Science.gov

Sample records for load forecasting system

  1. A Microcomputer Implementation Of An Intelligent Data Acquisition And Load Forecasting System

    NASA Astrophysics Data System (ADS)

    Rahman, Saifur

    1987-01-01

    This paper reports on the hardware and the programming aspects of an intelligent data acquisition and load forecasting system that has been implemented on a desktop microcomputer. The objective was to develop a low cost and reliable system that would collect forecasted weather data, real-time electric utility load data, archive them, and issue an electric utility load forecast in 1-hour, 6-hour and upto 24-hour increments within a midnight-to-midnight time frame. Data are collected, over commercial telephone lines, from remote locations (often hundreds of miles apart), filtered and then processed. The archived data are used to form monthly summaries of hourly electric utility load (MW) and weather conditions in the area. A set of pre-selected rules are then applied on this database to develop the desired load forecast. All this work is done in a totally automated fashion, i.e., without any human intervention. The data acquisition and load forecasting system is based on an AT&T 3B2/300 UNIX based desktop microcomputer. The 3B2 serves as the "heart" of the system and performs the functions of data collection, processing, archiving, load forecasting and display. It is a multi-tasking, multi-user machine and at it's present configuration can support four users and a "super user", or system manager.

  2. Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

    NASA Astrophysics Data System (ADS)

    Radziukynas, V.; Klementavičius, A.

    2016-04-01

    The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

  3. Fuzzy-neural network based short term peak and average load forecasting (STPA LF) system with network security

    SciTech Connect

    Mandal, S.K.; Agrawal, A.

    1997-12-31

    In this paper an attempt is made to forecast load using fuzzy neural network (FNN) for an integrated power system. Here, the proposed system uses a two stage FNN for a short term peak and average load forecasting (STPALF). The first stage FNN deals with the load forecasting and the second stage algorithm can be worked independently for network security. This technique is used to forecast load accurately on week days as well as holidays, weekends and some special occasions considering historical data of load and weather information and also take necessary control action for network security.

  4. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

    PubMed

    Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

    2016-01-01

    Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF. PMID:26835237

  5. Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

    NASA Technical Reports Server (NTRS)

    Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

    1996-01-01

    The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the

  6. Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-09-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique

  7. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter

  8. Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

    SciTech Connect

    Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.

    2012-07-01

    The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.

  9. An approach to distribution short-term load forecasting

    SciTech Connect

    Stratton, R.C.; Gaustad, K.L.

    1995-03-01

    This paper reports on the developments and findings of the Distribution Short-Term Load Forecaster (DSTLF) research activity. The objective of this research is to develop a distribution short-term load forecasting technology consisting of a forecasting method, development methodology, theories necessary to support required technical components, and the hardware and software tools required to perform the forecast The DSTLF consists of four major components: monitored endpoint load forecaster (MELF), nonmonitored endpoint load forecaster (NELF), topological integration forecaster (TIF), and a dynamic tuner. These components interact to provide short-term forecasts at various points in the, distribution system, eg., feeder, line section, and endpoint. This paper discusses the DSTLF methodology and MELF component MELF, based on artificial neural network technology, predicts distribution endpoint loads for an hour, a day, and a week in advance. Predictions are developed using time, calendar, historical load, and weather data. The overall DSTLF architecture and a prototype MELF module for retail endpoints have been developed. Future work will be focused on refining and extending MELF and developing NELF and TIF capabilities.

  10. The framework of weighted subset-hood Mamdani fuzzy rule based system rule extraction (MFRBS-WSBA) for forecasting electricity load demand

    NASA Astrophysics Data System (ADS)

    Mansor, Rosnalini; Kasim, Maznah Mat; Othman, Mahmod

    2016-08-01

    Fuzzy rules are very important elements that should be taken consideration seriously when applying any fuzzy system. This paper proposes the framework of Mamdani Fuzzy Rule-based System with Weighted Subset-hood Based Algorithm (MFRBS-WSBA) in the fuzzy rule extraction for electricity load demand forecasting. The framework consist of six main steps: (1) Data Collection and Selection; (2) Preprocessing Data; (3) Variables Selection; (4) Fuzzy Model; (5) Comparison with Other FIS and (6) Performance Evaluation. The objective of this paper is to show the fourth step in the framework which applied the new electricity load forecasting rule extraction by WSBA method. Electricity load demand in Malaysia data is used as numerical data in this framework. These preliminary results show that the WSBA method can be one of alternative methods to extract fuzzy rules for forecast electricity load demand

  11. Load forecast and need for power

    SciTech Connect

    1995-10-01

    This portion of the Energy Vision 2020 draft report discusses the models used for forecasting the load growth over the period of this report. To deal with uncertainties in load growth, TVA has used a range of forecasts: low, medium, and high. Based on the medium forecast, TVA has determined that an additional 800 MWe will be required by 1998 and 16,500 MWe by 2020. based on the high growth forecast, additional power will be needed in 1997 and increasing thereafter. Based on the low growth forecast, no additional capacity would be needed during the period of this report. These estimates include a reserve margin of 15% through 1997, 13% average through the period 1998 to 2010, and 12% average during the remainder of the reporting period.

  12. The Application of the Pso Based BP Network in Short-Term Load Forecasting

    NASA Astrophysics Data System (ADS)

    Zhaoyu, Pian; Shengzhu, Li; Hong, Zhang; Nan, Zhang

    The load forecast level in power system is a important symbol to measure operations and management of power system. This paper summarized the research conditions of the short-term load forecasting using artificial neural network method, and analyzed the characteristics of electrical load and factors of influencing power load forecasting accuracy. The paper used the particle swarm optimization neural network method in short-term load forecasting of power grid. Based on the analysis history loads in California power system, we established the load forecasting model considering the various affecting factors, and normalized the input load, meanwhile quantified date, atmosphere and other factors. The example showed that the model of neural network based on the particle swarm optimization algorithm can improve the prediction precision and speed, it's performance prediction is superior to the model based on BP neural network load forecasting.

  13. An operational power management method for the grid containing renewable power systems utilizing short-term weather and load forecasting data

    NASA Astrophysics Data System (ADS)

    Aula, Fadhil T.; Lee, Samuel C.

    2013-04-01

    This paper addresses the problems associated with power management of the grid containing renewable power systems and proposes a method for enhancing its operational power management. Since renewable energy provides uncertain and uncontrollable energy resources, the renewable power systems can only generate irregular power. This power irregularity creates problems affecting the grid power management process and influencing the parallel operations of conventional power plants on the grid. To demonstrate this power management method for this type of grid, weatherdependent wind and photovoltaic power systems are chosen an example. This study also deals with other uncertain quantities which are system loads. In this example, the management method is based on adapting short-term weather and load forecasting data. The new load demand curve (NLDC) can be produced by merging the loads with the power generated from the renewable power systems. The NLDC is used for setting the loads for the baseload power plants and knowing when other plants are needed to increase or decrease their supplies to the grid. This will decrease the irregularity behavior effects of the renewable power system and at the same time will enhance the smoothing of the power management for the grid. The aim of this paper is to show the use of the weather and load forecasting data to achieve the optimum operational power management of the grid contains renewable power systems. An illustrative example of such a power system is presented and verified by simulation.

  14. Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

    PubMed Central

    Hu, Zhongyi; Xiong, Tao

    2013-01-01

    Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425

  15. 7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... load forecast and the financial forecast require input assumptions for wholesale power costs... projections from the load forecast to develop system design criteria. The assumptions and data common to all the documents must be consistent. (g) Coordination. Power supply borrowers and their members that...

  16. Load forecasting using artificial neural networks

    SciTech Connect

    Pham, K.D.

    1995-12-31

    Artificial neural networks, modeled after their biological counterpart, have been successfully applied in many diverse areas including speech and pattern recognition, remote sensing, electrical power engineering, robotics and stock market forecasting. The most commonly used neural networks are those that gained knowledge from experience. Experience is presented to the network in form of the training data. Once trained, the neural network can recognized data that it has not seen before. This paper will present a fundamental introduction to the manner in which neural networks work and how to use them in load forecasting.

  17. Weather forecasting expert system study

    NASA Technical Reports Server (NTRS)

    1985-01-01

    Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

  18. 7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... supplier or member system forecasting and planning activities. (2) Resources used to develop the load... scenarios, weather conditions, and other uncertainties that borrowers may decide to address in their analysis include: (i) Most-probable assumptions, with normal weather; (ii) Pessimistic assumptions,...

  19. NASA Products to Enhance Energy Utility Load Forecasting

    NASA Technical Reports Server (NTRS)

    Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

    2012-01-01

    Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

  20. Short term load forecasting of anomalous load using hybrid soft computing methods

    NASA Astrophysics Data System (ADS)

    Rasyid, S. A.; Abdullah, A. G.; Mulyadi, Y.

    2016-04-01

    Load forecast accuracy will have an impact on the generation cost is more economical. The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Particle Swarm proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers.

  1. A Development of Very Short-Term Load Forecasting Based on Chaos Theory

    NASA Astrophysics Data System (ADS)

    Kawauchi, Seiji; Sugihara, Hiroaki; Sasaki, Hiroshi

    It is indispensable to accurately perform the short-term load forecasting of 10 minutes ahead in order to avoid undesirable disturbances in power system operations. The authors have so far developed such a forecasting method based on the conventional chaos theory. However, this approach is unable to give accurate forecasting results in case where the loads consecutively exceed than the historical maximum or lower than the minimum. Also, electric furnace loads with steep fluctuations have been another factor to degrade the forecast accuracy. This paper presents an improved forecasting method based on Chaos theory. Especially, the potential of the Local Fuzzy Reconstruction Method, a variant of the localized reconstruction methods, is fully exploited to realize accurate forecast as much as possible. To resolve the forecast deterioration due to sudden change loads such as by electric furnaces, they are separated from the rest and smoothing operations are carried out afterwards. The separated loads are forecasted independently from the remaining components. Several error correction methods are incorporated to enhance the proposed forecasting method. Furthermore, a consistent measure of obtaining the optimal combination of parameters to be used in the forecasting method is given. The effectiveness of the proposed methods is verified by using real load data for one year.

  2. Satellite freeze forecast system

    NASA Technical Reports Server (NTRS)

    Martsolf, J. D. (Principal Investigator)

    1983-01-01

    Provisions for back-up operations for the satellite freeze forecast system are discussed including software and hardware maintenance and DS/1000-1V linkage; troubleshooting; and digitized radar usage. The documentation developed; dissemination of data products via television and the IFAS computer network; data base management; predictive models; the installation of and progress towards the operational status of key stations; and digital data acquisition are also considered. The d addition of dew point temperature into the P-model is outlined.

  3. Forecasting in Complex Systems

    NASA Astrophysics Data System (ADS)

    Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

    2014-12-01

    Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification

  4. Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

    SciTech Connect

    Hodge, B. M.; Lew, D.; Milligan, M.

    2013-01-01

    Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of the day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.

  5. Risk Quantification for ANN Based Short-Term Load Forecasting

    NASA Astrophysics Data System (ADS)

    Iwashita, Daisuke; Mori, Hiroyuki

    A new risk assessment method for short-term load forecasting is proposed. The proposed method makes use of an Artificial Neural Network (ANN) to forecast one-step ahead daily maximum loads and evaluate uncertainty of in load forecasting. As ANN the model, the Radial Basis Function (RBF) network is employed to forecast loads due to the good performance. Sufficient realistic pseudo-scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of Moment Matching is used to improve the accuracy of the multivariate normal distribution. The Peak Over Threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting.

  6. Reconstructing Clusters for Preconditioned Short-term Load Forecasting

    NASA Astrophysics Data System (ADS)

    Itagaki, Tadahiro; Mori, Hiroyuki

    This paper presents a new preconditioned method for short-term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short-term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen-based neural network. That enhances a short-term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one-step ahead daily maximum load forecasting.

  7. Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market

    SciTech Connect

    Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.

    2008-11-15

    For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)

  8. Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

    SciTech Connect

    Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

    2013-11-01

    As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

  9. Comparison of very short-term load forecasting techniques

    SciTech Connect

    Liu, K.; Kwan, C.; Lewis, F.L.; Subbarayan, S.; Shoults, R.R.; Manry, M.T.; Naccarino, J.

    1996-05-01

    Three practical techniques--Fuzzy Logic (FL), Neural Networks (NN), and Auto-regressive model (AR)--for very short-term load forecasting have been proposed and discussed in this paper. Their performances are evaluated through a simulation study. The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict the very short-term load trends on-line. FL and NN can be good candidates for this application.

  10. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect

    United States. Bonneville Power Administration.

    1994-02-01

    This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

  11. 7 CFR 1710.209 - Approval requirements for load forecast work plans.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 11 2010-01-01 2010-01-01 false Approval requirements for load forecast work plans... LOANS AND GUARANTEES Load Forecasts § 1710.209 Approval requirements for load forecast work plans. (a... utility plant of $500 million or more must maintain an approved load forecast work plan. RUS...

  12. 7 CFR 1710.209 - Approval requirements for load forecast work plans.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... approved load forecast work plan must describe the borrower's process and methods to be used in producing... 7 Agriculture 11 2011-01-01 2011-01-01 false Approval requirements for load forecast work plans... LOANS AND GUARANTEES Load Forecasts § 1710.209 Approval requirements for load forecast work plans....

  13. 7 CFR 1710.209 - Approval requirements for load forecast work plans.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... approved load forecast work plan must describe the borrower's process and methods to be used in producing... 7 Agriculture 11 2014-01-01 2014-01-01 false Approval requirements for load forecast work plans... LOANS AND GUARANTEES Load Forecasts § 1710.209 Approval requirements for load forecast work plans....

  14. 7 CFR 1710.209 - Approval requirements for load forecast work plans.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... approved load forecast work plan must describe the borrower's process and methods to be used in producing... 7 Agriculture 11 2013-01-01 2013-01-01 false Approval requirements for load forecast work plans... LOANS AND GUARANTEES Load Forecasts § 1710.209 Approval requirements for load forecast work plans....

  15. 7 CFR 1710.209 - Approval requirements for load forecast work plans.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... approved load forecast work plan must describe the borrower's process and methods to be used in producing... 7 Agriculture 11 2012-01-01 2012-01-01 false Approval requirements for load forecast work plans... LOANS AND GUARANTEES Load Forecasts § 1710.209 Approval requirements for load forecast work plans....

  16. An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study

    SciTech Connect

    De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

    2011-01-17

    This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

  17. The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across England and Wales

    NASA Astrophysics Data System (ADS)

    Schellekens, J.; Weerts, A. H.; Moore, R. J.; Pierce, C. E.; Hildon, S.

    2011-03-01

    Operational flood forecasting systems share a fundamental challenge: forecast uncertainty which needs to be considered when making a flood warning decision. One way of representing this uncertainty is through employing an ensemble approach. This paper presents research funded by the Environment Agency in which ensemble rainfall forecasts are utilised and tested for operational use. The form of ensemble rainfall forecast used is the Met Office short-range product called MOGREPS. It is tested for operational use within the Environment Agency's National Flood Forecasting System (NFFS) for England and Wales. Currently, the NFFS uses deterministic forecasts only. The operational configuration of the NFFS for Thames Region is extended to trial the use of the new ensemble rainfall forecasts in support of probabilistic flood forecasting. Evaluation includes considering issues of model performance, configuration (how to fit the ensemble forecasts within the current configurations), data volumes, run times and options for displaying probabilistic forecasts. Although ensemble rainfall forecasts available from MOGREPS are not extensive enough to fully verify product performance, it is concluded that their use within current Environment Agency regional flood forecasting systems can provide better information to the forecaster than use of the deterministic forecasts alone. Of note are the small number of false alarms of river flow exceedance generated when using MOGREPS as input and that small flow events are also forecasted rather well, notwithstanding the rather coarse resolution of the MOGREPS grid (24 km) compared to the studied catchments. In addition, it is concluded that, with careful configuration in NFFS, MOGREPS can be used in existing systems without a significant increase in system load.

  18. Weather Forecasting Systems and Methods

    NASA Technical Reports Server (NTRS)

    Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

    2014-01-01

    A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

  19. 7 CFR 1710.202 - Requirement to prepare a load forecast-power supply borrowers.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... and provide an approved load forecast in support of any request for RUS financial assistance. The... provide an approved load forecast in support of any request for RUS financial assistance. The member power... forecasting information in the approved load forecast of its power supply borrower. The approved...

  20. Short-term load forecasting using neural network for future smart grid application

    NASA Astrophysics Data System (ADS)

    Zennamo, Joseph Anthony, III

    Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

  1. G&T adds versatile load management system

    SciTech Connect

    Nickel, J.R.; Baker, E.D.; Holt, J.W.; Chan, M.L.

    1995-04-01

    Wolverine`s load management system was designed in response to the need to reduce peak demand. The Energy Management System (EMS) prepares short term (seven day) load forecasts, based on a daily peak demand forecst, augmented by a similar day profile based on weather conditions. The software combines the similar day profile with the daily peak demand forecast to yield an hourly load forecast for an entire week. The software uses the accepted load forecast case in many application functions, including interchange scheduling, unit commitment, and transaction evaluation. In real time, the computer updates the accepted forecast hourly, based in actual changes in the weather and load. The load management program executes hourly. The program uses impact curves to calculate a load management strategy that reduces the load forecast below a desired load threshold.

  2. The Invasive Species Forecasting System

    NASA Technical Reports Server (NTRS)

    Schnase, John; Most, Neal; Gill, Roger; Ma, Peter

    2011-01-01

    The Invasive Species Forecasting System (ISFS) provides computational support for the generic work processes found in many regional-scale ecosystem modeling applications. Decision support tools built using ISFS allow a user to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of management concern, such as a national park, monument, forest, or refuge. This type of decision product helps resource managers plan invasive species protection, monitoring, and control strategies for the lands they manage. Until now, scientists and resource managers have lacked the data-assembly and computing capabilities to produce these maps quickly and cost efficiently. ISFS focuses on regional-scale habitat suitability modeling for invasive terrestrial plants. ISFS s component architecture emphasizes simplicity and adaptability. Its core services can be easily adapted to produce model-based decision support tools tailored to particular parks, monuments, forests, refuges, and related management units. ISFS can be used to build standalone run-time tools that require no connection to the Internet, as well as fully Internet-based decision support applications. ISFS provides the core data structures, operating system interfaces, network interfaces, and inter-component constraints comprising the canonical workflow for habitat suitability modeling. The predictors, analysis methods, and geographic extents involved in any particular model run are elements of the user space and arbitrarily configurable by the user. ISFS provides small, lightweight, readily hardened core components of general utility. These components can be adapted to unanticipated uses, are tailorable, and require at most a loosely coupled, nonproprietary connection to the Web. Users can invoke capabilities from a command line; programmers can integrate ISFS's core components into more complex systems and services. Taken together, these

  3. Improved Neural Networks with Random Weights for Short-Term Load Forecasting

    PubMed Central

    Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

    2015-01-01

    An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825

  4. Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

    PubMed

    Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

    2015-01-01

    An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825

  5. Interactive Forecasting with the National Weather Service River Forecast System

    NASA Technical Reports Server (NTRS)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  6. Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

    SciTech Connect

    Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.; Diao, Ruisheng; Lu, Ning

    2014-04-14

    To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.

  7. 7 CFR 1710.202 - Requirement to prepare a load forecast-power supply borrowers.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... and provide an approved load forecast in support of any request for RUS financial assistance. The... provide an approved load forecast in support of any request for RUS financial assistance. The member power... 7 Agriculture 11 2010-01-01 2010-01-01 false Requirement to prepare a load forecast-power...

  8. A Very Short-Term Load Forecasting of Long-Term Fluctuation Components in the Electric Power Demand

    NASA Astrophysics Data System (ADS)

    Kawauchi, Seiji; Sasaki, Hiroshi

    It is indispensable to forecast accurately the very short-term load demand to avoid undesirable disturbances in power system operations which deteriorate economical generations. The authors have so far developed a short-term forecasting method by using Local Fuzzy Reconstruction Method, a variant of the methods based on the chaos theory. However, this approach is unable to give accurate forecasting results in case where load demand consecutively exceeds the historical maximum or is lower than the minimum because forecasting is performed by the historical data themselves. Also, in forecasting holidays in summer, forecasting result of weekdays might appear due to similar demand trend. This paper presents novel demand forecasting methods that are able to make accurate forecasts by resolving the above mentioned problems. First, the new method improves the accuracy by extrapolating forecasted transition from the current point. Secondly, to eliminate miss forecast which may be occurred on holidays in summer, historical data are labeled by the information of the day of the week to distinguish similarly behaved weekdays’ load patterns. The proposed methods are applied to 10, 30, and 60 minutes ahead demand forecasting, and the accuracy is improved 10% to 20% compared with the method previously proposed.

  9. A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

    PubMed

    Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

    2013-10-01

    Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models. PMID:23924415

  10. Short-term load and wind power forecasting using neural network-based prediction intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time. PMID:24807030

  11. The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US

    NASA Astrophysics Data System (ADS)

    Oh, J.; Sinha, T.; Sankarasubramanian, A.

    2014-08-01

    It is well known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depends on the skill of daily precipitation forecasts over the watershed. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that daily nutrient loadings and the associated concentration could be predicted using daily precipitation forecasts and previously observed streamflow as surrogates of antecedent land surface conditions. By selecting 18 relatively undeveloped basins in the southeast US (SEUS), we evaluate the skill in predicting observed total nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing the daily streamflow forecasts using the retrospective weather forecasts based on K-nearest neighbor (K-NN) resampling approach and then forcing the forecasted streamflow with a nutrient load estimation (LOADEST) model to obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN loadings and the associated concentration were computed using rank correlation and RMSE (root mean square error), by comparing the respective forecast values with the WQN observations for the selected 18 Hydro-Climatic Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings and their concentration have statistically significant skill in predicting the respective daily observations in the WQN database at all 18 stations over the SEUS. Only two stations showed statistically insignificant relationships in predicting the observed nitrogen concentration. We also found that the skill in predicting the observed TN loadings increases with the increase in drainage area, which indicates that the large-scale precipitation reforecasts correlate better with precipitation and streamflow over large watersheds. To overcome the limited samplings of TN in the WQN

  12. The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the Southeast US

    NASA Astrophysics Data System (ADS)

    Oh, J.; Sinha, T.; Sankarasubramanian, A.

    2013-12-01

    It is well-known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depend on the skill of daily precipitation forecasts over the watershed. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that daily nutrient loadings and the associated concentration could be predicted using daily precipitation forecasts and previously observed streamflow as surrogates of antecedent land surface conditions. By selecting 18 relatively undeveloped basins in the Southeast US (SEUS), we evaluate the skill in predicting observed total nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing the daily streamflow forecasts using the retrospective weather forecasts based on K-nearest neighbor (K-NN) resampling approach and then forcing the forecasted streamflow with a nutrient load estimation (LOADEST) model to obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN loadings and the associated concentration were computed using rank correlation and RMSE, by comparing the respective forecast values with the WQN observations for the selected 18 Hydro-Climatic Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings and their concentration have statistically significant skill in predicting the respective daily observations in the WQN database at all the 18 stations over the SEUS. Only two stations showed statistically insignificant relationship in predicting the observed nitrogen concentration. We also found that the skill in predicting the observed TN loadings increase with increase in drainage area which indicates that the large-scale precipitation reforecasts correlate better with precipitation and streamflow over large watersheds. To overcome the limited samplings of TN in the WQN data, we extended the

  13. Efficient Resources Provisioning Based on Load Forecasting in Cloud

    PubMed Central

    Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

    2014-01-01

    Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. PMID:24701160

  14. Efficient resources provisioning based on load forecasting in cloud.

    PubMed

    Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

    2014-01-01

    Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. PMID:24701160

  15. Assessment of reservoir system variable forecasts

    NASA Astrophysics Data System (ADS)

    Kistenmacher, Martin; Georgakakos, Aris P.

    2015-05-01

    Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

  16. Short-term Operating Strategy with Consideration of Load Forecast and Generating Unit Uncertainty

    NASA Astrophysics Data System (ADS)

    Sarjiya; Eua-Arporn, Bundhit; Yokoyama, Akihiko

    One of the common problems faced by many electric utilities concernes with the uncertainty from both load forecast error and generating unit unavailability. This uncertainty might lead to uneconomic operation if it is not managed properly in the planning stage. Utilities may have many operational tools, e.g. unit commitment, economic dispatch. However, they require a proper operating strategy, taking into account uncertainties. This paper explicitly demonstrates how to include the uncertainties to obtain the best operating strategy for any power systems. The uncertainty of the load forecast is handled using decision analysis method, meanwhile the uncertainty of the generating unit is approached by inclusion of risk cost to the total cost. In addition, three spinning reserve strategies based on deterministic criteria are incorporated in the development of scenario. Meanwhile, Mixed Integer Linear Programming method is utilized to generate unit commitment decision in each created scenario. The best strategy which gives the minimum total cost is selected among the developed scenarios. The proposed method has been tested using a modified of IEEE 24-bus system. Sensitivity analysis with respect to the number of unit, expected unserved energy price, standard deviation of load forecast, and probability of load level is reported.

  17. 7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... management activities, if applicable; (6) Graphic representations of the variables specifically identified by... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for...

  18. 7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... management activities, if applicable; (6) Graphic representations of the variables specifically identified by... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for...

  19. 7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by...

  20. 7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by...

  1. 7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by...

  2. 7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by...

  3. 7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by...

  4. Load sensing system

    DOEpatents

    Sohns, Carl W.; Nodine, Robert N.; Wallace, Steven Allen

    1999-01-01

    A load sensing system inexpensively monitors the weight and temperature of stored nuclear material for long periods of time in widely variable environments. The system can include an electrostatic load cell that encodes weight and temperature into a digital signal which is sent to a remote monitor via a coaxial cable. The same cable is used to supply the load cell with power. When multiple load cells are used, vast

  5. A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

    SciTech Connect

    Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

    2013-12-18

    This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

  6. 7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 11 2010-01-01 2010-01-01 false Minimum approval requirements for all load forecasts. 1710.205 Section 1710.205 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts...

  7. 7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... for RUS financial assistance. The distribution borrower may comply with this requirement by participation in and inclusion of its load forecasting information in the approved load forecast of its power... financial assistance. The distribution borrower may comply with this requirement by participation in...

  8. A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

    SciTech Connect

    Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

    2013-07-25

    This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

  9. Load sensing system

    DOEpatents

    Sohns, C.W.; Nodine, R.N.; Wallace, S.A.

    1999-05-04

    A load sensing system inexpensively monitors the weight and temperature of stored nuclear material for long periods of time in widely variable environments. The system can include an electrostatic load cell that encodes weight and temperature into a digital signal which is sent to a remote monitor via a coaxial cable. The same cable is used to supply the load cell with power. When multiple load cells are used, vast inventories of stored nuclear material can be continuously monitored and inventoried of minimal cost. 4 figs.

  10. 7 CFR 1710.204 - Filing requirements for borrowers that must maintain an approved load forecast on an ongoing basis.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... an approved load forecast on an ongoing basis. 1710.204 Section 1710.204 Agriculture Regulations of... AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts § 1710.204 Filing requirements for borrowers that must maintain an approved load forecast on an ongoing basis....

  11. 7 CFR 1710.207 - RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an...

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... distribution borrowers not required to maintain an approved load forecast on an ongoing basis. 1710.207 Section... GUARANTEES Load Forecasts § 1710.207 RUS criteria for approval of load forecasts by distribution borrowers... distribution borrowers that are unaffiliated with a power supply borrower, or by distribution borrowers...

  12. A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

    PubMed Central

    Moriano, Javier; Rodríguez, Francisco Javier; Martín, Pedro; Jiménez, Jose Antonio; Vuksanovic, Branislav

    2016-01-01

    In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected. PMID:26771613

  13. A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting.

    PubMed

    Moriano, Javier; Rodríguez, Francisco Javier; Martín, Pedro; Jiménez, Jose Antonio; Vuksanovic, Branislav

    2016-01-01

    In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected. PMID:26771613

  14. Flood Forecasting in River System Using ANFIS

    NASA Astrophysics Data System (ADS)

    Ullah, Nazrin; Choudhury, P.

    2010-10-01

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  15. Flood Forecasting in River System Using ANFIS

    SciTech Connect

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  16. A global flash flood forecasting system

    NASA Astrophysics Data System (ADS)

    Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

    2016-04-01

    The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial

  17. Global Storm Surge Forecasting and Information System

    NASA Astrophysics Data System (ADS)

    Buckman, Lorraine; Verlaan, Martin; Weerts, Albrecht

    2015-04-01

    The Global Storm Surge Forecasting and Information System is a first-of-its-kind operational forecasting system for storm surge prediction on a global scale, taking into account tidal and extra-tropical storm events in real time. The system, built and hosted by Deltares, provides predictions of water level and surge height up to 10 days in advance from numerical simulations and measurement data integrated within an operational IT environment. The Delft-FEWS software provides the operational environment in which wind forecasts and measurement data are collected and processed, and serves as a platform from which to run the numerical model. The global Delft3D model is built on a spherical, flexible mesh with a resolution around 5 km in near-shore coastal waters and an offshore resolution of 50 km to provide detailed information at the coast while limiting the computational time required. By using a spherical grid, the model requires no external boundary conditions. Numerical global wind forecasts are used as forcing for the model, with plans to incorporate regional meteorological forecasts to better capture smaller, tropical storms using the Wind Enhanced Scheme for generation of tropical winds (WES). The system will be automated to collect regional wind forecasts and storm warning bulletins which are incorporated directly into the model calculations. The forecasting system provides real-time water level and surge information in areas that currently lack local storm surge prediction capability. This information is critical for coastal communities in planning their flood strategy and during disaster response. The system is also designed to supply boundary conditions for coupling finer-scale regional models. The Global Storm Surge Forecasting and Information System is run within the Deltares iD-Lab initiative aiming at collaboration with universities, consultants and interested organizations. The results of the system will be made available via standards such as net

  18. 7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... distribution borrower that is a member of a power supply borrower with a total utility plant of less than $500... utility plant, whichever is greater. The distribution borrower may comply with this requirement by... forecast or an approved load forecast work plan. (d) A distribution borrower with a total utility plant...

  19. Solar Energy Forecast System Development and Implementation

    NASA Astrophysics Data System (ADS)

    Jascourt, S. D.; Kirk-Davidoff, D. B.; Cassidy, C.

    2012-12-01

    Forecast systems for predicting real-time solar energy generation are being developed along similar lines to those of more established wind forecast systems, but the challenges and constraints are different. Clouds and aerosols play a large role, and for tilted photovoltaic panels and solar concentrating plants, the direct beam irradiance, which typically has much larger forecast errors than global horizontal irradiance, must be utilized. At MDA Information Systems, we are developing a forecast system based on first principles, with the well-validated REST2 clear sky model (Gueymard, 2008) at its backbone. In tuning the model and addressing aerosol scattering and surface albedo, etc., we relied upon the wealth of public data sources including AERONET (aerosol optical depth at different wavelengths), Suominet (GPS integrated water vapor), NREL MIDC solar monitoring stations, SURFRAD (includes upwelling shortwave), and MODIS (albedo in different wavelength bands), among others. The forecast itself utilizes a blend of NWP model output, which must be brought down to finer time resolution based on the diurnal cycle rather than simple interpolation. Many models currently do not output the direct beam irradiance, and one that does appears to have a bias relative to its global horizontal irradiance, with equally good performance attained by utilizing REST2 and the model global radiation to estimate the direct component. We will present a detailed assessment of various NWP solar energy products, evaluating forecast skill at a range of photovoltaic installations.

  20. Shuttle car loading system

    NASA Technical Reports Server (NTRS)

    Collins, E. R., Jr. (Inventor)

    1985-01-01

    A system is described for loading newly mined material such as coal, into a shuttle car, at a location near the mine face where there is only a limited height available for a loading system. The system includes a storage bin having several telescoping bin sections and a shuttle car having a bottom wall that can move under the bin. With the bin in an extended position and filled with coal the bin sections can be telescoped to allow the coal to drop out of the bin sections and into the shuttle car, to quickly load the car. The bin sections can then be extended, so they can be slowly filled with more while waiting another shuttle car.

  1. Coastal ocean forecasting systems in Europe

    NASA Astrophysics Data System (ADS)

    Dugan, John

    During my tour as the liaison oceanographer at the Office of Naval Research's European branch, I conducted a focused study of coastal ocean forecasting systems. This study is of direct interest to ONR because of an increased interest in the coastal zone and to the civilian U.S. oceanographic community because of numerous problems in the coastal zone that could be alleviated with an operational forecasting system. The Europeans have a long history of excellent research and developmental work in this area. The Europeans' distinguished history in coastal ocean forecasting is due in part to their strong dependence on the sea. However, the original motivation for these systems was the recognition early in this century that weather conditions were responsible for damaging storm surges around the periphery of the North Sea and that science could predict these catastrophic floods. Forecasting systems called tide-surge prediction systems, which provide warnings of impending flood conditions, were designed and constructed and are operational in the various meteorological centers of the nations surrounding the North Sea. Over time, the services have been extended to provide forecasts of ocean waves, water depth for navigation, and currents for a large customer base. These systems now are being extended further into the three-dimensional domain that is required for management of problems associated with water quality, pollution, and aquaculture and fisheries interests.

  2. Drought Forecasting System of the Netherlands

    NASA Astrophysics Data System (ADS)

    Weerts, A. H.; Berendrecht, W. L.; Veldhuizen, A.; Goorden, N.; Vernimmen, R.; Lourens, A.; Prinsen, G.; Mulder, M.; Kroon, T.; Stam, J.

    2009-04-01

    During periods of droughts the National Coordinating Committee for Water Distribution of the Netherlands has to decide how the available surface water is used and allocated between different users (agriculture, navigation, industry etc). To support this decision making, real-time information is needed about the availability of surface water, groundwater levels, saturation of the root zone, etc. This real-time information must give insight into the current state of the system as well as into its state in the near future (i.e. 10 days ahead). For this purpose, the National Hydrological Instrument (NHI), running on a daily time step and consisting of a nationwide distribution model and surface water model coupled with a MODFLOW-METASWAP model of the saturated-unsaturated zone of the whole of the Netherlands, driven by measured and forecasted precipitation and evaporation (ECMWF-DET and -EPS), is used to obtain insight into the actual and forecasted states of the surface, ground and soil water in the Netherlands. The tool also gives insight in the actual and forecasted water demands by the different actors. The whole system is operationalised within Delft-FEWS, an operational forecasting system to manage data and models in a real time environment. The surface water and groundwater models can be compared with surface water measurements (discharges and water levels) and groundwater level measurements in real-time. ECMWF reforecasts will be used to gain insight in the performance of the drought forecasting system.

  3. Probabilistic Water quality trading model conditioned on season-ahead nutrient load forecasts

    NASA Astrophysics Data System (ADS)

    Arumugam, S.; Oh, J.

    2010-12-01

    Successful water quality trading programs in the country rely on expected point and nonpoint nutrient loadings from multiple sources. Pollutant sources, through nutrient transactions, are in pursuit of minimum allocation strategies that can keep both the loadings and the associated concentrations under the target limit. It is well established in the hydroclimatic literature that interannual variability in seasonal streamflow could be explained partially using SST conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. We intend to bridge these two findings to develop probabilistic nutrient loading model for supporting water quality trading in the Tar River basin, NC. Utilizing the precipitation forecasts derived from ECHAM4.5 General Circulation Model, we develop season-ahead forecasts of total nitrogen (TN) and total phosphorus (TP) by forcing the calibrated water quality model with seasonal streamflow forecasts. Based on the season-head loadings, the probability of violation of desired nutrient concentration for the currently allowed loadings is also estimated. Through retrospective analyses using forecasted streamflow and the associated loadings, the probabilistic water quality trading model estimates the nutrient reduction strategies that can ensure the net loadings from both sources being below the target loadings. Challenges in applying the proposed framework for actual trading are also discussed.

  4. Timetable of an operational flood forecasting system

    NASA Astrophysics Data System (ADS)

    Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

    2010-05-01

    At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by

  5. Flood Warning and Forecasting System in Slovakia

    NASA Astrophysics Data System (ADS)

    Leskova, Danica

    2016-04-01

    In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

  6. The FOAM operational deep ocean forecasting system

    NASA Astrophysics Data System (ADS)

    Hines, A.; Barciela, R.; Bell, M.; Holland, P.; Martin, M.; McCulloch, M.; Storkey, D.

    2003-04-01

    The Forecasting Ocean Assimilation Model (FOAM) has been developed at the Met Office to provide operational real-time forecasts of the deep ocean to the Royal Navy. The model is built around the ocean and sea-ice components of the Met Office's Unified Model (UM), which is also used in coupled ocean-ice-atmosphere climate prediction. FOAM is forced by 6-hourly surface fluxes from the Met Office's Numerical Weather Prediction (NWP) system, and assimilates in situ profile data, in situ and satellite SST data, satellite derived sea-ice concentration data, and satellite altimeter sea surface height data. The operational system consists of a 1 degree resolution global model and a 1/3 degree resolution model covering the North Atlantic and Arctic oceans. The model suite runs daily, delivering forecast products directly to a visualisation system at the Royal Navy. The operational system also includes automatic verification of analyses and forecasts. A 1/9 degree model of the North Atlantic is being run daily on a pre-operational basis as part of GODAE and MERSEA. Output from this model is available on the internet in real time.

  7. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    SciTech Connect

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  8. Load Control System Reliability

    SciTech Connect

    Trudnowski, Daniel

    2015-04-03

    This report summarizes the results of the Load Control System Reliability project (DOE Award DE-FC26-06NT42750). The original grant was awarded to Montana Tech April 2006. Follow-on DOE awards and expansions to the project scope occurred August 2007, January 2009, April 2011, and April 2013. In addition to the DOE monies, the project also consisted of matching funds from the states of Montana and Wyoming. Project participants included Montana Tech; the University of Wyoming; Montana State University; NorthWestern Energy, Inc., and MSE. Research focused on two areas: real-time power-system load control methodologies; and, power-system measurement-based stability-assessment operation and control tools. The majority of effort was focused on area 2. Results from the research includes: development of fundamental power-system dynamic concepts, control schemes, and signal-processing algorithms; many papers (including two prize papers) in leading journals and conferences and leadership of IEEE activities; one patent; participation in major actual-system testing in the western North American power system; prototype power-system operation and control software installed and tested at three major North American control centers; and, the incubation of a new commercial-grade operation and control software tool. Work under this grant certainly supported the DOE-OE goals in the area of “Real Time Grid Reliability Management.”

  9. Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system

    NASA Astrophysics Data System (ADS)

    Sigmond, M.; Fyfe, J. C.; Flato, G. M.; Kharin, V. V.; Merryfield, W. J.

    2013-02-01

    AbstractWe assess the seasonal <span class="hlt">forecast</span> skill of pan-Arctic sea ice area in a dynamical <span class="hlt">forecast</span> <span class="hlt">system</span> that includes interactive atmosphere, ocean, and sea ice components. <span class="hlt">Forecast</span> skill is quantified by the correlation skill score computed from 12 month ensemble <span class="hlt">forecasts</span> initialized in each month between January 1979 to December 2009. We find that <span class="hlt">forecast</span> skill is substantial for all lead times and predicted seasons except spring but is mainly due to the strong downward trend in observations for lead times of about 4 months and longer. Skill is higher when evaluated against an observation-based dataset with larger trends. The <span class="hlt">forecast</span> skill when linear trends are removed from the <span class="hlt">forecasts</span> and verifying observations is small and generally not statistically significant at lead times greater than 2 to 3 months, except for January/February when <span class="hlt">forecast</span> skill is moderately high up to an 11 month lead time. For short lead times, high trend-independent <span class="hlt">forecast</span> skill is found for October, while low skill is found for November/December. This is consistent with the seasonal variation of observed lag correlations. For most predicted months and lead times, trend-independent <span class="hlt">forecast</span> skill exceeds that of an anomaly persistence <span class="hlt">forecast</span>, highlighting the potential for dynamical <span class="hlt">forecast</span> <span class="hlt">systems</span> to provide valuable seasonal predictions of Arctic sea ice.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002PhDT........65Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002PhDT........65Z"><span id="translatedtitle">Neural network based <span class="hlt">load</span> and price <span class="hlt">forecasting</span> and confidence interval estimation in deregulated power markets</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Li</p> <p></p> <p>With the deregulation of the electric power market in New England, an independent <span class="hlt">system</span> operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term <span class="hlt">load</span> and MCP <span class="hlt">forecasting</span> and corresponding confidence interval estimation methodologies. In this research, the complexity of <span class="hlt">load</span> and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for <span class="hlt">load</span> <span class="hlt">forecasting</span>, a neural network based <span class="hlt">load</span> <span class="hlt">forecaster</span> was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT........78R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT........78R"><span id="translatedtitle">Data driven models applied in building <span class="hlt">load</span> <span class="hlt">forecasting</span> for residential and commercial buildings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rahman, SM Mahbobur</p> <p></p> <p>A significant portion of the operating costs of utilities comes from energy production. Machine learning methods are widely used for short-term <span class="hlt">load</span> <span class="hlt">forecasts</span> for commercial buildings and also the utility grid. These <span class="hlt">forecasts</span> are used to minimize unit power production costs for the energy managers for better planning of power units and <span class="hlt">load</span> management. In this work, three different state-of-art machine learning methods i.e. Artificial Neural Network, Support Vector Regression and Gaussian Process Regression are applied in hour ahead and 24 --hour ahead building energy <span class="hlt">forecasting</span>. The work uses four residential buildings and one commercial building located in Downtown, San Antonio as test-bed using energy consumption data from those buildings monitored in real-time. Uncertainty quantification analysis is conducted to understand the confidence in each <span class="hlt">forecast</span> using Bayesian Network. Using a combination of weather variables and historical <span class="hlt">load</span>, <span class="hlt">forecasting</span> is done in a supervised way based on a moving window training algorithm. A range of comparisons between different <span class="hlt">forecasting</span> models in terms of relative accuracy are then presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/457578','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/457578"><span id="translatedtitle">Multipurpose simulation <span class="hlt">systems</span> for regional development <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kostina, N.I.</p> <p>1995-09-01</p> <p>We examine the development of automaton-modeling multipurpose simulation <span class="hlt">systems</span> as an efficient form of simulation software for MIS. Such <span class="hlt">systems</span> constitute a single problem-oriented package of applications based on a general simulation model, which is equipped with a task source language, interaction tools, file management tools, and an output document editor. The simulation models are described by the method of probabilistic-automaton modeling, which ensures standard representation of models and standardization of the modeling algorithm. Examples of such <span class="hlt">systems</span> include the demographic <span class="hlt">forecasting</span> <span class="hlt">system</span> DEPROG, the VOKON <span class="hlt">system</span> for assessing the quality of consumer services in terms of free time, and the SONET <span class="hlt">system</span> for servicing partially accessible customers. The development of computer-aided <span class="hlt">systems</span> for production and economic control is now moving to the second state, namely operationalization of optimization and <span class="hlt">forecasting</span> problems, whose solution may account for the main economic effect of MIS. Computation and information problems, which were the main focus of the first stage of MIS development, are thus acquiring the role of a source of information for optimization and <span class="hlt">forecasting</span> problems in addition to their direct contribution to preparation and analysis of current production and economic information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816659F&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816659F&link_type=ABSTRACT"><span id="translatedtitle">Evaluation and first <span class="hlt">forecasts</span> of the German Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> 1 (GCFS1)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fröhlich, Kristina; Baehr, Johanna; Müller, Wolfgang; Bunzel, Felix; Pohlmann, Holger; Dobrynin, Mikhail</p> <p>2016-04-01</p> <p>We present the near-operational seasonal <span class="hlt">forecast</span> <span class="hlt">system</span> GCFS1 (German Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> version 1), based on the CMIP5 version of the global coupled climate model MPI-ESM-LR. For GCFS1 we also present a detailed assessment on the predictive skill of the model. GCFS1 has been developed in cooperation between the Max Planck Institute for Meteorology, University of Hamburg and German Meteorological Service (DWD), the <span class="hlt">forecasts</span> are conducted by DWD. The <span class="hlt">system</span> is running at ECMWF with a re-<span class="hlt">forecast</span> ensemble of 15 member and a <span class="hlt">forecast</span> ensemble of 30 member. The re-<span class="hlt">forecasts</span> are initialised with full field nudging in the atmosphere (using ERA Interim), in the ocean (using ORAS4) and in the sea-ice component (using NSIDC sea-ice concentration). For the initialization of the <span class="hlt">forecasts</span> analyses from the ECMWF NWP model and recent ORAS4 analyses are taken. The ensemble perturbations are, for both re-<span class="hlt">forecasts</span> and <span class="hlt">forecasts</span>, generated through bred vectors in the ocean which provide initial perturbations for the ensemble in combination with simple physics perturbations in the atmosphere. Evaluation of the re-<span class="hlt">forecasted</span> climatologies will be presented for different variables, start dates and regions. The first winter <span class="hlt">forecast</span> during the strong El Niño phase is also subject of evaluation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940009911','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940009911"><span id="translatedtitle">PC4CAST: A tool for DSN <span class="hlt">load</span> <span class="hlt">forecasting</span> and capacity planning</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Loyola, S. J.</p> <p>1993-01-01</p> <p>Effectively planning the use and evolution of the Deep Space Network (DSN) is a complex problem involving many parameters. The tool that models many of these complexities, yet requires simple structured inputs and provides concise easy-to-understand metrics to aid in the planning process is discussed. The tool, PC4CAST, is used for both <span class="hlt">load</span> <span class="hlt">forecasting</span> (predicting how well planned that DSN resources meet expected demand) and as a decision support tool in the capacity-planning process (determining the relative benefits of capacity expansion options). It is now in use in the TDA Planning Office, has been used in numerous studies, and is also being used by the JPL Multimission Operations <span class="hlt">System</span> Office (MOSO) as an integral part of Resource Allocation Team activities. Experience using the tool has helped to identify additional requirements that will further improve the planning process, which can be met by future PC4CAST versions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830027185','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830027185"><span id="translatedtitle">A Satellite Frost <span class="hlt">Forecasting</span> <span class="hlt">System</span> for Florida</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Martsolf, J. D.</p> <p>1981-01-01</p> <p>Since the first of two minicomputers that are the main components of the satellite frost <span class="hlt">forecast</span> <span class="hlt">system</span> was delivered in 1977, the <span class="hlt">system</span> has evolved appreciably. A geostationary operational environmental satellite (GOES) <span class="hlt">system</span> provides the satellite data. The freeze of January 12-14, 1981, was documented with increasing interest in potential of such <span class="hlt">systems</span>. Satellite data is now acquired digitally rather than by redigitizing the GOES-Tap transmissions. Data acquisition is now automated, i.e., the computers are programmed to operate the <span class="hlt">system</span> with little, if any, operation intervention.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1016379','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1016379"><span id="translatedtitle">UNCERTAINTY IN THE GLOBAL <span class="hlt">FORECAST</span> <span class="hlt">SYSTEM</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Werth, D.; Garrett, A.</p> <p>2009-04-15</p> <p>We validated one year of Global <span class="hlt">Forecast</span> <span class="hlt">System</span> (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for <span class="hlt">forecasts</span> extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the <span class="hlt">forecast</span> period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour <span class="hlt">forecast</span> RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour <span class="hlt">forecasts</span> to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4646483','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4646483"><span id="translatedtitle">A Beacon Transmission Power Control Algorithm Based on Wireless Channel <span class="hlt">Load</span> <span class="hlt">Forecasting</span> in VANETs</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan</p> <p>2015-01-01</p> <p>In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel <span class="hlt">loading</span>, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel <span class="hlt">loading</span> are selected to construct the KF-BCLF, which is a channel <span class="hlt">load</span> <span class="hlt">forecasting</span> algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the <span class="hlt">forecasted</span> channel <span class="hlt">load</span>, the channel <span class="hlt">load</span> was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this <span class="hlt">forecast</span> with the measured channel <span class="hlt">loads</span>, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel <span class="hlt">load</span>, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26571042','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26571042"><span id="translatedtitle">A Beacon Transmission Power Control Algorithm Based on Wireless Channel <span class="hlt">Load</span> <span class="hlt">Forecasting</span> in VANETs.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan</p> <p>2015-01-01</p> <p>In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel <span class="hlt">loading</span>, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel <span class="hlt">loading</span> are selected to construct the KF-BCLF, which is a channel <span class="hlt">load</span> <span class="hlt">forecasting</span> algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the <span class="hlt">forecasted</span> channel <span class="hlt">load</span>, the channel <span class="hlt">load</span> was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this <span class="hlt">forecast</span> with the measured channel <span class="hlt">loads</span>, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel <span class="hlt">load</span>, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20000004367&hterms=System+Planning+Corporation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSystem%2BPlanning%2BCorporation','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20000004367&hterms=System+Planning+Corporation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSystem%2BPlanning%2BCorporation"><span id="translatedtitle">Mission Requirements and Data <span class="hlt">Systems</span> Support <span class="hlt">Forecast</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1993-01-01</p> <p>This document was developed by the Flight Mission Support Office and prepared by the <span class="hlt">Forecast</span> Analysis Section of the Bendix Field Engineering Corporation (BFEC) to provide NASA management with detailed mission information. It is one of a number of sources used in planning Mission Operations and Data <span class="hlt">Systems</span> resource commitments in support of mission requirements. All mission dates are based on information available as of May 28, 1993.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ChJOL.tmp..133W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ChJOL.tmp..133W"><span id="translatedtitle">Development of an oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> for offshore China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Yonggang; Wei, Zexun; An, Wei</p> <p>2015-12-01</p> <p>An oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> for offshore China was developed based on Visual C++. The oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> includes an ocean environmental <span class="hlt">forecast</span> model and an oil spill model. The ocean environmental <span class="hlt">forecast</span> model was designed to include timesaving methods, and comprised a parametrical wind wave <span class="hlt">forecast</span> model and a sea surface current <span class="hlt">forecast</span> model. The oil spill model was based on the "particle method" and fulfills the prediction of oil particle behavior by considering the drifting, evaporation and emulsification processes. A specific database was embedded into the oil spill <span class="hlt">forecast</span> <span class="hlt">system</span>, which contained fundamental information, such as the properties of oil, reserve of emergency equipment and distribution of marine petroleum platform. The oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> was successfully applied as part of an oil spill emergency exercise, and provides an operational service in the Research and Development Center for Offshore Oil Safety and Environmental Technology.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ChJOL..34..859W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ChJOL..34..859W&link_type=ABSTRACT"><span id="translatedtitle">Development of an oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> for offshore China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Yonggang; Wei, Zexun; An, Wei</p> <p>2016-07-01</p> <p>An oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> for offshore China was developed based on Visual C++. The oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> includes an ocean environmental <span class="hlt">forecast</span> model and an oil spill model. The ocean environmental <span class="hlt">forecast</span> model was designed to include timesaving methods, and comprised a parametrical wind wave <span class="hlt">forecast</span> model and a sea surface current <span class="hlt">forecast</span> model. The oil spill model was based on the "particle method" and fulfills the prediction of oil particle behavior by considering the drifting, evaporation and emulsification processes. A specific database was embedded into the oil spill <span class="hlt">forecast</span> <span class="hlt">system</span>, which contained fundamental information, such as the properties of oil, reserve of emergency equipment and distribution of marine petroleum platform. The oil spill <span class="hlt">forecast</span> <span class="hlt">system</span> was successfully applied as part of an oil spill emergency exercise, and provides an operational service in the Research and Development Center for Offshore Oil Safety and Environmental Technology.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011AGUFM.H53G1497C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011AGUFM.H53G1497C&link_type=ABSTRACT"><span id="translatedtitle">Seasonal streamflow <span class="hlt">forecasting</span> with the global hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span> FEWS-World</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.</p> <p>2011-12-01</p> <p>The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological <span class="hlt">forecasts</span> and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological <span class="hlt">forecasting</span> <span class="hlt">systems</span>. For these regions, a global <span class="hlt">forecasting</span> <span class="hlt">system</span> which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value. FEWS-World is developed for this purpose. The <span class="hlt">system</span> incorporates the global hydrological model PCR-GLOBWB and delivers streamflow <span class="hlt">forecasts</span> on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the <span class="hlt">system</span> to <span class="hlt">forecast</span> discharge extremes; and value is its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive <span class="hlt">forecasting</span> mode. The eventual goal is to transfer FEWS-World to operational <span class="hlt">forecasting</span> mode, where the <span class="hlt">system</span> will use operational seasonal <span class="hlt">forecasts</span> from the European Center for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF). The results will be disseminated on the internet to provide valuable information for users in data and model-poor regions of the world. The preliminary skill assessment of PCR-GLOBWB in reproducing flow extremes is carried out for a selection of 20 large rivers of the world. The model is run for a historical period, with a meteorological forcing data set based on observations from the Climate Research Unit of the University of East Anglia, and the ERA-40 reanalysis of ECMWF. Model skill in reproducing monthly anomalies as well as floods and droughts is assessed by applying verification measures developed for deterministic meteorological <span class="hlt">forecasts</span>. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title7-vol11/pdf/CFR-2011-title7-vol11-sec1710-205.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title7-vol11/pdf/CFR-2011-title7-vol11-sec1710-205.pdf"><span id="translatedtitle">7 CFR 1710.205 - Minimum approval requirements for all <span class="hlt">load</span> <span class="hlt">forecasts</span>.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... 7 Agriculture 11 2011-01-01 2011-01-01 false Minimum approval requirements for all <span class="hlt">load</span> <span class="hlt">forecasts</span>. 1710.205 Section 1710.205 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004cosp...35.1730B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004cosp...35.1730B"><span id="translatedtitle">The <span class="hlt">forecasting</span> Ocean assimilation model (FOAM) <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bell, M. J.; Acreman, D.; Barciela, R.; Hines, A.; Martin, M. J.; Sellar, A.; Stark, J.; Storkey, D.</p> <p></p> <p>The FOAM <span class="hlt">system</span> is built around the ocean and sea-ice components of the Met Office's Unified Model (UM), developed by the Hadley Centre for coupled ocean-ice-atmosphere climate prediction. It is forced by 6-hourly surface fluxes from the Met Office's Numerical Weather Prediction (NWP) <span class="hlt">system</span>, and assimilates temperature and salinity profiles from in situ instruments, surface temperature, sea-ice concentration and sea surface height data. A coarse resolution global configuration of FOAM on a 1 ° latitude-longitude grid with 20 vertical levels was implemented in the Met Office's operational suite in 1997. Nested models with grid spacings ranging from 30 km to 6 km are used to provide detailed <span class="hlt">forecasts</span> for selected regions. The models are run each morning and typically produce 5-day <span class="hlt">forecasts</span>. Real-time daily and archived analyses for the North Atlantic are freely available at http://nerc-essc.reading.ac.uk/las for research and developmentpurposes. We will present results from studies of the accuracy of the <span class="hlt">forecasts</span> and how it depends on the data types assimilated and the assimilation scheme used. We will also briefly describe the developments being made to assimilate sea-ice concentration and velocity data and incorporate the HadOCC NPZD (nutrient-phytoplankton-zooplankton-detritus) model and assimilation of ocean colour data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016SPD....4720701L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016SPD....4720701L&link_type=ABSTRACT"><span id="translatedtitle">The Discriminant Analysis Flare <span class="hlt">Forecasting</span> <span class="hlt">System</span> (DAFFS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.</p> <p>2016-05-01</p> <p>The Discriminant Analysis Flare <span class="hlt">Forecasting</span> <span class="hlt">System</span> (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk <span class="hlt">forecasts</span> of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides <span class="hlt">forecasts</span> which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day <span class="hlt">forecasts</span>), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant <span class="hlt">system</span> design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H23F1685M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H23F1685M"><span id="translatedtitle">Hydrological <span class="hlt">Forecasting</span> in Mexico: Extending the University of Washington West-wide Seasonal Hydrologic <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Munoz-Arriola, F.; Thomas, G.; Wood, A.; Wagner-Gomez, A.; Lobato-Sanchez, R.; Lettenmaier, D. P.</p> <p>2007-12-01</p> <p>Hydrologic <span class="hlt">forecasting</span> in areas constrained by the availability of hydrometeorological records is a notable challenge in water resource management. Techniques from the University of Washington West-wide Seasonal Hydrologic <span class="hlt">Forecast</span> <span class="hlt">system</span> www.hydro.washington.edu/<span class="hlt">forecast</span>/westwide) for generating daily nowcasts in areas with sparse and time-varying station coverage have been extended from the western U.S. into Mexico. The primary <span class="hlt">forecasting</span> approaches consist of ensembles based on the NWS ensemble streamflow prediction method (ESP; essentially resampling of climatology) and on NCEP Coupled <span class="hlt">Forecast</span> <span class="hlt">System</span> (CFS) outputs. These in turn are used to force the Variable Infiltration Capacity (VIC) macroscale hydrology model to produce streamflow ensembles. The initial hydrologic state utilized in the seasonal <span class="hlt">forecasting</span> is generated by VIC using daily real-time hydrologic nowcasts, produced using forcings derived via an 'index-station percentile' approach from meteorological station data accessed in real time from Servicio Meteorológico Nacional (SMN). One-year lead time streamflow <span class="hlt">forecasts</span> at monthly time step are produced at a set of major river locations in Mexico. As a case study, the streamflow <span class="hlt">forecasts</span>, along with <span class="hlt">forecasts</span> of reservoir evaporation, are used as input to the Simulation-Optimization (SIMOP) model of the Rio Yaqui <span class="hlt">system</span>, one of the major agricultural production centers of Mexico. This is the first step in an eventual planned water management implementation over all of Mexico.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title7-vol11/pdf/CFR-2010-title7-vol11-sec1710-206.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title7-vol11/pdf/CFR-2010-title7-vol11-sec1710-206.pdf"><span id="translatedtitle">7 CFR 1710.206 - Approval requirements for <span class="hlt">load</span> <span class="hlt">forecasts</span> prepared pursuant to approved <span class="hlt">load</span> <span class="hlt">forecast</span> work plans.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual <span class="hlt">loads</span>. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title7-vol11/pdf/CFR-2014-title7-vol11-sec1710-206.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title7-vol11/pdf/CFR-2014-title7-vol11-sec1710-206.pdf"><span id="translatedtitle">7 CFR 1710.206 - Approval requirements for <span class="hlt">load</span> <span class="hlt">forecasts</span> prepared pursuant to approved <span class="hlt">load</span> <span class="hlt">forecast</span> work plans.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual <span class="hlt">loads</span>. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title7-vol11/pdf/CFR-2012-title7-vol11-sec1710-206.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title7-vol11/pdf/CFR-2012-title7-vol11-sec1710-206.pdf"><span id="translatedtitle">7 CFR 1710.206 - Approval requirements for <span class="hlt">load</span> <span class="hlt">forecasts</span> prepared pursuant to approved <span class="hlt">load</span> <span class="hlt">forecast</span> work plans.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual <span class="hlt">loads</span>. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..1616899T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..1616899T&link_type=ABSTRACT"><span id="translatedtitle">The Mediterranean <span class="hlt">Forecasting</span> <span class="hlt">System</span>: recent developments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tonani, Marina; Oddo, Paolo; Korres, Gerasimos; Clementi, Emanuela; Dobricic, Srdjan; Drudi, Massimiliano; Pistoia, Jenny; Guarnieri, Antonio; Romaniello, Vito; Girardi, Giacomo; Grandi, Alessandro; Bonaduce, Antonio; Pinardi, Nadia</p> <p>2014-05-01</p> <p>Recent developments of the Mediterranean Monitoring and <span class="hlt">Forecasting</span> Centre of the EU-Copernicus marine service, the Mediterranean <span class="hlt">Forecasting</span> <span class="hlt">System</span> (MFS), are presented. MFS provides <span class="hlt">forecast</span>, analysis and reanalysis for the physical and biogeochemical parameters of the Mediterranean Sea. The different components of the <span class="hlt">system</span> are continuously updated in order to provide to the users the best available product. This work is focus on the physical component of the <span class="hlt">system</span>. The physical core of MFS is composed by an ocean general circulation model (NEMO) coupled with a spectral wave model (Wave Watch-III). The NEMO model provides to WW-III surface currents and SST fields, while WW-III returns back to NEMO the neutral component of the surface drag coefficient. Satellite Sea Level Anomaly observations and in-situ T & S vertical profiles are assimilated into this <span class="hlt">system</span> using a variational assimilation scheme based on 3DVAR (Dobricic, 2008) . Sensitive experiments have been performed in order to assess the impact of the assimilation of the latest available SLA missions, Altika and Cryosat together with the long term available mission of Jason2. The results show a significant improvement of the MFS skill due to the multi-mission along track assimilation. The primitive equations module has been recently upgraded with the introduction of the atmospheric pressure term and a new, explicit, numerical scheme has been adopted to solve the barotropic component of the equations of motion. The SLA satellite observations for data assimilation have been consequently modified in order to account for the new atmospheric pressure term introduced in the equations. This new <span class="hlt">system</span> has been evaluated using tide gauge coastal buoys and the satellite along track data. The quality of the SSH has improved significantly while a minor impact has been observed on the other state variables (temperature, salinity and currents). Experiments with a higher resolution NWP (numerical weather prediction</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AIPC.1635..817M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AIPC.1635..817M&link_type=ABSTRACT"><span id="translatedtitle">Performance of fuzzy approach in Malaysia short-term electricity <span class="hlt">load</span> <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee</p> <p>2014-12-01</p> <p>Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity <span class="hlt">load</span> <span class="hlt">forecasting</span> is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity <span class="hlt">load</span> <span class="hlt">forecasting</span>. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in <span class="hlt">forecasting</span> electricity <span class="hlt">load</span> when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity <span class="hlt">load</span>. The result indicated that day types, public holidays and several lags of electricity <span class="hlt">load</span> were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/6047545','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/6047545"><span id="translatedtitle">An adaptive nonlinear predictor with orthogonal escalator structure for short-term <span class="hlt">load</span> <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Lu, Q.C.; Grady, W.M.; Crawford, M.M.; Anderson, G.M.</p> <p>1989-02-01</p> <p>An adaptive Hammerstein model with an orthogonal escalator structure as well as a lattice structure for joint processes is developed for short-term <span class="hlt">load</span> <span class="hlt">forecasting</span> from one hour to several hours in the future. The method uses a Hammerstein nonlinear time-varying functional relationship between <span class="hlt">load</span> and temperature. Parameters in both linear and nonlinear parts of the predictor are updated systematically using a scalar orthogonalization procedure. Matrix operations are avoided, thereby allowing better model tracking ability, numerical properties, and performance. Prediction results using actual <span class="hlt">load</span>-temperature data demonstrate that this algorithm performs better than the commonly used matrix-oriented recursive least-square algorithm (RLS) for one-hour-ahead <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41A1148C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41A1148C"><span id="translatedtitle">Seasonal Runoff <span class="hlt">Forecasts</span> Based on the Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Version 2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, L.; Mo, K. C.; Shukla, S.; Lettenmaier, D. P.</p> <p>2012-12-01</p> <p>Seasonal runoff <span class="hlt">forecasts</span> are needed for many hydroclimatological applications, such as drought outlook, agricultural planning, seasonal hydrologic prediction, and multi-purpose reservoir management. Recently, NOAA National Centers for Environmental Prediction (NCEP) has transitioned to their second generation of the Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> (CFSv2) in operation. CFSv2 is a coupled ocean-atmosphere-land model with advanced physics, increased resolution, refined initialization, and improved land surface model, and provides <span class="hlt">forecasts</span> up to nine months in advance. Information on the accuracy and skill of the CFSv2 <span class="hlt">forecasts</span> is sought for the daily operation of many applications. In this study, we conduct an assessment of the prediction skill of seasonal runoff <span class="hlt">forecasts</span> from CFSv2 using its retrospective <span class="hlt">forecasts</span> from 1982 to 2009. <span class="hlt">Forecast</span> skill of spatially aggregated cumulative runoff (CR) from direct CFSv2 <span class="hlt">forecasts</span> and those obtained from the Variable Infiltration Capacity (VIC) model driven by daily precipitation, temperature, and wind <span class="hlt">forecasts</span> from CFSv2 (i.e., hydroclimate <span class="hlt">forecasts</span>) are compared with <span class="hlt">forecasts</span> based on the ensemble streamflow prediction (ESP) technique. All <span class="hlt">forecasts</span> are verified against historical VIC simulations with input forcing of precipitation and temperature derived from a set of 2131 high-quality index stations selected from the National Climatic Data Center's (NCDC's) Cooperative Observer stations across the contiguous United States. The monthly CR is spatially aggregated to 48 sub-regions created by merging the 221 U.S. Geological Survey (USGS) hydrologic sub-regions in order to evaluate regional characteristics. Preliminary results suggest that <span class="hlt">forecast</span> skill of CR is seasonally and regionally dependent. Direct runoff <span class="hlt">forecasts</span> from CFSv2 have the lowest skill on average, indicating limited use for hydrological drought prediction. Month-1 CR prediction from hydroclimate <span class="hlt">forecasts</span> is superior than that from the other two <span class="hlt">forecast</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26058571','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26058571"><span id="translatedtitle">Internal phosphorus <span class="hlt">load</span> in a Mexican reservoir: <span class="hlt">forecast</span> and validation.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hansen, Anne M; Márquez-Pacheco, Henri</p> <p>2015-11-01</p> <p>To determine the internal phosphorus <span class="hlt">load</span> (IPL) as a function of redox potential (Eh) in a Mexican reservoir, the results from a phosphorus (P) release experiment were extrapolated to temporal and spatial variations of Eh in sediments, and an IPL-Eh of 24.2 ± 2.5 t/yr was obtained. This result is compared with the P mass balance (MB) in the reservoir, where the IPL-MB is determined as the difference between P inputs to the reservoir and the outputs. Inputs of P are the sum of the external P <span class="hlt">load</span> from the hydrological basin, the IPL, and P in atmospheric precipitation; outputs of P are the sum of sedimented P, and the removal of P in water and biomass, and the resulting IPL-MB, is 26.4 ± 4.9 t/yr. In addition, P concentrations in sediment cores (SCs) are analyzed, and the historical release of P from sediments determined, resulting in an IPL-SC of 23.5 ± 1.4 t/yr. The different IPL results are similar, as average values are within the standard deviation of IPL-MB. It is concluded that analysis of the variations in Eh in sediments allows determination of the reservoir's IPL. Six-weekly IPL-Eh and IPL-MB values are analyzed, and it can be seen that IPL occurs mainly during the period from May to August, when the water column is thermally stratified. PMID:26058571</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.4443H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.4443H&link_type=ABSTRACT"><span id="translatedtitle">The Red Sea Modeling and <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hoteit, Ibrahim; Gopalakrishnan, Ganesh; Latif, Hatem; Toye, Habib; Zhan, Peng; Kartadikaria, Aditya R.; Viswanadhapalli, Yesubabu; Yao, Fengchao; Triantafyllou, George; Langodan, Sabique; Cavaleri, Luigi; Guo, Daquan; Johns, Burt</p> <p>2015-04-01</p> <p>Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological <span class="hlt">systems</span> in the global ocean. This contribution will present our efforts to build advanced modeling and <span class="hlt">forecasting</span> capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling <span class="hlt">system</span> compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research <span class="hlt">Forecasting</span> (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling <span class="hlt">system</span> (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5946S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5946S"><span id="translatedtitle">The Canadian coupled multi-seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sebatian Fontecilla, Juan</p> <p>2013-04-01</p> <p>The Canadian coupled multi-seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span> Since a year now, the Meteorological Service of Canada has its first coupled operational multi-seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span>. The Canadian Meteorological Centre (CMC) in collaboration with the Canadian Centre for Climate Modeling and Analysis (CCCma) has implemented a one-tier climate prediction <span class="hlt">system</span> which has replaced the old two-tier 4 model <span class="hlt">forecasting</span> <span class="hlt">system</span> used for <span class="hlt">forecasts</span> of months 1 to 4, and the CCA statistical <span class="hlt">forecasting</span> <span class="hlt">system</span> used for <span class="hlt">forecasts</span> of months 4 to 12. The coupled atmosphere-ocean-sea ice <span class="hlt">system</span> combines ensemble <span class="hlt">forecasts</span> from the CanCM3 and CanCM4 versions of CCCma's coupled global climate model and provide dynamical atmospheric, oceanic and sea ice predictions for lead times out to 12 months. This <span class="hlt">system</span>, developed under the second Coupled Historical <span class="hlt">Forecasting</span> Project (CHFP2) will be described briefly. <span class="hlt">Forecast</span> skill improvements will be shown. The implementation of this new <span class="hlt">system</span> permits the issuance of ENSO and arctic sea ice <span class="hlt">forecasts</span>, which were not possible before. The predictive skill of NINO3.4 index from this new coupled <span class="hlt">system</span> will compared against the skill from other centers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H41J..07T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H41J..07T&link_type=ABSTRACT"><span id="translatedtitle">Advances in Global Flood <span class="hlt">Forecasting</span> <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thielen-del Pozo, J.; Pappenberger, F.; Burek, P.; Alfieri, L.; Kreminski, B.; Muraro, D.</p> <p>2012-12-01</p> <p>A trend of increasing number of heavy precipitation events over many regions in the world during the past century has been observed (IPCC, 2007), but conclusive results on a changing frequency or intensity of floods have not yet been established. However, the socio-economic impact particularly of floods is increasing at an alarming trend. Thus anticipation of severe events is becoming a key element of society to react timely to effectively reduce socio-economic damage. Anticipation is essential on local as well as on national or trans-national level since management of response and aid for major disasters requires a substantial amount of planning and information on different levels. Continental and trans-national flood <span class="hlt">forecasting</span> <span class="hlt">systems</span> already exist. The European Flood Awareness <span class="hlt">System</span> (EFAS) has been developed in close collaboration with the National services and is going operational in 2012, enhancing the national <span class="hlt">forecasting</span> centres with medium-range probabilistic added value information while at the same time providing the European Civil Protection with harmonised information on ongoing and upcoming floods for improved aid management. Building on experiences and methodologies from EFAS, a Global Flood Awareness <span class="hlt">System</span> (GloFAS) has now been developed jointly between researchers from the European Commission Joint Research Centre (JRC) and the European Centre for Medium-Range Weather <span class="hlt">Forecast</span> (ECWMF). The prototype couples HTESSEL, the land-surface scheme of the ECMWF NWP model with the LISFLOOD hydrodynamic model for the flow routing in the river network. GloFAS is set-up on global scale with horizontal grid spacing of 0.1 degree. The <span class="hlt">system</span> is driven with 51 ensemble members from VAREPS with a time horizon of 15 days. In order to allow for the routing in the large rivers, the coupled model is run for 45 days assuming zero rainfall after day 15. Comparison with observations have shown that in some rivers the <span class="hlt">system</span> performs quite well while in others the hydro</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H53G1499W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H53G1499W"><span id="translatedtitle">Setup of the GLOWASIS seasonal global water scarcity <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winsemius, H.; Weerts, A.; Candogan, N.; Dutra, E.; van Beek, R.; Wisser, D.; Westerhoff, R.</p> <p>2011-12-01</p> <p>The EU-FP7 project "Global Water Scarcity Information Service" (GLOWASIS) is aimed at pre-validating a GMES Global Service for Water Scarcity Information. This includes improving and piloting our ability to <span class="hlt">forecast</span> water scarcity at global scale. Here, we present first results of the GLOWASIS seasonal global water scarcity <span class="hlt">forecasting</span> <span class="hlt">system</span>. This <span class="hlt">forecasting</span> <span class="hlt">system</span> provides seasonal probabilistic <span class="hlt">forecasts</span> of water scarcity indicators over the whole globe. The <span class="hlt">system</span> is built within the data and model integration shell Delft-FEWS. The GLOWASIS <span class="hlt">system</span> integrates reanalysis data from the European Centre for Medium-ranged Weather <span class="hlt">Forecasts</span> (ECMWF), ECMWF seasonal probabilistic <span class="hlt">forecasts</span>, information on water demand and use, the global hydrological model PCRGLOB-WB and user interfacing. The <span class="hlt">system</span> can provide a <span class="hlt">forecast</span> each month with a lead time of 6 months with daily time steps. Given the large amounts of data and computation time required to run a full <span class="hlt">forecast</span> ensemble, the <span class="hlt">system</span> is set up to run ensembles over multiple cores. A large number of hindcasts are made with the <span class="hlt">system</span>. These hindcasts are used to demonstrate which water scarcity indicators are useful to <span class="hlt">forecast</span> at seasonal time scales, where these indicators may provide satisfactory skill and with which lead time they can be meaningfully <span class="hlt">forecasted</span>. Further investigation will focus on improvement of skill by means of data assimilation of remotely sensed data sources such as soil moisture, snow water equivalent and water levels, and by better parameterisation of the hydrological model</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC33C..03W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC33C..03W"><span id="translatedtitle">Drought Monitoring and <span class="hlt">Forecasting</span> Using the Princeton/U Washington National Hydrologic <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.</p> <p>2011-12-01</p> <p>Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic <span class="hlt">forecast</span> <span class="hlt">system</span> can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a <span class="hlt">system</span>. Therefore it is important to understand the <span class="hlt">forecast</span> skill of the <span class="hlt">system</span> before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) <span class="hlt">system</span>, are assessed for <span class="hlt">forecasting</span> skill in drought prediction across the U.S., both singularly and as a multi-model <span class="hlt">system</span> The Princeton/U Washington national hydrologic monitoring and <span class="hlt">forecast</span> <span class="hlt">system</span> is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological <span class="hlt">forecast</span> <span class="hlt">system</span> to support U.S. operational drought prediction. Using our <span class="hlt">system</span>, the seasonal <span class="hlt">forecasts</span> are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal <span class="hlt">forecasts</span> of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information <span class="hlt">System</span> (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the <span class="hlt">forecast</span> <span class="hlt">system</span> to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2902173','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2902173"><span id="translatedtitle">Skill assessment for an operational algal bloom <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.</p> <p>2010-01-01</p> <p>An operational <span class="hlt">forecast</span> <span class="hlt">system</span> for harmful algal blooms (HABs) in southwest Florida is analyzed for <span class="hlt">forecasting</span> skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB <span class="hlt">forecasts</span> are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These <span class="hlt">forecasts</span> include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification <span class="hlt">forecasts</span>, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport <span class="hlt">forecasts</span> of HABs are also evaluated against the water samples. Due to the resolution of the <span class="hlt">forecasts</span> and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation <span class="hlt">forecasts</span> were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a <span class="hlt">forecast</span> accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the <span class="hlt">forecast</span> could be meaningfully assessed. The results show that the <span class="hlt">forecasts</span> identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive <span class="hlt">forecasts</span> occurred at any given beach. As the <span class="hlt">forecasts</span> were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate <span class="hlt">forecast</span></p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JMS....76..151S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JMS....76..151S"><span id="translatedtitle">Skill assessment for an operational algal bloom <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.</p> <p>2009-02-01</p> <p>An operational <span class="hlt">forecast</span> <span class="hlt">system</span> for harmful algal blooms (HABs) in southwest Florida is analyzed for <span class="hlt">forecasting</span> skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB <span class="hlt">forecasts</span> are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These <span class="hlt">forecasts</span> include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification <span class="hlt">forecasts</span>, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport <span class="hlt">forecasts</span> of HABs are also evaluated against the water samples. Due to the resolution of the <span class="hlt">forecasts</span> and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation <span class="hlt">forecasts</span> were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a <span class="hlt">forecast</span> accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the <span class="hlt">forecast</span> could be meaningfully assessed. The results show that the <span class="hlt">forecasts</span> identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive <span class="hlt">forecasts</span> occurred at any given beach. As the <span class="hlt">forecasts</span> were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate <span class="hlt">forecast</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003ITEIS.123.1847T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003ITEIS.123.1847T"><span id="translatedtitle">Development of <span class="hlt">Load</span> Balancing <span class="hlt">Systems</span> in a Parallel MRP <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tsukishima, Takahiro; Sato, Masahiro; Onari, Hisashi</p> <p></p> <p>The application of parallel computing <span class="hlt">system</span> to MRP (Material Requirements Planning) is essential to achieve a real-time demand <span class="hlt">forecasting</span> for a whole Supply Chain which consists of Multiple enterprises near future. The MRP using loosely connected multi-computer <span class="hlt">system</span> is examined here. New methods of synchronization, <span class="hlt">load</span> balancing and data access are required to keep high parallel efficiency increasing PE’s(Processing Elements). In this paper <span class="hlt">load</span> balancing and data access methods are proposed. The prototype <span class="hlt">system</span> can keep 96% parallel efficiency for the MRP with 120, 000 items on the 6 PE’s structure and can be robust against unbalanced <span class="hlt">load</span>. The processing speed increases in liner fashion.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4544Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4544Z"><span id="translatedtitle">An Operational Environmental Meteorology <span class="hlt">Forecasting</span> <span class="hlt">system</span> for Eastern China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhou, Guangqiang; Xu, Jianming; Xie, Ying; Wu, Jianbin; Yu, Zhongqi; Chang, Luyu</p> <p>2015-04-01</p> <p>Since 2012 an operational environmental meteorology <span class="hlt">forecasting</span> <span class="hlt">system</span> was setup to provide daily <span class="hlt">forecasts</span> of environmental meteorology pollutants for the Eastern China region. Initialized with 0.5 degree GFS meteorological fields, the <span class="hlt">system</span> uses the WRF-Chem model to provide daily 96-hour <span class="hlt">forecasts</span>. Model <span class="hlt">forecasts</span> for meteorological fields and pollutants concentrations (e.g. PM2.5 and O3) as well as haze conditions are displayed through an open platform. Verifications of the model results in terms of statistical and graphical products are also displayed at the website. Currently, the modeling <span class="hlt">system</span> provides strong support for the daily AQI <span class="hlt">forecasting</span> of Shanghai, and it also provides guidance products for other meteorological agencies in the Eastern China region. Here the modeling <span class="hlt">system</span> design will be presented, together with long-term verification results for PM2.5 and O3<span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/649899','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/649899"><span id="translatedtitle">A multi-echelon menu item <span class="hlt">forecasting</span> <span class="hlt">system</span> for hospitals.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Messersmith, A M; Moore, A N; Hoover, L W</p> <p>1978-05-01</p> <p>A multi-echelon <span class="hlt">system</span> was designed to generate statistical <span class="hlt">forecasts</span> of menu-item demand in hospitals from one- through twenty-eight-day intervals prior to patient meal service. The three interdependent echelons were: (1) <span class="hlt">Forecasting</span> patient census, (2) estimating diet category census, and (3) calculating menu-item demand. Eighteen weeks of supper data were utilized to analyze diet category distribution patterns and menu-item preferences, to test <span class="hlt">forecasting</span> models, and to evaluate the performance of the <span class="hlt">forecasting</span> <span class="hlt">system</span>. A cost function was used to evaluate the efficiency of the mathematical <span class="hlt">forecasting</span> <span class="hlt">system</span> and manual technique over a nine-week period. The cost of menu-item <span class="hlt">forecast</span> errors resulting from the use of adaptive exponential smoothing and Box-Jenkins formulations was approximately 40 per cent less than costs associated with the manual <span class="hlt">system</span>. PMID:649899</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012EGUGA..1411921W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012EGUGA..1411921W&link_type=ABSTRACT"><span id="translatedtitle">Skill of global hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span> FEWS GLOWASIS using climatic ESP <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weerts, A. H.; Candogan, N.; Winsemius, H. C.; van Beek, R.; Westerhoff, R.</p> <p>2012-04-01</p> <p><span class="hlt">Forecasting</span> of water availability and scarcity is a prerequisite for the management of hydropower reservoirs, basin-scale management of water resources, agriculture and disaster relief. The EU 7th Framework Program project Global Water Scarcity Information Service (GLOWASIS) aims to pre-validate a service that provides real-time global-scale information on water scarcity. In this contribution, we demonstrate what skill (compared to a climatology) may be reached with a global seasonal ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span> consisting of: a) a global hydrological model PCR-GLOBWB; b) an updating procedure for initial <span class="hlt">forecasting</span> states, based on the best-guess global rainfall information. As best guess, a combination of ERA-Interim Reanalysis rainfall and Global Precipitation Climatology Project (GPCP) observations is being used; c) a <span class="hlt">forecast</span> based on Ensemble Streamflow Prediction (ESP)procedure and reverse ESP procedure (Wood and Lettenmaier, 2008). In the ESP procedure, a meteorological <span class="hlt">forecast</span> ensemble is generated based on past years of observation series (i.e. climatological observations). As observations, the combination of ERA-Interim and GPCP is used. In reverse ESP, an ensemble is generated by using a set of initial states from a large sample of updates at the specific month of interest, and <span class="hlt">forecasts</span> are produced using one observed set. This analysis allows us to measure how much skill may be expected from hydrological inertia and climatology alone, leaving aside for the moment potential skill improvement by using seasonal meteorological <span class="hlt">forecasts</span>. In future work, we will measure how much skill improvement compared to the <span class="hlt">forecasts</span> mentioned above may be reached, when ECMWF Seasonal <span class="hlt">forecasts</span> are used. This will allow us to measure the contributions to skill of each component (initial state inertia, hydrology and meteorological inputs) of the <span class="hlt">forecast</span> <span class="hlt">system</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E.195B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E.195B"><span id="translatedtitle"><span class="hlt">System</span> Science approach to Space Weather <span class="hlt">forecast</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Balikhin, Michael A.</p> <p></p> <p>There are many dynamical <span class="hlt">systems</span> in nature that are so complex that mathematical models of their behaviour can not be deduced from first principles with the present level of our knowledge. Obvious examples are organic cell, human brain, etc often attract <span class="hlt">system</span> scientists. A example that is closer to space physics is the terrestrial magnetosphere. The <span class="hlt">system</span> approach has been developed to understand such complex objects from the observation of their dynamics. The <span class="hlt">systems</span> approach employs advanced data analysis methodologies to identify patterns in the overall <span class="hlt">system</span> behaviour and provides information regarding the linear and nonlinear processes involved in the dynamics of the <span class="hlt">system</span>. This, in combination with the knowledge deduced from the first principles, creates the opportunity to find mathematical relationships that govern the evolution of a particular physical <span class="hlt">system</span>. Advances and problems of <span class="hlt">systems</span> science applications to provide a reliable <span class="hlt">forecasts</span> of space weather phenomena such as geomagnetic storms, substorms and radiation belts particle fluxes are reviewed and compared with the physics based models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4273C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4273C"><span id="translatedtitle">Skill of a global seasonal ensemble streamflow <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc</p> <p>2013-04-01</p> <p><span class="hlt">Forecasting</span> of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow <span class="hlt">forecasts</span> are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological <span class="hlt">forecasting</span> on a global scale could be valuable especially for developing regions of the world, where effective hydrological <span class="hlt">forecasting</span> <span class="hlt">systems</span> are scarce. In this study, we investigate the <span class="hlt">forecasting</span> skill of the global seasonal streamflow <span class="hlt">forecasting</span> <span class="hlt">system</span> FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive <span class="hlt">forecasting</span> mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal <span class="hlt">forecasting</span> with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological <span class="hlt">forecasts</span>. Thus the meteorological forcing errors were not assessed. Yet, in a <span class="hlt">forecasting</span> setup, the predictive skill of a hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span> is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive <span class="hlt">forecasting</span> mode, the model is forced with actual ensemble <span class="hlt">forecasts</span> from the seasonal <span class="hlt">forecast</span> archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A51I..01K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A51I..01K"><span id="translatedtitle">The NCEP Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Reanalysis (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kistler, R.</p> <p>2010-12-01</p> <p>The NCEP Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Reanalysis (CFSR) was completed for the 31-year period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high resolution, coupled atmosphere-ocean-land surface-sea ice <span class="hlt">system</span> to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real time product into the future. New features of the CFSR include (1) coupling of atmosphere and ocean during the generation of the 6 hour guess field, (2) an interactive sea-ice model, and (3) assimilation of satellite radiances by the Grid-point Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean’s latitudinal spacing is 0.25 deg at the equator, extending to a global 0.5 deg beyond the tropics, with 40 levels to a depth of 4737m. The global land surface model has 4 soil levels and the global sea ice model has 3 layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979-2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in-situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled smooth transitions of the climate record due to evolutionary changes in the satellite observing <span class="hlt">system</span>. CFSR atmospheric, oceanic and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5 deg x 0.5 deg in latitude and longitude. The CFSR data will be distributed by NCDC and NCAR. This reanalysis will serve many purposes, including providing the basis for most of NCEP Climate Prediction Center’s operational climate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.B41F0124L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.B41F0124L&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Forecasting</span> the Performance of Agroforestry <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Luedeling, E.; Shepherd, K.</p> <p>2014-12-01</p> <p>Agroforestry has received considerable attention from scientists and development practitioners in recent years. It is recognized as a cornerstone of many traditional agricultural <span class="hlt">systems</span>, as well as a new option for sustainable land management in currently treeless agricultural landscapes. Agroforestry <span class="hlt">systems</span> are diverse, but most manifestations supply substantial ecosystem services, including marketable tree products, soil fertility, water cycle regulation, wildlife habitat and carbon sequestration. While these benefits have been well documented for many existing <span class="hlt">systems</span>, projecting the outcomes of introducing new agroforestry <span class="hlt">systems</span>, or <span class="hlt">forecasting</span> <span class="hlt">system</span> performance under changing environmental or climatic conditions, remains a substantial challenge. Due to the various interactions between <span class="hlt">system</span> components, the multiple benefits produced by trees and crops, and the host of environmental, socioeconomic and cultural factors that shape agroforestry <span class="hlt">systems</span>, mechanistic models of such <span class="hlt">systems</span> quickly become very complex. They then require a lot of data for site-specific calibration, which presents a challenge for their use in new environmental and climatic domains, especially in data-scarce environments. For supporting decisions on the scaling up of agroforestry technologies, new projection methods are needed that can capture <span class="hlt">system</span> complexity to an adequate degree, while taking full account of the fact that data on many <span class="hlt">system</span> variables will virtually always be highly uncertain. This paper explores what projection methods are needed for supplying decision-makers with useful information on the performance of agroforestry in new places or new climates. Existing methods are discussed in light of these methodological needs. Finally, a participatory approach to performance projection is proposed that captures <span class="hlt">system</span> dynamics in a holistic manner and makes probabilistic projections about expected <span class="hlt">system</span> performance. This approach avoids the temptation to take</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5824A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5824A"><span id="translatedtitle">Exercises for the VAST demonstration volcanic ash <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arnold, Delia; Bialek, Jakub; O'Dowd, Collin; Iren Kristiansen, Nina; Martin, Damien; Maurer, Christian; Miklos, Erika; Prata, Fred; Radulescu, Razvan; Sollum, Espen; Sofiev, Mikhail; Stebel, Kerstin; Stohl, Andreas; Vira, Julius; Wotawa, Gerhard</p> <p>2014-05-01</p> <p>Within the ESA-funded international project VAST (Volcanic Ash Strategic Initiative Team) a demonstration service for volcanic ash <span class="hlt">forecasting</span> and source term estimate is planned. This service takes advantage of the operationally available EO data for constraining the source term and multi-input and multi-model ensemble approaches to account, at a certain extent, for the uncertainties associated to the meteorological data used to drive the <span class="hlt">forecast</span> models and the models themselves. In order to test the approach and current capabilities of the team, a set of exercises was carried out in 2013 including fictitious scenarios that would potentially affect the European airspace giving significant fine ash <span class="hlt">loads</span> at usual cruise levels. The recent activity of Etna, with events in Autumn and Winter 2013 with clear transport over Europe, is providing a good test case for the evaluation of the <span class="hlt">system</span>, from the early warning to the ensemble modeling tools, in a real case scenario. Although the releases were not a potential threat for aviation at an European scale, the local airport of Catania, at a close distance, was affected. For one recent Etna eruption and the former exercises we present here the performance of the <span class="hlt">system</span> and the ensemble results. The combination atmospheric dispersion model-meteorology used are: FLEXPART-ECMWF/GFS/WRF, WRF-Chem and SILAM.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AdSR....8...77M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AdSR....8...77M"><span id="translatedtitle">First outcomes from the CNR-ISAC monthly <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mastrangelo, D.; Malguzzi, P.; Rendina, C.; Drofa, O.; Buzzi, A.</p> <p>2012-04-01</p> <p>A monthly probabilistic <span class="hlt">forecasting</span> <span class="hlt">system</span> is experimentally operated at the ISAC institute of the National Council of Research of Italy. The <span class="hlt">forecasting</span> <span class="hlt">system</span> is based on GLOBO, an atmospheric general circulation model developed at the same institute. The model is presently run on a monthly basis to produce an ensemble of 32 <span class="hlt">forecasts</span> initialized with GFS-NCEP perturbed analyses. Reforecasts, initialized with ECMWF ERA-Interim reanalyses of the 1989-2009 period, are also produced to determine modelled climatology of the month to <span class="hlt">forecast</span>. The modelled monthly climatology is then used to calibrate the ensemble <span class="hlt">forecast</span> of daily precipitation, geopotential height and temperature on standard pressure levels. In this work, we present the <span class="hlt">forecasting</span> <span class="hlt">system</span> and a preliminary evaluation of the model systematic and <span class="hlt">forecast</span> errors in terms of non-probabilistic scores of the 500-hPa geopotential height. Results show that the proposed <span class="hlt">forecasting</span> <span class="hlt">system</span> outperforms the climatology in the first two weeks of integrations. The adopted calibration based on weighted bias correction is found to reduce the systematic and the <span class="hlt">forecast</span> errors.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.2008C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2008C"><span id="translatedtitle">Value assessment of a global hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Candogan Yossef, N.; Winsemius, H.; van Beek, L. P. H.; van Beek, E.; Bierkens, M. F. P.</p> <p>2012-04-01</p> <p>The inter-annual variability in streamflow presents risks and opportunities in the management of water resources <span class="hlt">systems</span>. Reliable hydrological <span class="hlt">forecasts</span>, effective communication and proper response allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological <span class="hlt">forecasting</span> <span class="hlt">systems</span>. A global <span class="hlt">forecasting</span> <span class="hlt">system</span> which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value for these regions. FEWS-World <span class="hlt">system</span> is developed for this purpose. It is based on the Delft-FEWS (flood early warning <span class="hlt">system</span>) developed by Deltares and incorporates the global hydrological model PCR-GLOBWB. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the <span class="hlt">system</span> to <span class="hlt">forecast</span> discharge extremes; and value as its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive <span class="hlt">forecasting</span> mode. For the assessment in historical simulation mode a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF) was used. For the assessment in retroactive <span class="hlt">forecasting</span> mode the model was forced with ensemble <span class="hlt">forecasts</span> from the seasonal <span class="hlt">forecast</span> archives of ECMWF. The eventual goal is to transfer FEWS-World to operational <span class="hlt">forecasting</span> mode, where the <span class="hlt">system</span> will use operational seasonal <span class="hlt">forecasts</span> from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world. The results of the preliminary assessment show that although <span class="hlt">forecasting</span> skill decreases with increasing lead time, the value of <span class="hlt">forecasts</span> does not necessarily decrease. The <span class="hlt">forecast</span> requirements and response options of several water related sectors was</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010IEITI..91.1234K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010IEITI..91.1234K&link_type=ABSTRACT"><span id="translatedtitle">Hybrid Intrusion <span class="hlt">Forecasting</span> Framework for Early Warning <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo</p> <p></p> <p>Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security <span class="hlt">systems</span> such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these <span class="hlt">systems</span> respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion <span class="hlt">forecasting</span> <span class="hlt">system</span> framework for an early warning <span class="hlt">system</span>. The proposed <span class="hlt">system</span> utilizes three types of <span class="hlt">forecasting</span> methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the <span class="hlt">forecasting</span> technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion <span class="hlt">forecasting</span> method outperforms each of three <span class="hlt">forecasting</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010IJTPE.130..329I&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010IJTPE.130..329I&link_type=ABSTRACT"><span id="translatedtitle">Daily Peak <span class="hlt">Load</span> <span class="hlt">Forecasting</span> of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki</p> <p></p> <p>By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to <span class="hlt">forecast</span> the movement of the electric power demand carefully in advance. And using that <span class="hlt">forecast</span> as the source, supply and demand management plan should be made. Thus <span class="hlt">load</span> <span class="hlt">forecasting</span> is said to be an important job among demand investment of electric power companies. So far, <span class="hlt">forecasting</span> method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of <span class="hlt">forecasting</span> accuracy. Those <span class="hlt">forecasting</span> accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new <span class="hlt">forecasting</span> method with higher accuracy is needed. In this paper, to develop the <span class="hlt">forecasting</span> accuracy of the former methods, the daily peak <span class="hlt">load</span> <span class="hlt">forecasting</span> method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830021507','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830021507"><span id="translatedtitle">Satellite freeze <span class="hlt">forecast</span> <span class="hlt">system</span>: Executive summary</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Martsolf, J. D. (Principal Investigator)</p> <p>1983-01-01</p> <p>A satellite-based temperature monitoring and prediction <span class="hlt">system</span> consisting of a computer controlled acquisition, processing, and display <span class="hlt">system</span> and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze <span class="hlt">forecasting</span> <span class="hlt">system</span> (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1230263-load-leveling-battery-system-costs','SCIGOV-ESTSC'); return false;" href="http://www.osti.gov/scitech/biblio/1230263-load-leveling-battery-system-costs"><span id="translatedtitle"><span class="hlt">Load</span> Leveling Battery <span class="hlt">System</span> Costs</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech/">Energy Science and Technology Software Center (ESTSC)</a></p> <p></p> <p>1994-10-12</p> <p>SYSPLAN evaluates capital investment in customer side of the meter <span class="hlt">load</span> leveling battery <span class="hlt">systems</span>. Such <span class="hlt">systems</span> reduce the customer's monthly electrical demand charge by reducing the maximum power <span class="hlt">load</span> supplied by the utility during the customer's peak demand. <span class="hlt">System</span> equipment consists of a large array of batteries, a current converter, and balance of plant equipment and facilities required to support the battery and converter <span class="hlt">system</span>. The <span class="hlt">system</span> is installed on the customer's side of themore » meter and controlled and operated by the customer. Its economic feasibility depends largely on the customer's <span class="hlt">load</span> profile. <span class="hlt">Load</span> shape requirements, utility rate structures, and battery equipment cost and performance data serve as bases for determining whether a <span class="hlt">load</span> leveling battery <span class="hlt">system</span> is economically feasible for a particular installation. Life-cycle costs for <span class="hlt">system</span> hardware include all costs associated with the purchase, installation, and operation of battery, converter, and balance of plant facilities and equipment. The SYSPLAN spreadsheet software is specifically designed to evaluate these costs and the reduced demand charge benefits; it completes a 20 year period life cycle cost analysis based on the battery <span class="hlt">system</span> description and cost data. A built-in sensitivity analysis routine is also included for key battery cost parameters. The life cycle cost analysis spreadsheet is augmented by a <span class="hlt">system</span> sizing routine to help users identify <span class="hlt">load</span> leveling <span class="hlt">system</span> size requirements for their facilities. The optional XSIZE <span class="hlt">system</span> sizing spreadsheet which is included can be used to identify a range of battery <span class="hlt">system</span> sizes that might be economically attractive. XSIZE output consisting of <span class="hlt">system</span> operating requirements can then be passed by the temporary file SIZE to the main SYSPLAN spreadsheet.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008ACP.....8.3473N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008ACP.....8.3473N"><span id="translatedtitle">Data assimilation of dust aerosol observations for the CUACE/dust <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niu, T.; Gong, S. L.; Zhu, G. F.; Liu, H. L.; Hu, X. Q.; Zhou, C. H.; Wang, Y. Q.</p> <p>2008-07-01</p> <p>A data assimilation <span class="hlt">system</span> (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment Dust (CUACE/Dust) <span class="hlt">forecast</span> <span class="hlt">system</span> and applied in the operational <span class="hlt">forecasts</span> of sand and dust storm (SDS) in spring 2006. The <span class="hlt">system</span> is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust <span class="hlt">loading</span> retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the <span class="hlt">forecasting</span> capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation <span class="hlt">system</span>, a 41% enhancement. The <span class="hlt">forecast</span> results with DAS usually agree with the dust <span class="hlt">loading</span> retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of <span class="hlt">forecast</span> model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020060754','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020060754"><span id="translatedtitle">Observing <span class="hlt">System</span> <span class="hlt">Forecast</span> Experiments at the DAO</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Atlas, Robert</p> <p>2001-01-01</p> <p>Since the advent of meteorological satellites in the 1960's, numerous experiments have been conducted in order to evaluate the impact of these and other data on atmospheric analysis and prediction. Such studies have included both OSE'S and OSSE's. The OSE's were conducted to evaluate the impact of specific observations or classes of observations on analyses and <span class="hlt">forecasts</span>. Such experiments have been performed for selected types of conventional data and for various satellite data sets as they became available. (See for example the 1989 ECMWF/EUMETSAT workshop proceedings on "The use of satellite data in operational numerical weather prediction" and the references contained therein.) The ODYSSEY were conducted to evaluate the potential for future observing <span class="hlt">systems</span> to improve Numerical Weather Prediction NWP and to plan for the Global Weather Experiment and more recently for EVANS (Atlas et al., 1985a; Arnold and Day, 1986; Hoffman et al., 1990). In addition, OSSE's have been run to evaluate trade-offs in the design of observing <span class="hlt">systems</span> and observing networks (Atlas and Emmitt, 1991; Rohaly and Krishnamurti, 1993), and to test new methodology for data assimilation (Atlas and Bloom, 1989).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007JMS....65..299A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007JMS....65..299A&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Forecasting</span> front displacements with a satellite based ocean <span class="hlt">forecasting</span> (SOFT) <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alvarez, A.; Orfila, A.; Basterretxea, G.; Tintoré, J.; Vizoso, G.; Fornes, A.</p> <p>2007-03-01</p> <p>Relatively long term time series of satellite data are nowadays available. These spatio-temporal time series of satellite observations can be employed to build empirical models, called satellite based ocean <span class="hlt">forecasting</span> (SOFT) <span class="hlt">systems</span>, to <span class="hlt">forecast</span> certain aspects of future ocean states. The <span class="hlt">forecast</span> skill of SOFT <span class="hlt">systems</span> predicting the sea surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly focussed on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatio-temporal variability is more complex and propagating structures are frequently present. In this case, traditional SOFT <span class="hlt">systems</span> based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction <span class="hlt">systems</span>. Instead, SOFT <span class="hlt">systems</span> based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases. In this work we study and compare the performance of an EOF and CEOF based SOFT <span class="hlt">systems</span> <span class="hlt">forecasting</span> the SST at weekly time scales of a propagating mesoscale structure. The SOFT <span class="hlt">system</span> was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from both SOFT <span class="hlt">systems</span> are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT <span class="hlt">systems</span> are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012PhDT........52P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012PhDT........52P&link_type=ABSTRACT"><span id="translatedtitle">Energy management of a university campus utilizing short-term <span class="hlt">load</span> <span class="hlt">forecasting</span> with an artificial neural network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Palchak, David</p> <p></p> <p>Electrical <span class="hlt">load</span> <span class="hlt">forecasting</span> is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term <span class="hlt">load</span> <span class="hlt">forecast</span> is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a <span class="hlt">forecasted</span> daily electrical <span class="hlt">load</span> profile. The proposed algorithm for short-term <span class="hlt">load</span> <span class="hlt">forecasting</span> is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical <span class="hlt">load</span> is evaluated using a number of error measurements that seek to quantify the best application of the <span class="hlt">forecast</span>. The energy management presented utilizes historical electrical <span class="hlt">load</span> data from the local service provider to optimize the time of day that electrical <span class="hlt">loads</span> are being managed. Finally, the utilization of <span class="hlt">forecasts</span> in the presented energy management scenario is evaluated based on cost and energy savings.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002SPIE.4544...11P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002SPIE.4544...11P&link_type=ABSTRACT"><span id="translatedtitle">SOFT project: a new <span class="hlt">forecasting</span> <span class="hlt">system</span> based on satellite data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim</p> <p>2002-01-01</p> <p>The aim of the SOFT project is to develop a new ocean <span class="hlt">forecasting</span> <span class="hlt">system</span> by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean <span class="hlt">forecasting</span> <span class="hlt">system</span> using genetic algorithms based on satellite data; and (2) to integrate the above new <span class="hlt">system</span> into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' <span class="hlt">systems</span> that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the <span class="hlt">forecast</span> skill will be carried out by comparing the <span class="hlt">forecasts</span> fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the <span class="hlt">forecasting</span> <span class="hlt">system</span>. Validation with in situ data will indicate the capabilities of the satellite based <span class="hlt">forecast</span> information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid <span class="hlt">systems</span> while the second set will be used to validate the hybrid and traditional numerical model results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012CG.....41...72G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012CG.....41...72G"><span id="translatedtitle">Improvement of the Valencia region ultraviolet index (UVI) <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gómez, I.; Marín, M. J.; Pastor, F.; Estrela, M. J.</p> <p>2012-04-01</p> <p>The CEAM Foundation (Valencia, Spain) has developed an operational ultraviolet index (UVI) <span class="hlt">forecasting</span> <span class="hlt">system</span> based on the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The main objective of this <span class="hlt">system</span> is to provide the general public with a tool to minimize the impact of ultraviolet (UV) radiation, which can cause important human health problems. The <span class="hlt">system</span> presented in this paper has been developed in collaboration with the Environment Department of the Regional Government of Valencia, and it replaces the one running until 2007. The new <span class="hlt">system</span> substitutes the previously used Ozone Monitoring Instrument (OMI) observed data with the total ozone column data <span class="hlt">forecasted</span> from the Global <span class="hlt">Forecasting</span> <span class="hlt">System</span> (GFS) model. This has allowed the <span class="hlt">forecasting</span> period to be increased from only 1 day in the original <span class="hlt">system</span> to 3 days, with daily updates. The UVI <span class="hlt">forecast</span> presented herein uses maps to show the hourly daytime evolution of the UV index on selected locations as well as the maximum UVI expected in the area of interest for the following 3 days (D, D+1, and D+2). The locations selected correspond to measurement stations equipped with erythemal radiation instruments. The UVI <span class="hlt">forecast</span> information, the erythemal radiation experimental data, and other outreach information are supplied to the public through both the CEAM Meteorology and Climatology Program Web page and the Environment Department of the Regional Government of Valencia Web page.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...622924V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...622924V"><span id="translatedtitle">Self-Organizing Maps-based ocean currents <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir</p> <p>2016-03-01</p> <p>An ocean surface currents <span class="hlt">forecasting</span> <span class="hlt">system</span>, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two <span class="hlt">systems</span> differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their <span class="hlt">forecasting</span> skills were tested on independent datasets. The SOM-based <span class="hlt">forecasting</span> <span class="hlt">system</span> has a slightly better <span class="hlt">forecasting</span> skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26979129','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26979129"><span id="translatedtitle">Self-Organizing Maps-based ocean currents <span class="hlt">forecasting</span> <span class="hlt">system</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir</p> <p>2016-01-01</p> <p>An ocean surface currents <span class="hlt">forecasting</span> <span class="hlt">system</span>, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two <span class="hlt">systems</span> differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their <span class="hlt">forecasting</span> skills were tested on independent datasets. The SOM-based <span class="hlt">forecasting</span> <span class="hlt">system</span> has a slightly better <span class="hlt">forecasting</span> skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training. PMID:26979129</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.3730B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.3730B"><span id="translatedtitle">Rainfall Hazards Prevention based on a Local Model <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buendia, F.; Ojeda, B.; Buendia Moya, G.; Tarquis, A. M.; Andina, D.</p> <p>2009-04-01</p> <p>Rainfall is one of the most important events of human life and society. Some rainfall phenomena like floods or hailstone are a threat to the agriculture, business and even life. However in the meteorological observatories there are methods to detect and alarm about this kind of events, nowadays the prediction techniques based on synoptic measurements need to be improved to achieve medium term feasible <span class="hlt">forecasts</span>. Any deviation in the measurements or in the model description makes the <span class="hlt">forecast</span> to diverge in time from the real atmosphere evolution. In this paper the advances in a local rainfall <span class="hlt">forecasting</span> <span class="hlt">system</span> based on time series estimation with General Regression Neural Networks are presented. The <span class="hlt">system</span> is introduced, explaining the measurements, methodology and the current state of the development. The aim of the work is to provide a complementary criteria to the current <span class="hlt">forecast</span> <span class="hlt">systems</span>, based on the daily atmosphere observation and tracking over a certain place.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4793242','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4793242"><span id="translatedtitle">Self-Organizing Maps-based ocean currents <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir</p> <p>2016-01-01</p> <p>An ocean surface currents <span class="hlt">forecasting</span> <span class="hlt">system</span>, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two <span class="hlt">systems</span> differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their <span class="hlt">forecasting</span> skills were tested on independent datasets. The SOM-based <span class="hlt">forecasting</span> <span class="hlt">system</span> has a slightly better <span class="hlt">forecasting</span> skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training. PMID:26979129</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JGRD..114.6206M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JGRD..114.6206M"><span id="translatedtitle">Aerosol analysis and <span class="hlt">forecast</span> in the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> Integrated <span class="hlt">Forecast</span> <span class="hlt">System</span>: Forward modeling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Morcrette, J.-J.; Boucher, O.; Jones, L.; Salmond, D.; Bechtold, P.; Beljaars, A.; Benedetti, A.; Bonet, A.; Kaiser, J. W.; Razinger, M.; Schulz, M.; Serrar, S.; Simmons, A. J.; Sofiev, M.; Suttie, M.; Tompkins, A. M.; Untch, A.</p> <p>2009-03-01</p> <p>This paper presents the aerosol modeling now part of the ECMWF Integrated <span class="hlt">Forecasting</span> <span class="hlt">System</span> (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the <span class="hlt">forecast</span> model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The <span class="hlt">forecast</span>-only model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation <span class="hlt">system</span> described in part 2 of this paper.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/287806','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/287806"><span id="translatedtitle">Marine <span class="hlt">loading</span> vapor control <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Babet, F.H.</p> <p>1996-09-01</p> <p>The EPA and State air quality control boards have mandated the collection and destruction or recovery of vapors generated by the <span class="hlt">loading</span> of some hydrocarbons and chemicals into marine vessels. This is a brief overview of the main US Coast Guard requirements for marine vapor control <span class="hlt">systems</span>. As with most regulations, they are open to interpretation. In an attempt to more clearly define the intent of the regulations, the US Coast Guard has issued guidelines to assist the certifying entities in ensuring compliance with intended regulations. If a company is contemplating the installation of a marine <span class="hlt">loading</span> vapor control <span class="hlt">system</span>, the authors strongly recommend that one engage the services of a certifying entity, either as the designer, or an advisor and ultimately the certifier of the <span class="hlt">system</span>. This should be done well up front in the design of the <span class="hlt">system</span> to avoid costly mistakes which can occur as a result of lack of knowledge or misinterpretation of the regulations and guidelines.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/sciencecinema/biblio/987290','SCIGOVIMAGE-SCICINEMA'); return false;" href="http://www.osti.gov/sciencecinema/biblio/987290"><span id="translatedtitle">Science and Engineering of an Operational Tsunami <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/sciencecinema/">ScienceCinema</a></p> <p>Gonzalez, Frank</p> <p>2010-01-08</p> <p>After a review of tsunami statistics and the destruction caused by tsunamis, a means of <span class="hlt">forecasting</span> tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning <span class="hlt">system</span>. The <span class="hlt">forecast</span> is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami <span class="hlt">forecast</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/987290','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/987290"><span id="translatedtitle">Science and Engineering of an Operational Tsunami <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Gonzalez, Frank</p> <p>2009-04-06</p> <p>After a review of tsunami statistics and the destruction caused by tsunamis, a means of <span class="hlt">forecasting</span> tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning <span class="hlt">system</span>. The <span class="hlt">forecast</span> is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami <span class="hlt">forecast</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/7047935','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/7047935"><span id="translatedtitle">Pattern fuel assembly <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Ahmed, H.J.; Gerkey, K.S.; Miller, T.W.; Wylie, M.E.</p> <p>1986-12-02</p> <p>This patent describes an interactive <span class="hlt">system</span> for facilitating preloading of fuel rods into magazines, which comprises: an operator work station adapted for positioning between a supply of fuel rods of predetermined types, and the magazine defining grid locations for a predetermined fuel assembly; display means associated with the work station; scanner means associated with the work station and adapted for reading predetermined information accompanying the fuel rods; a rectangular frame adapted for attachment to one end of the fuel assembly <span class="hlt">loading</span> magazine; prompter/detector means associated with the frame for detecting insertion of a fuel rod into the magazine; and processing means responsive to the scanner means and the sensing means for prompting the operator via the display means to pre-<span class="hlt">load</span> the fuel rods into desired grid locations in the magazine. An apparatus is described for facilitating pre-<span class="hlt">loading</span> of fuel rods in predetermined grid locations of a fuel assembly <span class="hlt">loading</span> magazine, comprising: a rectangular frame adapted for attachment to one end of the fuel assembly <span class="hlt">loading</span> magazine; and means associated with the frame for detecting insertion of fuel rods into the magazine.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/552797','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/552797"><span id="translatedtitle">Power <span class="hlt">system</span> very short-term <span class="hlt">load</span> prediction</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Trudnowski, D.J.; Johnson, J.M.; Whitney, P.</p> <p>1997-02-01</p> <p>A fundamental objective of a power-<span class="hlt">system</span> operating and control scheme is to maintain a match between the <span class="hlt">system`s</span> overall real-power <span class="hlt">load</span> and generation. To accurately maintain this match, modern energy management <span class="hlt">systems</span> require estimates of the future total <span class="hlt">system</span> <span class="hlt">load</span>. Several strategies and tools are available for estimating <span class="hlt">system</span> <span class="hlt">load</span>. Nearly all of these estimate the future <span class="hlt">load</span> in 1-hour steps over several hours (or time frames very close to this). While hourly <span class="hlt">load</span> estimates are very useful for many operation and control decisions, more accurate estimates at closer intervals would also be valuable. This is especially true for emerging Area Generation Control (AGC) strategies such as look-ahead AGC. For these short-term estimation applications, future <span class="hlt">load</span> estimates out to several minutes at intervals of 1 to 5 minutes are required. The currently emerging operation and control strategies being developed by the BPA are dependent on accurate very short-term <span class="hlt">load</span> estimates. To meet this need, the BPA commissioned the Pacific Northwest National Laboratory (PNNL) and Montana Tech (an affiliate of the University of Montana) to develop an accurate <span class="hlt">load</span> prediction algorithm and computer codes that automatically update and can reliably perform in a closed-loop controller for the BPA <span class="hlt">system</span>. The requirements include accurate <span class="hlt">load</span> estimation in 5-minute steps out to 2 hours. This report presents the results of this effort and includes: a methodology and algorithms for short-term <span class="hlt">load</span> prediction that incorporates information from a general hourly <span class="hlt">forecaster</span>; specific algorithm parameters for implementing the predictor in the BPA <span class="hlt">system</span>; performance and sensitivity studies of the algorithms on BPA-supplied data; an algorithm for filtering power <span class="hlt">system</span> <span class="hlt">load</span> samples as a precursor to inputting into the predictor; and FORTRAN 77 subroutines for implementing the algorithms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H31A0823P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H31A0823P"><span id="translatedtitle">Verification and error sources of the California Seasonal Hydrologic <span class="hlt">Forecast</span> (Cali<span class="hlt">Forecast</span>) <span class="hlt">System</span> over the Feather River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Park, G.; Imam, B.; Sorooshian, S.</p> <p>2008-12-01</p> <p>Operational water resource planning and management heavily rely on the seasonal streamflow <span class="hlt">forecasts</span> of reservoir. The California Hydrologic <span class="hlt">Forecast</span> <span class="hlt">System</span>, a regional implementation of the West-Wide Seasonal Hydrologic <span class="hlt">forecast</span> <span class="hlt">System</span> over the state of California at the University of California-Irvine in a 1/8th degree resolution, provides probabilistic <span class="hlt">forecasts</span> in the form of ensemble streamflow predictions (ESP) to facilitate our need in the state of California. Similar to any other hydrologic <span class="hlt">forecast</span> <span class="hlt">systems</span>, Cali<span class="hlt">Forecast</span>, however, contains significant <span class="hlt">forecast</span> errors and uncertainties that are propagated from many sources. These within the Cali<span class="hlt">Forecast</span> <span class="hlt">system</span>, includes uncertainty associated with the interpolation techniques (Index station method) for the precipitation input, validity of ESP with respect to the climate change, efficiency of snow assimilation scheme, error in naturalized streamflow, and many others. This presentation will attempt to verify the ESP <span class="hlt">forecasts</span> over the Feather River Basin that is a major tributary to the Sacramento River Basin, provide understanding of error sources using existing verification metrics, and finally suggest next steps towards improving <span class="hlt">forecast</span> skills.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/10166298','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/10166298"><span id="translatedtitle">Short-Termed Integrated <span class="hlt">Forecasting</span> <span class="hlt">System</span>: 1993 Model documentation report</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Not Available</p> <p>1993-05-01</p> <p>The purpose of this report is to define the Short-Term Integrated <span class="hlt">Forecasting</span> <span class="hlt">System</span> (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly <span class="hlt">forecasts</span> of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate <span class="hlt">forecasts</span> for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets <span class="hlt">forecasts</span> are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..524..789H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..524..789H"><span id="translatedtitle">Ensemble Bayesian <span class="hlt">forecasting</span> <span class="hlt">system</span> Part I: Theory and algorithms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herr, Henry D.; Krzysztofowicz, Roman</p> <p>2015-05-01</p> <p>The ensemble Bayesian <span class="hlt">forecasting</span> <span class="hlt">system</span> (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble <span class="hlt">forecaster</span> (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage <span class="hlt">forecasts</span> and probabilistic flood <span class="hlt">forecasts</span>) or even thousands (for probabilistic stage transition <span class="hlt">forecasts</span>). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian <span class="hlt">forecasting</span> <span class="hlt">system</span> with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble <span class="hlt">forecast</span> of large size. Such a <span class="hlt">forecast</span> quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010HESS...14.1639T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010HESS...14.1639T"><span id="translatedtitle">A past discharge assimilation <span class="hlt">system</span> for ensemble streamflow <span class="hlt">forecasts</span> over France - Part 2: Impact on the ensemble streamflow <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thirel, G.; Martin, E.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Regimbeau, F.; Habets, F.</p> <p>2010-08-01</p> <p>The use of ensemble streamflow <span class="hlt">forecasts</span> is developing in the international flood <span class="hlt">forecasting</span> services. Ensemble streamflow <span class="hlt">forecast</span> <span class="hlt">systems</span> can provide more accurate <span class="hlt">forecasts</span> and useful information about the uncertainty of the <span class="hlt">forecasts</span>, thus improving the assessment of risks. Nevertheless, these <span class="hlt">systems</span>, like all hydrological <span class="hlt">forecasts</span>, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation <span class="hlt">system</span>, on an ensemble streamflow prediction <span class="hlt">system</span> over France. An assimilation <span class="hlt">system</span> was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow <span class="hlt">forecasts</span> at Météo-France. This assimilation <span class="hlt">system</span>, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow <span class="hlt">forecasts</span> of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather <span class="hlt">Forecasts</span> (ECMWF) 10-day Ensemble Prediction <span class="hlt">System</span> (EPS). Two different configurations of the assimilation <span class="hlt">system</span> were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation <span class="hlt">system</span> on the ensemble streamflow <span class="hlt">forecasts</span> were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow <span class="hlt">forecasts</span>. It is shown that the assimilation <span class="hlt">system</span> improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5277448','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/biblio/5277448"><span id="translatedtitle">Automated fuel pin <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Christiansen, D.W.; Brown, W.F.; Steffen, J.M.</p> <p></p> <p>An automated <span class="hlt">loading</span> <span class="hlt">system</span> for nuclear reactor fuel elements utilizes a gravity feed conveyor which permits individual fuel pins to roll along a constrained path perpendicular to their respective lengths. The individual lengths of fuel cladding are directed onto movable transports, where they are aligned coaxially with the axes of associated handling equipment at appropriate production stations. Each fuel pin can be be reciprocated axially and/or rotated about its axis as required during handling steps. The fuel pins are inerted as a batch prior to welding of end caps by one of two disclosed welding <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/865643','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/servlets/purl/865643"><span id="translatedtitle">Automated fuel pin <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Christiansen, David W.; Brown, William F.; Steffen, Jim M.</p> <p>1985-01-01</p> <p>An automated <span class="hlt">loading</span> <span class="hlt">system</span> for nuclear reactor fuel elements utilizes a gravity feed conveyor which permits individual fuel pins to roll along a constrained path perpendicular to their respective lengths. The individual lengths of fuel cladding are directed onto movable transports, where they are aligned coaxially with the axes of associated handling equipment at appropriate production stations. Each fuel pin can be reciprocated axially and/or rotated about its axis as required during handling steps. The fuel pins are inserted as a batch prior to welding of end caps by one of two disclosed welding <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JEE....63..153S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JEE....63..153S"><span id="translatedtitle">Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term <span class="hlt">Load</span> <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj</p> <p>2012-05-01</p> <p>For power industries electricity <span class="hlt">load</span> <span class="hlt">forecast</span> plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning <italic>etc</italic>. A new technique for long term <span class="hlt">load</span> <span class="hlt">forecasting</span> (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and <span class="hlt">forecasted</span> data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014HESS...18.3353C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014HESS...18.3353C&link_type=ABSTRACT"><span id="translatedtitle">Real-time drought <span class="hlt">forecasting</span> <span class="hlt">system</span> for irrigation management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.</p> <p>2014-09-01</p> <p>In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. In dry periods, water shortage problems can be enhanced by conflicting uses of water, such as irrigation, industry and power production (hydroelectric and thermoelectric). Furthermore, in the last decade the social perspective in relation to this issue has been increasing due to the possible impact of climate change and global warming scenarios which emerge from the IPCC Fifth Assessment Report (IPCC, 2013). Hence, the increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the PREGI real-time drought <span class="hlt">forecasting</span> <span class="hlt">system</span>; PREGI is an Italian acronym that means "hydro-meteorological <span class="hlt">forecast</span> for irrigation management". The <span class="hlt">system</span>, planned as a tool for irrigation optimization, is based on meteorological ensemble <span class="hlt">forecasts</span> (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to <span class="hlt">forecast</span> the soil water content on a maize field in the Muzza Bassa Lodigiana (MBL) consortium in northern Italy. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR (time domain reflectivity) probes; the reliability of this <span class="hlt">forecasting</span> <span class="hlt">system</span> and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought <span class="hlt">forecasting</span> <span class="hlt">system</span> is able to have a high reliability of <span class="hlt">forecast</span> at least for 7-10 days ahead of time.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1513808V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1513808V"><span id="translatedtitle">FEWS Vecht, a crossing boundaries flood <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van Heeringen, Klaas-Jan; Filius, Pieter; Tromp, Gerben; Renner, Tobias</p> <p>2013-04-01</p> <p>The river Vecht is a cross boundary river, starting in Germany and flowing to the Netherlands. The river is completely dependant on rainfall in the catchment. Being one of the smaller big rivers in the Netherlands, there was still no operational <span class="hlt">forecasting</span> <span class="hlt">system</span> avaible because of the hugh number of involved organisations (2 in Germany, 5 in the Netherlands) and many other stake holders. In 2011 a first operational <span class="hlt">forecasting</span> <span class="hlt">system</span> has been build by using the Delft-FEWS software. It collects the real time fluvial and meteorological observations from all the organisations, in that sense being a portal where all the collected information is available and can be consistantly interpreted as a whole. In 2012 an HBV rainfall runoff model and a Sobek 1D hydraulic model has been build. These models have been integrated into the FEWS <span class="hlt">system</span> and are operationally running since the 2012 autumn. The <span class="hlt">system</span> <span class="hlt">forecasts</span> 5 days ahead using a 5 days ECMWF rainfall ensemble <span class="hlt">forecast</span>. It enables making scenarios, especially useful for the operation of storage reservoirs. During the 2012 Christmas days a (relatively small) T=2 flood occurred (Q=175-200 m3/s) and proved the <span class="hlt">system</span> to run succesfully. Dissemination of the <span class="hlt">forecasts</span> is performed by using the FEWS <span class="hlt">system</span> in all organisations, connected to the central <span class="hlt">system</span> through internet. There is also a (password protected) website available that provides the current <span class="hlt">forecast</span> to all stake holders in the catchment. The challenge of the project was not to make the models and to build the fews, but to connect all data and all operators together into one <span class="hlt">system</span>, even cross boundary. Also in that sense the FEWS Vecht <span class="hlt">system</span> has proved to be very succesful.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009gdca.conf..276G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009gdca.conf..276G&link_type=ABSTRACT"><span id="translatedtitle">Research and Development for Technology Evolution Potential <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Changqing; Cao, Shukun; Wang, Yuzeng; Ai, Changsheng; Ze, Xiangbo</p> <p></p> <p>Technology <span class="hlt">forecasting</span> is a powerful weapon for many enterprises to gain an animate future. Evolutionary potential radar plot is a necessary step of some valuable methods to help the technology managers with right technical strategy. A software <span class="hlt">system</span> for Technology Evolution Potential <span class="hlt">Forecasting</span> (TEPF) with automatic radar plot drawing is introduced in this paper. The framework of the <span class="hlt">system</span> and the date structure describing the concrete evolution pattern are illustrated in details. And the algorithm for radar plot drawing is researched. It is proved that the TEPF <span class="hlt">system</span> is an effective tool during the technology strategy analyzing process with a referenced case study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011NHESS..11.2419B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011NHESS..11.2419B"><span id="translatedtitle">Wet snow hazard for power lines: a <span class="hlt">forecast</span> and alert <span class="hlt">system</span> applied in Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.</p> <p>2011-09-01</p> <p>Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather <span class="hlt">forecast</span> can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the <span class="hlt">load</span> of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of <span class="hlt">forecast</span> and alert <span class="hlt">system</span>, WOLF (Wet snow Overload aLert and <span class="hlt">Forecast</span>), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' <span class="hlt">load</span> for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and <span class="hlt">forecasted</span> icing conditions are presented and discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140001445','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140001445"><span id="translatedtitle">Automated <span class="hlt">Loads</span> Analysis <span class="hlt">System</span> (ATLAS)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gardner, Stephen; Frere, Scot; O’Reilly, Patrick</p> <p>2013-01-01</p> <p>ATLAS is a generalized solution that can be used for launch vehicles. ATLAS is used to produce modal transient analysis and quasi-static analysis results (i.e., accelerations, displacements, and forces) for the payload math models on a specific Shuttle Transport <span class="hlt">System</span> (STS) flight using the shuttle math model and associated forcing functions. This innovation solves the problem of coupling of payload math models into a shuttle math model. It performs a transient <span class="hlt">loads</span> analysis simulating liftoff, landing, and all flight events between liftoff and landing. ATLAS utilizes efficient and numerically stable algorithms available in MSC/NASTRAN.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010HESSD...7.2455T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010HESSD...7.2455T"><span id="translatedtitle">A past discharge assimilation <span class="hlt">system</span> for ensemble streamflow <span class="hlt">forecasts</span> over France - Part 2: Impact on the ensemble streamflow <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thirel, G.; Martin, E.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Regimbeau, F.; Habets, F.</p> <p>2010-04-01</p> <p>The use of ensemble streamflow <span class="hlt">forecasts</span> is developing in the international flood <span class="hlt">forecasting</span> services. Such <span class="hlt">systems</span> can provide more accurate <span class="hlt">forecasts</span> and useful information about the uncertainty of the <span class="hlt">forecasts</span>, thus improving the assessment of risks. Nevertheless, these <span class="hlt">systems</span>, like all hydrological <span class="hlt">forecasts</span>, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation <span class="hlt">system</span>, on an ensemble streamflow prediction <span class="hlt">system</span> over France. An assimilation <span class="hlt">system</span> was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow <span class="hlt">forecasts</span> at Météo-France. This assimilation <span class="hlt">system</span>, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow <span class="hlt">forecasts</span> of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather <span class="hlt">Forecasts</span> (ECMWF) 10-day Ensemble Prediction <span class="hlt">System</span> (EPS). Two different configurations of the assimilation <span class="hlt">system</span> were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation <span class="hlt">system</span> on the ensemble streamflow <span class="hlt">forecasts</span> were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow <span class="hlt">forecasts</span>. It is shown that the assimilation <span class="hlt">system</span> improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=116409&keyword=fleet&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=68602668&CFTOKEN=42675561','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=116409&keyword=fleet&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=68602668&CFTOKEN=42675561"><span id="translatedtitle">EMISSIONS PROCESSING FOR THE ETA/ CMAQ AIR QUALITY <span class="hlt">FORECAST</span> <span class="hlt">SYSTEM</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>NOAA and EPA have created an Air Quality <span class="hlt">Forecast</span> (AQF) <span class="hlt">system</span>. This AQF <span class="hlt">system</span> links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of the...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/6168600','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/6168600"><span id="translatedtitle">METEOR - an artificial intelligence <span class="hlt">system</span> for convective storm <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Elio, R.; De haan, J.; Strong, G.S.</p> <p>1987-03-01</p> <p>An AI <span class="hlt">system</span> called METEOR, which uses the meteorologist's heuristics, strategies, and statistical tools to <span class="hlt">forecast</span> severe hailstorms in Alberta, is described, emphasizing the information and knowledge that METEOR uses to mimic the <span class="hlt">forecasting</span> procedure of an expert meteorologist. METEOR is then discussed as an AI <span class="hlt">system</span>, emphasizing the ways in which it is qualitatively different from algorithmic or statistical approaches to prediction. Some features of METEOR's design and the AI techniques for representing meteorological knowledge and for reasoning and inference are presented. Finally, some observations on designing and implementing intelligent consultants for meteorological applications are made. 7 references.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A13D3202K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A13D3202K"><span id="translatedtitle">Improvement of <span class="hlt">forecasting</span> <span class="hlt">system</span> with optimal interpolation focusing on korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kang, J.; Koo, Y. S.</p> <p>2014-12-01</p> <p>A <span class="hlt">system</span> for <span class="hlt">forecasting</span> future air quality can play an important role as part of an air quality management <span class="hlt">system</span> working in concert with more traditional emissions-based approaches. However, there are still a lot of uncertainties in modeling atmospheric. Data assimilation makes use of observation in order to reduce the uncertainties. This paper presents experiments of PM10(particulate matter <10㎛ in diameter) data assimilation with the optimal interpolation method. In order to improve the performance of chemical transport models (CTM) models in predicting pollutant concentrations for PM10, data assimilation techniques can be used. Model (CMAQ : Community Multiscale Air Quality Model) to simulate and assimilate PM10 concentration over Korea peninsula. The observations are provided by AAQMS (Ambient Air Quality Monitoring Stations in Korea).Data assimilation techniques combine measurements of the pollutant concentrations with model results to obtain better estimates of the true concentration levels(unknown). The method is then applied in operational-<span class="hlt">forecast</span> conditions. It is found that the assimilation of PM10 observations significantly improves the one-day <span class="hlt">forecast</span> of PM10, whereas the improvement is non significant for the tow-day <span class="hlt">forecast</span>. We focus on the horizontal and temporal impacts of the data assimilation. The strategy followed in this paper with the optimal interpolation could be useful for operational <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007ACPD....7.8309N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007ACPD....7.8309N"><span id="translatedtitle">Data assimilation of dust aerosol observations for CUACE/Dust <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niu, T.; Gong, S. L.; Zhu, G. F.; Liu, H. L.; Hu, X. Q.; Zhou, C. H.; Wang, Y. Q.</p> <p>2007-06-01</p> <p>A data assimilation <span class="hlt">system</span> (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment - Dust (CUACE/Dust) <span class="hlt">forecast</span> <span class="hlt">system</span> and applied in the operational <span class="hlt">forecasts</span> of sand and dust storm (SDS) in spring 2006. The <span class="hlt">system</span> is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust <span class="hlt">loading</span> retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in <span class="hlt">forecasting</span> the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation <span class="hlt">system</span>, a 41% enhancement. The assimilation results usually agree with the dust <span class="hlt">loading</span> retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=248375','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=248375"><span id="translatedtitle"><span class="hlt">Forecast</span> and virtual weather driven plant disease risk modeling <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>We describe a <span class="hlt">system</span> in use and development that leverages public weather station data, several spatialized weather <span class="hlt">forecast</span> types, leaf wetness estimation, generic plant disease models, and online statistical evaluation. Convergent technological developments in all these areas allow, with funding f...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005CSR....25.2122X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005CSR....25.2122X"><span id="translatedtitle">The GoMOOS nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xue, Huijie; Shi, Lei; Cousins, Stephen; Pettigrew, Neal R.</p> <p>2005-11-01</p> <p>A circulation nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span> has been developed for the Gulf of Maine as an integral component of the Gulf of Maine Ocean Observing <span class="hlt">System</span> (GoMOOS). It has been used daily since 2001 to produce short-term <span class="hlt">forecasts</span> of the circulation and hydrographic properties in the Gulf of Maine. One of the expectations is that the nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span> can provide consistent SST to fill in AVHRR data gaps and eventually produce reliable 3D temperature and flow fields for fisheries and other applications. The framework of the nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span> is presented, including an algorithm for assimilating satellite-derived SST. Comparisons between the predicted and the observed temperature (both in situ and satellite-derived) and velocity are discussed. In general, the assimilation algorithm is stable and produces robust SST patterns. Seasonal variations in temperature and the coastal current are reasonably reproduced. Correlation between the modeled and observed fields in the synoptic band is summarized for individual buoys in monthly bins. The Root-Mean-Square (RMS) errors for the M 2 tidal ellipse are estimated at 1.9 and 1.2 cm s -1 for the major and minor axis, respectively, while the RMS error in ellipse orientation is at 11°.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFMOS54A..07M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFMOS54A..07M&link_type=ABSTRACT"><span id="translatedtitle">An operational global ocean <span class="hlt">forecast</span> <span class="hlt">system</span> and its applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.</p> <p>2012-12-01</p> <p>A global Real-Time Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This <span class="hlt">system</span> is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The <span class="hlt">forecast</span> <span class="hlt">system</span> is run once a day and produces a 6 day long <span class="hlt">forecast</span> using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The <span class="hlt">forecast</span> <span class="hlt">system</span> is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global <span class="hlt">Forecast</span> <span class="hlt">System</span> (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this <span class="hlt">forecast</span> <span class="hlt">system</span> will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H53F0537N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H53F0537N"><span id="translatedtitle">Streamflow <span class="hlt">Forecasting</span> Using Nuero-Fuzzy Inference <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nanduri, U. V.; Swain, P. C.</p> <p>2005-12-01</p> <p>The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow <span class="hlt">forecasting</span> is widely recognized and emphasized by many in water resources fraternity. Real-time <span class="hlt">forecasts</span> of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical <span class="hlt">system</span> of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The <span class="hlt">system</span> is influenced by large number of factors and variables. The large spatial extent of the <span class="hlt">systems</span> forces the uncertainty into the hydrologic information. A variety of methods have been proposed for <span class="hlt">forecasting</span> reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological <span class="hlt">forecasting</span> is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference <span class="hlt">System</span> (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the <span class="hlt">system</span>. It is a judicious combination of the Neural Networks and fuzzy <span class="hlt">systems</span>. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy <span class="hlt">system</span> mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead <span class="hlt">forecasts</span> are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRC..120.8327H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRC..120.8327H"><span id="translatedtitle">Short-term sea ice <span class="hlt">forecasting</span>: An assessment of ice concentration and ice drift <span class="hlt">forecasts</span> using the U.S. Navy's Arctic Cap Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin</p> <p>2015-12-01</p> <p>In this study the <span class="hlt">forecast</span> skill of the U.S. Navy operational Arctic sea ice <span class="hlt">forecast</span> <span class="hlt">system</span>, the Arctic Cap Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span> (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day <span class="hlt">forecasts</span> of Arctic sea ice and ocean conditions. Many quantities are <span class="hlt">forecast</span> by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration <span class="hlt">forecast</span> skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS <span class="hlt">forecasts</span> are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for <span class="hlt">forecasts</span> up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping <span class="hlt">System</span> (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term <span class="hlt">forecast</span> skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19940000570&hterms=steel+bridges&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsteel%2Bbridges','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19940000570&hterms=steel+bridges&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsteel%2Bbridges"><span id="translatedtitle"><span class="hlt">System</span> Measures <span class="hlt">Loads</span> In Bolts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Allison, Sidney G.</p> <p>1994-01-01</p> <p>Improved technique for ultrasonic nondestructive measurement of <span class="hlt">loads</span> in bolts involves use of pulsed phase-locked loop interferometer. Provides for correction of errors and for automatic readout of <span class="hlt">loads</span> in bolts. Actual bolt <span class="hlt">load</span> measured, using transducers rebonded after bolts tightened. Calibration block and thermometer added. Technique applicable to critical fasteners in aerospace applications, nuclear reactors, petroleum and other chemical processing plants, steel bridges, and other structures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/951585','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/951585"><span id="translatedtitle">Weather <span class="hlt">forecast</span>-based optimization of integrated energy <span class="hlt">systems</span>.</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.</p> <p>2009-03-01</p> <p>In this work, we establish an on-line optimization framework to exploit detailed weather <span class="hlt">forecast</span> information in the operation of integrated energy <span class="hlt">systems</span>, such as buildings and photovoltaic/wind hybrid <span class="hlt">systems</span>. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical <span class="hlt">forecast</span> information, and operational constraints. To obtain the weather <span class="hlt">forecast</span> information, we employ a state-of-the-art <span class="hlt">forecasting</span> model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20160005808&hterms=https&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dhttps%253A','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20160005808&hterms=https&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dhttps%253A"><span id="translatedtitle">Solar Storm GIC <span class="hlt">Forecasting</span>: Solar Shield Extension Development of the End-User <span class="hlt">Forecasting</span> <span class="hlt">System</span> Requirements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.</p> <p>2015-01-01</p> <p>A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield <span class="hlt">system</span> to enhance its <span class="hlt">forecasting</span> capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the <span class="hlt">forecasting</span> capabilities of the new Solar Shield, a key undertaking is to extend the prediction <span class="hlt">system</span> coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the <span class="hlt">system</span> http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930022381','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930022381"><span id="translatedtitle">Expert <span class="hlt">system</span> development for probabilistic <span class="hlt">load</span> simulation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ho, H.; Newell, J. F.</p> <p>1991-01-01</p> <p>A knowledge based <span class="hlt">system</span> LDEXPT using the intelligent data base paradigm was developed for the Composite <span class="hlt">Load</span> Spectra (CLS) project to simulate the probabilistic <span class="hlt">loads</span> of a space propulsion <span class="hlt">system</span>. The knowledge base approach provides a systematic framework of organizing the <span class="hlt">load</span> information and facilitates the coupling of the numerical processing and symbolic (information) processing. It provides an incremental development environment for building generic probabilistic <span class="hlt">load</span> models and book keeping the associated <span class="hlt">load</span> information. A large volume of <span class="hlt">load</span> data is stored in the data base and can be retrieved and updated by a built-in data base management <span class="hlt">system</span>. The data base <span class="hlt">system</span> standardizes the data storage and retrieval procedures. It helps maintain data integrity and avoid data redundancy. The intelligent data base paradigm provides ways to build expert <span class="hlt">system</span> rules for shallow and deep reasoning and thus provides expert knowledge to help users to obtain the required probabilistic <span class="hlt">load</span> spectra.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.3400C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3400C"><span id="translatedtitle">Real-time drought <span class="hlt">forecasting</span> <span class="hlt">system</span> for irrigation managment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Masseroni, Daniele; Meucci, Stefania; Pala, Francesca; Salerno, Raffaele; Meazza, Giuseppe; Chiesa, Marco; Mancini, Marco</p> <p>2013-04-01</p> <p>In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in in European areas traditionally rich of water such as the Po Valley. In dry periods, the problem of water shortage can be enhanced by conflictual use of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective on this issue is increasing due to climate change and global warming scenarios which come out from the last IPCC Report. The increased frequency of dry periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the real-time drought <span class="hlt">forecasting</span> <span class="hlt">system</span> Pre.G.I., an Italian acronym that stands for "Hydro-Meteorological <span class="hlt">forecast</span> for irrigation management". The <span class="hlt">system</span> is based on ensemble prediction at long range (30 days) with hydrological simulation of water balance to <span class="hlt">forecast</span> the soil water content in every parcel over the Consorzio Muzza basin. The studied area covers 74,000 ha in the middle of the Po Valley, near the city of Lodi. The hydrological ensemble <span class="hlt">forecasts</span> are based on 20 meteorological members of the non-hydrostatic WRF model with 30 days as lead-time, provided by Epson Meteo Centre, while the hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The hydrological model was validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station. Reliability of the <span class="hlt">forecasting</span> <span class="hlt">system</span> and its benefits was assessed on some cases-study occurred in the recent years.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..1610155C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..1610155C&link_type=ABSTRACT"><span id="translatedtitle">Using seasonal <span class="hlt">forecasts</span> in a drought <span class="hlt">forecasting</span> <span class="hlt">system</span> for water management: case-study of the Arzal dam in Brittany</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crochemore, Louise; Ramos, Maria-Helena; Perrin, Charles; Penasso, Aldo</p> <p>2014-05-01</p> <p>The Arzal dam is located at the outlet of the Vilaine River basin (10,000 km2) in Brittany, France. It controls a reservoir (50 hm3) managed for multiple water uses: drinking water, flood control, irrigation, sailing and fish by-passing. Its location in the estuary creates a physical divide between upstream freshwater and downstream saline water. The reservoir thus plays an essential role in the regional water management <span class="hlt">system</span>. Its operational management during the summer season poses several challenges, mainly related to the quantification of future water inflows and the risks of having restricted water availability for its different uses. Indeed, the occurrence of severe drought periods between May and October may increase the risk of salt intrusion and drinking water contamination due to lock operations. Therefore it is important to provide decision-makers with reliable low-flow <span class="hlt">forecasts</span> and risk-based visualization tools, which will support their choice of the best strategy for allocation of water among different users and stakeholders. This study focuses on an integrated hydro-meteorological <span class="hlt">forecasting</span> <span class="hlt">system</span> developed to <span class="hlt">forecast</span> low flows upstream the Arzal dam and based on a lumped hydrological model. Medium-range meteorological <span class="hlt">forecasts</span> from the ECMWF ensemble prediction <span class="hlt">system</span> (51 scenarios up to 9 days ahead) are combined with seasonal meteorological <span class="hlt">forecasts</span> also from ECMWF to provide extended streamflow <span class="hlt">forecasts</span> for the summer period. The performance of the <span class="hlt">forecasts</span> obtained by this method is compared with the performance of two benchmarks: (i) flow <span class="hlt">forecasts</span> obtained using an ensemble of past observed precipitation series as precipitation scenarios, i.e. without any use of <span class="hlt">forecasts</span> from meteorological models and (ii) flow <span class="hlt">forecasts</span> obtained using the seasonal <span class="hlt">forecasts</span> only, i.e. without medium-term information. First, the performance of ensemble <span class="hlt">forecasts</span> is evaluated and compared by means of probabilistic scores. Then, a risk</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830021510','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830021510"><span id="translatedtitle">Satellite freeze <span class="hlt">forecast</span> <span class="hlt">system</span>. Operating/troubleshooting manual</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Martsolf, J. D. (Principal Investigator)</p> <p>1983-01-01</p> <p>Examples of operational procedures are given to assist users of the satellites freeze <span class="hlt">forecasting</span> <span class="hlt">system</span> (SFFS) in logging in on to the computer, executing the programs in the menu, logging off the computer, and setting up the automatic <span class="hlt">system</span>. Directions are also given for displaying, acquiring, and listing satellite maps; for communicating via terminal and monitor displays; and for what to do when the SFFS doesn't work. Administrative procedures are included.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFMOS53A..02X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFMOS53A..02X"><span id="translatedtitle">The GoMOOS Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xue, H.; Shi, L.; Cousins, S.</p> <p>2004-12-01</p> <p>A circulation nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span> was developed for the Gulf of Maine as an integral component of the Gulf of Maine Ocean Observing <span class="hlt">System</span> (GoMOOS) technical program. The <span class="hlt">system</span> has been used daily to produce short-term <span class="hlt">forecasts</span> of the circulation and physical properties in the Gulf of Maine. One of the expectations is that the <span class="hlt">system</span> can provide consistent SST to fill in AVHRR gaps and eventually produce reliable 3D temperature and flow fields for fishery applications. We first present the framework of the nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span>, which includes an algorithm to assimilate satellite derived SST. Comparisons between the modeled and the observed temperature and velocity (both in situ and satellite derived) are discussed. In general, the assimilation algorithm is stable and produces SST patterns mimicking the AVHRR. Seasonal variations in temperature and the coastal current are well reproduced. Correlation between the modeled and observed fields in the synoptic band is summarized for individual buoys in monthly bins. Comparisons of spectral characteristics suggest that the <span class="hlt">system</span> successfully captures the wind-driven events, whereas it is less satisfactory in simulating high frequency variability in summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41J..06S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41J..06S"><span id="translatedtitle">An Operational Flood <span class="hlt">Forecast</span> <span class="hlt">System</span> for the Indus Valley</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shrestha, K.; Webster, P. J.</p> <p>2012-12-01</p> <p>The Indus River is central to agriculture, hydroelectric power, and the potable water supply in Pakistan. The ever-present risk of drought - leading to poor soil conditions, conservative dam practices, and higher flood risk - amplifies the consequences of abnormally large precipitation events during the monsoon season. Preparation for the 2010 and 2011 floods could have been improved by coupling quantitative precipitation <span class="hlt">forecasts</span> to a distributed hydrological model. The nature of slow-rise discharge on the Indus and overtopping of riverbanks in this basin indicate that medium-range (1-10 day) probabilistic weather <span class="hlt">forecasts</span> can be used to assess flood risk at critical points in the basin. We describe a process for transforming these probabilities into an alert <span class="hlt">system</span> for supporting flood mitigation and response decisions on a daily basis. We present a fully automated two-dimensional flood <span class="hlt">forecast</span> methodology based on meteorological variables from the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF) Variable Ensemble Prediction <span class="hlt">System</span> (VarEPS). Energy and water fluxes are calculated in 25km grid cells using macroscale hydrologic parameterizations from the UW Variable Infiltration Capacity (VIC) model. A linear routing model transports grid cell surface runoff and baseflow within each grid cell to the outlet and into the stream network. The overflow points are estimated using flow directions, flow velocities, and maximum discharge thresholds from each grid cell. Flood waves are then deconvolved from the in-channel discharge time series and propagated into adjacent cells until a storage criterion based on average grid cell elevation is met. Floodwaters are drained back into channels as a continuous process, thus simulating spatial extent, depth, and persistence on the plains as the ensemble <span class="hlt">forecast</span> evolves with time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005WRR....4102005C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005WRR....4102005C"><span id="translatedtitle">Fuzzy exemplar-based inference <span class="hlt">system</span> for flood <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin</p> <p>2005-02-01</p> <p>Fuzzy inference <span class="hlt">systems</span> have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference <span class="hlt">system</span> (FEIS) for flood <span class="hlt">forecasting</span>. The FEIS is based on a fuzzy inference <span class="hlt">system</span>, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood <span class="hlt">forecasting</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014JHyd..519.2832B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014JHyd..519.2832B&link_type=ABSTRACT"><span id="translatedtitle">A <span class="hlt">System</span> for Continuous Hydrological Ensemble <span class="hlt">Forecasting</span> (SCHEF) to lead times of 9 days</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bennett, James C.; Robertson, David E.; Shrestha, Durga Lal; Wang, Q. J.; Enever, David; Hapuarachchi, Prasantha; Tuteja, Narendra K.</p> <p>2014-11-01</p> <p>This study describes a <span class="hlt">System</span> for Continuous Hydrological Ensemble <span class="hlt">Forecasting</span> (SCHEF) designed to <span class="hlt">forecast</span> streamflows to lead times of 9 days. SCHEF is intended to support optimal management of water resources for consumptive and environmental purposes and ultimately to support the management of impending floods. Deterministic rainfall <span class="hlt">forecasts</span> from the ACCESS-G numerical weather prediction (NWP) model are post-processed using a Bayesian joint probability model to correct biases and quantify uncertainty. Realistic temporal and spatial characteristics are instilled in the rainfall <span class="hlt">forecast</span> ensemble with the Schaake shuffle. The ensemble rainfall <span class="hlt">forecasts</span> are then used as inputs to the GR4H hydrological model to produce streamflow <span class="hlt">forecasts</span>. A hydrological error correction is applied to ensure <span class="hlt">forecasts</span> transit smoothly from recent streamflow observations. SCHEF <span class="hlt">forecasts</span> streamflows skilfully for a range of hydrological and climate conditions. Skill is particularly evident in <span class="hlt">forecasts</span> of streamflows at lead times of 1-6 days. <span class="hlt">Forecasts</span> perform best in temperate perennially flowing rivers, while <span class="hlt">forecasts</span> are poorest in intermittently flowing rivers. The poor streamflow <span class="hlt">forecasts</span> in intermittent rivers are primarily the result of poor rainfall <span class="hlt">forecasts</span>, rather than an inadequate representation of hydrological processes. <span class="hlt">Forecast</span> uncertainty becomes more reliably quantified at longer lead times; however there is considerable scope for improving the reliability of streamflow <span class="hlt">forecasts</span> at all lead times. Additionally, we show that the choice of <span class="hlt">forecast</span> time-step can influence <span class="hlt">forecast</span> accuracy: <span class="hlt">forecasts</span> generated at a 1-h time-step tend to be more accurate than at longer time-steps (e.g. 1-day). This is largely because at shorter time-steps the hydrological error correction is able to correct streamflow <span class="hlt">forecasts</span> with more recent information, rather than the ability of GR4H to simulate hydrological processes better at shorter time-steps. SCHEF will form the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016HESS...20.2453Y&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016HESS...20.2453Y&link_type=ABSTRACT"><span id="translatedtitle">An experimental seasonal hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span> over the Yellow River basin - Part 2: The added value from climate <span class="hlt">forecast</span> models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, Xing</p> <p>2016-06-01</p> <p>This is the second paper of a two-part series on introducing an experimental seasonal hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span> over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate <span class="hlt">forecast</span> models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The <span class="hlt">forecast</span> skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature <span class="hlt">forecasts</span> than the precipitation <span class="hlt">forecasts</span>, the grand NMME ensemble mean <span class="hlt">forecast</span> has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce <span class="hlt">forecasts</span> of soil moisture, runoff and streamflow. And the NMME/VIC <span class="hlt">forecasts</span> are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture <span class="hlt">forecasts</span>, and the former has higher skill than the latter only for the <span class="hlt">forecasts</span> at long leads and for those initialized in the rainy season. The <span class="hlt">forecast</span> skill for runoff is lower for both <span class="hlt">forecast</span> approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcDyn..66..221V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcDyn..66..221V"><span id="translatedtitle">Observation impact analysis methods for storm surge <span class="hlt">forecasting</span> <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Verlaan, Martin; Sumihar, Julius</p> <p>2016-02-01</p> <p>This paper presents a simple method for estimating the impact of assimilating individual or group of observations on <span class="hlt">forecast</span> accuracy improvement. This method is derived from the nsemble-based observation impact analysis method of Liu and Kalnay (Q J R Meteorol Soc 134:1327-1335, 2008). The method described here is different in two ways from their method. Firstly, it uses a quadratic function of model-minus-observation residuals as a measure of <span class="hlt">forecast</span> accuracy, instead of model-minus-analysis. Secondly, it simply makes use of time series of observations and the corresponding model output generated without data assimilation. These time series are usually available in an operational database. Hence, it is simple to implement. It can be used before any data assimilation is implemented. Therefore, it is useful as a design tool of a data assimilation <span class="hlt">system</span>, namely for selecting which observations to assimilate. The method can also be used as a diagnostic tool, for example, to assess if all observation contributes positively to the accuracy improvement. The method is applicable for <span class="hlt">systems</span> with stationary error process and fixed observing network. Using twin experiments with a simple one-dimensional advection model, the method is shown to work perfectly in an idealized situation. The method is used to evaluate the observation impact in the operational storm surge <span class="hlt">forecasting</span> <span class="hlt">system</span> based on the Dutch Continental Shelf Model version 5 (DCSMv5).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19790017276','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19790017276"><span id="translatedtitle">Global crop production <span class="hlt">forecasting</span> data <span class="hlt">system</span> analysis</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Castruccio, P. A. (Principal Investigator); Loats, H. L.; Lloyd, D. G.</p> <p>1978-01-01</p> <p>The author has identified the following significant results. Findings led to the development of a theory of radiometric discrimination employing the mathematical framework of the theory of discrimination between scintillating radar targets. The theory indicated that the functions which drive accuracy of discrimination are the contrast ratio between targets, and the number of samples, or pixels, observed. Theoretical results led to three primary consequences, as regards the data <span class="hlt">system</span>: (1) agricultural targets must be imaged at correctly chosen times, when the relative evolution of the crop's development is such as to maximize their contrast; (2) under these favorable conditions, the number of observed pixels can be significantly reduced with respect to wall-to-wall measurements; and (3) remotely sensed radiometric data must be suitably mixed with other auxiliary data, derived from external sources.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFMPA21B1306C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFMPA21B1306C"><span id="translatedtitle">A Relocatable Environmental Prediction <span class="hlt">System</span> for Volcanic Ash <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cook, J.; Geiszler, D.</p> <p>2009-12-01</p> <p>Timeliness is an essential component for any <span class="hlt">system</span> generating volcanic ash <span class="hlt">forecasts</span> for aviation. Timeliness implies that the steps required for estimating the concentration of volcanic ash in the atmosphere are streamlined into a process that can accurately identify the volcano’s source function, utilize atmospheric conditions to predict the movement of the volcanic ash plume, and ultimately produce a volcanic ash <span class="hlt">forecast</span> product in a useable format for aviation interests. During the past decade, the Naval Research Laboratory (NRL) has developed a suite of software integrated with the Coupled Ocean/Atmosphere Mesoscale Prediction <span class="hlt">System</span> (COAMPS®) that is designed with a similar automated purpose in support of the Navy’s operational (24/7) schedule and diverse mission requirements worldwide. The COAMPS-OS® (On-demand <span class="hlt">System</span>) provides web-based interfaces to COAMPS that allows Navy users to rapidly (in a few minutes) set up and start a new <span class="hlt">forecast</span> in response to short-fused requests. A unique capability in COAMPS unlike many regional numerical weather prediction models is the option to initialize a volcanic ash plume and use the model’s full three-dimensional atmospheric grid (e.g. winds and precipitation) to predict the movement and concentration of the plume. This paper will describe the efforts to automate volcanic ash <span class="hlt">forecasts</span> using COAMPS-OS including the specification of the source function, initialization and configuration of COAMPS, and generation of output products for aviation. This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA. COAMPS® and COAMPS-OS® are registered trademarks of the Naval Research Laboratory.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=186404&keyword=scales+AND+theoretical&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=76280495&CFTOKEN=63990230','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=186404&keyword=scales+AND+theoretical&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=76280495&CFTOKEN=63990230"><span id="translatedtitle">Ecological <span class="hlt">Forecasting</span>: Microbial Contamination and Atmospheric <span class="hlt">Loadings</span> of Nutrients to Land and Water</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The development of ecological <span class="hlt">forecasts</span>, namely, methodologies to predict the chemical, biological, and physical changes in terrestrial and aquatic ecosystems is desirable so that effective strategies for reducing the adverse impacts of human activities and extreme natural events...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19870052239&hterms=Study+skills&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3D%2528Study%2Bskills%2529','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19870052239&hterms=Study+skills&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3D%2528Study%2Bskills%2529"><span id="translatedtitle"><span class="hlt">Forecasting</span> <span class="hlt">forecast</span> skill</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kalnay, Eugenia; Dalcher, Amnon</p> <p>1987-01-01</p> <p>It is shown that it is possible to predict the skill of numerical weather <span class="hlt">forecasts</span> - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of <span class="hlt">forecasts</span> started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing <span class="hlt">systems</span>. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent <span class="hlt">forecasts</span> as well as regions with poor <span class="hlt">forecasts</span>, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble <span class="hlt">forecast</span> dispersion provided a very good prediction of the quality of the individual <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130010528','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130010528"><span id="translatedtitle">Anvil <span class="hlt">Forecast</span> Tool in the Advanced Weather Interactive Processing <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe H., III; Hood, Doris</p> <p>2009-01-01</p> <p>Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil <span class="hlt">forecasting</span> as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display <span class="hlt">System</span> (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model <span class="hlt">forecast</span> winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or <span class="hlt">forecasted</span> winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing <span class="hlt">System</span> (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.582P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.582P"><span id="translatedtitle">Global and Limited-Area Ensemble Prediction <span class="hlt">Systems</span> deployed for Wind Power <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Petroliagis, T. I.; Jacques, M.; Montani, A.; Bremen, L. V.; Heinemann, D.</p> <p>2010-09-01</p> <p>The integration of wind generation into power <span class="hlt">systems</span> is affected by uncertainties in the <span class="hlt">forecasting</span> of expected power output. Misestimating of meteorological conditions or large <span class="hlt">forecasting</span> errors (phase errors, near cut-off speeds etc.) has proved to be very costly for infrastructures (i.e. unexpected <span class="hlt">loads</span> on turbines) and reduces the value of wind energy for end-users. The state-of-the-art in wind power <span class="hlt">forecasting</span> has focused so far on the 'usual' operating conditions rather than on extreme events. Thus, the current wind <span class="hlt">forecasting</span> technology presents several strong bottlenecks. End-users urge for dedicated approaches to reduce large prediction errors or predict extremes from a local scale (gusts, shears) up to a European scale as extremes and <span class="hlt">forecast</span> errors may propagate. The aim of the new European FP7 Project, namely SAFEWIND, is to substantially improve wind power predictability in challenging or extreme situations and at different temporal and spatial scales. One of the areas that SAFEWIND concentrates on is the use of Global and Local Area Model (LAM) Ensemble Prediction <span class="hlt">System</span> (EPS) <span class="hlt">forecasts</span> for improved wind predictions on local to regional scales. More specifically, the ECMWF (European Centre for Medium-Range Weather <span class="hlt">Forecasts</span>) EPS has been deployed as the backbone Global EPS platform for the SAFEWIND. The operational ECMWF EPS uses perturbations based on initial and evolved singular vectors. Model uncertainties are presented currently by the stochastic physics scheme that perturbs the parametrised physics tendencies by multiplicative noise. The current horizontal resolution of the ECMWF EPS is roughly 32 km while its corresponding 'deterministic' IFS (Integrated <span class="hlt">Forecast</span> <span class="hlt">System</span>) <span class="hlt">forecast</span> and analysis fields have a resolution of about 16 km. On the other hand, LEPS (Limited-Area Ensemble Prediction <span class="hlt">System</span>) components have been provided by the COSMO-LEPS. This <span class="hlt">system</span> is based on the non-hydrostatic COSMO-model developed within the COnsortium for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010EGUGA..1211993C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010EGUGA..1211993C&link_type=ABSTRACT"><span id="translatedtitle">A multidisciplinary <span class="hlt">system</span> for monitoring and <span class="hlt">forecasting</span> Etna volcanic plumes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coltelli, Mauro; Prestifilippo, Michele; Spata, Gaetano; Scollo, Simona; Andronico, Daniele</p> <p>2010-05-01</p> <p>One of the most active volcanoes in the world is Mt. Etna, in Italy, characterized by frequent explosive activity from the central craters and from fractures opened along the volcano flanks which, during the last years, caused several damages to aviation and forced the closure of the Catania International Airport. To give precise warning to the aviation authorities and air traffic controller and to assist the work of VAACs, a novel <span class="hlt">system</span> for monitoring and <span class="hlt">forecasting</span> Etna volcanic plumes, was developed at the Istituto Nazionale di Geofisica e Vulcanologia, sezione di Catania, the managing institution for the surveillance of Etna volcano. Monitoring is carried out using multispectral infrared measurements from the Spin Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation geosynchronous satellite able to track the volcanic plume with a high time resolution, visual and thermal cameras used to monitor the explosive activity, three continuous wave X-band disdrometers which detect ash dispersal and fallout, sounding balloons used to evaluate the atmospheric fields, and finally field data collected after the end of the eruptive event needed to extrapolate important features of explosive activity. <span class="hlt">Forecasting</span> is carried out daily using automatic procedures which download weather <span class="hlt">forecast</span> data obtained by meteorological mesoscale models from the Italian Air Force national Meteorological Office and from the hydrometeorological service of ARPA-SIM; run four different tephra dispersal models using input parameters obtained by the analysis of the deposits collected after few hours since the eruptive event similar to 22 July 1998, 21-24 July 2001 and 2002-03 Etna eruptions; plot hazard maps on ground and in air and finally publish them on a web-site dedicated to the Italian Civil Protection. The <span class="hlt">system</span> has been already tested successfully during several explosive events occurring at Etna in 2006, 2007 and 2008. These events produced eruption</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JASTP.102..329G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JASTP.102..329G"><span id="translatedtitle">GIM-TEC adaptive ionospheric weather assessment and <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gulyaeva, T. L.; Arikan, F.; Hernandez-Pajares, M.; Stanislawska, I.</p> <p>2013-09-01</p> <p>The Ionospheric Weather Assessment and <span class="hlt">Forecast</span> (IWAF) <span class="hlt">system</span> is a computer software package designed to assess and predict the world-wide representation of 3-D electron density profiles from the Global Ionospheric Maps of Total Electron Content (GIM-TEC). The unique <span class="hlt">system</span> products include daily-hourly numerical global maps of the F2 layer critical frequency (foF2) and the peak height (hmF2) generated with the International Reference Ionosphere extended to the plasmasphere, IRI-Plas, upgraded by importing the daily-hourly GIM-TEC as a new model driving parameter. Since GIM-TEC maps are provided with 1- or 2-days latency, the global maps <span class="hlt">forecast</span> for 1 day and 2 days ahead are derived using an harmonic analysis applied to the temporal changes of TEC, foF2 and hmF2 at 5112 grid points of a map encapsulated in IONEX format (-87.5°:2.5°:87.5°N in latitude, -180°:5°:180°E in longitude). The <span class="hlt">system</span> provides online the ionospheric disturbance warnings in the global W-index map establishing categories of the ionospheric weather from the quiet state (W=±1) to intense storm (W=±4) according to the thresholds set for instant TEC perturbations regarding quiet reference median for the preceding 7 days. The accuracy of IWAF <span class="hlt">system</span> predictions of TEC, foF2 and hmF2 maps is superior to the standard persistence model with prediction equal to the most recent ‘true’ map. The paper presents outcomes of the new service expressed by the global ionospheric foF2, hmF2 and W-index maps demonstrating the process of origin and propagation of positive and negative ionosphere disturbances in space and time and their <span class="hlt">forecast</span> under different scenarios.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20090009340&hterms=Cyclones&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DCyclones','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20090009340&hterms=Cyclones&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DCyclones"><span id="translatedtitle">AIRS Impact on the Analysis and <span class="hlt">Forecast</span> Track of Tropical Cyclone Nargis in a Global Data Assimilation and <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.</p> <p>2009-01-01</p> <p>Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather <span class="hlt">forecasting</span> <span class="hlt">systems</span>, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation <span class="hlt">systems</span> (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. <span class="hlt">Forecasts</span> produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and <span class="hlt">forecasting</span> <span class="hlt">system</span> is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.9760B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.9760B"><span id="translatedtitle">Operational flood <span class="hlt">forecasting</span> <span class="hlt">system</span> of Umbria Region "Functional Centre</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.</p> <p>2009-04-01</p> <p>The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert <span class="hlt">system</span> during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management <span class="hlt">System</span>, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological <span class="hlt">forecasting</span> models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert <span class="hlt">system</span> is referred to 6 different warning areas in which the territory has been divided into and based on a threshold <span class="hlt">system</span> of three different increasing critical levels according</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010095','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010095"><span id="translatedtitle">The Experimental Regional Ensemble <span class="hlt">Forecast</span> <span class="hlt">System</span> (ExREF): Its Use in NWS <span class="hlt">Forecast</span> Operations and Preliminary Verification</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve</p> <p>2014-01-01</p> <p>The Experimental Regional Ensemble <span class="hlt">Forecast</span> (ExREF) <span class="hlt">system</span> is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global <span class="hlt">Systems</span> Division (GSD) of the NOAA Earth <span class="hlt">System</span> Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global <span class="hlt">Forecasting</span> <span class="hlt">System</span> (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in <span class="hlt">forecasting</span> extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather <span class="hlt">Forecast</span> Office and California Nevada River <span class="hlt">Forecast</span> Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing <span class="hlt">System</span> (AWIPS I and II) to provide guidance on the <span class="hlt">forecasting</span> of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River <span class="hlt">Forecast</span> Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the <span class="hlt">forecasters</span> can build confidence in the use of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1816370O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816370O"><span id="translatedtitle">Usefulness of ECMWF <span class="hlt">system</span>-4 ensemble seasonal climate <span class="hlt">forecasts</span> for East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ogutu, Geoffrey; Franssen, Wietse; Supit, Iwan; Omondi, Philip; Hutjes, Ronald</p> <p>2016-04-01</p> <p>This study evaluates whether European Centre for Medium-Range Weather <span class="hlt">Forecast</span> (ECMWF) <span class="hlt">system</span>-4 seasonal <span class="hlt">forecasts</span> can potentially be used as input for impact analysis over East Africa. To be of any use, these <span class="hlt">forecasts</span> should have skill. We used the 15-member ensemble runs and tested potential <span class="hlt">forecast</span> skill of precipitation (tp), near surface air temperature (tas) and surface downwelling shortwave radiation (rsds) for future use in impact models. Probabilistic measures verified the ECMWF ensemble <span class="hlt">forecasts</span> against the WATCH Forcing Data methodology applied to ERA-Interim data (WFDEI) climatology for the period 1981-2010. The Ranked Probability Skill Score (RPSS) assesses the overall <span class="hlt">forecast</span> skill, whereas the Relative Operating Curve Skill Score (ROCSS) analyses skill of the <span class="hlt">forecasted</span> tercile at both grid cell and over sub-regions with homogeneous rainfall characteristics. The results show that predictability of the three variables varies with season, location and <span class="hlt">forecast</span> month (lead-time) before start of the seasons. Quantile-quantile bias correction clears biases in the raw <span class="hlt">forecasts</span> but does not improve probabilistic skills. The October-December (OND) tp <span class="hlt">forecasts</span> show skill over a larger area up to lead-time of three months compared to the March-May (MAM) and June-August (JJA) seasons. Temperature <span class="hlt">forecasts</span> are skillful up to a minimum three months lead-time in all seasons, while the rsds is less skillful. ROCSS analyses indicate high skill in simulation of upper- and lower-tercile <span class="hlt">forecasts</span> compared to simulation of the middle-terciles. Upper- and lower-tercile precipitation <span class="hlt">forecasts</span> are 20-80% better than climatology over a larger area at 0-3 month lead-time; tas <span class="hlt">forecasts</span> are 40-100% better at shorter lead-times while rsds <span class="hlt">forecasts</span> are less skillful in all seasons. The <span class="hlt">forecast</span> <span class="hlt">system</span> captures manifestations of strong El Niño and La Niña years in terms of precipitation but the skill scores are region dependent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.8569M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.8569M"><span id="translatedtitle">The Establishment of an Operational Earthquake <span class="hlt">Forecasting</span> <span class="hlt">System</span> in Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marzocchi, Warner; Lombardi, Anna Maria; Casarotti, Emanuele</p> <p>2014-05-01</p> <p>Just after the Mw 6.2 earthquake that hit L'Aquila, on April 6 2009, the Civil Protection nominated an International Commission on Earthquake <span class="hlt">Forecasting</span> (ICEF) that paved the way to the development of the Operational Earthquake <span class="hlt">Forecasting</span> (OEF), defined as the "procedures for gathering and disseminating authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes". In this paper we introduce the first official OEF <span class="hlt">system</span> in Italy that has been developed by the new-born Centro di Pericolosità Sismica at the Istituto Nazionale di Geofisica e Vulcanologia. The <span class="hlt">system</span> provides every day an update of the weekly probabilities of ground shaking over the whole Italian territory. In this presentation, we describe in detail the philosophy behind the <span class="hlt">system</span>, the scientific details, and the output format that has been preliminary defined in agreement with Civil Protection. To our knowledge, this is the first operational <span class="hlt">system</span> that fully satisfies the ICEF guidelines. Probably, the most sensitive issue is related to the communication of such a kind of message to the population. Acknowledging this inherent difficulty, in agreement with Civil Protection we are planning pilot tests to be carried out in few selected areas in Italy; the purpose of such tests is to check the effectiveness of the message and to receive feedbacks.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.A11D3043S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.A11D3043S&link_type=ABSTRACT"><span id="translatedtitle">Integrating Windblown Dust <span class="hlt">Forecasts</span> with Public Safety and Health <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sprigg, W. A.</p> <p>2014-12-01</p> <p>Experiments in real-time prediction of desert dust emissions and downstream plume concentrations (~ 3.5 km near-surface spatial resolution) succeed to the point of challenging public safety and public health services to beta test a dust storm warning and advisory <span class="hlt">system</span> in lowering risks of highway and airline accidents and illnesses such as asthma and valley fever. Key beta test components are: high-resolution models of dust emission, entrainment and diffusion, integrated with synoptic weather observations and <span class="hlt">forecasts</span>; satellite-based detection and monitoring of soil properties on the ground and elevated above; high space and time resolution for health surveillance and transportation advisories.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.236B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.236B"><span id="translatedtitle">An Operational Coastal <span class="hlt">Forecasting</span> <span class="hlt">System</span> in Galicia (NW Spain)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Balseiro, C. F.; Carracedo, P.; Pérez, E.; Pérez, V.; Taboada, J.; Venacio, A.; Vilasa, L.</p> <p>2009-09-01</p> <p>The Galician coast (NW Iberian Peninsula coast) and mainly the Rias Baixas (southern Galician rias) are one of the most productive ecosystems in the world, supporting a very active fishing and aquiculture industry. This high productivity lives together with a high human pressure and an intense maritime traffic, which means an important environmental risk. Besides that, Harmful Algae Blooms (HAB) are common in this area, producing important economical losses in aquiculture. In this context, the development of an Operational Hydrodynamic Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> is the first step to the development of a more sophisticated Ocean Integrated Decision Support Tool. A regional oceanographic <span class="hlt">forecasting</span> <span class="hlt">system</span> in the Galician Coast has been developed by MeteoGalicia (the Galician regional meteorological agency) inside ESEOO project to provide <span class="hlt">forecasts</span> on currents, sea level, water temperature and salinity. This <span class="hlt">system</span> is based on hydrodynamic model MOHID, forced with the operational meteorological model WRF, supported daily at MeteoGalicia . Two grid meshes are running nested at different scales, one of ~2km at the shelf scale and the other one with a resolution of 500 m at the rias scale. ESEOAT (Puertos del Estado) model provide salinity and temperature fields which are relaxed at all depth along the open boundary of the regional model (~6km). Temperature and salinity initial fields are also obtained from this application. Freshwater input from main rivers are included as forcing in MOHID model. Monthly mean discharge data from gauge station have been provided by Aguas de Galicia. Nowadays a coupling between an hydrological model (SWAT) and the hydrodynamic one are in development with the aim to verify the impact of the rivers discharges. The <span class="hlt">system</span> runs operationally daily, providing two days of <span class="hlt">forecast</span>. First model verifications had been performed against Puertos del Estado buoys and Xunta de Galicia buoys network along the Galician coast. High resolution model results</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/1942131','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/1942131"><span id="translatedtitle">Biomechanical <span class="hlt">load</span> analysis of cantilevered implant <span class="hlt">systems</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Osier, J F</p> <p>1991-01-01</p> <p>Historically, dental implants have been placed in areas where quality bone exists. The maxillary sinus areas and mandibular canal proximities have been avoided. From these placements, various cantilevered prosthetic applications have emerged. This analysis uses static engineering principles to define the <span class="hlt">loads</span> (i.e., forces) placed upon the implants. These principles make use of Newton's first and third laws of mechanics by summing the forces and moments to zero. These summations then generate mathematical equations and their algebraic solutions. Three implant <span class="hlt">systems</span> are analyzed. The first is a two-implant <span class="hlt">system</span>. The second is a three-implant cross-arch stabilized <span class="hlt">system</span> usually found in mandibular replacements of lower full dentures. The third is a five-implant <span class="hlt">system</span> which is identical to the three-implant cantilevered <span class="hlt">system</span> but which uses implants in the first molar area, thereby negating the cantilevered <span class="hlt">load</span> magnification of the three-implant design. These analyses demonstrate that, in a cantilevered application, the implant closest to the point of <span class="hlt">load</span> application (usually the most posterior implant) takes the largest compressive <span class="hlt">load</span>. Implants opposite the <span class="hlt">load</span> application (generally the anterior implant) are in tension. These <span class="hlt">loads</span> on the implants are normally magnified over the biting force and can easily reach 2 1/2 to five times the biting <span class="hlt">load</span>. PMID:1942131</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010sucs.conf...34S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010sucs.conf...34S"><span id="translatedtitle">Design of a <span class="hlt">Forecasting</span> Service <span class="hlt">System</span> for Monitoring of Vulnerabilities of Sensor Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Jae-Gu; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo</p> <p></p> <p>This study aims to reduce security vulnerabilities of sensor networks which transmit data in an open environment by developing a <span class="hlt">forecasting</span> service <span class="hlt">system</span>. The <span class="hlt">system</span> is to remove or monitor causes of breach incidents in advance. To that end, this research first examines general security vulnerabilities of sensor networks and analyzes characteristics of existing <span class="hlt">forecasting</span> <span class="hlt">systems</span>. Then, 5 steps of a <span class="hlt">forecasting</span> service <span class="hlt">system</span> are proposed in order to improve security responses.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.3187M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.3187M"><span id="translatedtitle">An automatic <span class="hlt">system</span> for on-line flash flood <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Makin, I.; Rumyantsev, D.; Shemanayev, K.; Shkarbanov, R.</p> <p>2012-04-01</p> <p>The research group at Russian State Hydrometeorological University continues developing hydrologic software, called SLS+, which might be useful for background flash flood <span class="hlt">forecasting</span> in poorly gauged regions. Now the SLS+ software has a user-friendly web interface for on-line background flash flood <span class="hlt">forecasting</span> in training and operational (real time or near real time) modes, and allows issuing stream flow <span class="hlt">forecasts</span> based on precipitation and evaporation data obtained either from archives, or from field sensors, respectively. The <span class="hlt">system</span> currently includes two hydrological models, the Sacramento Soil Moisture Accounting model (USA) and Multi-Layer Conceptual Model (Russia). These models can be calibrated either manually, or automatically based on four calibration algorithms: Shuffled Complex Evolution algorithm (SCE), which is quite useful if (1) a number of calibrated parameters does not exceed 6-7 and boundaries of the parameter space are well defined and (2) the parameter space is not too wide; Basic Stepwise Line Search (SLS) algorithm, which is efficient and computationally "inexpensive", if an initial point for pattern optimization is well defined; SLS-2L algorithm (where 2L is an abbreviation for "two loops" or "two cycles"), which is used in regions with scarce soil data and allows first to predetermine the soil hydraulic parameters, and then use these parameters for the refined model parameterization; SLS-E algorithm (where E stands for "Ensemble generation"), which implies the generation of ensembles of one or several forcing processes (for instance, effective precipitation and evaporation) and model calibration for each of those ensembles. This method is primarily designed for models with undistracted parameters at a relatively low density of ground-based meteorological observation network. Currently the trial version of the <span class="hlt">system</span> is available for testing upon request.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/70727','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/70727"><span id="translatedtitle">Traffic congestion <span class="hlt">forecasting</span> model for the INFORM <span class="hlt">System</span>. Final report</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Azarm, A.; Mughabghab, S.; Stock, D.</p> <p>1995-05-01</p> <p>This report describes a computerized traffic <span class="hlt">forecasting</span> model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM <span class="hlt">system</span> roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information <span class="hlt">system</span>. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical <span class="hlt">forecasting</span> of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy <span class="hlt">forecasted</span> (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C23B0781W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C23B0781W"><span id="translatedtitle">Sea Ice in the NCEP Climate <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, X.; Grumbine, R. W.</p> <p>2015-12-01</p> <p>Sea ice is known to play a significant role in the global climate <span class="hlt">system</span>. For a weather or climate <span class="hlt">forecast</span> <span class="hlt">system</span> (CFS), it is important that the realistic distribution of sea ice is represented. Sea ice prediction is challenging; sea ice can form or melt, it can move with wind and/or ocean current; sea ice interacts with both the air above and ocean underneath, it influences by, and has impact on the air and ocean conditions. NCEP has developed coupled CFS (version 2, CFSv2) and carried out CFS reanalysis (CFSR), which includes a coupled model with the NCEP global <span class="hlt">forecast</span> <span class="hlt">system</span>, a land model, an ocean model (GFDL MOM4), and a sea ice model. In this work, we present the NCEP coupled model, the CFSv2 sea ice component that includes a dynamic thermodynamic sea ice model and a simple "assimilation" scheme, how sea ice has been assimilated in CFSR, the characteristics of the sea ice from CFSR and CFSv2, and the improvements of sea ice needed for future CFS (version 3) and the CFSR.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.3042V&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.3042V&link_type=ABSTRACT"><span id="translatedtitle">A quality assessment of the MARS crop yield <span class="hlt">forecasting</span> <span class="hlt">system</span> for the European Union</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van der Velde, Marijn; Bareuth, Bettina</p> <p>2015-04-01</p> <p>Timely information on crop production <span class="hlt">forecasts</span> can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing <span class="hlt">forecasts</span> of European crop production levels since 1993. The operational crop production <span class="hlt">forecasting</span> is carried out with the MARS Crop Yield <span class="hlt">Forecasting</span> <span class="hlt">System</span> (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly <span class="hlt">forecasts</span> of crop yield at national and European Union level. The crop production <span class="hlt">forecasts</span> are published in the so-called MARS bulletins. <span class="hlt">Forecasting</span> crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield <span class="hlt">forecasts</span> of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final <span class="hlt">forecast</span> before harvest. Two simple benchmarks to assess the skill of the <span class="hlt">forecasts</span> were defined as comparing the <span class="hlt">forecasts</span> to 1) a <span class="hlt">forecast</span> equal to the average yield and 2) a <span class="hlt">forecast</span> using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield <span class="hlt">forecasts</span> of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been <span class="hlt">forecast</span> superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield <span class="hlt">forecasts</span> can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080012286','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080012286"><span id="translatedtitle">Electric power distribution and <span class="hlt">load</span> transfer <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bradford, Michael P. (Inventor); Parkinson, Gerald W. (Inventor); Grant, Ross M. (Inventor)</p> <p>1989-01-01</p> <p>A power distribution <span class="hlt">system</span> includes a plurality of power sources and <span class="hlt">load</span> transfer units including transistors and diodes connected in series and leading to a common power output, each of the transistors being controller switchable subject to voltage levels of the respective input and output sides of said transistors, and the voltage and current level of said common power output. The <span class="hlt">system</span> is part of an interconnection scheme in which all but one of the power sources is connected to a single <span class="hlt">load</span> transfer unit, enabling the survival of at least a single power source with the failure of one of the <span class="hlt">load</span> transfer units.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.9451V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9451V"><span id="translatedtitle">Operational water management of Rijnland water <span class="hlt">system</span> and pilot of ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span> for flood control</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van der Zwan, Rene</p> <p>2013-04-01</p> <p>The Rijnland water <span class="hlt">system</span> is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support <span class="hlt">system</span>. This decision support <span class="hlt">system</span> consists of deterministic hydro-meteorological <span class="hlt">forecasts</span> with a 24-hr <span class="hlt">forecast</span> horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall <span class="hlt">forecast</span> is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control <span class="hlt">system</span>. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer <span class="hlt">forecast</span> horizon and a probabilistic <span class="hlt">forecast</span>. Therefore ensemble precipitation <span class="hlt">forecasts</span> of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational <span class="hlt">system</span> providing 10-day water level ensemble <span class="hlt">forecasts</span>. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble <span class="hlt">forecasts</span> for risk-based operational management of the Rijnland water <span class="hlt">system</span> will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.9203R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.9203R&link_type=ABSTRACT"><span id="translatedtitle">WMOP: The SOCIB Western Mediterranean Sea OPerational <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Renault, Lionel; Juza, Mélanie; Garau, Bartolomé; Sayol, Juan Manuel; Orfila, Alejandro; Tintoré, Joaquín</p> <p>2013-04-01</p> <p>Development of science based ocean-<span class="hlt">forecasting</span> <span class="hlt">systems</span> at global, regional, sub-regional and local scales is needed to increase our understanding of ocean processes and to support knowledge based management of the marine environment. In this context, WMOP (Western Mediterranean sea /Balearic OPerational <span class="hlt">system</span>) is the <span class="hlt">forecasting</span> subsystem component of SOCIB, the new Balearic Islands Coastal Observing and <span class="hlt">Forecasting</span> <span class="hlt">System</span>. The WMOP <span class="hlt">system</span> is operational since the end of 2010. The ROMS model is forced every 3 hours with atmospheric forcing derived from AEMET/Hirlam and daily boundary conditions provided by MFS2 from MyOcean/MOON. Model domain is implemented over an area extending from Gibraltar strait to Corsica/Sardinia (from 6°W to 9°E and from 35°N to 44.5°N), including Balearic Sea and Gulf of Lion. The grid is 631 x 539 points with a resolution of ~1.5km, which allows good representation of mesoscale and submesoscale features (first baroclinic Rossby radius ~10-15 km) of key relevance in this region. The model has 30 sigma levels, and the vertical s coordinate is stretched for boundary layer resolution, also essential to capture extreme events water masses formation and dynamical effects. Bottom topography is derived from a 2' resolution database. Online validation procedures based on inter-comparison of model outputs against observing <span class="hlt">systems</span> and reference models such as MFS and Mercator are used to assess at what level the numerical models are able to reproduce the features observed from in-situ <span class="hlt">systems</span> and remote sensing. The intrinsic three-dimensional variability of the coastal ocean and open-ocean exchanges imply the need of muti-plaform observing <span class="hlt">systems</span> covering a variety of scales. Fixed moorings provide a good temporal resolution but poor spatial coverage, while satellite products provide a good spatial coverage but just on the surface layer. Gliders can provide a reasonable spatial variability in both horizontal and vertical axes. Thus, inter</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcSci..10.1013D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014OcSci..10.1013D"><span id="translatedtitle"><span class="hlt">Forecasting</span> the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Drillet, Y.; Lellouche, J. M.; Levier, B.; Drévillon, M.; Le Galloudec, O.; Reffray, G.; Regnier, C.; Greiner, E.; Clavier, M.</p> <p>2014-12-01</p> <p>Operational <span class="hlt">systems</span> operated by Mercator Ocean provide daily ocean <span class="hlt">forecasts</span>, and combining these <span class="hlt">forecasts</span> we can produce ensemble <span class="hlt">forecast</span> and uncertainty estimates. This study focuses on the mixed-layer depth in the Northeast Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Ocean operational <span class="hlt">systems</span> provide daily <span class="hlt">forecasts</span> at a horizontal resolution of 1/4, 1/12 and 1/36° with different physics; (2) glider deployment under the OSMOSIS project provides observation of the changes in mixed-layer depth; (3) the ocean stratifies in May, but mixing events induced by gale force wind are observed and <span class="hlt">forecast</span> by the <span class="hlt">systems</span>. Statistical scores and <span class="hlt">forecast</span> error quantification for each <span class="hlt">system</span> and for the combined products are presented. Skill scores indicate that <span class="hlt">forecasts</span> are consistently better than persistence, and temporal correlations between <span class="hlt">forecast</span> and observations are greater than 0.8 even for the 4-day <span class="hlt">forecast</span>. The impact of atmospheric <span class="hlt">forecast</span> error, and for the wind field in particular (miss or time delay of a wind burst <span class="hlt">forecast</span>), is also quantified in terms of occurrence and intensity of mixing or stratification events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.C41D0756R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.C41D0756R&link_type=ABSTRACT"><span id="translatedtitle">An Intercomparison of Predicted Sea Ice Concentration from Global Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> & Arctic Cap Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rosemond, K.</p> <p>2015-12-01</p> <p>The objective of this research is to provide an evaluation of improvements in marginal ice zone (MIZ) and pack ice estimations from the Global Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> (GOFS) model compared to the current operational model, the Arctic Cap Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span> (ACNFS). This will be determined by an intercomparison between the subjectively estimated operational ice concentration data from the National Ice Center (NIC) MIZ analysis and the ice concentration estimates from GOFS and ACNFS. This will help ascertain which nowcast from the models compares best to the NIC operational data stream needed for vessel support. It will also provide a quantitative assessment of GOFS and ACNFS performance and be used in the Operational Evaluation (OPEVAL) report from the NIC to NRL. The intercomparison results are based on statistical evaluations through a series of map overlays from both models ACNFS, GOFS with the NIC's MIZ data. All data was transformed to a common grid and difference maps were generated to determine which model had the greatest difference compared to the MIZ ice concentrations. This was provided daily for both the freeze-up and meltout seasons. Results indicated the GOFS model surpassed the ACNFS model, however both models were comparable. These results will help US Navy and NWS Anchorage ice <span class="hlt">forecasters</span> understand model biases and know which model guidance is likely to provide the best estimate of future ice conditions.The objective of this research is to provide an evaluation of improvements in marginal ice zone (MIZ) and pack ice estimations from the Global Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> (GOFS) model compared to the current operational model, the Arctic Cap Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span> (ACNFS). This will be determined by an intercomparison between the subjectively estimated operational ice concentration data from the National Ice Center (NIC) MIZ analysis and the ice concentration estimates from GOFS and ACNFS. This will help ascertain which nowcast from the models</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860018157','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860018157"><span id="translatedtitle"><span class="hlt">Load</span> positioning <span class="hlt">system</span> with gravity compensation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hollow, R. H.</p> <p>1984-01-01</p> <p>A <span class="hlt">load</span> positioning <span class="hlt">system</span> with gravity compensation has a servomotor, position sensing feedback potentiometer and velocity sensing tachometer in a conventional closed loop servo arrangement to cause a lead screw and a ball nut to vertically position a <span class="hlt">load</span>. Gravity compensating components comprise the DC motor, gears, which couple torque from the motor to the lead screw, and constant current power supply. The constant weight of the <span class="hlt">load</span> applied to the lead screw via the ball nut tend to cause the lead screw to rotate, the constant torque of which is opposed by the constant torque produced by the motor when fed from the constant current source. The constant current is preset as required by the potentiometer to effect equilibration of the <span class="hlt">load</span> which thereby enables the positioning servomotor to see the <span class="hlt">load</span> as weightless under both static and dynamic conditions. Positioning acceleration and velocity performance are therefore symmetrical.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A41P..01W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A41P..01W"><span id="translatedtitle">Thirty Years of Improving the NCEP Global <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>White, G. H.; Manikin, G.; Yang, F.</p> <p>2014-12-01</p> <p>Current eight day <span class="hlt">forecasts</span> by the NCEP Global <span class="hlt">Forecast</span> <span class="hlt">System</span> are as accurate as five day <span class="hlt">forecasts</span> 30 years ago. This revolution in weather <span class="hlt">forecasting</span> reflects increases in computer power, improvements in the assimilation of observations, especially satellite data, improvements in model physics, improvements in observations and international cooperation and competition. One important component has been and is the diagnosis, evaluation and reduction of systematic errors. The effect of proposed improvements in the GFS on systematic errors is one component of the thorough testing of such improvements by the Global Climate and Weather Modeling Branch. Examples of reductions in systematic errors in zonal mean temperatures and winds and other fields will be presented. One challenge in evaluating systematic errors is uncertainty in what reality is. Model initial states can be regarded as the best overall depiction of the atmosphere, but can be misleading in areas of few observations or for fields not well observed such as humidity or precipitation over the oceans. Verification of model physics is particularly difficult. The Environmental Modeling Center emphasizes the evaluation of systematic biases against observations. Recently EMC has placed greater emphasis on synoptic evaluation and on precipitation, 2-meter temperatures and dew points and 10 meter winds. A weekly EMC map discussion reviews the performance of many models over the United States and has helped diagnose and alleviate significant systematic errors in the GFS, including a near surface summertime evening cold wet bias over the eastern US and a multi-week period when the GFS persistently developed bogus tropical storms off Central America. The GFS exhibits a wet bias for light rain and a dry bias for moderate to heavy rain over the continental United States. Significant changes to the GFS are scheduled to be implemented in the fall of 2014. These include higher resolution, improved physics and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED555619.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED555619.pdf"><span id="translatedtitle">Faculty Teaching <span class="hlt">Loads</span> in the UNC <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Schalin, Jay</p> <p>2014-01-01</p> <p>This paper explores the teaching <span class="hlt">loads</span> of faculty in the University of North Carolina (UNC) <span class="hlt">system</span>. Salaries for faculty members are the single largest cost of higher education in the UNC <span class="hlt">system</span>, accounting for approximately half of expenditures. The <span class="hlt">system</span>'s funding formula for its 16 college campuses is largely dependent upon the number of…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvE..91c2915B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvE..91c2915B"><span id="translatedtitle">Nonparametric <span class="hlt">forecasting</span> of low-dimensional dynamical <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berry, Tyrus; Giannakis, Dimitrios; Harlim, John</p> <p>2015-03-01</p> <p>This paper presents a nonparametric modeling approach for <span class="hlt">forecasting</span> stochastic dynamical <span class="hlt">systems</span> on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical <span class="hlt">systems</span> with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25871180','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25871180"><span id="translatedtitle">Nonparametric <span class="hlt">forecasting</span> of low-dimensional dynamical <span class="hlt">systems</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Berry, Tyrus; Giannakis, Dimitrios; Harlim, John</p> <p>2015-03-01</p> <p>This paper presents a nonparametric modeling approach for <span class="hlt">forecasting</span> stochastic dynamical <span class="hlt">systems</span> on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical <span class="hlt">systems</span> with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation. PMID:25871180</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19770021261','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19770021261"><span id="translatedtitle"><span class="hlt">Load</span> control <span class="hlt">system</span>. [for space shuttle external tank ground tests</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Grosse, J. C.</p> <p>1977-01-01</p> <p>The <span class="hlt">load</span> control <span class="hlt">system</span> developed for the shuttle external structural tests is described. The <span class="hlt">system</span> consists of a <span class="hlt">load</span> programming/display module, and a <span class="hlt">load</span> control module along with the following hydraulic <span class="hlt">system</span> components: servo valves, dump valves, hydraulic <span class="hlt">system</span> components, and servo valve manifold blocks. One <span class="hlt">load</span> programming/display subsystem can support multiple <span class="hlt">load</span> control subsystem modules.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19790063237&hterms=deep+webb&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Ddeep%2Bwebb','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19790063237&hterms=deep+webb&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Ddeep%2Bwebb"><span id="translatedtitle"><span class="hlt">Forecasting</span> of <span class="hlt">loading</span> on the Deep Space Network for proposed future NASA mission sets</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Webb, W. A.</p> <p>1979-01-01</p> <p>The paper describes a computer program, DSNLOAD, which provides the Deep Space Network (DSN) <span class="hlt">loading</span> information given a proposed future NASA mission set. The DSNLOAD model includes required pre- and post-calibration periods, and station 'overhead' such as maintenance or 'down' time. The analysis is presented which transforms station view period data for the mission set into <span class="hlt">loading</span> matrices used to assess <span class="hlt">loading</span> requirement. Assessment of future <span class="hlt">loading</span> on the DSN for a set of NASA missions by estimating the tracking situation and presenting the DSN <span class="hlt">loading</span> data, and a flowchart for selecting a possible future mission, determining a heliocentric orbit for the mission, generating view period schedules, and converting these schedules into basic <span class="hlt">loading</span> data for each mission for each station are given. The tracking schedule model which considers the tracking schedule to be represented by passes of maximum required length and centered within the view period of available tracking time for each mission is described, and, finally, an example of typical <span class="hlt">loading</span> study is provided.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014Nonli..27R..51B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014Nonli..27R..51B"><span id="translatedtitle">Short- and long-term <span class="hlt">forecast</span> for chaotic and random <span class="hlt">systems</span> (50 years after Lorenz's paper)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bunimovich, Leonid A.</p> <p>2014-09-01</p> <p>We briefly review a history of the impact of the famous 1963 paper by E Lorenz on hydrodynamics, physics and mathematics communities on both sides of the iron curtain. This paper was an attempt to apply the ideas and methods of dynamical <span class="hlt">systems</span> theory to the problem of weather <span class="hlt">forecast</span>. Its major discovery was the phenomenon of chaos in dissipative dynamical <span class="hlt">systems</span> which makes such <span class="hlt">forecasts</span> rather problematic, if at all possible. In this connection we present some recent results which demonstrate that both a short-term and a long-term <span class="hlt">forecast</span> are actually possible for the most chaotic dynamical (as well as for the most random, like IID and Markov chain) <span class="hlt">systems</span>. Moreover, there is a sharp transition between the time interval where one may use a short-term <span class="hlt">forecast</span> and the times where a long-term <span class="hlt">forecast</span> is applicable. Finally we discuss how these findings could be incorporated into the <span class="hlt">forecast</span> strategy outlined in the Lorenz's paper.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/864311','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/servlets/purl/864311"><span id="translatedtitle">Fuel cell stack compressive <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Fahle, Ronald W.; Reiser, Carl A.</p> <p>1982-01-01</p> <p>A fuel cell module comprising a stack of fuel cells with reactant gas manifolds sealed against the external surfaces of the stack includes a constraint <span class="hlt">system</span> for providing a compressive <span class="hlt">load</span> on the stack wherein the constraint <span class="hlt">system</span> maintains the stack at a constant height (after thermal expansion) and allows the compressive <span class="hlt">load</span> to decrease with time as a result of the creep characteristics of the stack. Relative motion between the manifold sealing edges and the stack surface is virtually eliminated by this constraint <span class="hlt">system</span>; however it can only be used with a stack having considerable resiliency and appropriate thermal expansion and creep characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012OcScD...9.1437C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012OcScD...9.1437C"><span id="translatedtitle">Towards an integrated <span class="hlt">forecasting</span> <span class="hlt">system</span> for pelagic fisheries</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Christensen, A.; Butenschön, M.; Gürkan, Z.; Allen, I. J.</p> <p>2012-03-01</p> <p>First results of a coupled modeling and <span class="hlt">forecasting</span> <span class="hlt">system</span> for the pelagic fisheries are being presented. The <span class="hlt">system</span> consists currently of three mathematically fundamentally different model subsystems: POLCOMS-ERSEM providing the physical-biogeochemical environment implemented in the domain of the North-West European shelf and the SPAM model which describes sandeel stocks in the North Sea. The third component, the SLAM model, connects POLCOMS-ERSEM and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the base of hindcast data. The coupled <span class="hlt">forecasting</span> <span class="hlt">system</span> is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeels stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigen dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2-6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1999PhDT........86S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1999PhDT........86S"><span id="translatedtitle">Interpretation, modeling and <span class="hlt">forecasting</span> runoff of regional hydrogeologic <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shun, Tongying</p> <p>1999-10-01</p> <p>Long-range modeling of a precipitation-runoff process has become indispensable to predict/<span class="hlt">forecast</span> runoff and study the impact of modern anthropogenic factors and land change use on watersheds. The purpose of this thesis research is to interpret, model and <span class="hlt">forecast</span> complex drainage basins using advanced signal processing technique and a physically-based low-dimensional dynamic model. The first emphasis is placed on a hydrogeologic interpretation of a complex drainage basin. The space- time patterns of annual, interannual, and decadal components of precipitation, temperature, and runoff (P- T-R) using long-record time series across the steep topographic gradient of the Wasatch Front in northern Utah, are examined. The singular spectrum analysis is used to detect dominant oscillations and spatial patterns in the data and to discuss the relation to the unique mountain and basin hydrologic setting. For precipitation and temperature, only the annual/seasonal spectral peaks were found to be significantly different from the underlying noise floor. Spectral peaks in runoff show increasing low-frequency components at intermediate and low elevation. A conceptual hydrogeologic model for the mountain and basin <span class="hlt">system</span> proposes how losing streams and deep upwelling groundwater in the alluvial aquifer could explain the strong low-frequency component in streams. The research shows that weak interannual and decadal oscillations in the climate signal are strengthened where groundwater discharge dominates streamflow. The second emphasis is focused on developing a long-range physically-based precipitation-runoff model. A low- dimensional integral-balance model is developed for a hydrologic <span class="hlt">system</span> where multiple time scales of basin storage play the dominant role on a precipitation-runoff process. The genetic algorithm (GA) technique is implemented for parameter identification with the observed data. The model is developed for the Upper West Branch of the Susquehanna River in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890005967','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890005967"><span id="translatedtitle">Common source-multiple <span class="hlt">load</span> vs. separate source-individual <span class="hlt">load</span> photovoltaic <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Appelbaum, Joseph</p> <p>1989-01-01</p> <p>A comparison of <span class="hlt">system</span> performance is made for two possible <span class="hlt">system</span> setups: (1) individual <span class="hlt">loads</span> powered by separate solar cell sources; and (2) multiple <span class="hlt">loads</span> powered by a common solar cell source. A proof for resistive <span class="hlt">loads</span> is given that shows the advantage of a common source over a separate source photovoltaic <span class="hlt">system</span> for a large range of <span class="hlt">loads</span>. For identical <span class="hlt">loads</span>, both <span class="hlt">systems</span> perform the same.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1817275C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1817275C&link_type=ABSTRACT"><span id="translatedtitle">Mediterranea <span class="hlt">Forecasting</span> <span class="hlt">System</span>: a focus on wave-current coupling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Clementi, Emanuela; Delrosso, Damiano; Pistoia, Jenny; Drudi, Massimiliano; Fratianni, Claudia; Grandi, Alessandro; Pinardi, Nadia; Oddo, Paolo; Tonani, Marina</p> <p>2016-04-01</p> <p>The Mediterranean <span class="hlt">Forecasting</span> <span class="hlt">System</span> (MFS) is a numerical ocean prediction <span class="hlt">system</span> that produces analyses, reanalyses and short term <span class="hlt">forecasts</span> for the entire Mediterranean Sea and its Atlantic Ocean adjacent areas. MFS became operational in the late 90's and has been developed and continuously improved in the framework of a series of EU and National funded programs and is now part of the Copernicus Marine Service. The MFS is composed by the hydrodynamic model NEMO (Nucleus for European Modelling of the Ocean) 2-way coupled with the third generation wave model WW3 (WaveWatchIII) implemented in the Mediterranean Sea with 1/16 horizontal resolution and forced by ECMWF atmospheric fields. The model solutions are corrected by the data assimilation <span class="hlt">system</span> (3D variational scheme adapted to the oceanic assimilation problem) with a daily assimilation cycle, using a background error correlation matrix varying seasonally and in different sub-regions of the Mediterranean Sea. The focus of this work is to present the latest modelling <span class="hlt">system</span> upgrades and the related achieved improvements. In order to evaluate the performance of the coupled <span class="hlt">system</span> a set of experiments has been built by coupling the wave and circulation models that hourly exchange the following fields: the sea surface currents and air-sea temperature difference are transferred from NEMO model to WW3 model modifying respectively the mean momentum transfer of waves and the wind speed stability parameter; while the neutral drag coefficient computed by WW3 model is passed to NEMO that computes the turbulent component. In order to validate the modelling <span class="hlt">system</span>, numerical results have been compared with in-situ and remote sensing data. This work suggests that a coupled model might be capable of a better description of wave-current interactions, in particular feedback from the ocean to the waves might assess an improvement on the prediction capability of wave characteristics, while suggests to proceed toward a fully</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Avalanches&pg=2&id=EJ573907','ERIC'); return false;" href="http://eric.ed.gov/?q=Avalanches&pg=2&id=EJ573907"><span id="translatedtitle">A Methodology To Allow Avalanche <span class="hlt">Forecasting</span> on an Information Retrieval <span class="hlt">System</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Purves, R. S.; Sanderson, M.</p> <p>1998-01-01</p> <p>Presents adaptations and tests undertaken to allow an information retrieval <span class="hlt">system</span> to <span class="hlt">forecast</span> the likelihood of avalanches on a particular day; the <span class="hlt">forecasting</span> process uses historical data of the weather and avalanche conditions for a large number of days. Describes a method for adapting these data into a form usable by a text-based IR <span class="hlt">system</span> and…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED305121.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED305121.pdf"><span id="translatedtitle">Developing Environmental Scanning/<span class="hlt">Forecasting</span> <span class="hlt">Systems</span> To Augment Community College Planning.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Morrison, James L.; Held, William G.</p> <p></p> <p>A description is provided of a conference session that was conducted to explore the structure and function of an environmental scanning/<span class="hlt">forecasting</span> <span class="hlt">system</span> that could be used in a community college to facilitate planning. Introductory comments argue that a college that establishes an environmental scanning and <span class="hlt">forecasting</span> <span class="hlt">system</span> is able to identify…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5143O&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5143O&link_type=ABSTRACT"><span id="translatedtitle">The Eruption <span class="hlt">Forecasting</span> Information <span class="hlt">System</span> (EFIS) database project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ogburn, Sarah; Harpel, Chris; Pesicek, Jeremy; Wellik, Jay; Pallister, John; Wright, Heather</p> <p>2016-04-01</p> <p>The Eruption <span class="hlt">Forecasting</span> Information <span class="hlt">System</span> (EFIS) project is a new initiative of the U.S. Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) with the goal of enhancing VDAP's ability to <span class="hlt">forecast</span> the outcome of volcanic unrest. The EFIS project seeks to: (1) Move away from relying on the collective memory to probability estimation using databases (2) Create databases useful for pattern recognition and for answering common VDAP questions; e.g. how commonly does unrest lead to eruption? how commonly do phreatic eruptions portend magmatic eruptions and what is the range of antecedence times? (3) Create generic probabilistic event trees using global data for different volcano 'types' (4) Create background, volcano-specific, probabilistic event trees for frequently active or particularly hazardous volcanoes in advance of a crisis (5) Quantify and communicate uncertainty in probabilities A major component of the project is the global EFIS relational database, which contains multiple modules designed to aid in the construction of probabilistic event trees and to answer common questions that arise during volcanic crises. The primary module contains chronologies of volcanic unrest, including the timing of phreatic eruptions, column heights, eruptive products, etc. and will be initially populated using chronicles of eruptive activity from Alaskan volcanic eruptions in the GeoDIVA database (Cameron et al. 2013). This database module allows us to query across other global databases such as the WOVOdat database of monitoring data and the Smithsonian Institution's Global Volcanism Program (GVP) database of eruptive histories and volcano information. The EFIS database is in the early stages of development and population; thus, this contribution also serves as a request for feedback from the community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1212947E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1212947E"><span id="translatedtitle">Statistical modelling of <span class="hlt">forecast</span> errors for multiple lead-times and a <span class="hlt">system</span> of reservoirs</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Engeland, Kolbjorn; Steinsland, Ingelin; Kolberg, Sjur</p> <p>2010-05-01</p> <p>Water resources management, e.g. operation of reservoirs, is amongst others based on <span class="hlt">forecasts</span> of inflow provided by a precipitation-runoff model. The <span class="hlt">forecasted</span> inflow is normally given as one value, even though it is an uncertain value. There is a growing interest to account for uncertain information in decision support <span class="hlt">systems</span>, e.g. how to operate a hydropower reservoir to maximize the gain. One challenge is to develop decision support <span class="hlt">systems</span> that can use uncertain information. The contribution from the hydrological modeler is to derive a <span class="hlt">forecast</span> distribution (from which uncertainty intervals can be computed) for the inflow predictions. In this study we constructed a statistical model for the <span class="hlt">forecast</span> errors for daily inflow into a <span class="hlt">system</span> of four hydropower reservoirs in Ulla-Førre in Western Norway. A distributed hydrological model was applied to generate the inflow <span class="hlt">forecasts</span> using weather <span class="hlt">forecasts</span> provided by ECM for lead-times up to 10 days. The precipitation <span class="hlt">forecasts</span> were corrected for systematic bias. A statistical model based on auto-regressive innovations for Box-Cox-transformed observations and <span class="hlt">forecasts</span> was constructed for the <span class="hlt">forecast</span> errors. The parameters of the statistical model were conditioned on climate and the internal snow state in the hydrological model. The model was evaluated according to the reliability of the <span class="hlt">forecast</span> distribution, the width of the <span class="hlt">forecast</span> distribution, and efficiency of the median <span class="hlt">forecast</span> for the 10 lead times and the four catchments. The interpretation of the results had to be done carefully since the inflow data have a large uncertainty.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000SPIE.4192...36F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000SPIE.4192...36F"><span id="translatedtitle">Intelligent <span class="hlt">forecasting</span> compensatory control <span class="hlt">system</span> for profile machining</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fung, Eric H. K.; Chuen, C. W.; Lee, L. M.</p> <p>2000-10-01</p> <p>Precision machining is becoming increasingly important in modern industry because many modern products require high form accuracy. An affordable approach to improve the accuracy of the surface profile of a workpiece is to adopt the on-line error <span class="hlt">forecasting</span> and compensation control (FCC) techniques. In the present study, the consideration of variation of cutting force as a result of piezoactuator movement requires the formulation of ARMAX models. The time-series analysis based on ARMAX technique has an advantage over the traditional spectral method in that the latter can lead to the over-parameterization of the accompanying model. The roundness measurement results obtained from the practical experiments and the derived improvement percentages are grouped under one or more of the <span class="hlt">system</span> parameters which include the ARMAX orders, feed rate, depth of cut, material, and forgetting factor. An expert <span class="hlt">system</span> has been successfully developed to implement the rules using the Prolog language for helping the users to select suitable parameters for the FCC <span class="hlt">system</span> of the lathe machine. Based on the measurement data, it can be shown that the lathe machine, when equipped with the ARMAX-based FCC <span class="hlt">system</span>, can yield a minimum value of average improvement of 26% under the testing conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950013335','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950013335"><span id="translatedtitle">Transport aircraft <span class="hlt">loading</span> and balancing <span class="hlt">system</span>: Using a CLIPS expert <span class="hlt">system</span> for military aircraft <span class="hlt">load</span> planning</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Richardson, J.; Labbe, M.; Belala, Y.; Leduc, Vincent</p> <p>1994-01-01</p> <p>The requirement for improving aircraft utilization and responsiveness in airlift operations has been recognized for quite some time by the Canadian Forces. To date, the utilization of scarce airlift resources has been planned mainly through the employment of manpower-intensive manual methods in combination with the expertise of highly qualified personnel. In this paper, we address the problem of facilitating the <span class="hlt">load</span> planning process for military aircraft cargo planes through the development of a computer-based <span class="hlt">system</span>. We introduce TALBAS (Transport Aircraft <span class="hlt">Loading</span> and BAlancing <span class="hlt">System</span>), a knowledge-based <span class="hlt">system</span> designed to assist personnel involved in preparing valid <span class="hlt">load</span> plans for the C130 Hercules aircraft. The main features of this <span class="hlt">system</span> which are accessible through a convivial graphical user interface, consists of the automatic generation of valid cargo arrangements given a list of items to be transported, the user-definition of <span class="hlt">load</span> plans and the automatic validation of such <span class="hlt">load</span> plans.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=143665&keyword=independent+AND+variables&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65041812&CFTOKEN=41601335','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=143665&keyword=independent+AND+variables&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65041812&CFTOKEN=41601335"><span id="translatedtitle">A SIMPLE MODEL FOR <span class="hlt">FORECASTING</span> THE EFFECTS OF NITROGEN <span class="hlt">LOADS</span> ON CHESAPEAKE BAY HYPOXIA</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The causes and consequences of oxygen depletion in Chesapeake Bay have been the focus of research, assessment, and policy action over the past several decades. An ongoing scientific re-evaluation of what nutrients <span class="hlt">load</span> reductions are necessary to meet the water quality goals is ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=89736&keyword=vehicle+AND+management+AND+system&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77763245&CFTOKEN=93839682','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=89736&keyword=vehicle+AND+management+AND+system&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77763245&CFTOKEN=93839682"><span id="translatedtitle">THE EMISSION PROCESSING <span class="hlt">SYSTEM</span> FOR THE ETA/CMAQ AIR QUALITY <span class="hlt">FORECAST</span> <span class="hlt">SYSTEM</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>NOAA and EPA have created an Air Quality <span class="hlt">Forecast</span> (AQF) <span class="hlt">system</span>. This AQF <span class="hlt">system</span> links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of th...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997BAMS...78.2851V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997BAMS...78.2851V"><span id="translatedtitle">Performance of an Advanced MOS <span class="hlt">System</span> in the 1996-97 National Collegiate Weather <span class="hlt">Forecasting</span> Contest.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vislocky, Robert L.; Fritsch, J. Michael</p> <p>1997-12-01</p> <p>A prototype advanced model output statistics (MOS) <span class="hlt">forecast</span> <span class="hlt">system</span> that was entered in the 1996-97 National Collegiate Weather <span class="hlt">Forecast</span> Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype <span class="hlt">system</span> uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS <span class="hlt">forecasts</span>, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the <span class="hlt">forecast</span> site. The <span class="hlt">forecasts</span> are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus <span class="hlt">forecast</span> (i.e., the average of the <span class="hlt">forecasts</span> from all of the human participants), 2) the combination of NGM raw output (for precipitation <span class="hlt">forecasts</span>) and NGM MOS guidance (for temperature <span class="hlt">forecasts</span>), and 3) the combination of Eta Model raw output (for precipitation <span class="hlt">forecasts</span>) and AVN MOS guidance (for temperature <span class="hlt">forecasts</span>).Results show that the advanced MOS <span class="hlt">system</span> finished in 20th place out of 737 original entrants, or better than approximately 97% of the human <span class="hlt">forecasters</span> who entered the contest. Moreover, the advanced MOS <span class="hlt">system</span> was slightly better than consensus (23d place). The fact that an objective <span class="hlt">forecast</span> <span class="hlt">system</span> finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in <span class="hlt">forecast</span> competitions. Equally significant is that the advanced MOS <span class="hlt">system</span> was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSMIN41A..04F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSMIN41A..04F"><span id="translatedtitle">A Weather Analysis and <span class="hlt">Forecasting</span> <span class="hlt">System</span> for Baja California, Mexico</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Farfan, L. M.</p> <p>2006-05-01</p> <p>The weather of the Baja California Peninsula, part of northwestern Mexico, is mild and dry most of the year. However, during the summer, humid air masses associated with tropical cyclones move northward in the eastern Pacific Ocean. Added features that create a unique meteorological situation include mountain ranges along the spine of the peninsula, warm water in the Gulf of California, and the cold California Current in the Pacific. These features interact with the environmental flow to induce conditions that play a role in the occurrence of localized, convective <span class="hlt">systems</span> during the approach of tropical cyclones. Most of these events occur late in the summer, generating heavy precipitation, strong winds, lightning, and are associated with significant property damage to the local populations. Our goal is to provide information on the characteristics of these weather <span class="hlt">systems</span> by performing an analysis of observations derived from a regional network. This includes imagery from radar and geostationary satellite, and data from surface stations. A set of real-time products are generated in our research center and are made available to a broad audience (researchers, students, and business employees) by using an internet site. Graphical products are updated anywhere from one to 24 hours and includes predictions from numerical models. <span class="hlt">Forecasts</span> are derived from an operational model (GFS) and locally generated simulations based on a mesoscale model (MM5). Our analysis and <span class="hlt">forecasting</span> <span class="hlt">system</span> has been in operation since the summer of 2005 and was used as a reference for a set of discussions during the development of eastern Pacific tropical cyclones. This basin had 15 named storms and none of them made landfall on the west coast of Mexico; however, four <span class="hlt">systems</span> were within 800 km from the area of interest, resulting in some convective activity. During the whole season, a group of 30 users from our institution, government offices, and local businesses received daily information</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009HESS...13.2221V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009HESS...13.2221V"><span id="translatedtitle">An evaluation of the Canadian global meteorological ensemble prediction <span class="hlt">system</span> for short-term hydrological <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Velázquez, J. A.; Petit, T.; Lavoie, A.; Boucher, M.-A.; Turcotte, R.; Fortin, V.; Anctil, F.</p> <p>2009-11-01</p> <p>Hydrological <span class="hlt">forecasting</span> consists in the assessment of future streamflow. Current deterministic <span class="hlt">forecasts</span> do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble <span class="hlt">forecasts</span> are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction <span class="hlt">system</span>, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved <span class="hlt">system</span> is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. <span class="hlt">Forecasts</span> are produced in a 3 h time step for a 3-day <span class="hlt">forecast</span> horizon. The deterministic <span class="hlt">forecast</span> is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble <span class="hlt">forecasts</span> are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h <span class="hlt">forecasts</span>. However, the hydrological ensemble <span class="hlt">forecasts</span> are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009HESSD...6.4891V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009HESSD...6.4891V"><span id="translatedtitle">An evaluation of the canadian global meteorological ensemble prediction <span class="hlt">system</span> for short-term hydrological <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Velázquez, J. A.; Petit, T.; Lavoie, A.; Boucher, M.-A.; Turcotte, R.; Fortin, V.; Anctil, F.</p> <p>2009-07-01</p> <p>Hydrological <span class="hlt">forecasting</span> consists in the assessment of future streamflow. Current deterministic <span class="hlt">forecasts</span> do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble <span class="hlt">forecasts</span> are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction <span class="hlt">system</span>, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved <span class="hlt">system</span> is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. <span class="hlt">Forecasts</span> are produced in a 3 h time step for a 3-day <span class="hlt">forecast</span> horizon. The deterministic <span class="hlt">forecast</span> is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble <span class="hlt">forecasts</span> are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h <span class="hlt">forecasts</span>. However, the hydrological ensemble <span class="hlt">forecasts</span> are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1999AIPC..465..259L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1999AIPC..465..259L"><span id="translatedtitle">The application of hybrid artificial intelligence <span class="hlt">systems</span> for <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lees, Brian; Corchado, Juan</p> <p>1999-03-01</p> <p>The results to date are presented from an ongoing investigation, in which the aim is to combine the strengths of different artificial intelligence methods into a single problem solving <span class="hlt">system</span>. The premise underlying this research is that a <span class="hlt">system</span> which embodies several cooperating problem solving methods will be capable of achieving better performance than if only a single method were employed. The work has so far concentrated on the combination of case-based reasoning and artificial neural networks. The relative merits of artificial neural networks and case-based reasoning problem solving paradigms, and their combination are discussed. The integration of these two AI problem solving methods in a hybrid <span class="hlt">systems</span> architecture, such that the neural network provides support for learning from past experience in the case-based reasoning cycle, is then presented. The approach has been applied to the task of <span class="hlt">forecasting</span> the variation of physical parameters of the ocean. Results obtained so far from tests carried out in the dynamic oceanic environment are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1193237','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1193237"><span id="translatedtitle">3D cloud detection and tracking <span class="hlt">system</span> for solar <span class="hlt">forecast</span> using multiple sky imagers</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul</p> <p>2015-06-23</p> <p>We propose a <span class="hlt">system</span> for <span class="hlt">forecasting</span> short-term solar irradiance based on multiple total sky imagers (TSIs). The <span class="hlt">system</span> utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for <span class="hlt">forecasting</span> surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar <span class="hlt">forecasting</span>. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance <span class="hlt">forecast</span> models at various sites. We confirm that this <span class="hlt">system</span> can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance <span class="hlt">forecasts</span> with short <span class="hlt">forecast</span> horizons from the obtained images. Finally, we vet our <span class="hlt">forecasting</span> <span class="hlt">system</span> at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our <span class="hlt">system</span> achieves at least a 26% improvement for all irradiance <span class="hlt">forecasts</span> between one and fifteen minutes.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1193237-cloud-detection-tracking-system-solar-forecast-using-multiple-sky-imagers','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1193237-cloud-detection-tracking-system-solar-forecast-using-multiple-sky-imagers"><span id="translatedtitle">3D cloud detection and tracking <span class="hlt">system</span> for solar <span class="hlt">forecast</span> using multiple sky imagers</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul</p> <p>2015-06-23</p> <p>We propose a <span class="hlt">system</span> for <span class="hlt">forecasting</span> short-term solar irradiance based on multiple total sky imagers (TSIs). The <span class="hlt">system</span> utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for <span class="hlt">forecasting</span> surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar <span class="hlt">forecasting</span>. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance <span class="hlt">forecast</span> models at various sites. We confirm that this <span class="hlt">system</span> can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance <span class="hlt">forecasts</span> with short <span class="hlt">forecast</span> horizons from the obtained images. Finally, we vet our <span class="hlt">forecasting</span> <span class="hlt">system</span> at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our <span class="hlt">system</span> achieves at least a 26% improvement for all irradiance <span class="hlt">forecasts</span> between one and fifteen minutes.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AdWR...71..200R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AdWR...71..200R"><span id="translatedtitle">Short-term optimal operation of water <span class="hlt">systems</span> using ensemble <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raso, L.; Schwanenberg, D.; van de Giesen, N. C.; van Overloop, P. J.</p> <p>2014-09-01</p> <p>Short-term water <span class="hlt">system</span> operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic <span class="hlt">systems</span>. Applied to open water <span class="hlt">systems</span>, MPC provides integrated, optimal, and proactive management, when <span class="hlt">forecasts</span> are available. Notwithstanding these properties, if <span class="hlt">forecast</span> uncertainty is not properly taken into account, the <span class="hlt">system</span> performance can critically deteriorate. Ensemble <span class="hlt">forecast</span> is a way to represent short-term <span class="hlt">forecast</span> uncertainty. An ensemble <span class="hlt">forecast</span> is a set of possible future trajectories of a meteorological or hydrological <span class="hlt">system</span>. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management. The theoretical innovation presented here is the use of ensemble <span class="hlt">forecasts</span> for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to <span class="hlt">forecast</span> uncertainty. Adaptivity reduces the sensitivity to wrong <span class="hlt">forecasts</span> and improves the operational performance. TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1982STIN...8327066B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1982STIN...8327066B"><span id="translatedtitle"><span class="hlt">Load</span> leveling on industrial refrigeration <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bierenbaum, H. S.; Kraus, A. D.</p> <p>1982-01-01</p> <p>A computer model was constructed of a brewery with a 2000 horsepower compressor/refrigeration <span class="hlt">system</span>. The various conservation and <span class="hlt">load</span> management options were simulated using the validated model. The savings available for implementing the most promising options were verified by trials in the brewery. Result show that an optimized methodology for implementing <span class="hlt">load</span> leveling and energy conservation consisted of: (1) adjusting (or tuning) refrigeration <span class="hlt">systems</span> controller variables to minimize unnecessary compressor starts, (2) The primary refrigeration <span class="hlt">system</span> operating parameters, compressor suction pressure, and discharge pressure are carefully controlled (modulated) to satisfy product quality constraints (as well as in-process material cooling rates and temperature levels) and energy evaluating the energy cost savings associated with reject heat recovery, and (4) a decision is made to implement the reject heat recovery <span class="hlt">system</span> based on a cost/benefits analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/435371','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/435371"><span id="translatedtitle">A linear programming model for reducing <span class="hlt">system</span> peak through customer <span class="hlt">load</span> control programs</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kurucz, C.N.; Brandt, D.; Sim, S.</p> <p>1996-11-01</p> <p>A Linear Programming (LP) model was developed to optimize the amount of <span class="hlt">system</span> peak <span class="hlt">load</span> reduction through scheduling of control periods in commercial/industrial and residential <span class="hlt">load</span> control programs at Florida Power and Light Company. The LP model can be used to determine both long and short term control scheduling strategies and for planning the number of customers which should be enrolled in each program. Results of applying the model to a <span class="hlt">forecasted</span> late 1990s summer peak day <span class="hlt">load</span> shape are presented. It is concluded that LP solutions provide a relatively inexpensive and powerful approach to planning and scheduling <span class="hlt">load</span> control. Also, it is not necessary to model completely general scheduling of control periods in order to obtain near best solutions to peak <span class="hlt">load</span> reduction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1220632-recent-trends-variable-generation-forecasting-its-value-power-system','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1220632-recent-trends-variable-generation-forecasting-its-value-power-system"><span id="translatedtitle">Recent Trends in Variable Generation <span class="hlt">Forecasting</span> and Its Value to the Power <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; et al</p> <p>2014-12-23</p> <p>We report that the rapid deployment of wind and solar energy generation <span class="hlt">systems</span> has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power <span class="hlt">forecasts</span> is still viewed by the power industry as being quite high, and many barriers to <span class="hlt">forecast</span> adoption by power <span class="hlt">system</span> operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power <span class="hlt">forecasts</span>. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation <span class="hlt">forecasting</span> and have taken great strides to enhance their usage of <span class="hlt">forecasting</span>. In parallel, power <span class="hlt">system</span> markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power <span class="hlt">forecasting</span> technologies in the U.S., the role of <span class="hlt">forecasting</span> in an evolving power <span class="hlt">system</span> framework, and the benefits to intended <span class="hlt">forecast</span> users.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1220632','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1220632"><span id="translatedtitle">Recent Trends in Variable Generation <span class="hlt">Forecasting</span> and Its Value to the Power <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; Hodge, Bri-Mathias; Finley, Catherine; Nakafuji, Dora; Peterson, Jack L.; Maggio, David; Marquis, Melinda</p> <p>2014-12-23</p> <p>We report that the rapid deployment of wind and solar energy generation <span class="hlt">systems</span> has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power <span class="hlt">forecasts</span> is still viewed by the power industry as being quite high, and many barriers to <span class="hlt">forecast</span> adoption by power <span class="hlt">system</span> operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power <span class="hlt">forecasts</span>. Additionally, several utilities and grid operators have recognized the value of adopting variable generation <span class="hlt">forecasting</span> and have taken great strides to enhance their usage of <span class="hlt">forecasting</span>. In parallel, power <span class="hlt">system</span> markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power <span class="hlt">forecasting</span> technologies in the U.S., the role of <span class="hlt">forecasting</span> in an evolving power <span class="hlt">system</span> framework, and the benefits to intended <span class="hlt">forecast</span> users.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014E%26ES...17a2058H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014E%26ES...17a2058H"><span id="translatedtitle">Winter wheat quality monitoring and <span class="hlt">forecasting</span> <span class="hlt">system</span> based on remote sensing and environmental factors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie</p> <p>2014-03-01</p> <p>To achieve dynamic winter wheat quality monitoring and <span class="hlt">forecasting</span> in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and <span class="hlt">forecasting</span> <span class="hlt">system</span> by using a remote sensing index and environmental factors. The winter wheat quality trend was <span class="hlt">forecasted</span> before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and <span class="hlt">forecasting</span> models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for <span class="hlt">forecasting</span> the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and <span class="hlt">forecasting</span> model used in this <span class="hlt">system</span> get greatly improved accuracy. Winter wheat quality was monitored and <span class="hlt">forecasted</span> based on the above models, and this <span class="hlt">system</span> was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring <span class="hlt">system</span> was presented in Beijing, the monitoring and <span class="hlt">forecasting</span> results was outputted as thematic maps.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43A0690C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43A0690C"><span id="translatedtitle">Using ensemble NWP wind power <span class="hlt">forecasts</span> to improve national power <span class="hlt">system</span> management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cannon, D.; Brayshaw, D.; Methven, J.; Coker, P.; Lenaghan, D.</p> <p>2014-12-01</p> <p>National power <span class="hlt">systems</span> are becoming increasingly sensitive to atmospheric variability as generation from wind (and other renewables) increases. As such, the days-ahead predictability of wind power has significant implications for power <span class="hlt">system</span> management. At this time horizon, power <span class="hlt">system</span> operators plan transmission line outages for maintenance. In addition, <span class="hlt">forecast</span> users begin to form backup strategies to account for the uncertainty in wind power predictions. Under-estimating this uncertainty could result in a failure to meet <span class="hlt">system</span> security standards, or in the worst instance, a shortfall in total electricity supply. On the other hand, overly conservative assumptions about the <span class="hlt">forecast</span> uncertainty incur costs associated with the unnecessary holding of reserve power. Using the power <span class="hlt">system</span> of Great Britain (GB) as an example, we construct time series of GB-total wind power output using wind speeds from either reanalyses or global weather <span class="hlt">forecasts</span>. To validate the accuracy of these data sets, wind power reconstructions using reanalyses and <span class="hlt">forecast</span> analyses over a recent period are compared to measured GB-total power output. The results are found to be highly correlated on time scales greater than around 6 hours. Results are presented using ensemble wind power <span class="hlt">forecasts</span> from several national and international <span class="hlt">forecast</span> centres (obtained through TIGGE). Firstly, the skill with which global ensemble <span class="hlt">forecasts</span> can represent the uncertainty in the GB-total power output at up to 10 days ahead is quantified. Following this, novel ensemble <span class="hlt">forecast</span> metrics are developed to improve estimates of <span class="hlt">forecast</span> uncertainty within the context of power <span class="hlt">system</span> operations, thus enabling the development of more cost effective strategies. Finally, the predictability of extreme events such as prolonged low wind periods or rapid changes in wind power output are examined in detail. These events, if poorly <span class="hlt">forecast</span>, induce high stress scenarios that could threaten the security of the power</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC42B..04C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC42B..04C"><span id="translatedtitle">Operational Solar <span class="hlt">Forecasting</span> <span class="hlt">System</span> for DNI and GHI for Horizons Covering 5 Minutes to 72 Hours</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coimbra, C. F.</p> <p>2014-12-01</p> <p>I will describe the methodology used to develop and deploy operationally a comprehensive solar <span class="hlt">forecasting</span> <span class="hlt">system</span> for both concentrated and non-concentrated solar technologies. This operational <span class="hlt">forecasting</span> <span class="hlt">system</span> ingests data from local telemetry, remote sensing and Numerical Weather Prediction (NWP) models, processes all the diferent types of data (time series, sky images, satellite images, gridded data, etc.) to produce concatenated solar <span class="hlt">forecasts</span> from 5 minutes out to 72 hours into the future. Each <span class="hlt">forecast</span> is optimized with stochastic learning techniques that include input selection, model topology optimization, model output statistics, metric fitness optimization and machine learning. These <span class="hlt">forecasts</span> are used by solar generators (plant managers), utilities and independent <span class="hlt">system</span> operators for operations, scheduling, dispatching and market participation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1818197C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1818197C&link_type=ABSTRACT"><span id="translatedtitle">Mediterranean monitoring and <span class="hlt">forecasting</span> operational <span class="hlt">system</span> for Copernicus Marine Service</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coppini, Giovanni; Drudi, Massimiliano; Korres, Gerasimos; Fratianni, Claudia; Salon, Stefano; Cossarini, Gianpiero; Clementi, Emanuela; Zacharioudaki, Anna; Grandi, Alessandro; Delrosso, Damiano; Pistoia, Jenny; Solidoro, Cosimo; Pinardi, Nadia; Lecci, Rita; Agostini, Paola; Cretì, Sergio; Turrisi, Giuseppe; Palermo, Francesco; Konstantinidou, Anna; Storto, Andrea; Simoncelli, Simona; Di Pietro, Pier Luigi; Masina, Simona; Ciliberti, Stefania Angela; Ravdas, Michalis; Mancini, Marco; Aloisio, Giovanni; Fiore, Sandro; Buonocore, Mauro</p> <p>2016-04-01</p> <p>The MEDiterranean Monitoring and <span class="hlt">Forecasting</span> Center (Med-MFC) is part of the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), provided on an operational mode by Mercator Ocean in agreement with the European Commission. Specifically, Med MFC <span class="hlt">system</span> provides regular and systematic information about the physical state of the ocean and marine ecosystems for the Mediterranean Sea. The Med-MFC service started in May 2015 from the pre-operational <span class="hlt">system</span> developed during the MyOcean projects, consolidating the understanding of regional Mediterranean Sea dynamics, from currents to biogeochemistry to waves, interfacing with local data collection networks and guaranteeing an efficient link with other Centers in Copernicus network. The Med-MFC products include analyses, 10 days <span class="hlt">forecasts</span> and reanalysis, describing currents, temperature, salinity, sea level and pelagic biogeochemistry. Waves products will be available in MED-MFC version in 2017. The consortium, composed of INGV (Italy), HCMR (Greece) and OGS (Italy) and coordinated by the Euro-Mediterranean Centre on Climate Change (CMCC, Italy), performs advanced R&D activities and manages the service delivery. The Med-MFC infrastructure consists of 3 Production Units (PU), for Physics, Biogechemistry and Waves, a unique Dissemination Unit (DU) and Archiving Unit (AU) and Backup Units (BU) for all principal components, guaranteeing a resilient configuration of the service and providing and efficient and robust solution for the maintenance of the service and delivery. The Med-MFC includes also an evolution plan, both in terms of research and operational activities, oriented to increase the spatial resolution of products, to start wave products dissemination, to increase temporal extent of the reanalysis products and improving ocean physical modeling for delivering new products. The scientific activities carried out in 2015 concerned some improvements in the physical, biogeochemical and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H44E..03H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H44E..03H&link_type=ABSTRACT"><span id="translatedtitle">A Global Hydrological Ensemble <span class="hlt">Forecasting</span> <span class="hlt">System</span>: Uncertainty Quantification and Data Assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hong, Y.; Zhang, Y.; Xue, X.; Wang, X.; Gourley, J. J.; Kirstetter, P.</p> <p>2012-12-01</p> <p>A Global Hydrological Ensemble <span class="hlt">Forecasting</span> <span class="hlt">System</span> (GHEFS) driven by TRMM Multi-satellite Prediction Analysis (TMPA) precipitation ensembles and Global Ensemble <span class="hlt">Forecast</span> <span class="hlt">System</span> (GEFS) Quantitative Precipitation <span class="hlt">Forecast</span> (QPF) ensembles, via the Coupled Routing and Excess STorage (CREST) distributed hydrological model, provides deterministic and probabilistic (e.g. 95% confidence boundaries) simulations of streamflow. The TMPA inputs enable flood monitoring and short-term <span class="hlt">forecasts</span> while the GEFS ensembles provide for <span class="hlt">forecasts</span> up to a seven-day lead time. This talk will focus on a quantification of the <span class="hlt">system</span>'s uncertainty and streamflow ensemble prediction generation using the following three techniques: 1) an error model that first quantifies and then perturbs both temporal and spatial variability of the real-time, TMPA precipitation estimates by considering the version-7 research product as the reference rainfall product; 2) in <span class="hlt">forecast</span> mode, utilization of the Ensemble Transform method to account for the uncertainty of GEFS <span class="hlt">forecasts</span> from its initial condition errors; 3) a sequential data assimilation approach - the Ensemble Square Root Kalman Filter (EnSRF) applied to update the CREST model's internal states whenever observations (e.g. streamflow, soil moisture, and actual ET etc.) are available. The GHEFS is validated in several basins in the U.S. and other continents in terms of flood detection capability (e.g. CSI, NSCE, Peak, Timing), showing improved prognostic capability by offering more time for responding agencies and yielding unique uncertainty information about the magnitude of the <span class="hlt">forecast</span> impacts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.4481W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.4481W"><span id="translatedtitle">On the impact of stochastic parametrisations in the ECMWF seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic</p> <p>2014-05-01</p> <p>Seasonal climate predictions several months ahead based on dynamical atmosphere-ocean GCMs are part of the routinely operational <span class="hlt">forecasts</span> issued by the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF). Here, the seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span> is a seamless extension of ECMWF's medium-range ensemble weather <span class="hlt">forecasting</span> <span class="hlt">system</span> for the atmosphere coupled to a state-of-the-art ocean model. Model uncertainty in the atmosphere is represented by two schemes, the Stochastically Perturbed Physical Tendency (SPPT) scheme and the Stochastic Kinetic Energy Backscatter (SKEB) scheme. This contributions looks at the impact of these two stochastic parametrisation schemes on the model performance for seasonal <span class="hlt">forecasts</span>. It is found that these schemes reduce long-standing model biases in the Indonesian warm pool area dominated by intense convection. The simulation of MJO events in the seasonal <span class="hlt">forecasts</span> has improved due to the stochastic parametrisations. Both schemes substantially increase the ensemble spread for El Niño SST <span class="hlt">forecasts</span> and thus make the ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span> better calibrated. In addition, the stochastic parametrisations also have a positive effect on the simulation of atmospheric quasi-stationary circulation regimes over the extratropical Pacific-North America region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFMGC31B1037V&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFMGC31B1037V&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Systemic</span> change increases <span class="hlt">forecast</span> uncertainty of land use change models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Verstegen, J. A.; Karssenberg, D.; van der Hilst, F.; Faaij, A.</p> <p>2013-12-01</p> <p>Cellular Automaton (CA) models of land use change are based on the assumption that the relationship between land use change and its explanatory processes is stationary. This means that model structure and parameterization are usually kept constant over time, ignoring potential <span class="hlt">systemic</span> changes in this relationship resulting from societal changes, thereby overlooking a source of uncertainty. Evaluation of the stationarity of the relationship between land use and a set of spatial attributes has been done by others (e.g., Bakker and Veldkamp, 2012). These studies, however, use logistic regression, separate from the land use change model. Therefore, they do not gain information on how to implement the spatial attributes into the model. In addition, they often compare observations for only two points in time and do not check whether the change is statistically significant. To overcome these restrictions, we assimilate a time series of observations of real land use into a land use change CA (Verstegen et al., 2012), using a Bayesian data assimilation technique, the particle filter. The particle filter was used to update the prior knowledge about the parameterization and model structure, i.e. the selection and relative importance of the drivers of location of land use change. In a case study of sugar cane expansion in Brazil, optimal model structure and parameterization were determined for each point in time for which observations were available (all years from 2004 to 2012). A <span class="hlt">systemic</span> change, i.e. a statistically significant deviation in model structure, was detected for the period 2006 to 2008. In this period the influence on the location of sugar cane expansion of the driver sugar cane in the neighborhood doubled, while the influence of slope and potential yield decreased by 75% and 25% respectively. Allowing these <span class="hlt">systemic</span> changes to occur in our CA in the future (up to 2022) resulted in an increase in model <span class="hlt">forecast</span> uncertainty by a factor two compared to the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.6760R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.6760R"><span id="translatedtitle">Hydrologic <span class="hlt">Forecasting</span> at the US National Weather Service in the 21st Century: Transition from the NWS River <span class="hlt">Forecast</span> <span class="hlt">System</span> (NWSRFS) to the Community Hydrologic Prediction <span class="hlt">System</span> (CHPS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Restrepo, Pedro; Roe, Jon; Dietz, Christine; Werner, Micha; Gijsbers, Peter; Hartman, Robert; Opitz, Harold; Olsen, Billy; Halquist, John; Shedd, Robert</p> <p>2010-05-01</p> <p>The US National Weather Service developed the River <span class="hlt">Forecast</span> <span class="hlt">System</span> (NWSRFS) since the 1970s as the platform for performing hydrologic <span class="hlt">forecasts</span>. The <span class="hlt">system</span>, originally developed for the computers of that era, was optimized for speed of execution and compact and fast data storage and retrieval. However, with modern computers those features became less of a driver, and, instead, the ability to maintain and transition of new developments in data and modeling research into operations have become the top <span class="hlt">system</span> priorities for hydrologic <span class="hlt">forecasting</span> software applications. To address those two new priorities, and to allow the hydrologic research community at large to be able to contribute models and <span class="hlt">forecasting</span> techniques, the National Weather Service proposed the development of the Community Hydrologic Prediction <span class="hlt">System</span> (CHPS). CHPS must be sufficiently flexible not only to ensure current operational models and data remain available, but also to integrate readily modeling approaches and data from the wider community of practitioners and scientists involved in hydro-meteorological <span class="hlt">forecasting</span>. Portability considerations require the computational infrastructure to be programmed in a language such as Java, and data formats conform to open standards such as XML. After examining a number of potential candidates, the NWS settled on the Delft Flood Early Warning <span class="hlt">System</span> (Delft FEWS) from Deltares as the basis for CHPS, since it shares the basic design characteristics, the underlying community philosophy and was being successfully used in operations in several countries. This paper describes the characteristics of CHPS and the transition path to make it operational and available to the community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AIPC.1761b0070J&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AIPC.1761b0070J&link_type=ABSTRACT"><span id="translatedtitle">An innovative <span class="hlt">forecasting</span> and dashboard <span class="hlt">system</span> for Malaysian dengue trends</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd</p> <p>2016-08-01</p> <p>Dengue fever has been recognized in over 100 countries and 2.5 billion people live in areas where dengue is endemic. It is currently a serious arthropod-borne disease, affecting around 50-100 million people worldwide every year. Dengue fever is also prevalent in Malaysia with numerous cases including mortality recorded over the past year. In 2012, a total of 21,900 cases of dengue fever were reported with 35 deaths. Dengue, a mosquito-transmitted virus, causes a high fever accompanied by significant pain in afflicted patient and the Aedes Aegypti mosquito is the primary disease carrier. Knowing the dangerous effect of dengue fever, thus one of the solutions is to implement an innovative <span class="hlt">forecasting</span> and dashboard <span class="hlt">system</span> of dengue spread in Malaysia, with emphasize on an early prediction of dengue outbreak. Specifically, the model developed will provide with a valuable insight into strategically managing and controlling the future dengue epidemic. Importantly, this research will deliver the message to health policy makers such as The Ministry of Health Malaysia (MOH), practitioners, and researchers of the importance to integrate their collaboration in exploring the potential strategies in order to reduce the future burden of the increase in dengue transmission cases in Malaysia.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005BoLMe.116..363C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005BoLMe.116..363C"><span id="translatedtitle">The Australian Air Quality <span class="hlt">Forecasting</span> <span class="hlt">System</span>: Exploring First Steps Towards Determining The Limits of Predictability For Short-Term Ozone <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cope, M. E.; Hess, G. D.; Lee, S.; Tory, K. J.; Burgers, M.; Dewundege, P.; Johnson, M.</p> <p>2005-08-01</p> <p>Physical parameterisations of turbulent transfer processes in the atmospheric boundary layer, such as the stability parameterisations developed by Joost Businger, and recent advances in computing capabilities, have been important factors leading to the emergence of operational, numerical air quality <span class="hlt">forecasting</span> <span class="hlt">systems</span>. The present paper investigates the performance of the Australian Air Quality <span class="hlt">Forecasting</span> <span class="hlt">System</span> (AAQFS) in <span class="hlt">forecasting</span> the peak 1 h ozone for the current or next day. These 24/36 h <span class="hlt">forecasts</span> are generated for the Sydney and Melbourne regions and issued twice daily. Quantitative evidence is presented of the potential for the AAQFS to provide accurate numerical air quality <span class="hlt">forecasts</span>. A second goal is to provide an initial benchmark for investigating the limits of predictability for air quality in the Sydney and Melbourne regions by looking at the dependence of the <span class="hlt">forecasts</span> on the domain spatial scale (while maintaining the same model grid resolution), the starting time and length of the <span class="hlt">forecast</span> (0000 UTC starts are 36-h <span class="hlt">forecasts</span> and 1200 UTC starts are 24-h <span class="hlt">forecasts</span>), and the sophistication of the photochemical mechanism (simple chemistry, Generic Reaction Set (GRS) and complex chemistry, Carbon Bond IV (CBIV)). The probability of detection by the <span class="hlt">forecast</span> model is much better than persistence, showing considerable skill. The normalised bias, in general, decreases going from regional scale to sub-regional scale and becomes negative at the station scale. In Melbourne the gross error increases as the domain spatial scale decreases, but in Sydney there is a dip in the error at the sub-regional scale due to a sampling artifact. Better results are obtained at the smaller domain scales for 1200 UTC <span class="hlt">forecasts</span> in Sydney. These are attributed to the shorter <span class="hlt">forecast</span> period and secondarily to greater model spin-up effects at 0000 UTC. In Melbourne the results are ambiguous. Similar conclusions are derived from scatter plots of <span class="hlt">forecasts</span> versus observations</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150000728','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150000728"><span id="translatedtitle">Spectral Analysis of <span class="hlt">Forecast</span> Error Investigated with an Observing <span class="hlt">System</span> Simulation Experiment</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Prive, N. C.; Errico, Ronald M.</p> <p>2015-01-01</p> <p>The spectra of analysis and <span class="hlt">forecast</span> error are examined using the observing <span class="hlt">system</span> simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing <span class="hlt">System</span> version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily <span class="hlt">forecasts</span> to 336 hours to generate a control case. Verification of <span class="hlt">forecast</span> errors using the Nature Run as truth is compared with verification of <span class="hlt">forecast</span> errors using self-analysis; significant underestimation of <span class="hlt">forecast</span> errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early <span class="hlt">forecast</span>, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The <span class="hlt">forecast</span> errors of the two experiments become more correlated during the early <span class="hlt">forecast</span> period, with correlations increasing for up to 72 hours before beginning to decrease.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/104265','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/104265"><span id="translatedtitle">Traffic flow <span class="hlt">forecasting</span> for intelligent transportation <span class="hlt">systems</span>. Final report, January 1993-June 1995</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Smith, B.L.; Demetsky, M.J.</p> <p>1995-06-01</p> <p>The capability to <span class="hlt">forecast</span> traffic volume in an operational setting has been identified as a critical need for intelligent transportation <span class="hlt">systems</span> (ITS). In particular, traffic volume <span class="hlt">forecasts</span> will directly support proactive traffic control and accurate travel time estimation. However, previous attempts to develop traffic volume <span class="hlt">forecasting</span> models have met with limited success. The research focused on developing such models for two sites on the Capital Beltway in Northern Virginia. Four models were developed and tested for the single-interval <span class="hlt">forecasting</span> problem, which is defined as estimating traffic flow 15 minutes into the future. The four models were the historical average, time series, neural network, and nonparametric regression models. The nonparametric regression model significantly outperformed the others. Based on its success on the single-interval <span class="hlt">forecasting</span> problem, the nonparametric regression approach was used to develop and test a model for the multiple-interval <span class="hlt">forecasting</span> problem. This problem is defined as estimating traffic flow for a series of time periods into the future in 15-minute intervals. The model performed well in this application. In general, the model was portable, accurate, and easy to deploy in a field environment. Finally, an ITS <span class="hlt">system</span> architecture was developed to take full advantage of the <span class="hlt">forecasting</span> capability. The architecture illustrates the potential for significantly improved ITS services with enhanced analysis components, such as traffic volume <span class="hlt">forecasting</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESS...18.3907S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESS...18.3907S"><span id="translatedtitle">A seasonal agricultural drought <span class="hlt">forecast</span> <span class="hlt">system</span> for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; McNally, A.; Husak, G.; Funk, C.</p> <p>2014-10-01</p> <p>The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought <span class="hlt">forecasts</span> for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought <span class="hlt">forecast</span> <span class="hlt">system</span> for East Africa (EA) that provides decision support for the Famine Early Warning <span class="hlt">Systems</span> Network's (FEWS NET) science team. We evaluate this <span class="hlt">forecast</span> <span class="hlt">system</span> for a region of equatorial EA (2° S-8° N, 36-46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our <span class="hlt">forecast</span> <span class="hlt">system</span> simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM <span class="hlt">forecasts</span> generated by this <span class="hlt">system</span> on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM <span class="hlt">forecasts</span> with start-of-season (SOS) (5 March) SM conditions resulted in useful SM <span class="hlt">forecast</span> skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the <span class="hlt">forecast</span> was initialized with midseason (i.e., 5</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920020972','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920020972"><span id="translatedtitle">National Launch <span class="hlt">System</span> cycle 1 <span class="hlt">loads</span> and models data book</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bugg, F.; Brunty, J.; Ernsberger, G.; Mcghee, D.; Gagliano, L.; Harrington, F.; Meyer, D.; Blades, E.</p> <p>1992-01-01</p> <p>This document contains preliminary cycle 1 <span class="hlt">loads</span> for the National Launch <span class="hlt">System</span> (NLS) 1 and 2 vehicles. The <span class="hlt">loads</span> provided and recommended as design <span class="hlt">loads</span> represent the maximum <span class="hlt">load</span> expected during prelaunch and flight regimes, i.e., limit <span class="hlt">loads</span>, except that propellant tank ullage pressure has not been included. Ullage pressure should be added to the <span class="hlt">loads</span> book values for cases where the addition results in higher <span class="hlt">loads</span>. The <span class="hlt">loads</span> must be multiplied by the appropriate factors of safety to determine the ultimate <span class="hlt">loads</span> for which the structure must be capable.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1159376','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1159376"><span id="translatedtitle">Impact of Improved Solar <span class="hlt">Forecasts</span> on Bulk Power <span class="hlt">System</span> Operations in ISO-NE: Preprint</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.</p> <p>2014-09-01</p> <p>The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its <span class="hlt">forecasting</span> has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power <span class="hlt">system</span> operations. This study examines the value of improved solar power <span class="hlt">forecasting</span> for the Independent <span class="hlt">System</span> Operator-New England <span class="hlt">system</span>. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27612712','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27612712"><span id="translatedtitle">A novel dual-frequency <span class="hlt">loading</span> <span class="hlt">system</span> for studying mechanobiology of <span class="hlt">load</span>-bearing tissue.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Chunqiu; Qiu, Lulu; Gao, Lilan; Guan, Yinjie; Xu, Qiang; Zhang, Xizheng; Chen, Qian</p> <p>2016-12-01</p> <p>In mechanobiological research, an appropriate <span class="hlt">loading</span> <span class="hlt">system</span> is an essential tool to mimic mechanical signals in a native environment. To achieve this goal, we have developed a novel <span class="hlt">loading</span> <span class="hlt">system</span> capable of applying dual-frequency <span class="hlt">loading</span> including both a low-frequency high-amplitude <span class="hlt">loading</span> and a high-frequency low-amplitude <span class="hlt">loading</span>, according to the mechanical conditions experienced by bone and articular cartilage tissues. The low-frequency high-amplitude <span class="hlt">loading</span> embodies the main force from muscular contractions and/or reaction forces while the high-frequency low-amplitude <span class="hlt">loading</span> represents an assistant force from small muscles, ligaments and/or other tissue in order to maintain body posture during human activities. Therefore, such dual frequency <span class="hlt">loading</span> <span class="hlt">system</span> may reflect the natural characteristics of complex mechanical <span class="hlt">load</span> on bone or articular cartilage than the single frequency <span class="hlt">loading</span> often applied during current mechanobiological experiments. The dual-frequency <span class="hlt">loading</span> <span class="hlt">system</span> is validated by experimental tests using precision miniature plane-mirror interferometers. The dual-frequency <span class="hlt">loading</span> results in significantly more solute transport in articular cartilage than that of the low-frequency high-amplitude <span class="hlt">loading</span> regiment alone, as determined by quantitative fluorescence microscopy of tracer distribution in articular cartilage. Thus, the <span class="hlt">loading</span> <span class="hlt">system</span> can provide a new method to mimic mechanical environment in bone and cartilage, thereby revealing the in vivo mechanisms of mechanosensation, mechanotransduction and mass-transport, and improving mechanical conditioning of cartilage and/or bone constructs for tissue engineering. PMID:27612712</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4063030','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4063030"><span id="translatedtitle">A Space Weather <span class="hlt">Forecasting</span> <span class="hlt">System</span> with Multiple Satellites Based on a Self-Recognizing Network</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tokumitsu, Masahiro; Ishida, Yoshiteru</p> <p>2014-01-01</p> <p>This paper proposes a space weather <span class="hlt">forecasting</span> <span class="hlt">system</span> at geostationary orbit for high-energy electron flux (>2 MeV). The <span class="hlt">forecasting</span> model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed <span class="hlt">forecasting</span> model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate <span class="hlt">forecasting</span> for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather <span class="hlt">forecasting</span> based on the satellite network with in-situ sensing. PMID:24803190</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26910315','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26910315"><span id="translatedtitle">Evaluating probabilistic dengue risk <span class="hlt">forecasts</span> from a prototype early warning <span class="hlt">system</span> for Brazil.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier</p> <p>2016-01-01</p> <p>Recently, a prototype dengue early warning <span class="hlt">system</span> was developed to produce probabilistic <span class="hlt">forecasts</span> of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue <span class="hlt">forecasts</span> across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the <span class="hlt">forecast</span> model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the <span class="hlt">forecast</span> model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the <span class="hlt">forecast</span> model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. PMID:26910315</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1990JPDC....9..331M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1990JPDC....9..331M"><span id="translatedtitle">Adaptive <span class="hlt">load</span> sharing in heterogeneous distributed <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mirchandaney, Ravi; Towsley, Don; Stankovic, John A.</p> <p>1990-08-01</p> <p>In this paper, we study the performance characteristics of simple <span class="hlt">load</span> sharing algorithms for heterogeneous distributed <span class="hlt">systems</span>. We assume that nonnegligible delays are encountered in transferring jobs from one node to another. We analyze the effects of these delays on the performance of two threshold-based algorithms called Forward and Reverse. We formulate queuing theoretic models for each of the algorithms operating in heterogeneous <span class="hlt">systems</span> under the assumption that the job arrival process at each node in Poisson and the service times and job transfer times are exponentially distributed. The models are solved using the Matrix-Geometric solution technique. These models are used to study the effects of different parameters and algorithm variations on the mean job response time: e.g., the effects of varying the thresholds, the impact of changing the probe limit, the impact of biasing the probing, and the optimal response times over a large range of <span class="hlt">loads</span> and delays. Wherever relevant, the results of the models are compared with the M/M/ 1 model, representing no <span class="hlt">load</span> balancing (hereafter referred to as NLB), and the M/M/K model, which is an achievable lower bound (hereafter referred to as LB).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007AGUFM.H32C..05P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007AGUFM.H32C..05P&link_type=ABSTRACT"><span id="translatedtitle">Application of a Multi-Scheme Ensemble Prediction <span class="hlt">System</span> and an Ensemble Classification Method to Streamflow <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pahlow, M.; Moehrlen, C.; Joergensen, J.; Hundecha, Y.</p> <p>2007-12-01</p> <p>Europe has experienced a number of unusually long-lasting and intense rainfall events in the last decade, resulting in severe floods in most European countries. Ensemble <span class="hlt">forecasts</span> emerged as a valuable resource to provide decision makers in case of emergency with adequate information to protect downstream areas. However, <span class="hlt">forecasts</span> should not only provide a best guess of the state of the stream network, but also an estimate of the range of possible outcomes. Ensemble <span class="hlt">forecast</span> techniques are a suitable tool to obtain the required information. Furthermore a wide range of uncertainty that may impact hydrological <span class="hlt">forecasts</span> can be accounted for using an ensemble of <span class="hlt">forecasts</span>. The <span class="hlt">forecasting</span> <span class="hlt">system</span> used in this study is based on a multi-scheme ensemble prediction method and <span class="hlt">forecasts</span> the meteorological uncertainty on synoptic scales as well as the resulting <span class="hlt">forecast</span> error in weather derived products. Statistical methods are used to directly transform raw weather output to derived products and thereby utilize the statistical capabilities of each ensemble <span class="hlt">forecast</span>. The <span class="hlt">forecasting</span> <span class="hlt">system</span> MS-EPS (Multi-Scheme Ensemble Prediction <span class="hlt">System</span>) used in this study is a limited area ensemble prediction <span class="hlt">system</span> using 75 different numerical weather prediction (NWP) model parameterisations. These individual 'schemes' each differ in their formulation of the fast meteorological processes. The MS-EPS <span class="hlt">forecasts</span> are used as input for a hydrological model (HBV) to generate an ensemble of streamflow <span class="hlt">forecasts</span>. Determining the most probable <span class="hlt">forecast</span> from an ensemble of <span class="hlt">forecasts</span> requires suitable statistical tools. They must enable a <span class="hlt">forecaster</span> to interpret the model output, to condense the information and to provide the desired product. For this purpose, a probabilistic multi-trend filter (pmt-filter) for statistical post-processing of the hydrological ensemble <span class="hlt">forecasts</span> is used in this study. An application of the <span class="hlt">forecasting</span> <span class="hlt">system</span> is shown for a watershed located in the eastern part of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20050240237&hterms=spinal+cord&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3D%2528spinal%2Bcord%2529','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20050240237&hterms=spinal+cord&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3D%2528spinal%2Bcord%2529"><span id="translatedtitle"><span class="hlt">Load</span>-dependent regulation of neuromuscular <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ohira, Yoshinobu; Kawano, Fuminori; Stevens, James L.; Wang, Xiao D.; Ishihara, Akihiko</p> <p>2004-01-01</p> <p>Roles of gravitational <span class="hlt">loading</span>, sarcomere length, and/or tension development on the electromyogram (EMG) of soleus and afferent neurogram recorded at the L5 segmental level of spinal cord were investigated during parabolic flight of a jet airplane or hindlimb suspension in conscious rats. Both EMG and neurogram levels were increased when the gravity levels were elevated from 1-G to 2-G during the parabolic flight. They were decreased when the hindlimbs were unloaded by exposure to actual microgravity or by suspension. These phenomena were related to passive shortening of muscle fibers and/or sarcomeres. Unloading-related decrease in sarcomere length was greater at the central rather than the proximal and distal regions of fibers. These activities and tension development were not detected when the mean sarcomere length was less than 2.03 micrometers. It is suggested that <span class="hlt">load</span>-dependent regulation of neuromuscular <span class="hlt">system</span> is related to the tension development which is influenced by sarcomere length.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/974024','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/974024"><span id="translatedtitle">On-line economic optimization of energy <span class="hlt">systems</span> using weather <span class="hlt">forecast</span> information.</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.</p> <p>2009-01-01</p> <p>We establish an on-line optimization framework to exploit weather <span class="hlt">forecast</span> information in the operation of energy <span class="hlt">systems</span>. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating <span class="hlt">forecasts</span> generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the <span class="hlt">forecast</span> trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building <span class="hlt">system</span> to demonstrate the developments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AdSR...11...49A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AdSR...11...49A"><span id="translatedtitle">Comparison of the economic impact of different wind power <span class="hlt">forecast</span> <span class="hlt">systems</span> for producers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.</p> <p>2014-05-01</p> <p>Deterministic <span class="hlt">forecasts</span> of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic <span class="hlt">forecast</span> consists of a single value for each time in the future for the variable to be predicted, while probabilistic <span class="hlt">forecasting</span> informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a <span class="hlt">forecast</span> of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic <span class="hlt">forecasts</span> alone. In fact, by using probabilistic <span class="hlt">forecasting</span> it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for <span class="hlt">forecast</span> errors valid in that market. In this work we have applied a probabilistic wind power <span class="hlt">forecast</span> <span class="hlt">systems</span> based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price <span class="hlt">forecasting</span> partly reduces the economic benefit gained by using a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010070048','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010070048"><span id="translatedtitle">An Advanced Buffet <span class="hlt">Load</span> Alleviation <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Burnham, Jay K.; Pitt, Dale M.; White, Edward V.; Henderson, Douglas A.; Moses, Robert W.</p> <p>2001-01-01</p> <p>This paper describes the development of an advanced buffet <span class="hlt">load</span> alleviation (BLA) <span class="hlt">system</span> that utilizes distributed piezoelectric actuators in conjunction with an active rudder to reduce the structural dynamic response of the F/A-18 aircraft vertical tails to buffet <span class="hlt">loads</span>. The BLA <span class="hlt">system</span> was defined analytically with a detailed finite-element-model of the tail structure and piezoelectric actuators. Oscillatory aerodynamics were included along with a buffet forcing function to complete the aeroservoelastic model of the tail with rudder control surface. Two single-input-single-output (SISO) controllers were designed, one for the active rudder and one for the active piezoelectric actuators. The results from the analytical open and closed loop simulations were used to predict the <span class="hlt">system</span> performance. The objective of this BLA <span class="hlt">system</span> is to extend the life of vertical tail structures and decrease their life-cycle costs. This <span class="hlt">system</span> can be applied to other aircraft designs to address suppression of structural vibrations on military and commercial aircraft.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1111474B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1111474B"><span id="translatedtitle">Evaluation of an operational streamflow <span class="hlt">forecasting</span> <span class="hlt">system</span> driven by ensemble precipitation <span class="hlt">forecasts</span> : a case study for the Gatineau watershed</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boucher, M.-A.; Perreault, L.; Tremblay, D.; Gaudet, J.; Minville, M.; Anctil, F.</p> <p>2009-04-01</p> <p>Among the various sources of uncertainty for hydrological <span class="hlt">forecasts</span>, the uncertainty linked to meteorological inputs prevail. Precipitation is particularly difficult to <span class="hlt">forecast</span> and observed values are often poor representation of the true precipitation field. In order to account for the uncertainty related to precipitation data, it can be interesting to produce ensemble streamflow <span class="hlt">forecasts</span> by feeding a hydrological model with ensemble precipitation <span class="hlt">forecasts</span> issued by atmospheric models. In this study, we use ensemble precipitation <span class="hlt">forecasts</span> to drive Hydrotel, a distributed hydrological model. We concentrate on the Gatineau watershed, which serves as an experimental watershed for Hydro-Québec, the major hydropower producer in Quebec. The main goal of this study is to demonstrate that ensemble precipitation <span class="hlt">forecasts</span> can improve streamflow <span class="hlt">forecasting</span> for the watershed of interest. The ensemble precipitation <span class="hlt">forecasts</span> were produced by Environnement Canada from march first of 2002 to december 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic <span class="hlt">forecast</span>) and GEM (8 members). The corresponding deterministic precipitation <span class="hlt">forecast</span> issued by SEF model is also used with Hydrotel in order to compare ensemble streamflow <span class="hlt">forecasts</span> with their deterministic counterparts. The quality of the precipitation <span class="hlt">forecasts</span> is first assessed, using the continuous ranked probability score (CRPS), the logarithmic score, rank histograms and reliability diagrams. The performance of the corresponding streamflow <span class="hlt">forecasts</span> obtained at the end of the process is also evaluated using the same quality assessment tools.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/5903930','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/5903930"><span id="translatedtitle">Navy mobility fuels <span class="hlt">forecasting</span> <span class="hlt">system</span> report: World petroleum trade <span class="hlt">forecasts</span> for the year 2000</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Das, S.</p> <p>1991-12-01</p> <p>The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual <span class="hlt">forecasts</span> published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760016771','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760016771"><span id="translatedtitle">Digital data processing <span class="hlt">system</span> dynamic <span class="hlt">loading</span> analysis</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lagas, J. J.; Peterka, J. J.; Tucker, A. E.</p> <p>1976-01-01</p> <p>Simulation and analysis of the Space Shuttle Orbiter Digital Data Processing <span class="hlt">System</span> (DDPS) are reported. The mated flight and postseparation flight phases of the space shuttle's approach and landing test configuration were modeled utilizing the Information Management <span class="hlt">System</span> Interpretative Model (IMSIM) in a computerized simulation modeling of the ALT hardware, software, and workload. <span class="hlt">System</span> requirements simulated for the ALT configuration were defined. Sensitivity analyses determined areas of potential data flow problems in DDPS operation. Based on the defined <span class="hlt">system</span> requirements and the sensitivity analyses, a test design is described for adapting, parameterizing, and executing the IMSIM. Varying <span class="hlt">load</span> and stress conditions for the model execution are given. The analyses of the computer simulation runs were documented as results, conclusions, and recommendations for DDPS improvements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A31F0184M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A31F0184M"><span id="translatedtitle">An integrated <span class="hlt">system</span> for wind energy <span class="hlt">forecast</span> using meteorological models and statistical post-processing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.</p> <p>2009-12-01</p> <p>With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric <span class="hlt">system</span> implies a strong requirement for accurate wind <span class="hlt">forecasts</span>, that can be used to <span class="hlt">forecast</span> this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a <span class="hlt">system</span> for 72h energy <span class="hlt">forecast</span> in mainland Portugal was setup, using 6km resolution meteorological <span class="hlt">forecasts</span>, forced by global GFS <span class="hlt">forecasts</span> by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed <span class="hlt">system</span>, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological <span class="hlt">forecast</span>, and converts the <span class="hlt">forecasted</span> wind fields into <span class="hlt">forecasted</span> energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind <span class="hlt">forecasts</span>, requiring fully developed meteorological models. In general, results from 2 full years of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1164089','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1164089"><span id="translatedtitle">Impact of Improved Solar <span class="hlt">Forecasts</span> on Bulk Power <span class="hlt">System</span> Operations in ISO-NE (Presentation)</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.</p> <p>2014-11-01</p> <p>The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its <span class="hlt">forecasting</span> has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power <span class="hlt">system</span> operations. This presentation is an overview of a study that examines the value of improved solar <span class="hlt">forecasts</span> on Bulk Power <span class="hlt">System</span> Operations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20110015439&hterms=river&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Driver','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20110015439&hterms=river&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Driver"><span id="translatedtitle">AIRS Impact on Analysis and <span class="hlt">Forecast</span> of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.</p> <p>2011-01-01</p> <p>A set of data assimilation and <span class="hlt">forecast</span> experiments are performed with the NASA Global data assimilation and <span class="hlt">forecast</span> <span class="hlt">system</span> GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and <span class="hlt">forecast</span> skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day <span class="hlt">forecasts</span>, than assimilation of clear-sky radiances. The improvement of precipitation <span class="hlt">forecast</span> skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1990STIN...9124466H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1990STIN...9124466H"><span id="translatedtitle"><span class="hlt">Load</span> transfer mechanisms in anchored geosynthetic <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hryciw, Roman D.</p> <p>1990-12-01</p> <p>Success of an anchored geosynthetic <span class="hlt">system</span> (AGS) depends on the satisfactory transfer of <span class="hlt">load</span> between: the surface-deployed geosynthetic and anchors (typically ribbed reinforcing rods) driven into the slope; the geosynthetic and soil; and the anchors and soil. A study was performed to evaluate the <span class="hlt">load</span> transfer mechanisms at these interfaces in an AGS. A mathematical model was developed for predicting the pullout resistance of plane ribbed inclusions. The model considered the contribution of both frictional and passive resistance components of pullout resistance. Optical observation of sand around the ribs was made to determine the behavior of soil around the moving ribs during pullout. A theoretical study disclosed that the optimum anchor orientation for stabilization of infinite slopes depends on several factors including slope angle and in-situ stresses. It typically ranges from 20 to 30 degree from the normal to the slope with the anchor driven upslope. An experimental study confirmed that the soil-geosynthetic interface friction angle may be correctly predicted from the residual or critical state friction angle of the sand. Equations were developed for <span class="hlt">load</span> transfer at curved soil-fabric interfaces. An experimental study verified that the increases in soil stress with distance from the anchor may be predicted by the developed equations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol1/pdf/CFR-2010-title14-vol1-sec23-395.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol1/pdf/CFR-2010-title14-vol1-sec23-395.pdf"><span id="translatedtitle">14 CFR 23.395 - Control <span class="hlt">system</span> <span class="hlt">loads</span>.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Structure Control Surface and <span class="hlt">System</span> <span class="hlt">Loads</span> § 23.395 Control <span class="hlt">system</span> <span class="hlt">loads</span>. (a) Each flight control <span class="hlt">system</span> and its supporting structure must be... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Control <span class="hlt">system</span> <span class="hlt">loads</span>. 23.395 Section...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70120632','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70120632"><span id="translatedtitle">GCLAS: a graphical constituent <span class="hlt">loading</span> analysis <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>McKallip, T.E.; Koltun, G.F.; Gray, J.R.; Glysson, G.D.</p> <p>2001-01-01</p> <p>The U. S. Geological Survey has developed a program called GCLAS (Graphical Constituent <span class="hlt">Loading</span> Analysis <span class="hlt">System</span>) to aid in the computation of daily constituent <span class="hlt">loads</span> transported in stream flow. Due to the relative paucity with which most water-quality data are collected, computation of daily constituent <span class="hlt">loads</span> is moderately to highly dependent on human interpretation of the relation between stream hydraulics and constituent transport. GCLAS provides a visual environment for evaluating the relation between hydraulic and other covariate time series and the constituent chemograph. GCLAS replaces the computer program Sedcalc, which is the most recent USGS sanctioned tool for constructing sediment chemographs and computing suspended-sediment <span class="hlt">loads</span>. Written in a portable language, GCLAS has an interactive graphical interface that permits easy entry of estimated values and provides new tools to aid in making those estimates. The use of a portable language for program development imparts a degree of computer platform independence that was difficult to obtain in the past, making implementation more straightforward within the USGS' s diverse computing environment. Some of the improvements introduced in GCLAS include (1) the ability to directly handle periods of zero or reverse flow, (2) the ability to analyze and apply coefficient adjustments to concentrations as a function of time, streamflow, or both, (3) the ability to compute discharges of constituents other than suspended sediment, (4) the ability to easily view data related to the chemograph at different levels of detail, and (5) the ability to readily display covariate time series data to provide enhanced visual cues for drawing the constituent chemograph.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AdAtS..33..544Z&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AdAtS..33..544Z&link_type=ABSTRACT"><span id="translatedtitle">Analyses and <span class="hlt">forecasts</span> of a tornadic supercell outbreak using a 3DVAR <span class="hlt">system</span> ensemble</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhuang, Zhaorong; Yussouf, Nusrat; Gao, Jidong</p> <p>2016-05-01</p> <p>As part of NOAA's "Warn-On-<span class="hlt">Forecast</span>" initiative, a convective-scale data assimilation and prediction <span class="hlt">system</span> was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The <span class="hlt">system</span> was then evaluated using retrospective short-range ensemble analyses and probabilistic <span class="hlt">forecasts</span> of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble <span class="hlt">system</span> provided the initial and boundary conditions for a 3-km convective-scale ensemble <span class="hlt">system</span>. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and <span class="hlt">forecast</span> experiments were conducted to evaluate the sensitivity of the <span class="hlt">system</span> to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good <span class="hlt">forecasts</span>, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and <span class="hlt">forecast</span> <span class="hlt">system</span> can provide reasonable <span class="hlt">forecasts</span> of tornadic supercell storms.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A43L..08K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A43L..08K"><span id="translatedtitle">Development of an Adaptable Display and Diagnostic <span class="hlt">System</span> for the Evaluation of Tropical Cyclone <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kucera, P. A.; Burek, T.; Halley-Gotway, J.</p> <p>2015-12-01</p> <p>NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental <span class="hlt">forecasts</span> of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC <span class="hlt">forecast</span> verification methods and the development of an adaptable TC display and diagnostic <span class="hlt">system</span>. The next generation display and diagnostic <span class="hlt">system</span> is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow <span class="hlt">forecasters</span> and research scientists to more deeply examine the performance of operational and experimental models. The <span class="hlt">system</span> is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The <span class="hlt">forecast</span> track and intensity along with associated observed track information are stored in an efficient MySQL database. The <span class="hlt">system</span> provides easy-to-use interactive display <span class="hlt">system</span>, and provides diagnostic tools to examine <span class="hlt">forecast</span> track stratified by intensity. Consensus <span class="hlt">forecasts</span> can be computed and displayed interactively. The <span class="hlt">system</span> is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity <span class="hlt">forecasts</span>, and improved graphical display tools. The display is also being enhanced to incorporate gridded <span class="hlt">forecast</span>, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic <span class="hlt">system</span> development and demonstration of the current capabilities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009PhDT.......163R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009PhDT.......163R"><span id="translatedtitle">The effects of changes in land cover and land use on nutrient <span class="hlt">loadings</span> to the Chesapeake Bay using <span class="hlt">forecasts</span> of urbanization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roberts, Allen Derrick</p> <p></p> <p>This dissertation examined the effects of land cover and land use (LC/LU) change on nutrient <span class="hlt">loadings</span> (mass for a specified time) to the Chesapeake Bay, after future projections of urbanization were applied. This was accomplished by quantifying the comprehensive impacts of landscape on nutrients throughout the watershed. In order to quantify <span class="hlt">forecasted</span> impacts of future development and LC/LU change, the current (2000) effects of landscape composition and configuration on total nitrogen (TN) and total phosphorus (TP) were examined. The effects of cover types were examined not only at catchment scales, but within riparian stream buffer to quantify the effects of spatial arrangement. Using the SPAtially Referenced Regressions On Watershed Attributes (SPARROW) model, several compositional and configurational metrics at both scales were significantly (p value ≤ 0.05) correlated to nutrient genesis and transport and helped estimate <span class="hlt">loadings</span> to the Chesapeake Bay with slightly better accuracy and precision. Remotely sensed <span class="hlt">forecasts</span> of future (2030) urbanization were integrated into SPARROWusing these metrics to project TN and TP <span class="hlt">loadings</span> into the future. After estimation of these metrics and other LC/LU-based sources, it was found that overall nutrient transport to the Chesapeake Bay will decrease due to agricultural land losses and fertilizer reductions. Although point and non-point source urban <span class="hlt">loadings</span> increased in the watershed, these gains were not enough to negate decreased agricultural impacts. In catchments <span class="hlt">forecasted</span> to undergo urban sprawl conditions by 2030, the response of TN locally generated within catchments varied. The <span class="hlt">forecasted</span> placement of smaller patches of development within agricultural lands of higher nutrient production was correlated to projected losses. However, shifting <span class="hlt">forecasted</span> growth onto or adjacent to existing development, not agricultural lands, resulted in projected gains. This indicated the importance of <span class="hlt">forecasted</span> spatial</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004JGRC..109.3023A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004JGRC..109.3023A&link_type=ABSTRACT"><span id="translatedtitle">Real-time <span class="hlt">forecasting</span> at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean <span class="hlt">forecasting</span> (SOFT) <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>ÁLvarez, A.; Orfila, A.; Tintoré, J.</p> <p>2004-03-01</p> <p>Satellites are the only <span class="hlt">systems</span> able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean <span class="hlt">forecasting</span> (SOFT) <span class="hlt">systems</span>, to <span class="hlt">forecast</span> certain aspects of future ocean states. SOFT <span class="hlt">systems</span> can predict satellite-observed fields at different timescales. The <span class="hlt">forecast</span> skill of SOFT <span class="hlt">systems</span> <span class="hlt">forecasting</span> the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT <span class="hlt">systems</span> <span class="hlt">forecasting</span>, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing <span class="hlt">forecasts</span> of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT <span class="hlt">systems</span> were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT <span class="hlt">systems</span> are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT <span class="hlt">system</span> <span class="hlt">forecasting</span> the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT <span class="hlt">system</span> <span class="hlt">forecasting</span> the SLA field.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUSM.A43B..04D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUSM.A43B..04D"><span id="translatedtitle">Pros and Cons of 1-tiered versus 2-tiered Seasonal <span class="hlt">Forecast</span> <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dewitt, D. G.; Goddard, L.; Li, S.</p> <p>2005-05-01</p> <p>All reasonable seasonal <span class="hlt">forecast</span> <span class="hlt">systems</span> have advantages and disadvantages.Some advantages/disadvantages may be theoretical; others may be practical. A clear understanding of where the limitations of a particular <span class="hlt">forecast</span> <span class="hlt">system</span> lie is helpful in making the most of the tool(s) in hand. In this presentation we examine the good, the bad and the ugly in both 1-tiered <span class="hlt">forecast</span> <span class="hlt">systems</span> (i.e. coupled ocean-atmosphere general circulation models or CGCMs) and 2-tiered <span class="hlt">forecast</span> <span class="hlt">systems</span> (i.e. atmospheric general circulation models or AGCMs). AGCMs are potentially hindered by unphysical air-sea fluxes in the mid-latitudes and warm pool regions, where the observations suggest that the atmosphere forces changes in the ocean, rather than the other way around. In CGCMs the ocean and atmosphere evolve harmoniously, but that is no guarantee that their air-sea fluxes are correct.And indeed, CGCMs have problems with climate drift and large-scale systematic biases because of difficulties in getting the proper air-sea fluxes. To what extent these physical limitations limit skill in seasonal climate prediction will be presented. Suggestions will be offered for how one might capitalize on the strengths of both types of dynamical <span class="hlt">forecast</span> <span class="hlt">systems</span>, while minimizing the weaknesses, in constructing a seasonal climate <span class="hlt">forecast</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016NHESS..16.1639Z&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016NHESS..16.1639Z&link_type=ABSTRACT"><span id="translatedtitle">Comparison and validation of global and regional ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span> for the South China Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, Xueming; Wang, Hui; Liu, Guimei; Régnier, Charly; Kuang, Xiaodi; Wang, Dakui; Ren, Shihe; Jing, Zhiyou; Drévillon, Marie</p> <p>2016-07-01</p> <p>In this paper, the performance of two operational ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span>, the global Mercator Océan (MO) Operational <span class="hlt">System</span>, developed and maintained by Mercator Océan in France, and the regional South China Sea Operational <span class="hlt">Forecasting</span> <span class="hlt">System</span> (SCSOFS), by the National Marine Environmental <span class="hlt">Forecasting</span> Center (NMEFC) in China, have been examined. Both <span class="hlt">systems</span> can provide science-based nowcast/<span class="hlt">forecast</span> products of temperature, salinity, water level, and ocean circulations. Comparison and validation of the ocean circulations, the structures of temperature and salinity, and some mesoscale activities, such as ocean fronts, typhoons, and mesoscale eddies, are conducted based on observed satellite and in situ data obtained in 2012 in the South China Sea. The results showed that MO performs better in simulating the ocean circulations and sea surface temperature (SST), and SCSOFS performs better in simulating the structures of temperature and salinity. For the mesoscale activities, the performance of SCSOFS is better than MO in simulating SST fronts and SST decrease during Typhoon Tembin compared with the previous studies and satellite data; but model results from both of SCSOFS and MO show some differences from satellite observations. In conclusion, some recommendations have been proposed for both <span class="hlt">forecast</span> <span class="hlt">systems</span> to improve their <span class="hlt">forecasting</span> performance in the near future based on our comparison and validation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6389R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6389R"><span id="translatedtitle">Semi-distributed flood <span class="hlt">forecasting</span> <span class="hlt">system</span> for the Middle Vistula reach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Romanowicz, Renata; Karamuz, Emilia; Osuch, Marzena</p> <p>2014-05-01</p> <p>The aim of this study is the development of an integrated <span class="hlt">forecasting</span> <span class="hlt">system</span> for the middle reach of the River Vistula. The <span class="hlt">system</span> consists of combined in series lumped parameter Stochastic Transfer Function models. In order to prolong the <span class="hlt">forecast</span> lead-time, the <span class="hlt">system</span> was extended to include gauging stations situated upstream of Zawichost. There is a number of tributaries located along the studied reach. The largest are Kamienna, Pilica and Wieprz. Therefore apart from Single- Input -Single-Output models (SISO), multiple input models were also developed (MISO). The <span class="hlt">system</span> is based on water levels instead of flows, in order to avoid errors related to rating curve transformation. The problem of the nonlinear transformation of <span class="hlt">system</span> inputs in order to separate the nonlinearity of the flow process to obtain the linear model dynamics is equally important for the accuracy of <span class="hlt">forecasts</span>. The possibility of linearizing the flow routing process was investigated using a State Dependent Parameter approach. The nonparametric relationship was parameterised using a power function. This procedure allowed the application of a model with a nonlinear transformation of input in the <span class="hlt">forecasting</span> mode. It is important to note that the applied methods are stochastic in nature and the structure of the models and their parameters are estimated from available observations, taking into account inherent observation and model approximation errors. As a result, <span class="hlt">forecasts</span> are estimated together with uncertainty bands. We apply a Kalman filter updating of model predictions as a data assimilation procedure. The procedure involves formulating the <span class="hlt">forecasting</span> problem in a state space form. Validation of the developed <span class="hlt">forecasting</span> <span class="hlt">system</span> shows that the quality of <span class="hlt">forecasts</span> obtained using a semi-distributed lumped parameter model is comparable with the <span class="hlt">forecasts</span> obtained using a distributed model with the advantage of obtaining <span class="hlt">forecast</span> uncertainty by the former. This work was supported by the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.3317G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.3317G&link_type=ABSTRACT"><span id="translatedtitle">Comparing One-way and Two-way Coupled Hydrometeorological <span class="hlt">Forecasting</span> <span class="hlt">Systems</span> for Flood <span class="hlt">Forecasting</span> in the Mediterranean Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Givati, Amir; Gochis, David; Rummler, Thomas; Kunstmann, Harald; Yu, Wei</p> <p>2016-04-01</p> <p>A pair of hydro-meteorological modeling <span class="hlt">systems</span> were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling <span class="hlt">systems</span> for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling <span class="hlt">System</span> (HEC-HMS) model and the Weather Research and <span class="hlt">Forecasting</span> (WRF) Hydro modeling <span class="hlt">system</span>. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then both modeling <span class="hlt">systems</span> were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from hydrometric stations at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of precipitation data, and less so, to hydrologic model formulation. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling <span class="hlt">system</span> produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological <span class="hlt">forecasts</span> for early flood warning applications. However more research needed in order to better understand the land-atmosphere coupling mechanisms driving hydrometeorological processes on a wider variety precipitation and terrestrial hydrologic <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/774851','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/774851"><span id="translatedtitle">Plutonium Immobilization Project <span class="hlt">System</span> Design Description for Can <span class="hlt">Loading</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kriikku, E.</p> <p>2001-02-15</p> <p>The purpose of this <span class="hlt">System</span> Design Description (SDD) is to specify the <span class="hlt">system</span> and component functions and requirements for the Can <span class="hlt">Loading</span> <span class="hlt">System</span> and provide a complete description of the <span class="hlt">system</span> (design features, boundaries, and interfaces), principles of operation (including upsets and recovery), and the <span class="hlt">system</span> maintenance approach. The Plutonium Immobilization Project (PIP) will immobilize up to 13 metric tons (MT) of U.S. surplus weapons usable plutonium materials.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H21A0812N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H21A0812N"><span id="translatedtitle">A Novel Hydro-information <span class="hlt">System</span> for Improving National Weather Service River <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nan, Z.; Wang, S.; Liang, X.; Adams, T. E.; Teng, W. L.; Liang, Y.</p> <p>2009-12-01</p> <p>A novel hydro-information <span class="hlt">system</span> has been developed to improve the <span class="hlt">forecast</span> accuracy of the NOAA National Weather Service River <span class="hlt">Forecast</span> <span class="hlt">System</span> (NWSRFS). An MKF-based (Multiscale Kalman Filter) spatial data assimilation framework, together with the NOAH land surface model, is employed in our <span class="hlt">system</span> to assimilate satellite surface soil moisture data to yield improved evapotranspiration. The latter are then integrated into the distributed version of the NWSRFS to improve its <span class="hlt">forecasting</span> skills, especially for droughts, but also for disaster management in general. Our <span class="hlt">system</span> supports an automated flow into the NWSRFS of daily satellite surface soil moisture data, derived from the TRMM Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer-Earth Observing <span class="hlt">System</span> (AMSR-E), and the forcing information of the North American Land Data Assimilation <span class="hlt">System</span> (NLDAS). All data are custom processed, archived, and supported by the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). An optional data fusing component is available in our <span class="hlt">system</span>, which fuses NEXRAD Stage III precipitation data with the NLDAS precipitation data, using the MKF-based framework, to provide improved precipitation inputs. Our <span class="hlt">system</span> employs a plug-in, structured framework and has a user-friendly, graphical interface, which can display, in real-time, the spatial distributions of assimilated state variables and other model-simulated information, as well as their behaviors in time series. The interface can also display watershed maps, as a result of the integration of the QGIS library into our <span class="hlt">system</span>. Extendibility and flexibility of our <span class="hlt">system</span> are achieved through the plug-in design and by an extensive use of XML-based configuration files. Furthermore, our <span class="hlt">system</span> can be extended to support multiple land surface models and multiple data assimilation schemes, which would further increase its capabilities. Testing of the integration of the current <span class="hlt">system</span> into the NWSRFS is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8653P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8653P"><span id="translatedtitle">Wave ensemble <span class="hlt">forecast</span> in the Western Mediterranean Sea, application to an early warning <span class="hlt">system</span>.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel</p> <p>2015-04-01</p> <p>The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave <span class="hlt">forecast</span> in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the <span class="hlt">forecast</span> to the end users, an ensemble wave <span class="hlt">forecast</span> <span class="hlt">system</span> is considered. The ensemble prediction <span class="hlt">systems</span> have already been used in the last decades for the meteorological <span class="hlt">forecast</span>; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the <span class="hlt">forecast</span>, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic <span class="hlt">forecast</span>. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global <span class="hlt">Forecast</span> <span class="hlt">System</span> (GFS) and Global Ensemble <span class="hlt">Forecast</span> <span class="hlt">System</span> (GEFS). The wind fields includes a deterministic <span class="hlt">forecast</span> (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a <span class="hlt">forecast</span> horizon of 8 days and represented in spaghetti diagrams, the spread of the <span class="hlt">system</span> and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave <span class="hlt">forecast</span>, meaningful</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JCoAM.233.2481W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JCoAM.233.2481W"><span id="translatedtitle">Product demand <span class="hlt">forecasts</span> using wavelet kernel support vector machine and particle swarm optimization in manufacture <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Qi</p> <p>2010-03-01</p> <p>Demand <span class="hlt">forecasts</span> play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment <span class="hlt">systems</span>. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent <span class="hlt">system</span> combining the wavelet kernel support vector machine and particle swarm optimization for demand <span class="hlt">forecasting</span>. The results of application in car sale series <span class="hlt">forecasting</span> show that the <span class="hlt">forecasting</span> approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol1/pdf/CFR-2011-title14-vol1-sec25-397.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol1/pdf/CFR-2011-title14-vol1-sec25-397.pdf"><span id="translatedtitle">14 CFR 25.397 - Control <span class="hlt">system</span> <span class="hlt">loads</span>.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Control <span class="hlt">system</span> <span class="hlt">loads</span>. 25.397 Section 25.397 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION AIRCRAFT AIRWORTHINESS STANDARDS: TRANSPORT CATEGORY AIRPLANES Structure Control Surface and <span class="hlt">System</span> <span class="hlt">Loads</span> § 25.397 Control <span class="hlt">system</span> <span class="hlt">loads</span>. (a) General. The maximum...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H14A..07G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H14A..07G"><span id="translatedtitle">Integrated <span class="hlt">Forecast</span>-Decision <span class="hlt">Systems</span> For River Basin Planning and Management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Georgakakos, A. P.</p> <p>2005-12-01</p> <p>A central application of climatology, meteorology, and hydrology is the generation of reliable <span class="hlt">forecasts</span> for water resources management. In principle, effective use of <span class="hlt">forecasts</span> could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of <span class="hlt">forecasts</span>. Second is the existence of decision support methods/<span class="hlt">systems</span> with the ability to properly utilize <span class="hlt">forecast</span> information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support <span class="hlt">systems</span> into the decision making processes. This presentation discusses several decision support <span class="hlt">systems</span> (DSS) using ensemble <span class="hlt">forecasting</span> that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective <span class="hlt">forecast</span>-decision support <span class="hlt">systems</span> must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015FrES..tmp...67W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015FrES..tmp...67W"><span id="translatedtitle">A one-way coupled atmospheric-hydrological modeling <span class="hlt">system</span> with combination of high-resolution and ensemble precipitation <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Zhiyong; Wu, Juan; Lu, Guihua</p> <p>2015-11-01</p> <p>Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood <span class="hlt">forecasting</span>. However, the uncertainties in precipitation <span class="hlt">forecasts</span> are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling <span class="hlt">system</span>, with a combination of high-resolution and ensemble precipitation <span class="hlt">forecasting</span>, has been developed. It consists of three high-resolution single models and four sets of ensemble <span class="hlt">forecasts</span> from the THORPEX Interactive Grande Global Ensemble database. The former provides higher <span class="hlt">forecasting</span> accuracy, while the latter provides the range of <span class="hlt">forecasts</span>. The combined precipitation <span class="hlt">forecasting</span> was then implemented to drive the Chinese National Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the <span class="hlt">system</span> can clearly give a set of <span class="hlt">forecasting</span> hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability <span class="hlt">forecasts</span>. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. <span class="hlt">Forecasters</span> could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather <span class="hlt">forecasts</span> will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016FrES...10..432W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016FrES...10..432W&link_type=ABSTRACT"><span id="translatedtitle">A one-way coupled atmospheric-hydrological modeling <span class="hlt">system</span> with combination of high-resolution and ensemble precipitation <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Zhiyong; Wu, Juan; Lu, Guihua</p> <p>2016-09-01</p> <p>Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood <span class="hlt">forecasting</span>. However, the uncertainties in precipitation <span class="hlt">forecasts</span> are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling <span class="hlt">system</span>, with a combination of high-resolution and ensemble precipitation <span class="hlt">forecasting</span>, has been developed. It consists of three high-resolution single models and four sets of ensemble <span class="hlt">forecasts</span> from the THORPEX Interactive Grande Global Ensemble database. The former provides higher <span class="hlt">forecasting</span> accuracy, while the latter provides the range of <span class="hlt">forecasts</span>. The combined precipitation <span class="hlt">forecasting</span> was then implemented to drive the Chinese National Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the <span class="hlt">system</span> can clearly give a set of <span class="hlt">forecasting</span> hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability <span class="hlt">forecasts</span>. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. <span class="hlt">Forecasters</span> could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather <span class="hlt">forecasts</span> will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080042406','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080042406"><span id="translatedtitle">Anvil <span class="hlt">Forecast</span> Tool in the Advanced Weather Interactive Processing <span class="hlt">System</span>, Phase II</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe H., III</p> <p>2008-01-01</p> <p>Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil <span class="hlt">forecasting</span> as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display <span class="hlt">Systems</span> (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model <span class="hlt">forecast</span> winds as input.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27078490','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27078490"><span id="translatedtitle">Comment on "Nonparametric <span class="hlt">forecasting</span> of low-dimensional dynamical <span class="hlt">systems</span> ".</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kondrashov, Dmitri; Chekroun, Mickaël D; Ghil, Michael</p> <p>2016-03-01</p> <p>The comparison performed in Berry et al. [Phys. Rev. E 91, 032915 (2015)] between the skill in predicting the El Niño-Southern Oscillation climate phenomenon by the prediction method of Berry et al. and the "past-noise" <span class="hlt">forecasting</span> method of Chekroun et al. [Proc. Natl. Acad. Sci. USA 108, 11766 (2011)] is flawed. Three specific misunderstandings in Berry et al. are pointed out and corrected. PMID:27078490</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120014987','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120014987"><span id="translatedtitle">Decadal Prediction Skill in the GEOS-5 <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ham, Yoo-Geun; Rienecker, Michael M.; Suarez, M.; Vikhliaev, Yury V.; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.</p> <p>2012-01-01</p> <p>A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office?s GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). The hindcasts are initialized every December from 1959 to 2010 following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from the atmospheric reanalysis (MERRA, the Modern-Era Retrospective Analysis for Research and Applications) generated using the GEOS-5 atmospheric model. The <span class="hlt">forecast</span> skill of a three-member-ensemble mean is compared to that of an experiment without initialization but forced with observed CO2. The results show that initialization acts to increase the <span class="hlt">forecast</span> skill of Northern Atlantic SST compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. The annual-mean Atlantic Meridional Overturning Circulation (AMOC) index is predictable up to a 5-year lead time, consistent with the predictable signal in upper ocean heat content over the Northern Atlantic. While the skill measured by Mean Squared Skill Score (MSSS) shows 50% improvement up to 10-year lead <span class="hlt">forecast</span> over the subtropical and mid-latitude Atlantic, however, prediction skill is relatively low in the subpolar gyre, due in part to the fact that the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region appears to be unrealistic. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150008258','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150008258"><span id="translatedtitle">Decadal Prediction Skill in the GEOS-5 <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ham, Yoo-Geun; Rienecker, Michele M.; Suarez, Max J.; Vikhliaev, Yury; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.</p> <p>2013-01-01</p> <p>A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean <span class="hlt">forecast</span> skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the <span class="hlt">forecast</span> skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year <span class="hlt">forecast</span> skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JHyd..367..125C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JHyd..367..125C"><span id="translatedtitle">Evolutionary artificial neural networks for hydrological <span class="hlt">systems</span> <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Yung-hsiang; Chang, Fi-John</p> <p>2009-03-01</p> <p>SummaryThe conventional ways of constructing artificial neural network (ANN) for a problem generally presume a specific architecture and do not automatically discover network modules appropriate for specific training data. Evolutionary algorithms are used to automatically adapt the network architecture and connection weights according to the problem environment without substantial human intervention. To improve on the drawbacks of the conventional optimal process, this study presents a novel evolutionary artificial neural network (EANN) for time series <span class="hlt">forecasting</span>. The EANN has a hybrid procedure, including the genetic algorithm and the scaled conjugate gradient algorithm, where the feedforward ANN architecture and its connection weights of neurons are simultaneously identified and optimized. We first explored the performance of the proposed EANN for the Mackey-Glass chaotic time series. The performance of the different networks was evaluated. The excellent performance in <span class="hlt">forecasting</span> of the chaotic series shows that the proposed algorithm concurrently possesses efficiency, effectiveness, and robustness. We further explored the applicability and reliability of the EANN in a real hydrological time series. Again, the results indicate the EANN can effectively and efficiently construct a viable <span class="hlt">forecast</span> module for the 10-day reservoir inflow, and its accuracy is superior to that of the AR and ARMAX models.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011ITEIS.131.1665Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011ITEIS.131.1665Y"><span id="translatedtitle">Optimal Planning Strategy for Large PV/Battery <span class="hlt">System</span> Based on Long-Term Insolation <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yona, Atsushi; Uchida, Kosuke; Senjyu, Tomonobu; Funabashi, Toshihisa</p> <p></p> <p>Photovoltaic (PV) <span class="hlt">systems</span> are rapidly gaining acceptance as some of the best alternative energy sources. Usually the power output of PV <span class="hlt">system</span> fluctuates depending on weather conditions. In order to control the fluctuating power output for PV <span class="hlt">system</span>, it requires control method of energy storage <span class="hlt">system</span>. This paper proposes an optimization approach to determine the operational planning of power output for PV <span class="hlt">system</span> with battery energy storage <span class="hlt">system</span> (BESS). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output for PV <span class="hlt">system</span>. The optimization method applies genetic algorithm (GA) considering PV power output <span class="hlt">forecast</span> error. The <span class="hlt">forecast</span> error is based on our previous works with the insolation <span class="hlt">forecasting</span> at one day ahead by using weather reported data, fuzzy theory and neural network(NN). The validity of the proposed method is confirmed by the computer simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AtmRe.100..150R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AtmRe.100..150R"><span id="translatedtitle">The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological <span class="hlt">forecast</span> <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rossa, Andrea; Liechti, Katharina; Zappa, Massimiliano; Bruen, Michael; Germann, Urs; Haase, Günther; Keil, Christian; Krahe, Peter</p> <p>2011-05-01</p> <p>Quantifying uncertainty in flood <span class="hlt">forecasting</span> is a difficult task, given the multiple and strongly non-linear model components involved in such a <span class="hlt">system</span>. Much effort has been and is being invested in the quest of dealing with uncertain precipitation observations and <span class="hlt">forecasts</span> and the propagation of such uncertainties through hydrological and hydraulic models predicting river discharges and risk for inundation. The COST 731 Action is one of these and constitutes a European initiative which deals with the quantification of <span class="hlt">forecast</span> uncertainty in hydro-meteorological <span class="hlt">forecast</span> <span class="hlt">systems</span>. COST 731 addresses three major lines of development: (1) combining meteorological and hydrological models to form a <span class="hlt">forecast</span> chain, (2) propagating uncertainty information through this chain and make it available to end users in a suitable form, (3) advancing high-resolution numerical weather prediction precipitation <span class="hlt">forecasts</span> by using non-conventional observations from, for instance, radar to determine details in the initial conditions on scales smaller than what can be resolved by conventional observing <span class="hlt">systems</span>. Recognizing the interdisciplinarity of the challenge COST 731 has organized its work forming Working Groups at the interfaces between the different scientific disciplines involved, i.e. between observation and atmospheric (and hydrological) modelling (WG-1), between atmospheric and hydrologic modelling (WG-2) and between hydrologic modelling and end-users (WG-3). This paper summarizes the COST 731 activities and its context, provides a review of the recent progress made in dealing with uncertainties in flood <span class="hlt">forecasting</span>, and sets the scene for the papers of this Thematic Issue. In particular, a bibliometric analysis highlights the strong recent increase in addressing the uncertainty analysis in flood <span class="hlt">forecasting</span> from an integrated perspective. Such a perspective necessarily involves the area of meteorology, hydrology, and decision making in order to take operational advantage</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412068R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412068R"><span id="translatedtitle">Real Time Air Quality <span class="hlt">Forecasting</span> <span class="hlt">System</span> for a Large Industrial Facility</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radonjic, Z.; Chambers, D.; Telenta, B.; Janjic, Z.</p> <p>2012-04-01</p> <p><span class="hlt">Forecasts</span> of air quality are provided using a weather <span class="hlt">forecasting</span> model coupled with an air dispersion model. The advanced mesoscale WRF- NMM (Weather Research and <span class="hlt">Forecasting</span> - Nonhydrostatic Mesoscale Model) is set up to provide meteorological <span class="hlt">forecasts</span> initially over a larger domain with resolution 3 by 3 km which is subsequently nested down to a smaller domain of 1 by 1 km horizontal resolution around a copper smelter in Serbia. The refined meteorological <span class="hlt">forecast</span> is used as input to drive the CALMET/CALPUFF modeling <span class="hlt">system</span> to predict hour by hour concentrations of the facility's key pollutant (SO2). CALMET/CALPUFF is the U.S. EPA's regulatory model for long-range transport and on a case by case basis is applied in complex terrain and shore-line settings. The CALMET/CALPUFF modeling <span class="hlt">system</span> is accepted as a regulatory model for short-range applications in several jurisdictions in Canada. The main goal of this paper is to demonstrate the good performance of the weather model in <span class="hlt">forecasting</span> mode with fine resolution and in complex terrain, as well as the comparison of predicted SO2 air concentrations with measurements taken at four nearby air quality ambient monitoring stations. The <span class="hlt">forecasts</span> of SO2 concentrations are used by the facility to adjust the production schedule to avoid high level concentrations in the city and maximize production during favourable meteorological conditions. Since the facility is located in a valley, during stagnant meteorological conditions there is a potential for the build up of high concentrations of SO2. With the use of this air quality <span class="hlt">forecasting</span> <span class="hlt">system</span>, the facility can avoid the worst meteorological situations and reduce concentrations in the populated areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70155250','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70155250"><span id="translatedtitle">A seasonal agricultural drought <span class="hlt">forecast</span> <span class="hlt">system</span> for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.</p> <p>2014-01-01</p> <p> The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought <span class="hlt">forecasts</span> for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought <span class="hlt">forecast</span> <span class="hlt">system</span> for East Africa (EA) that provides decision support for the Famine Early Warning <span class="hlt">Systems</span> Network's science team. We evaluate this <span class="hlt">forecast</span> <span class="hlt">system</span> for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our <span class="hlt">forecast</span> <span class="hlt">system</span> simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our <span class="hlt">forecasting</span> <span class="hlt">system</span> with hindcast runs (1993–2012). We found that initializing SM <span class="hlt">forecasts</span> with start-of-season (5 March) SM conditions resulted in useful SM <span class="hlt">forecast</span> skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the <span class="hlt">forecast</span> was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESSD..11.3049S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESSD..11.3049S"><span id="translatedtitle">A seasonal agricultural drought <span class="hlt">forecast</span> <span class="hlt">system</span> for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; McNally, A.; Husak, G.; Funk, C.</p> <p>2014-03-01</p> <p>The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought <span class="hlt">forecasts</span> for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought <span class="hlt">forecast</span> <span class="hlt">system</span> for East Africa (EA) that provides decision support for the Famine Early Warning <span class="hlt">Systems</span> Network's science team. We evaluate this <span class="hlt">forecast</span> <span class="hlt">system</span> for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our <span class="hlt">forecast</span> <span class="hlt">system</span> simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our <span class="hlt">forecasting</span> <span class="hlt">system</span> with hindcast runs (1993-2012). We found that initializing SM <span class="hlt">forecasts</span> with start-of-season (5 March) SM conditions resulted in useful SM <span class="hlt">forecast</span> skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the <span class="hlt">forecast</span> was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/21290863','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/21290863"><span id="translatedtitle">Waste information management <span class="hlt">system</span>: a web-based <span class="hlt">system</span> for DOE waste <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Geisler, T.J.; Shoffner, P.A.; Upadhyay, U.; Quintero, W.</p> <p>2007-07-01</p> <p>The implementation of the Department of Energy (DOE) mandated accelerated cleanup program has created significant potential technical impediments that must be overcome. The schedule compression will require close coordination and a comprehensive review and prioritization of the barriers that may impede treatment and disposition of the waste streams at each site. Many issues related to site waste treatment and disposal have now become potential critical path issues under the accelerated schedules. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE headquarters in Washington, D.C., need timely waste <span class="hlt">forecast</span> information regarding the volumes and types of waste that will be generated by DOE sites over the next 25 years. Each local DOE site has historically collected, organized, and displayed site waste <span class="hlt">forecast</span> information in separate and unique <span class="hlt">systems</span>. However, waste information from all sites needs a common application to allow interested parties to understand and view the complete complex-wide picture. A common application would allow identification of total waste volumes, material classes, disposition sites, choke points, and technological or regulatory barriers to treatment and disposal. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, has completed the development of this web-based <span class="hlt">forecast</span> <span class="hlt">system</span>. (authors)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H13D1003K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H13D1003K"><span id="translatedtitle">A Predictive ENSO Frequency-based Time Series Model for Nitrate <span class="hlt">Loads</span> in the Little River Watershed Using Observed or <span class="hlt">Forecast</span> NINO 3.4 Sea Surface Temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Keener, V. W.; Bosch, D. D.; Cho, J.; Jones, J. W.</p> <p>2009-12-01</p> <p>There is a recognized difficulty in dealing with climate variability on a global or regional level, either in the Global or Regional Circulation Models currently in use. Many current climate models do not simulate El-Niño/Southern Oscillation (ENSO), and therefore do not adequately reproduce precipitation in areas of the world where ENSO is a significant factor, such as the southeast United States. ENSO is a global climate phenomenon with strong effects on the weather patterns of the southeast United States. ENSO has predictable effects on stream flow, rainfall, crop yield, and nutrient <span class="hlt">loads</span>. As IPCC reports have concluded that climate variability and extreme events will be more common in the future, models that focus on understanding and predicting climate variability effects will be increasingly helpful for making robust management decisions in an uncertain world. Natural resource and agricultural Best Management Practices (BMP) are a popular solution to ultimately reduce non-point source nutrient pollution, a problem in many industrial or agricultural watersheds. However, as BMP selection and implementation is both voluntary and not well documented in effectiveness, a method which can give land or water managers a better idea of climatologically high-risk months and when to most efficiently put BMP’s into practice would be helpful. In order to predict nitrate <span class="hlt">loads</span> in an agricultural coastal plain watershed in Georgia, the Little River Watershed, we have extracted and rebuilt the 3-7 year periodic variability from 30 years of nutrient and sea surface temperature (SST) data. In this way, both the low-frequency variability non-stationarity of the data was preserved. An optimized monthly multivariate time series model predicting nitrate <span class="hlt">loads</span> based on ENSO SST data was constructed from the relevant frequency extracted data. This model was tested for a year of monthly nitrate <span class="hlt">load</span> predictions, and accuracy was assessed in 3, 6, 9, and 12 month <span class="hlt">forecasts</span> with</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980037015','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980037015"><span id="translatedtitle"><span class="hlt">Load</span> Balancing Using Time Series Analysis for Soft Real Time <span class="hlt">Systems</span> with Statistically Periodic <span class="hlt">Loads</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hailperin, Max</p> <p>1993-01-01</p> <p>This thesis provides design and analysis of techniques for global <span class="hlt">load</span> balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic <span class="hlt">loads</span>. It focuses on estimating the instantaneous average <span class="hlt">load</span> over all the processing elements. The major contribution is the use of explicit stochastic process models for both the <span class="hlt">loading</span> and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average <span class="hlt">load</span> estimates, and thus to facilitate global <span class="hlt">load</span> balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global <span class="hlt">system</span> <span class="hlt">load</span> ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static <span class="hlt">load</span>-balancing schemes but also to many adaptive methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcDyn..64.1803M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014OcDyn..64.1803M"><span id="translatedtitle">Verification of an ensemble prediction <span class="hlt">system</span> for storm surge <span class="hlt">forecast</span> in the Adriatic Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mel, Riccardo; Lionello, Piero</p> <p>2014-12-01</p> <p>In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level <span class="hlt">forecast</span> is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction <span class="hlt">system</span>) for operational <span class="hlt">forecasting</span> of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather <span class="hlt">Forecasts</span>) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. <span class="hlt">Forecast</span> uncertainty increases with the predicted value of the storm surge and with the <span class="hlt">forecast</span> lead time. The EMF (ensemble mean <span class="hlt">forecast</span>) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic <span class="hlt">forecast</span>), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the <span class="hlt">forecast</span> and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the <span class="hlt">forecast</span>. For large lead times the EPS spread and the <span class="hlt">forecast</span> error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the <span class="hlt">forecast</span> and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability <span class="hlt">forecast</span>) of the EPS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPhCS.632a2075P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPhCS.632a2075P"><span id="translatedtitle">A Novel <span class="hlt">Forecasting</span> <span class="hlt">System</span> for Solar Particle Events and Flares (FORSPEF)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Papaioannou, A.; Anastasiadis, A.; Sandberg, I.; Georgoulis, M. K.; Tsiropoula, G.; Tziotziou, K.; Jiggens, P.; Hilgers, A.</p> <p>2015-08-01</p> <p>Solar Energetic Particles (SEPs) result from intense solar eruptive events such as solar flares and coronal mass ejections (CMEs) and pose a significant threat for both personnel and infrastructure in space conditions. In this work, we present FORSPEF (<span class="hlt">Forecasting</span> Solar Particle Events and Flares), a novel dual <span class="hlt">system</span>, designed to perform <span class="hlt">forecasting</span> of SEPs based on <span class="hlt">forecasting</span> of solar flares, as well as independent SEP nowcasting. An overview of flare and SEP <span class="hlt">forecasting</span> methods of choice is presented. Concerning SEP events, we make use for the first time of the newly re-calibrated GOES proton data within the energy range 6.0-243 MeV and we build our statistics on an extensive time interval that includes roughly 3 solar cycles (1984-2013). A new comprehensive catalogue of SEP events based on these data has been compiled including solar associations in terms of flare (magnitude, location) and CME (width, velocity) characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20100002942&hterms=innovation+impact&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dinnovation%2Bimpact','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20100002942&hterms=innovation+impact&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dinnovation%2Bimpact"><span id="translatedtitle">Comparison of Observation Impacts in Two <span class="hlt">Forecast</span> <span class="hlt">Systems</span> using Adjoint Methods</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gelaro, Ronald; Langland, Rolf; Todling, Ricardo</p> <p>2009-01-01</p> <p>An experiment is being conducted to compare directly the impact of all assimilated observations on short-range <span class="hlt">forecast</span> errors in different operational <span class="hlt">forecast</span> <span class="hlt">systems</span>. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 <span class="hlt">forecast</span> <span class="hlt">systems</span> for the month of January 2007. In each <span class="hlt">system</span>, about 65% of the total reduction in 24-h <span class="hlt">forecast</span> error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the <span class="hlt">forecast</span>, while the rest degrade it. It is found that most of the total <span class="hlt">forecast</span> error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each <span class="hlt">system</span>, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the <span class="hlt">forecast</span> in certain locations common to both <span class="hlt">systems</span>, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing <span class="hlt">system</span> as well as about the strengths and weaknesses of the methodologies used to assimilate them.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1510197K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1510197K"><span id="translatedtitle">High resolution operational air quality <span class="hlt">forecast</span> for Poland and Central Europe with the GEM-AQ model - Eco<span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaminski, Jacek W.; Struzewska, Joanna</p> <p>2013-04-01</p> <p>The air quality <span class="hlt">forecast</span> is an important component of the environmental assessment <span class="hlt">system</span>. The "Clean Air for Europe" (CAFE) Directive 2008/50/EC stipulates a need for numerical modelling in order to support public information services to interpret measurements of pollutants concentrations and to prepare and evaluate air quality plans. Most European countries have developed model-based air quality modelling and information services. We will present the design strategy, development and implementation of a regional high resolution <span class="hlt">forecasting</span> <span class="hlt">system</span> that was implemented in Poland. The new national high resolution air quality <span class="hlt">forecasting</span> <span class="hlt">system</span> has evolved from a semi-operational chemical weather <span class="hlt">system</span> Eco<span class="hlt">Forecast</span>.EU which is based on the GEM-AQ model (Kaminski et al., 2008). GEM-AQ is a comprehensive chemical weather model where air quality processes (chemistry and aerosols), troposphere and stratospheric chemistry are implemented on-line in the operational weather prediction model, the Global Environmental Multiscale (GEM) model (Cote et al, 1998), developed at Environment Canada. For these applications, the model is run on a global variable resolution grid with horizontal spacing of 15 km over Europe. In the vertical there are 28 hybrid levels, with the top at 10 hPa. A high resolution nested <span class="hlt">forecast</span> at 5 km resolution over Poland (and surrounding countries) was implemented in December 2012. The <span class="hlt">forecast</span> is published once a day at www.Eco<span class="hlt">Forecast</span>.EU. The air quality <span class="hlt">forecast</span> is presented for ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, PM10 and PM2.5 as maps of daily maxima and daily averages. We will present results from the on-going model evaluation study over Central Europe (2010-2012). Modelling results were evaluated and compared with available observation of ozone and primary pollutants from air quality monitoring stations and from meteorological synoptic stations. Ozone exposure indices, as defined in the CAFE Directive, will be shown for the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.H22A..02V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.H22A..02V"><span id="translatedtitle">Probabilistic Runoff <span class="hlt">Forecasting</span> using a Limited-Area Ensemble Prediction <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Verbunt, M.; Walser, A.; Gurtz, J.; Montani, A.; Schär, C.</p> <p>2004-12-01</p> <p>A high-resolution atmospheric ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span>, based on 51 runs of a Limited Area Model (LAM) has been used to make probabilistic runoff <span class="hlt">forecasts</span> for the Alpine Rhine basin. The operational European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> Ensemble Prediction <span class="hlt">System</span> (ECMWF EPS) provides the initial and boundary conditions for the LAM integrations with the Local Model (LM) for a 5 day <span class="hlt">forecasting</span> period. The LM runs in a horizontal resolution of 0.09 degree (10 km) and provides output with an hourly interval. Output from this model is used to drive a distributed hydrological model with a resolution of 500 m and a time-step of one hour. Runoff generation in the Precipitation Runoff EVApotranspiration Hydrotope (PREVAH) model is based on the HBV-model. The model further contains modules, which calculate snow and glacier melt, after a combined radiation and temperature index approach. The case-study investigated is the spring 1999 flood event, when a combination of snowmelt and heavy precipitation caused severe floods in Central Europe. The area investigated is the upper part of the Rhine catchment (34550 km2) in Central Europe. This river catchment, characterized by highly complex topography, has an altitude range from 262 m up to 4225 m a.s.l. The hydrological model component has been calibrated for the period 1997-1998 using ground observations, and validated for 1999-2002. This study focuses on the feasibility of ensemble prediction data for runoff <span class="hlt">forecasting</span> and addresses the predictability of this flood event. <span class="hlt">Forecast</span> uncertainties are investigated and runoff predictions from the deterministic <span class="hlt">forecast</span> are compared with those obtained from probabilistic atmospheric <span class="hlt">forecasts</span>. Further analyses include the changes in predictability when using different quantities of representative members.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1986RMTME.......45K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1986RMTME.......45K"><span id="translatedtitle">Advanced machine tools, <span class="hlt">loading</span> <span class="hlt">systems</span> viewed</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kharkov, V. I.</p> <p>1986-03-01</p> <p>The machine-tooling complex built from a revolving lathe and a two-armed robot designed to machine short revolving bodies including parts with curvilinear and threaded surfaces from piece blanks in either small-series or series multiitem production is described. The complex consists of: (1) a model 1V340F30 revolving lathe with a vertical axis of rotation, 8-position revolving head on a cross carriage and an Elektronika NTs-31 on-line control <span class="hlt">system</span>; (2) a gantry-style two-armed M20-Ts robot with a 20-kilogram (20 x 2) <span class="hlt">load</span> capacity; and (3) an 8-position indexable blank table, one of whose positions is for initial unloading of finished parts. Subsequently, machined parts are set onto the position into which all of the blanks are unloaded. Complex enclosure allows adjustment and process correction during maintenance and convenient observation of the machining process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1214477B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1214477B"><span id="translatedtitle">Local Rainfall <span class="hlt">Forecast</span> <span class="hlt">System</span> based on Time Series Analysis and Neural Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buendia-Buendía, F. S.; López Carrión, F.; Tarquis, A. M.; Buendía Moya, G.; Andina, D.</p> <p>2010-05-01</p> <p>Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current <span class="hlt">forecasts</span> are based on high complex dynamic models. In this paper is presented a local rainfall <span class="hlt">forecast</span> <span class="hlt">system</span> based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several years taking data, expert meteorologists proposed this approximation to characterize the local weather behaviour, that is automated by this <span class="hlt">system</span>. The current <span class="hlt">system</span> predicts rainfall events over Valladolid within a time period of a month with a twelve hours accuracy. The different blocks of the <span class="hlt">system</span> is explained as well as the work introduces how to apply the <span class="hlt">forecast</span> <span class="hlt">system</span> to prevent economical impact hazards produced by rainfalls.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMNH43A1622M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMNH43A1622M"><span id="translatedtitle">Development of Hydrometeorological Monitoring and <span class="hlt">Forecasting</span> as AN Essential Component of the Early Flood Warning <span class="hlt">System</span>:</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manukalo, V.</p> <p>2012-12-01</p> <p>Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning <span class="hlt">System</span> is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and <span class="hlt">forecasts</span> are a core importance in this <span class="hlt">system</span>. The primary factors affecting reliability and a lead - time of <span class="hlt">forecasts</span> include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and <span class="hlt">forecasting</span> resulted in a need in reconsideration of the concept of integrated monitoring and <span class="hlt">forecasting</span> approach - from "sensors to database and <span class="hlt">forecasters</span>". Result presentation The Project: "Development of Flood Monitoring and <span class="hlt">Forecasting</span> in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to <span class="hlt">forecasting</span> centers; e) developing hydrometeoroogical <span class="hlt">forecasting</span> methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNG23C..07V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNG23C..07V"><span id="translatedtitle">Using Self-Organizing Maps in Creation of an Ocean <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vilibic, I.; Zagar, N.; Cosoli, S.; Dadic, V.; Ivankovic, D.; Jesenko, B.; Kalinic, H.; Mihanovic, H.; Sepic, J.; Tudor, M.</p> <p>2014-12-01</p> <p>We present the first results of the NEURAL project (www.izor.hr/neural), which is dedicated to creation of an efficient and reliable ocean surface current <span class="hlt">forecasting</span> <span class="hlt">system</span>. This <span class="hlt">system</span> is based on high-frequency (HF) radar measurements, numerical weather prediction (NWP) models and neural network algorithms (Self-Organizing Maps, SOM). Joint mapping of mesoscale ground winds and HF radars in a coastal area points to a high correlation between two sets, indicating that wind <span class="hlt">forecast</span> may be used as a basis for <span class="hlt">forecasting</span> ocean surface currents. NEURAL project consists of three modules: (i) the technological module which covers installation of new HF radars in the coastal area of the middle Adriatic, and implementation of data management procedures; (ii) the research module which deals with an assessment of different combinations of input variables (radial vs. Cartesian vectors, original vs. detided vs. filtered series, WRF-ARW vs. Aladin meteorological model), all in order to get the best hindcasted surface currents; and finally (iii) the operational module in which NWP operational products will be used for short-term <span class="hlt">forecasting</span> of ocean surface currents. Both historical and newly observed HF radar data, as well as reanalysis and operational NWP model runs will be used within the (ii) and (iii) modules of the project. Finally, the observed, hindcasted and <span class="hlt">forecasted</span> ocean current will be compared to the operational ROMS model outputs to compare skill reliability of the <span class="hlt">forecasting</span> <span class="hlt">system</span> based on neural network approach to the skill and reliability of numerical ocean models. We expect the <span class="hlt">forecasting</span> <span class="hlt">system</span> based on neural network approach to be more reliable than the one based on numerical ocean model as it is more exclusively based on measurements. Disadvantages of such a <span class="hlt">system</span> are that it can be applied only in areas where long series surface currents measurements exist and where the recognized patterns can be properly ascribed to a forcing field.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvE..93c6201K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvE..93c6201K"><span id="translatedtitle">Comment on "Nonparametric <span class="hlt">forecasting</span> of low-dimensional dynamical <span class="hlt">systems</span> "</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael</p> <p>2016-03-01</p> <p>The comparison performed in Berry et al. [Phys. Rev. E 91, 032915 (2015), 10.1103/PhysRevE.91.032915] between the skill in predicting the El Niño-Southern Oscillation climate phenomenon by the prediction method of Berry et al. and the "past-noise" <span class="hlt">forecasting</span> method of Chekroun et al. [Proc. Natl. Acad. Sci. USA 108, 11766 (2011), 10.1073/pnas.1015753108] is flawed. Three specific misunderstandings in Berry et al. are pointed out and corrected.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.3365T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.3365T"><span id="translatedtitle">A pan-African medium-range ensemble flood <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thiemig, V.; Bisselink, B.; Pappenberger, F.; Thielen, J.</p> <p>2015-08-01</p> <p>The African Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> (AFFS) is a probabilistic flood <span class="hlt">forecast</span> <span class="hlt">system</span> for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather <span class="hlt">Forecasts</span>) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the <span class="hlt">system</span> showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in <span class="hlt">forecasting</span> timing and severity of the floods, gave an example of the clear and concise output products, and showed that the <span class="hlt">system</span> is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood <span class="hlt">forecasting</span> <span class="hlt">system</span>, although issues related to the practical implication will still need to be investigated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUSMOS32A..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUSMOS32A..08S"><span id="translatedtitle">Improvements to the NOS Experimental Nowcast/<span class="hlt">Forecast</span> <span class="hlt">System</span> for Galveston Bay</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schmalz, R. A.</p> <p>2002-05-01</p> <p>The National Ocean Service (NOS) has developed an experimental nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span> in conjunction with its Physical Oceanographic Real Time <span class="hlt">System</span> (PORTS) for Galveston Bay. Both a Bay model and a one-way coupled, fine resolution Houston Ship Channel model (Schmalz, 1998) based on the Mellor-Blumberg (1987) three-dimensional sigma coordinate formulation have been used to provide daily 24 hour nowcasts and 36 hour <span class="hlt">forecasts</span> for water levels, currents, salinity, and temperature on an experimental basis over the past three years. During the nowcast, PORTS station derived wind and sea level pressure, USGS streamflow data, and Galveston Pleasure Pier water level station derived nontidal signals are used to provide the meteorological, freshwater inflow, and Gulf of Mexico subtidal water level forcings, respectively. During the <span class="hlt">forecast</span>, the National Weather Service's Aviation Atmospheric, Western Gulf River <span class="hlt">Forecast</span> Center <span class="hlt">forecast</span> model river flows, and Extratropical Storm Surge <span class="hlt">Forecast</span> Models are used to provide these forcings. Nowcast and <span class="hlt">forecast</span> results have been evaluated using the NOS (1999) statistical measures for water levels, currents, salinity, and temperature over the one year period April 2000 through March 2001. Based on the evaluation, an initial plan for incorporating the following improvements is outlined: 1) a rainfall-runoff model for the Houston metroplex, 2) statistical nowcasting of missing PORTS data, 3) the incorporation of overland flow and marsh storage, 4) alternate vertical coordinates, and 5) wave-current interaction. It is anticipated that water level response will be improved by 1 and 3, current response by 1 and 5, salinity and temperature stratification by 4, and <span class="hlt">system</span> robustness by 2, respectively. Plans for implementing these improvements with respect to operational <span class="hlt">system</span> considerations will be highlighted.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4948O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4948O"><span id="translatedtitle">An operational real-time flood <span class="hlt">forecasting</span> <span class="hlt">system</span> in Southern Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio</p> <p>2015-04-01</p> <p>A real-time flood <span class="hlt">forecasting</span> <span class="hlt">system</span> has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> (SFFS) has been built within the FEWS (Flood Early Warning <span class="hlt">System</span>) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for <span class="hlt">forecasting</span> the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal <span class="hlt">forecast</span>, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the <span class="hlt">forecast</span> lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the <span class="hlt">forecast</span> lead time and the distribution of the time occurrence of the flood peak, starting from one or more model <span class="hlt">forecasts</span>. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management <span class="hlt">system</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPRS..104..224C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPRS..104..224C"><span id="translatedtitle">Operational perspective of remote sensing-based forest fire danger <span class="hlt">forecasting</span> <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chowdhury, Ehsan H.; Hassan, Quazi K.</p> <p>2015-06-01</p> <p>Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is the <span class="hlt">forecasting</span> of fire danger conditions. Here, our aim was to critically analyse the following issues, (i) current operational forest fire danger <span class="hlt">forecasting</span> <span class="hlt">systems</span> and their limitations; (ii) remote sensing-based fire danger monitoring <span class="hlt">systems</span> and usefulness in operational perspective; (iii) remote sensing-based fire danger <span class="hlt">forecasting</span> <span class="hlt">systems</span> and their functional implications; and (iv) synergy between operational <span class="hlt">forecasting</span> <span class="hlt">systems</span> and remote sensing-based methods. In general, the operational <span class="hlt">systems</span> use point-based measurements of meteorological variables (e.g., temperature, wind speed and direction, relative humidity, precipitations, cloudiness, solar radiation, etc.) and generate danger maps upon employing interpolation techniques. Theoretically, it is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. During the last several decades, efforts were given to develop fire danger condition <span class="hlt">systems</span>, which could be broadly classified into two major groups: fire danger monitoring and <span class="hlt">forecasting</span> <span class="hlt">systems</span>. Most of the monitoring <span class="hlt">systems</span> focused on determining the danger during and/or after the period of image acquisition. A limited number of studies were conducted to <span class="hlt">forecast</span> fire danger conditions, which could be adaptable. Synergy between the operational <span class="hlt">systems</span> and remote sensing-based methods were investigated in the past but too much complex in nature. Thus, the elaborated understanding about these developments would be worthwhile to advance research in the area of fire danger in the context of making them operational.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19840013987&hterms=0000&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3D0000','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19840013987&hterms=0000&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3D0000"><span id="translatedtitle">SASS wind <span class="hlt">forecast</span> impact studies using the GLAS and NEPRF <span class="hlt">systems</span>: Preliminary conclusions</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kalnay, E.; Atlas, R.; Baker, W. E.; Duffy, D.; Halem, M.; Helfand, M.</p> <p>1984-01-01</p> <p>For this project, a version of the GLAS Analysis/<span class="hlt">Forecast</span> <span class="hlt">System</span> was developed that includes an objective dealiasing scheme as an integral part of the analysis cycle. With this <span class="hlt">system</span> the (100 sq km) binned SASS wind data generated by S. Peteherych of AER, Canada corresponding of the period 0000 GMT 7 September 1978 to 1200 GMT 13 September 1978 was objectively dealiased. The dealiased wind fields have been requested and received by JPL, NMC and the British Meteorological Office. The first 3.5 days of objectively dealiased fields were subjectively enhanced on the McIDAS <span class="hlt">system</span>. Approximately 20% of the wind directions were modified, and of these, about 70% were changed by less than 90 deg. Two SASS <span class="hlt">forecast</span> impact studies, were performed using the dealiased fields, with the GLAS and the NEPRF (Navy Environmental Prediction Research Facility) analysis/<span class="hlt">forecast</span> <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1051935','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1051935"><span id="translatedtitle">Wind Power <span class="hlt">Forecasting</span> Error Frequency Analyses for Operational Power <span class="hlt">System</span> Studies: Preprint</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Florita, A.; Hodge, B. M.; Milligan, M.</p> <p>2012-08-01</p> <p>The examination of wind power <span class="hlt">forecasting</span> errors is crucial for optimal unit commitment and economic dispatch of power <span class="hlt">systems</span> with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power <span class="hlt">forecasts</span> will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power <span class="hlt">forecasts</span> and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power <span class="hlt">forecasting</span> error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level <span class="hlt">forecasts</span> than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power <span class="hlt">forecasting</span> has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130011214','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130011214"><span id="translatedtitle">Global Positioning <span class="hlt">System</span> (GPS) Precipitable Water in <span class="hlt">Forecasting</span> Lightning at Spaceport Canaveral</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kehrer, Kristen C.; Graf, Brian; Roeder, William</p> <p>2006-01-01</p> <p>This paper evaluates the use of precipitable water (PW) from Global Positioning <span class="hlt">System</span> (GPS) in lightning prediction. Additional independent verification of an earlier model is performed. This earlier model used binary logistic regression with the following four predictor variables optimally selected from a candidate list of 23 candidate predictors: the current precipitable water value for a given time of the day, the change in GPS-PW over the past 9 hours, the KIndex, and the electric field mill value. This earlier model was not optimized for any specific <span class="hlt">forecast</span> interval, but showed promise for 6 hour and 1.5 hour <span class="hlt">forecasts</span>. Two new models were developed and verified. These new models were optimized for two operationally significant <span class="hlt">forecast</span> intervals. The first model was optimized for the 0.5 hour lightning advisories issued by the 45th Weather Squadron. An additional 1.5 hours was allowed for sensor dwell, communication, calculation, analysis, and advisory decision by the <span class="hlt">forecaster</span>. Therefore the 0.5 hour advisory model became a 2 hour <span class="hlt">forecast</span> model for lightning within the 45th Weather Squadron advisory areas. The second model was optimized for major ground processing operations supported by the 45th Weather Squadron, which can require lightning <span class="hlt">forecasts</span> with a lead-time of up to 7.5 hours. Using the same 1.5 lag as in the other new model, this became a 9 hour <span class="hlt">forecast</span> model for lightning within 37 km (20 NM)) of the 45th Weather Squadron advisory areas. The two new models were built using binary logistic regression from a list of 26 candidate predictor variables: the current GPS-PW value, the change of GPS-PW over 0.5 hour increments from 0.5 to 12 hours, and the K-index. The new 2 hour model found the following for predictors to be statistically significant, listed in decreasing order of contribution to the <span class="hlt">forecast</span>: the 0.5 hour change in GPS-PW, the 7.5 hour change in GPS-PW, the current GPS-PW value, and the KIndex. The new 9 hour <span class="hlt">forecast</span> model found</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H33D1632C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H33D1632C&link_type=ABSTRACT"><span id="translatedtitle">A Real-time Irrigation <span class="hlt">Forecasting</span> <span class="hlt">System</span> in Jiefangzha Irrigation District, China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cong, Z.</p> <p>2015-12-01</p> <p>In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can <span class="hlt">forecast</span> the soil moisture content in the next days based on the rainfall <span class="hlt">forecasting</span> and the crop evapotranspiration <span class="hlt">forecasting</span>. Then the irrigation should be considered when the <span class="hlt">forecasting</span> soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather <span class="hlt">forecasting</span> data are downloaded from the website of European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is <span class="hlt">forecasted</span> by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is <span class="hlt">forecasted</span> based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The <span class="hlt">forecasting</span> period is 10 days. The <span class="hlt">system</span> is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation <span class="hlt">forecasting</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMOS11A1984P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMOS11A1984P"><span id="translatedtitle">Development and evaluation of an ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span> for regional ocean wave at KMA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>PARK, J. S.</p> <p>2015-12-01</p> <p>KMA developed an ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span> for regional wave. The <span class="hlt">system</span> predicts ocean (wind) waves based on meteorological forcing from the KMA-EPSG (Ensemble Prediction <span class="hlt">System</span> for Global at KMA), which has been running twice per day on experiment. It consisted of 24 ensemble members including the control member. It made 87 hour <span class="hlt">forecasts</span> for 00 and 12 UTC each day. Its spatial resolution is 0.083° in latitude and longitude from 115E to 150E and from 20N to 50N regionally. The wind forcing for the 24 ensemble members are obtained from KMA-EPSG 10m wind fields and are updated every three hours.Hereafter, the ensemble ocean wave <span class="hlt">forecasts</span> will be evaluated using moored buoy data from 8 locations around the coast of South Korea. Statistical verification will be performed for the typhoon cases during summer in 2015. The RMSE (Root Mean Square Error) is calculated for four types of <span class="hlt">forecasts</span>: the ensemble control, the perturbed ensemble members, the mean of all ensemble members (including the control), and the existing operational deterministic waves <span class="hlt">forecasts</span>. Also, this study will be conducted probability analysis such as brier score (BS), economical value on the performance of the <span class="hlt">system</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcMod..93....7V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..93....7V"><span id="translatedtitle">A stochastic operational <span class="hlt">forecasting</span> <span class="hlt">system</span> of the Black Sea: Technique and validation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vandenbulcke, Luc; Barth, Alexander</p> <p>2015-09-01</p> <p>In this article, we present the latest version of an ensemble <span class="hlt">forecasting</span> <span class="hlt">system</span> of the hydrodynamics of the Black Sea, based on the GHER model. The <span class="hlt">system</span> includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The <span class="hlt">forecasting</span> <span class="hlt">system</span> also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the <span class="hlt">forecasting</span> <span class="hlt">system</span> is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the <span class="hlt">forecast</span> errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model <span class="hlt">forecasts</span> are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1613691T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1613691T"><span id="translatedtitle">A Preliminary Application of Ensemble Transform Kalman Filter <span class="hlt">System</span> to Rainfall <span class="hlt">Forecast</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tie, Wang</p> <p>2014-05-01</p> <p>WangTie(1,2),YangXiaofeng(1),ZangXin(1),GengLining(1),ZhouYan(1) (1)NanJing China-Spacenet Satellite Telecom CO., Ltd, Nanjing, 210061, China,(2)Institute of Atmospheric Physics, Chinese Academy of Sciences, LASG, Beijing, 100029, China The Ensemble Transform Kalman Filter(ETKF) method is applied to WRF model(Weather Research & <span class="hlt">Forecast</span> Model <span class="hlt">system</span>) Version 3.5.1, and the ETKF based data assimilation <span class="hlt">system</span> is constructed. The accurate of <span class="hlt">forecast</span> result and the sensitivity of initial data error on a heavy rainfall from 5th to 7th July 2003 in Jiangsu province China are investigated. The numerical result indicated that the ETKF method could decrease the <span class="hlt">forecast</span> error and improves the <span class="hlt">forecast</span> result either the synoptic scale <span class="hlt">system</span> and the meso-scale <span class="hlt">system</span>, the RMSEs of wind decrease less than 18 percent, while the temperature and humidity decrease about 6 precent. The <span class="hlt">forecast</span> of precipitation, especially the 12h accumulated precipitation is improved significant after ETKF assimilation. Keywords: intense rainfall, prediction error, Ensemble Transform Kalman Filter(ETKF), WRF model</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.2456S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.2456S"><span id="translatedtitle">Seasonal drought <span class="hlt">forecast</span> <span class="hlt">system</span> for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, Shraddhanand; McNally, Amy; Husak, Greg; Funk, Chris</p> <p>2014-05-01</p> <p>In East Africa, agriculture is mostly rainfed and hence sensitive to interannual rainfall variability, and the increasing food and water demands of a growing population place further stresses on the water resources of this region. Skillful seasonal agricultural drought <span class="hlt">forecasts</span> for this region can inform timely water and agricultural management decisions, support the proper allocation of the region's water resources, and help mitigate socio-economic losses. Here we describe the development and implementation of a seasonal drought <span class="hlt">forecast</span> <span class="hlt">system</span> that is being used for providing seasonal outlooks of agricultural drought in East Africa. We present a test case of the evaluation and applicability of this <span class="hlt">system</span> for March-April-May growing season over equatorial East Africa (latitude 20 south to 80 North and 360 E to 460E) that encompasses one of the most food insecure and climatically and socio-economically vulnerable regions in East Africa. This region experienced famine as recently as in 2011. The <span class="hlt">system</span> described here combines advanced satellite and re-analysis as well as station-based long term and real-time observations (e.g. NASA's TRMM, Infra-red remote sensing, Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Reanalysis), state-of-the-art dynamical climate <span class="hlt">forecast</span> <span class="hlt">system</span> (NCEP's Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Verison-2) and large scale land surface models (e.g. Variable Infiltration Capacity, NASA's Land Information <span class="hlt">System</span>) to provide <span class="hlt">forecasts</span> of seasonal rainfall, soil moisture and Water Requirement Satisfaction Index (WRSI) throughout the season - with an emphasis on times when water is the most critical: start of season/planting and the mid-season/crop reproductive phase. Based on the hindcast assessment of this <span class="hlt">system</span>, we demonstrate the value of this approach to the US Agency for International Development (USAID)'s efforts to mitigate future losses of lives and economic losses by allowing a proactive approach of drought management that includes early warning and timely action.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.7227R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.7227R&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Forecasting</span> skills of the ensemble hydro-meteorological <span class="hlt">system</span> for the Po river floods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio</p> <p>2013-04-01</p> <p>The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in <span class="hlt">forecasting</span> skill and information dissemination. Since 2005, an operational <span class="hlt">forecasting</span> and modelling <span class="hlt">system</span> for flood modelling and <span class="hlt">forecasting</span> has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This <span class="hlt">system</span>, referred to as FEWSPo, has also taken care of environmental aspects of flood <span class="hlt">forecast</span>. The FEWSPo <span class="hlt">system</span> has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (<span class="hlt">forecasted</span> - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood <span class="hlt">forecast</span> hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term <span class="hlt">forecast</span> (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..16.6864J&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..16.6864J&link_type=ABSTRACT"><span id="translatedtitle">Multi-platform operational validation of the Western Mediterranean SOCIB <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin</p> <p>2014-05-01</p> <p>The development of science-based ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span> at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and <span class="hlt">Forecasting</span> <span class="hlt">System</span>, www.socib.es) has developed an operational ocean <span class="hlt">forecasting</span> <span class="hlt">system</span> in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling <span class="hlt">System</span> (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean <span class="hlt">Forecasting</span> <span class="hlt">System</span> (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation <span class="hlt">system</span> has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean <span class="hlt">forecasting</span> <span class="hlt">system</span> before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily <span class="hlt">forecasts</span>, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813156S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813156S"><span id="translatedtitle">A simple method of observation impact analysis for operational storm surge <span class="hlt">forecasting</span> <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sumihar, Julius; Verlaan, Martin</p> <p>2016-04-01</p> <p>In this work, a simple method is developed for analyzing the impact of assimilating observations in improving <span class="hlt">forecast</span> accuracy of a model. The method simply makes use of observation time series and the corresponding model output that are generated without data assimilation. These two time series are usually available in an operational database. The method is therefore easy to implement. Moreover, it can be used before actually implementing any data assimilation to the <span class="hlt">forecasting</span> <span class="hlt">system</span>. In this respect, it can be used as a tool for designing a data assimilation <span class="hlt">system</span>, namely for searching for an optimal observing network. The method can also be used as a diagnostic tool, for example, for evaluating an existing operational data assimilation <span class="hlt">system</span> to check if all observations are contributing positively to the <span class="hlt">forecast</span> accuracy. The method has been validated with some twin experiments using a simple one-dimensional advection model as well as with an operational storm surge <span class="hlt">forecasting</span> <span class="hlt">system</span> based on the Dutch Continental Shelf model version 5 (DCSMv5). It has been applied for evaluating the impact of observations in the operational data assimilation <span class="hlt">system</span> with DCSMv5 and for designing a data assimilation <span class="hlt">system</span> for the new model DCSMv6. References: Verlaan, M. and J. Sumihar (2016), Observation impact analysis methods for storm surge <span class="hlt">forecasting</span> <span class="hlt">systems</span>, Ocean Dynamics, ODYN-D-15-00061R1 (in press) Zijl, F., J. Sumihar, and M. Verlaan (2015), Application of data assimilation for improved operational water level <span class="hlt">forecasting</span> of the northwest European shelf and North Sea, Ocean Dynamics, 65, Issue 12, pp 1699-1716.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1215074B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1215074B"><span id="translatedtitle">Local Rainfall <span class="hlt">Forecast</span> <span class="hlt">System</span> based on Time Series Analysis and Neural Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buendia, Fulgencio S.; Tarquis, A. M.; Buendia, G.; Andina, D.</p> <p>2010-05-01</p> <p>Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current <span class="hlt">forecasts</span> are based on high complex dynamic models. In this paper is presented a local rainfall <span class="hlt">forecast</span> <span class="hlt">system</span> based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several year taking data, expert meteorologists proposed this approximation to characterize the local weather behavior, that is being automated by this <span class="hlt">system</span> in different stages. However the whole <span class="hlt">system</span> is introduced, it is focused on the different rainfall events situation classification as well as the time series analysis and <span class="hlt">forecast</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=116334&keyword=fig&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65069067&CFTOKEN=37326142','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=116334&keyword=fig&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65069067&CFTOKEN=37326142"><span id="translatedtitle">VERIFICATION OF SURFACE LAYER OZONE <span class="hlt">FORECASTS</span> IN THE NOAA/EPA AIR QUALITY <span class="hlt">FORECAST</span> <span class="hlt">SYSTEM</span> IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>An air quality <span class="hlt">forecast</span> (AQF) <span class="hlt">system</span> has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The <span class="hlt">system</span> is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this <span class="hlt">system</span> is ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/20060096','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/20060096"><span id="translatedtitle">The effect of <span class="hlt">load</span> distribution within military <span class="hlt">load</span> carriage <span class="hlt">systems</span> on the kinetics of human gait.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Birrell, Stewart A; Haslam, Roger A</p> <p>2010-07-01</p> <p>Military personnel carry their equipment in <span class="hlt">load</span> carriage <span class="hlt">systems</span> (LCS) which consists of webbing and a Bergen (aka backpack). In scientific terms it is most efficient to carry <span class="hlt">load</span> as close to the body's centre of mass (CoM) as possible, this has been shown extensively with physiological studies. However, less is known regarding the kinetic effects of <span class="hlt">load</span> distribution. Twelve experienced <span class="hlt">load</span> carriers carried four different <span class="hlt">loads</span> (8, 16, 24 and 32 kg) in three LCS (backpack, standard and AirMesh). The three LCS represented a gradual shift to a more even <span class="hlt">load</span> distribution around the CoM. Results from the study suggest that shifting the CoM posteriorly by carrying <span class="hlt">load</span> solely in a backpack significantly reduced the force produced at toe-off, whilst also decreasing stance time at the heavier <span class="hlt">loads</span>. Conversely, distributing <span class="hlt">load</span> evenly on the trunk significantly decreased the maximum braking force by 10%. No other interactions between LCS and kinetic parameters were observed. Despite this important findings were established, in particular the effect of heavy <span class="hlt">load</span> carriage on maximum braking force. Although the total <span class="hlt">load</span> carried is the major cause of changes to gait patterns, the scientific testing of, and development of, future LCS can modify these risks. PMID:20060096</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PApGe.171..209M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PApGe.171..209M"><span id="translatedtitle">An Experimental High-Resolution <span class="hlt">Forecast</span> <span class="hlt">System</span> During the Vancouver 2010 Winter Olympic and Paralympic Games</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.</p> <p>2014-01-01</p> <p>Environment Canada ran an experimental numerical weather prediction (NWP) <span class="hlt">system</span> during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP <span class="hlt">system</span> has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the <span class="hlt">forecasts</span> from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best <span class="hlt">forecast</span> accuracy. Appreciable errors were noted in all models for the <span class="hlt">forecasts</span> of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic <span class="hlt">system</span> was skillful at <span class="hlt">forecasting</span> meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic <span class="hlt">forecasters</span> in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19970040261&hterms=cs+go&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcs%2Bgo','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19970040261&hterms=cs+go&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcs%2Bgo"><span id="translatedtitle"><span class="hlt">Load</span> Balancing Using Time Series Analysis for Soft Real Time <span class="hlt">Systems</span> with Statistically Periodic <span class="hlt">Loads</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hailperin, M.</p> <p>1993-01-01</p> <p>This thesis provides design and analysis of techniques for global <span class="hlt">load</span> balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic <span class="hlt">loads</span>. It focuses on estimating the instantaneous average <span class="hlt">load</span> over all the processing elements. The major contribution is the use of explicit stochastic process models for both the <span class="hlt">loading</span> and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average <span class="hlt">load</span> estimates, and thus to facilitate global <span class="hlt">load</span> balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global <span class="hlt">system</span> <span class="hlt">loading</span>, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static <span class="hlt">load</span>-balancing schemes but also to many adaptive <span class="hlt">load</span>-balancing methods. Results from a preliminary analysis of another <span class="hlt">system</span> and from simulation with a synthetic <span class="hlt">load</span> provide some evidence of more general applicability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24842026','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24842026"><span id="translatedtitle">Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic</p> <p>2014-06-28</p> <p>The finite resolution of general circulation models of the coupled atmosphere-ocean <span class="hlt">system</span> and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate <span class="hlt">system</span> in operational <span class="hlt">forecast</span> mode, and the latest seasonal <span class="hlt">forecasting</span> <span class="hlt">system--System</span> 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather <span class="hlt">forecasts</span>. Here, we present results of the impact of these schemes in <span class="hlt">System</span> 4 by contrasting the operational performance on seasonal time scales during the retrospective <span class="hlt">forecast</span> period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation <span class="hlt">forecasts</span> become smaller, whereas increases in ensemble spread lead to a better calibrated <span class="hlt">system</span> if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhDT.......509S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhDT.......509S"><span id="translatedtitle">WRF <span class="hlt">forecast</span> skill of the Great Plains low level jet and its correlation to <span class="hlt">forecast</span> skill of mesoscale convective <span class="hlt">system</span> precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Squitieri, Brian Joseph</p> <p></p> <p>One of the primary mechanisms for supporting summer nocturnal precipitation across the central United States is the Great Plains low-level Jet (LLJ). Mesoscale Convective <span class="hlt">Systems</span> (MCSs) are organized storm complexes that can be supported from the upward vertical motion supplied at the terminus of the LLJ, which bring beneficial rains to farmers. As such, a need for <span class="hlt">forecasting</span> these storm complexes exists. Correlating <span class="hlt">forecast</span> skills of the LLJ and MCS precipitation in high spatial resolution modeling was the main goal of this research. STAGE IV data was used as observations for MCS precipitation and the 00-hr 13 km RUC analysis was employed for evaluation of the LLJ. The 4 km WRF was used for high resolution <span class="hlt">forecast</span> simulations, with 2 microphysics and 3 planetary boundary layer schemes selected for a sensitivity study to see which model run best simulated reality. It was found that the <span class="hlt">forecast</span> skill of the potential temperature and directional components of the geostrophic and ageostrophic winds within the LLJ correlated well with MCS precipitation, especially early during LLJ evolution. Since the 20 real cases sampled consisted of three LLJ types (synoptic, inertial oscillation and transition), <span class="hlt">forecast</span> skill in other parameters such as deep layer and low level shear, convergence, frontogenesis and stability parameters were compared to MCS <span class="hlt">forecast</span> skill to see if consistent signals outside of the LLJ influenced MCS evolution in <span class="hlt">forecasts</span>. No correlations were found among these additional parameters. Given the variety of synoptic setups present, the lack of <span class="hlt">forecast</span> skill correlations between several variables and MCSs resulted as different synoptic or mesoscale mechanisms played varying roles if importance in different cases.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=environmental+AND+scanning&pg=3&id=EJ349531','ERIC'); return false;" href="http://eric.ed.gov/?q=environmental+AND+scanning&pg=3&id=EJ349531"><span id="translatedtitle">Establishing an Environmental Scanning/<span class="hlt">Forecasting</span> <span class="hlt">System</span> to Augment College and University Planning.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Morrison, James L.</p> <p>1987-01-01</p> <p>The major benefit of an environmental scanning/<span class="hlt">forecasting</span> <span class="hlt">system</span> is in providing critical information for strategic planning. Such a <span class="hlt">system</span> allows the institution to detect social, technological, economic, and political trends and potential events. The environmental scanning database developed by United Way of America is described. (MLW)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.274F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.274F"><span id="translatedtitle">Operational coupled atmosphere - ocean - ice <span class="hlt">forecast</span> <span class="hlt">system</span> for the Gulf of St. Lawrence, Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Faucher, M.; Roy, F.; Desjardins, S.; Fogarty, C.; Pellerin, P.; Ritchie, H.; Denis, B.</p> <p>2009-09-01</p> <p>A fully interactive coupled atmosphere-ocean-ice <span class="hlt">forecasting</span> <span class="hlt">system</span> for the Gulf of St. Lawrence (GSL) has been running in experimental mode at the Canadian Meteorological Centre (CMC) for the last two winter seasons. The goal of this project is to provide more accurate weather and sea ice <span class="hlt">forecasts</span> over the GSL and adjacent coastal areas by including atmosphere-oceanice interactions in the CMC operational <span class="hlt">forecast</span> <span class="hlt">system</span> using a formal coupling strategy between two independent modeling components. The atmospheric component is the Canadian operational GEM model (Côté et al. 1998) and the oceanic component is the ocean-ice model for the Gulf of St. Lawrence developed at the Maurice Lamontagne Institute (IML) (Saucier et al. 2003, 2004). The coupling between those two models is achieved by exchanging surface fluxes and variables through MPI communication. The re-gridding of the variables is done with a package developed at the Recherche en Prevision Numerique centre (RPN, Canada). Coupled atmosphere - ocean - ice <span class="hlt">forecasts</span> are issued once a day based on 00GMT data. Results for the past two years have demonstrated that the coupled <span class="hlt">system</span> produces improved <span class="hlt">forecasts</span> in and around the GSL during all seasons, proving that atmosphere-ocean-ice interactions are indeed important even for short-term Canadian weather <span class="hlt">forecasts</span>. This has important implications for other coupled modeling and data assimilation partnerships that are in progress involving EC, the Department of Fisheries and Oceans (DFO) and the National Defense (DND). Following this experimental phase, it is anticipated that this GSL <span class="hlt">system</span> will be the first fully interactive coupled <span class="hlt">system</span> to be implemented at CMC.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024238','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024238"><span id="translatedtitle">Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic</p> <p>2014-01-01</p> <p>The finite resolution of general circulation models of the coupled atmosphere–ocean <span class="hlt">system</span> and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate <span class="hlt">system</span> in operational <span class="hlt">forecast</span> mode, and the latest seasonal <span class="hlt">forecasting</span> system—<span class="hlt">System</span> 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather <span class="hlt">forecasts</span>. Here, we present results of the impact of these schemes in <span class="hlt">System</span> 4 by contrasting the operational performance on seasonal time scales during the retrospective <span class="hlt">forecast</span> period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation <span class="hlt">forecasts</span> become smaller, whereas increases in ensemble spread lead to a better calibrated <span class="hlt">system</span> if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A53C0191P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A53C0191P"><span id="translatedtitle">Development of On-line Wildfire Emissions for the Operational Canadian Air Quality <span class="hlt">Forecast</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.</p> <p>2013-12-01</p> <p>An emissions processing <span class="hlt">system</span> has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) <span class="hlt">forecast</span> <span class="hlt">system</span>. Since the GEM-MACH <span class="hlt">forecast</span> domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information <span class="hlt">System</span> (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) <span class="hlt">system</span> via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour <span class="hlt">forecasts</span> were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ <span class="hlt">forecasts</span> for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ <span class="hlt">forecasters</span> will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality <span class="hlt">forecast</span> <span class="hlt">system</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013PhDT........29Y&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013PhDT........29Y&link_type=ABSTRACT"><span id="translatedtitle">Investigation into a displacement bias in numerical weather prediction models' <span class="hlt">forecasts</span> of mesoscale convective <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yost, Charles</p> <p></p> <p>Although often hard to correctly <span class="hlt">forecast</span>, mesoscale convective <span class="hlt">systems</span> (MCSs) are responsible for a majority of warm-season, localized extreme rain events. This study investigates displacement errors often observed by <span class="hlt">forecasters</span> and researchers in the Global <span class="hlt">Forecast</span> <span class="hlt">System</span> (GFS) and the North American Mesoscale (NAM) models, in addition to the European Centre for Medium Range Weather <span class="hlt">Forecasts</span> (ECMWF) and the 4-km convection allowing NSSL-WRF models. Using archived radar data and Stage IV precipitation data from April to August of 2009 to 2011, MCSs were recorded and sorted into unique six-hour intervals. The locations of these MCSs were compared to the associated predicted precipitation field in all models using the Method for Object-Based Diagnostic Evaluation (MODE) tool, produced by the Developmental Testbed Center and verified through manual analysis. A northward bias exists in the location of the <span class="hlt">forecasts</span> in all lead times of the GFS, NAM, and ECMWF models. The MODE tool found that 74%, 68%, and 65% of the <span class="hlt">forecasts</span> were too far to the north of the observed rainfall in the GFS, NAM and ECMWF models respectively. The higher-resolution NSSL-WRF model produced a near neutral location <span class="hlt">forecast</span> error with 52% of the cases too far to the south. The GFS model consistently moved the MCSs too quickly with 65% of the cases located to the east of the observed MCS. The mean <span class="hlt">forecast</span> displacement error from the GFS and NAM were on average 266 km and 249 km, respectively, while the ECMWF and NSSL-WRF produced a much lower average of 179 km and 158 km. A case study of the Dubuque, IA MCS on 28 July 2011 was analyzed to identify the root cause of this bias. This MCS shattered several rainfall records and required over 50 people to be rescued from mobile home parks from around the area. This devastating MCS, which was a classic Training Line/Adjoining Stratiform archetype, had numerous northward-biased <span class="hlt">forecasts</span> from all models, which are examined here. As common with</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22494302','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22494302"><span id="translatedtitle">Demand <span class="hlt">forecasting</span> for automotive sector in Malaysia by <span class="hlt">system</span> dynamics approach</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Zulkepli, Jafri Abidin, Norhaslinda Zainal; Fong, Chan Hwa</p> <p>2015-12-11</p> <p>In general, Proton as an automotive company needs to <span class="hlt">forecast</span> future demand of the car to assist in decision making related to capacity expansion planning. One of the <span class="hlt">forecasting</span> approaches that based on judgemental or subjective factors is normally used to <span class="hlt">forecast</span> the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the <span class="hlt">system</span>, an accurate tool to <span class="hlt">forecast</span> the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to <span class="hlt">forecast</span> the demand of automotive Proton car industry in Malaysia using <span class="hlt">system</span> dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AIPC.1691c0031Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AIPC.1691c0031Z"><span id="translatedtitle">Demand <span class="hlt">forecasting</span> for automotive sector in Malaysia by <span class="hlt">system</span> dynamics approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zulkepli, Jafri; Fong, Chan Hwa; Abidin, Norhaslinda Zainal</p> <p>2015-12-01</p> <p>In general, Proton as an automotive company needs to <span class="hlt">forecast</span> future demand of the car to assist in decision making related to capacity expansion planning. One of the <span class="hlt">forecasting</span> approaches that based on judgemental or subjective factors is normally used to <span class="hlt">forecast</span> the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the <span class="hlt">system</span>, an accurate tool to <span class="hlt">forecast</span> the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to <span class="hlt">forecast</span> the demand of automotive Proton car industry in Malaysia using <span class="hlt">system</span> dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70020321','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70020321"><span id="translatedtitle"><span class="hlt">Forecasting</span> drought risks for a water supply storage <span class="hlt">system</span> using bootstrap position analysis</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tasker, Gary; Dunne, Paul</p> <p>1997-01-01</p> <p><span class="hlt">Forecasting</span> the likelihood of drought conditions is an integral part of managing a water supply storage and delivery <span class="hlt">system</span>. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery <span class="hlt">system</span>. For a given set of operating rules and water use requirements, water managers can use such a model to <span class="hlt">forecast</span> the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather <span class="hlt">forecasts</span> into the analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19880068095&hterms=LED+technology&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DLED%2Btechnology','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19880068095&hterms=LED+technology&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DLED%2Btechnology"><span id="translatedtitle">Technology <span class="hlt">forecast</span> and applications for autonomous, intelligent <span class="hlt">systems</span>. [for space station, shuttle, and interplanetary missions</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lum, Henry, Jr.; Heer, Ewald</p> <p>1988-01-01</p> <p>Significant research products which have emerged from the core program of NASA's Office of Aeronautics and Space Technology (OAST) are discussed. The Space Station Thermal Control <span class="hlt">System</span>, the Space Shuttle Integrated Communications Officer Station, the Launch Processing <span class="hlt">System</span>, the Expert Scheduling <span class="hlt">System</span> for Pioneer Venus Spacecraft, a Bayesian classification <span class="hlt">system</span>, and a spaceborne multiprocessor <span class="hlt">system</span> are included. The technology trends which led to these results are discussed and future developments in technology are <span class="hlt">forecasted</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H51E0873S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H51E0873S"><span id="translatedtitle">Incorporating Multi-model Ensemble Techniques Into a Probabilistic Hydrologic <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sonessa, M. Y.; Bohn, T. J.; Lettenmaier, D. P.</p> <p>2008-12-01</p> <p>Multi-model ensemble techniques have been shown to reduce bias and to aid in quantification of the effects of model uncertainty in hydrologic modeling. However, these techniques are only beginning to be applied in operational hydrologic <span class="hlt">forecast</span> <span class="hlt">systems</span>. To investigate the performance of a multi-model ensemble in the context of probabilistic hydrologic <span class="hlt">forecasting</span>, we have extended the University of Washington's West-wide Seasonal Hydrologic <span class="hlt">Forecasting</span> <span class="hlt">System</span> to use an ensemble of three models: the Variable Infiltration Capacity (VIC) model version 4.0.6, the NCEP NOAH model version 2.7.1, and the NWS grid-based Sacramento/Snow-17 model (SAC). The objective of this presentation is to assess the performance of the ensemble of the three models as compared to the performance of the models individually. Three <span class="hlt">forecast</span> points within the West-wide <span class="hlt">forecast</span> <span class="hlt">system</span> domain were used for this research: the Feather River at Oroville, CA, the Salmon River at White horse, ID, and the Colorado River at Grand Junction. The forcing and observed streamflow data are for years 1951-2005 for the Feather and Salmon Rivers; and 1951-2003 for the Colorado. The models were first run for the retrospective period, then bias-corrected, and model weights were then determined using multiple linear regression. We assessed the performance of the ensemble in comparison with the individual models in terms of correlation with observed flows and Root Mean Square Error, and Nash-Sutcliffe. We found that for evaluations of retrospective simulations in comparison with observations, the ensemble performed better overall than any of the models individually even though in few individual months individual models performed slightly better than the ensemble. To test <span class="hlt">forecast</span> skill, we performed Ensemble Streamflow Prediction (ESP) <span class="hlt">forecasts</span> for each year of the retrospective period, using forcings from all other years, for individual models and for the multi-model ensemble. To form the ensemble for the ESP</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.4804B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.4804B&link_type=ABSTRACT"><span id="translatedtitle">Post-processing of a low-flow <span class="hlt">forecasting</span> <span class="hlt">system</span> in the Thur basin (Switzerland)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogner, Konrad; Joerg-Hess, Stefanie; Bernhard, Luzi; Zappa, Massimiliano</p> <p>2015-04-01</p> <p>Low-flows and droughts are natural hazards with potentially severe impacts and economic loss or damage in a number of environmental and socio-economic sectors. As droughts develop slowly there is time to prepare and pre-empt some of these impacts. Real-time information and <span class="hlt">forecasting</span> of a drought situation can therefore be an effective component of drought management. Although Switzerland has traditionally been more concerned with problems related to floods, in recent years some unprecedented low-flow situations have been experienced. Driven by the climate change debate a drought information platform has been developed to guide water resources management during situations where water resources drop below critical low-flow levels characterised by the indices duration (time between onset and offset), severity (cumulative water deficit) and magnitude (severity/duration). However to gain maximum benefit from such an information <span class="hlt">system</span> it is essential to remove the bias from the meteorological <span class="hlt">forecast</span>, to derive optimal estimates of the initial conditions, and to post-process the stream-flow <span class="hlt">forecasts</span>. Quantile mapping methods for pre-processing the meteorological <span class="hlt">forecasts</span> and improved data assimilation methods of snow measurements, which accounts for much of the seasonal stream-flow predictability for the majority of the basins in Switzerland, have been tested previously. The objective of this study is the testing of post-processing methods in order to remove bias and dispersion errors and to derive the predictive uncertainty of a calibrated low-flow <span class="hlt">forecast</span> <span class="hlt">system</span>. Therefore various stream-flow error correction methods with different degrees of complexity have been applied and combined with the Hydrological Uncertainty Processor (HUP) in order to minimise the differences between the observations and model predictions and to derive posterior probabilities. The complexity of the analysed error correction methods ranges from simple AR(1) models to methods including</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3801470','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3801470"><span id="translatedtitle">Evaluation of a Wildfire Smoke <span class="hlt">Forecasting</span> <span class="hlt">System</span> as a Tool for Public Health Protection</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Brauer, Michael; Henderson, Sarah B.</p> <p>2013-01-01</p> <p>Background: Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. <span class="hlt">Forecasts</span> of smoke exposure can facilitate public health responses. Objectives: We evaluated the utility of a wildfire smoke <span class="hlt">forecasting</span> <span class="hlt">system</span> (BlueSky) for public health protection by comparing its <span class="hlt">forecasts</span> with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. Methods: We compared BlueSky PM2.5 <span class="hlt">forecasts</span> with PM2.5 measurements from air quality monitors, and BlueSky smoke plume <span class="hlt">forecasts</span> with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping <span class="hlt">System</span> remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either <span class="hlt">forecasted</span> or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. Results: We found modest agreement between <span class="hlt">forecasts</span> and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. Conclusions: BlueSky <span class="hlt">forecasts</span> showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection. Citation: Yao J, Brauer M, Henderson SB. 2013. Evaluation of a wildfire smoke</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMOS53G..08D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMOS53G..08D"><span id="translatedtitle">Ocean Properties in 2007 Described by the Mercator-Ocean Global Ocean Analysis and <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Drevillon, M.; Lellouche, J.; Greiner, E.; Verbrugge, N.; Remy, E.; Crosnier, L.</p> <p>2008-12-01</p> <p>Since the beginning of GODAE and also in the framework of the European projects MERSEA and now GMES/MyOcean, Mercator-Ocean has been designing a hierarchy of operational oceanography analysis and <span class="hlt">forecasting</span> <span class="hlt">systems</span>. These <span class="hlt">systems</span> are based on numerical models of the ocean and data assimilation <span class="hlt">systems</span> which interpolate in an optimal way all available observations of the ocean. The real time operation of these <span class="hlt">systems</span> began in 2001, in order to produce each week realistic 3-dimensional oceanic conditions (temperature, salinity, currents) two weeks back in time and a two weeks <span class="hlt">forecast</span>, driven at the surface by atmospheric conditions from the European Center for Medium Range Weather <span class="hlt">Forecast</span> (ECMWF). Since April 2008, the state-of-the-art Mercator Ocean <span class="hlt">forecasting</span> <span class="hlt">system</span> demonstrates that the use of the ocean and sea ice model NEMO and of the data assimilation <span class="hlt">system</span> SAM2 (Système d'Assimilation Mercator V2) can produce high quality real time analyses and <span class="hlt">forecast</span> of the ocean at the global scale, and up to the "eddy resolving" horizontal resolution. This <span class="hlt">system</span> currently comprises a global ocean configuration at 1/4° horizontal resolution and a North Atlantic and Mediterranean zoom at 1/12°, both having 50 levels on the vertical with a surface refinement. Both have been run and comprehensively validated over the year 2007. The realism of the description of the ocean physics, sea ice, water masses, and volume transports is assessed. Although some biases develop in regions where complex interactions take place between the different limitations of the <span class="hlt">system</span> (mostly the Antarctic and the tropics), the results show a "qualitative jump" of the physical and statistical skills of the <span class="hlt">system</span>. They reinforce the scientific feasibility of the future upgrade of the <span class="hlt">system</span> into a global high resolution configuration at 1/12°.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/5628844','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/5628844"><span id="translatedtitle">Model documentation report: Short-term Integrated <span class="hlt">Forecasting</span> <span class="hlt">System</span> demand model 1985. [(STIFS)</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Not Available</p> <p>1985-07-01</p> <p>The Short-Term Integrated <span class="hlt">Forecasting</span> <span class="hlt">System</span> (STIFS) Demand Model consists of a set of energy demand and price models that are used to <span class="hlt">forecast</span> monthly demand and prices of various energy products up to eight quarters in the future. The STIFS demand model is based on monthly data (unless otherwise noted), but the <span class="hlt">forecast</span> is published on a quarterly basis. All of the <span class="hlt">forecasts</span> are presented at the national level, and no regional detail is available. The model discussed in this report is the April 1985 version of the STIFS demand model. The relationships described by this model include: the specification of retail energy prices as a function of input prices, seasonal factors, and other significant variables; and the specification of energy demand by product as a function of price, a measure of economic activity, and other appropriate variables. The STIFS demand model is actually a collection of 18 individual models representing the demand for each type of fuel. The individual fuel models are listed below: motor gasoline; nonutility distillate fuel oil, (a) diesel, (b) nondiesel; nonutility residual fuel oil; jet fuel, kerosene-type and naphtha-type; liquefied petroleum gases; petrochemical feedstocks and ethane; kerosene; road oil and asphalt; still gas; petroleum coke; miscellaneous products; coking coal; electric utility coal; retail and general industry coal; electricity generation; nonutility natural gas; and utility petroleum. The demand estimates produced by these models are used in the STIFS integrating model to produce a full energy balance of energy supply, demand, and stock change. These <span class="hlt">forecasts</span> are published quarterly in the Outlook. Details of the major changes in the <span class="hlt">forecasting</span> methodology and an evaluation of previous <span class="hlt">forecast</span> errors are presented once a year in Volume 2 of the Outlook, the Methodology publication.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H33D1645R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H33D1645R"><span id="translatedtitle">Multivariate Climate-Weather <span class="hlt">Forecasting</span> <span class="hlt">System</span>: An Integrated Approach for Mitigating Agricultural Risks in Punjab</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ravindranath, A.; Devineni, N.</p> <p>2015-12-01</p> <p>While India has a long history of prediction of the All India Monsoon, work on spatially specific attributes of the monsoon, as well as monsoon break periods has only recently emerged. However, from a risk management context, prognostic information of a single variable such as total precipitation or average temperature will be of less utility especially for specific operational purposes. An integrated regional climate-weather <span class="hlt">forecast</span> <span class="hlt">system</span> covering precipitation, temperature and humidity etc. over the year will benefit the farmers in the context of a specific decision time table for irrigation scheduling as well as for pre-season crop choices. Hence, contrary to the existing <span class="hlt">forecasting</span> methods that develop multi time scale information of a single variable at a time, in this paper, we introduce an integrated regional multivariate climate-weather <span class="hlt">forecasting</span> <span class="hlt">system</span> that directly relates to agricultural decision making and risk mitigation. These multi-scale risk attributes include mutually dependent, spatially disaggregated statistics such as total rainfall, average temperature, growing degree days, relative humidity, total number of rainfall days/dry spell length, and cumulative water deficits that inform the potential irrigation water requirements for crops. Given that these attributes exhibit mutual dependence across space and time, we propose to explore common ocean-atmospheric conditions from the observations and the state of the art Global Circulation Models (GCMs) that can be utilized as the predictor variables for the <span class="hlt">forecasting</span> <span class="hlt">system</span>. Hierarchical Bayesian methods are be used to develop the integrated <span class="hlt">forecast</span> <span class="hlt">system</span>. The developed multivariate <span class="hlt">forecasts</span> will be adapted and disseminated as decision tools for the farmers under the extension projects in Punjab region of India.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008SPIE.7145E..0AW','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008SPIE.7145E..0AW"><span id="translatedtitle">Web-based hydrological modeling <span class="hlt">system</span> for flood <span class="hlt">forecasting</span> and risk mapping</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Lei; Cheng, Qiuming</p> <p>2008-10-01</p> <p>Mechanism of flood <span class="hlt">forecasting</span> is a complex <span class="hlt">system</span>, which involves precipitation, drainage characterizes, land use/cover types, ground water and runoff discharge. The application of flood <span class="hlt">forecasting</span> model require the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, pre-processing and manipulation, analysis and display of model results. The extensive datasets usually involve multiple organizations, but no single organization can collect and maintain all the multidisciplinary data. The possible usage of the available datasets remains limited primarily because of the difficulty associated with combining data from diverse and distributed data sources. Difficulty in linking data, analysis tools and model is one of the barriers to be overcome in developing real-time flood <span class="hlt">forecasting</span> and risk prediction <span class="hlt">system</span>. The current revolution in technology and online availability of spatial data, particularly, with the construction of Canadian Geospatial Data Infrastructure (CGDI), a lot of spatial data and information can be accessed in real-time from distributed sources over the Internet to facilitate Canadians' need for information sharing in support of decision-making. This has resulted in research studies demonstrating the suitability of the web as a medium for implementation of flood <span class="hlt">forecasting</span> and flood risk prediction. Web-based hydrological modeling <span class="hlt">system</span> can provide the framework within which spatially distributed real-time data accessed remotely to prepare model input files, model calculation and evaluate model results for flood <span class="hlt">forecasting</span> and flood risk prediction. This paper will develop a prototype web-base hydrological modeling <span class="hlt">system</span> for on-line flood <span class="hlt">forecasting</span> and risk mapping in the Oak Ridges Moraine (ORM) area, southern Ontario, Canada, integrating information retrieval, analysis and model analysis for near real time river runoff prediction, flood frequency prediction, flood risk and flood inundation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H51D1371D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H51D1371D"><span id="translatedtitle">Multivariate Climate-Weather <span class="hlt">Forecasting</span> <span class="hlt">System</span>: An Integrated Approach for Mitigating Agricultural Risks in India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Devineni, N.; Lall, U.; Perveen, S.</p> <p>2012-12-01</p> <p>While India has a long history of prediction of the All India Monsoon, work on spatially specific attributes of the monsoon, as well as monsoon break periods has only recently emerged. However, from a risk management context, prognostic information of a single variable such as total precipitation or average temperature will be of less utility especially for specific operational purposes. An integrated regional climate-weather <span class="hlt">forecast</span> <span class="hlt">system</span> covering precipitation, temperature and humidity etc. over the year will benefit the farmers in the context of a specific decision time table for irrigation scheduling as well as for pre-season crop choices. Hence, contrary to the existing <span class="hlt">forecasting</span> methods that develop multi time scale information of a single variable at a time, in this paper, we introduce an integrated regional multivariate climate-weather <span class="hlt">forecasting</span> <span class="hlt">system</span> that directly relates to agricultural decision making and risk mitigation. These multi-scale risk attributes include mutually dependent, spatially disaggregated statistics such as total rainfall, average temperature, growing degree days, relative humidity, total number of rainfall days/dry spell length, and cumulative water deficits that inform the potential irrigation water requirements for crops etc. Given that these attributes exhibit mutual dependence across space and time, we propose to explore common ocean-atmospheric conditions from the observations and the state of the art Global Circulation Models (GCMs) that can be utilized as the predictor variables for the <span class="hlt">forecasting</span> <span class="hlt">system</span>. Non parametric bootstrap resampling methods and Hierarchical Bayesian methods that can easily handle the high dimensionality of such problems will be used to develop the integrated <span class="hlt">forecast</span> <span class="hlt">system</span>. The developed multivariate <span class="hlt">forecasts</span> will be adapted and disseminated as decision tools for the farmers under the Columbia Water Center's pilot project in Punjab region of India.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AIPC.1218..921K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AIPC.1218..921K"><span id="translatedtitle">Nanogel Aerogel as <span class="hlt">Load</span> Bearing Insulation for Cryogenic <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Koravos, J. J.; Miller, T. M.; Fesmire, J. E.; Coffman, B. E.</p> <p>2010-04-01</p> <p><span class="hlt">Load</span> support structures in cryogenic storage, transport and processing <span class="hlt">systems</span> are large contributors to the total heat leak of the <span class="hlt">system</span>. Conventional insulation <span class="hlt">systems</span> require the use of these support members in order to stabilize the process fluid enclosure and prevent degradation of insulation performance due to compression. Removal of these support structures would substantially improve <span class="hlt">system</span> efficiency. Nanogel aerogel insulation performance is tested at vacuum pressures ranging from high vacuum to atmospheric pressure and under <span class="hlt">loads</span> from loosely packed to greater than 10,000 Pa. Insulation performance is determined using boil-off calorimetry with liquid nitrogen as the latent heat recipient. Two properties of the aerogel insulation material suit it to act as a <span class="hlt">load</span> bearing "structure" in a process vessel: (1) Ability to maintain thermal performance under <span class="hlt">load</span>; (2) Elasticity when subjected to <span class="hlt">load</span>. Results of testing provide positive preliminary indication that these properties allow Nanogel aerogel to effectively be used as a <span class="hlt">load</span> bearing insulation in cryogenic <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.9060P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.9060P"><span id="translatedtitle">Evaluation of two Operational Weather <span class="hlt">Forecasting</span> <span class="hlt">Systems</span> for the Mediterranean Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Papadopoulos, A.; Katsafados, P.</p> <p>2012-04-01</p> <p>This paper presents an intercomparison and evaluation of two weather <span class="hlt">forecasting</span> <span class="hlt">systems</span> for the Mediterranean Sea and the surrounding countries. The POSEIDON weather <span class="hlt">forecasting</span> <span class="hlt">system</span> has been developed in the framework of the project "Monitoring, <span class="hlt">forecasting</span> and information <span class="hlt">system</span> for the Greek Seas" at the Hellenic Centre for Marine Research (HCMR) in 1999. In the current HCMR's operational procedures the <span class="hlt">system</span> issues high-resolution (~5 km) weather <span class="hlt">forecasts</span> for 5 days ahead. It is based on an advanced version of the non-hydrostatic atmospheric Eta/NCEP model and is forced by the GFS model. To achieve better initialization a meteorological data assimilation package, the LAPS, has been implemented which employs all available real-time observations. Likewise, the Weather Research and <span class="hlt">Forecasting</span> (WRF) limited area model with the embedded Non-hydrostatic Mesoscale Model (NMM) dynamical core became operational at the Department of Geography at Harokopio University of Athens in 2008. It provides daily 120-hour weather <span class="hlt">forecasts</span> in a single domain covering the entire Mediterranean basin and the Black Sea at a resolution of 0.09° x0.09°. The performance of the two operational <span class="hlt">systems</span> has been assessed across the Mediterranean region and the surrounding countries using as reference the surface measurements available from the World Meteorological Organization (WMO) network unevenly distributed over the domain of integration. Surface observations from more than 900 conventional stations were used to verify and compare categorical <span class="hlt">forecasts</span> of the 10-m wind field, 2-m air temperature and sea level pressure every 3 hours and the accumulated 6-h precipitation. The verification of the operational <span class="hlt">systems</span> is based on the point-to-point comparison between the model generated variables and the relevant surface observations. Therefore, a verification procedure has been developed based on the estimation of traditional objective verification techniques such as bias, RMSE and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010ems..confE.329J&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010ems..confE.329J&link_type=ABSTRACT"><span id="translatedtitle">Error discrimination of an operational hydrological <span class="hlt">forecasting</span> <span class="hlt">system</span> at a national scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jordan, F.; Brauchli, T.</p> <p>2010-09-01</p> <p>The use of operational hydrological <span class="hlt">forecasting</span> <span class="hlt">systems</span> is recommended for hydropower production as well as flood management. However, the <span class="hlt">forecast</span> uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the <span class="hlt">forecasting</span> <span class="hlt">systems</span>, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather <span class="hlt">forecast</span> must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational <span class="hlt">forecasting</span> <span class="hlt">system</span> exists. The <span class="hlt">forecast</span> is based on the RS 3.0 <span class="hlt">system</span> [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both <span class="hlt">forecasting</span> components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing <span class="hlt">System</span> II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016OcMod.104..171P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016OcMod.104..171P&link_type=ABSTRACT"><span id="translatedtitle">Performance comparison of meso-scale ensemble wave <span class="hlt">forecasting</span> <span class="hlt">systems</span> for Mediterranean sea states</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pezzutto, Paolo; Saulter, Andrew; Cavaleri, Luigi; Bunney, Christopher; Marcucci, Francesca; Torrisi, Lucio; Sebastianelli, Stefano</p> <p>2016-08-01</p> <p>This paper compares the performance of two wind and wave short range ensemble <span class="hlt">forecast</span> <span class="hlt">systems</span> for the Mediterranean Sea. In particular, it describes a six month verification experiment carried out by the U.K. Met Office and Italian Air Force Meteorological Service, based on their respective <span class="hlt">systems</span>: the Met Office Global-Regional Ensemble Prediction <span class="hlt">System</span> and the Nettuno Ensemble Prediction <span class="hlt">System</span>. The latter is the ensemble version of the operational Nettuno <span class="hlt">forecast</span> <span class="hlt">system</span>. Attention is focused on the differences between the two implementations (e.g. grid resolution and initial ensemble members sampling) and their effects on the prediction skill. The cross-verification of the two ensemble <span class="hlt">systems</span> shows that from a macroscopic point of view the differences cancel out, suggesting similar skill. More in-depth analysis indicates that the Nettuno wave <span class="hlt">forecast</span> is better resolved but, on average, slightly less reliable than the Met Office product. Assessment of the added value of the ensemble techniques at short range in comparison with the deterministic <span class="hlt">forecast</span> from Nettuno, reveals that adopting the ensemble approach has small, but substantive, advantages.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMIN43B1187B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMIN43B1187B"><span id="translatedtitle">HydroMet: Real-time <span class="hlt">Forecasting</span> <span class="hlt">System</span> for Hydrologic Hazards</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Band, L. E.; Shin, D.; Hwang, T.; Goodall, J.; Reed, M.; Rynge, M.; Stillwell, L.; Galluppi, K.</p> <p>2007-12-01</p> <p>Recent devastating floods and severe droughts in North Carolina called attention to the need of a reliable nowcasting and <span class="hlt">forecasting</span> <span class="hlt">system</span> for these hydrologic hazards. In response to the demand, HydroMet project was launched by RENCI (Renaissance Computing Institute). On a supercomputer in the institute, we integrated (1) WRF (Weather Research and <span class="hlt">Forecasting</span>) for the mesoscale numerical weather prediction, (2) RHESSys (Regional Hydro-Ecologic Simulation <span class="hlt">System</span>) for the distributed modeling of runoff generation and soil moisture, and (3) LDAS (Land Data Assimilation <span class="hlt">Systems</span>) for upgrading the prediction accuracy of soil moisture and energy. By exploiting the powerful parallel computing architecture, the <span class="hlt">forecasting</span> <span class="hlt">system</span> was designed to assimilate and produce massive spatio-temporal data in real-time while recalibrating itself automatically. We applied the <span class="hlt">system</span> for western and central North Carolina as test sites, and <span class="hlt">forecasted</span> the propagation of flood waves, and the long-term trends of low channel flow and soil moisture at a fine spatial resolution. As we extend the application of the <span class="hlt">system</span> over the entire North Carolina, it is expected to provide timely and accurate information about floods and droughts in the area, which is prerequisite for more effective prevention and recovery from the hazards.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol1/pdf/CFR-2011-title14-vol1-sec23-395.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol1/pdf/CFR-2011-title14-vol1-sec23-395.pdf"><span id="translatedtitle">14 CFR 23.395 - Control <span class="hlt">system</span> <span class="hlt">loads</span>.</span></a></p> <p><a target="_blank" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Structure Control Surface and <span class="hlt">System</span> <span class="hlt">Loads</span> § 23.395 Control <span class="hlt">system</span> <span class="hlt">loads</span>. (a) Each flight control <span class="hlt">system</span> and its supporting structure must be... depending upon the accuracy and reliability of the data. (c) Pilot forces used for design are assumed to...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/325396','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/325396"><span id="translatedtitle">Design review report for the MCO <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Brisbin, S.A.</p> <p>1997-06-23</p> <p>This design report presents the design of the MCO <span class="hlt">Loading</span> <span class="hlt">System</span>. The report includes final design drawings, a <span class="hlt">system</span> description, failure modes and recovery plans, a <span class="hlt">system</span> operational description, and stress analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712437C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712437C"><span id="translatedtitle">Tests of oceanic stochastic parameterisation in a seasonal <span class="hlt">forecast</span> <span class="hlt">system</span>.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cooper, Fenwick; Andrejczuk, Miroslaw; Juricke, Stephan; Zanna, Laure; Palmer, Tim</p> <p>2015-04-01</p> <p>Over seasonal time scales, our aim is to compare the relative impact of ocean initial condition and model uncertainty, upon the ocean <span class="hlt">forecast</span> skill and reliability. Over seasonal timescales we compare four oceanic stochastic parameterisation schemes applied in a 1x1 degree ocean model (NEMO) with a fully coupled T159 atmosphere (ECMWF IFS). The relative impacts upon the ocean of the resulting eddy induced activity, wind forcing and typical initial condition perturbations are quantified. Following the historical success of stochastic parameterisation in the atmosphere, two of the parameterisations tested were multiplicitave in nature: A stochastic variation of the Gent-McWilliams scheme and a stochastic diffusion scheme. We also consider a surface flux parameterisation (similar to that introduced by Williams, 2012), and stochastic perturbation of the equation of state (similar to that introduced by Brankart, 2013). The amplitude of the stochastic term in the Williams (2012) scheme was set to the physically reasonable amplitude considered in that paper. The amplitude of the stochastic term in each of the other schemes was increased to the limits of model stability. As expected, variability was increased. Up to 1 month after initialisation, ensemble spread induced by stochastic parameterisation is greater than that induced by the atmosphere, whilst being smaller than the initial condition perturbations currently used at ECMWF. After 1 month, the wind forcing becomes the dominant source of model ocean variability, even at depth.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC41D0858A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC41D0858A"><span id="translatedtitle">Using the LAPS / WRF <span class="hlt">system</span> to Analyze and <span class="hlt">Forecast</span> Solar Radiation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Albers, S. C.; Jankov, I.</p> <p>2011-12-01</p> <p>The LAPS <span class="hlt">system</span> is being used to produce rapid update, high resolution analyses and <span class="hlt">forecasts</span> of solar radiation. The cloud analysis uses satellite, METARs, radar, aircraft and model first guess information to produce an hourly 3-D field of cloud fraction, cloud liquid, and cloud ice. The cloud analysis and satellite data together are used to produce a gridded analysis of total solar radiation. This is verified against solar radiation measurements that are independent (not used in the analysis). Two domains are being run and verified at present. The one with the most stations covers the Oklahoma mesonet with about 100 pyranometers. The total solar radiation <span class="hlt">forecast</span> is being run on two domains, and is being initialized using the same cloud analysis package that drives the analysis fields mentioned above. The Colorado domain produces hourly <span class="hlt">forecasts</span>, initialized every 6 hours. It is verified with about 20 Oklahoma mesonet stations. The HWT domain initializes WRF every 2 hours, with 15-minute output. <span class="hlt">Forecasts</span> are being compared with the Oklahoma mesonet. Real-time verification of the analyses (including images of the analysis), and <span class="hlt">forecasts</span> can be seen on our website 'laps.noaa.gov', and will be explored in this presentation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19870003647&hterms=complimentary+products&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dcomplimentary%2Bproducts','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19870003647&hterms=complimentary+products&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dcomplimentary%2Bproducts"><span id="translatedtitle">The assimilation of satellite soundings, winds and satellite products in a mesoscale analysis/<span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Diak, G. R.; Smith, W. L.</p> <p>1985-01-01</p> <p>Investigations in FY-85 were centered on three case study days in 1982. Two of these, March 6 and April 24, were Atmospheric Variability Experiment/Verical Atmospheric Sounder (AVE/VAS) days for which high spatial and temporal resolution RAOB and Vertical Atmospheric Sounder (VAS) data sets were available. The third investigation day, April 26, was a day of interesting severe weather. In the last part of FY-84 and early FY-85 we were able to demonstrate most importantly the complimentary nature of satellite soundings and winds in a <span class="hlt">forecast</span>/analysis <span class="hlt">system</span>. In our variational analysis scheme, cloud drift and water vapor winds enter into the height field as gradient information. The cloud drift winds especially, have the character of supplying information in cloudy areas where satellite soundings are not possible. In the April 26 experiments, analyses and <span class="hlt">forecasts</span> using the combination satellite winds and soundings were superior to those using only soundings. Good consistency was shown between independent satellite <span class="hlt">forecasts</span> from different initialization times run to the same verification time. A significant accomplishment in FY-85 was expanding experiments on April 26 to include quasi-continuous initialization inserting satellite soundings and winds from several different times into an analysis/<span class="hlt">forecast</span>. Contrary to the first set of experiments on April 26, here <span class="hlt">forecast</span> initialization fields were not independent, but contained satellite information from two data times.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24803190','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24803190"><span id="translatedtitle">A space weather <span class="hlt">forecasting</span> <span class="hlt">system</span> with multiple satellites based on a self-recognizing network.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tokumitsu, Masahiro; Ishida, Yoshiteru</p> <p>2014-01-01</p> <p>This paper proposes a space weather <span class="hlt">forecasting</span> <span class="hlt">system</span> at geostationary orbit for high-energy electron flux (>2 MeV). The <span class="hlt">forecasting</span> model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed <span class="hlt">forecasting</span> model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate <span class="hlt">forecasting</span> for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather <span class="hlt">forecasting</span> based on the satellite network with in-situ sensing. PMID:24803190</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4775211','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4775211"><span id="translatedtitle">Evaluating probabilistic dengue risk <span class="hlt">forecasts</span> from a prototype early warning <span class="hlt">system</span> for Brazil</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier</p> <p>2016-01-01</p> <p>Recently, a prototype dengue early warning <span class="hlt">system</span> was developed to produce probabilistic <span class="hlt">forecasts</span> of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue <span class="hlt">forecasts</span> across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the <span class="hlt">forecast</span> model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the <span class="hlt">forecast</span> model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the <span class="hlt">forecast</span> model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/919208','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/919208"><span id="translatedtitle">Final Report on California Regional Wind Energy <span class="hlt">Forecasting</span> Project:Application of NARAC Wind Prediction <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Chin, H S</p> <p>2005-07-26</p> <p>Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather <span class="hlt">forecasting</span> has been recognized as an important factor to further improve the wind energy <span class="hlt">forecast</span> for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather <span class="hlt">forecasting</span> capability to develop an improved regional wind energy <span class="hlt">forecasting</span> <span class="hlt">system</span>. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780010554','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780010554"><span id="translatedtitle">Analysis of data <span class="hlt">systems</span> requirements for global crop production <span class="hlt">forecasting</span> in the 1985 time frame</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Downs, S. W.; Larsen, P. A.; Gerstner, D. A.</p> <p>1978-01-01</p> <p>Data <span class="hlt">systems</span> concepts that would be needed to implement the objective of the global crop production <span class="hlt">forecasting</span> in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data <span class="hlt">system</span> requirements, and the influence of these requirements on the formulation of a conceptual data <span class="hlt">system</span> was analyzed. Any potential problem areas in meeting these data <span class="hlt">system</span> requirements were identified in an iterative process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GMD.....8.3523E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GMD.....8.3523E"><span id="translatedtitle">Validation of reactive gases and aerosols in the MACC global analysis and <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.</p> <p>2015-11-01</p> <p>The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated <span class="hlt">Forecasting</span> <span class="hlt">System</span> of the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation <span class="hlt">systems</span> validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC <span class="hlt">system</span> concerns <span class="hlt">forecasting</span> special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and <span class="hlt">forecast</span> products from the MACC <span class="hlt">system</span> reported in November 2014.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020021986&hterms=flight+data+recorder+impact&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dflight%2Bdata%2Brecorder%2Bimpact','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020021986&hterms=flight+data+recorder+impact&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dflight%2Bdata%2Brecorder%2Bimpact"><span id="translatedtitle">The Impact of British Airways Wind Observations on the Goddard Earth Observing <span class="hlt">System</span> Analyses and <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rukhovets, Leonid; Sienkiewicz, M.; Tenenbaum, J.; Kondratyeva, Y.; Owens, T.; Oztunali, M.; Atlas, Robert (Technical Monitor)</p> <p>2001-01-01</p> <p>British Airways flight data recorders can provide valuable meteorological information, but they are not available in real-time on the Global Telecommunication <span class="hlt">System</span>. Information from the flight recorders was used in the Global Aircraft Data Set (GADS) experiment as independent observations to estimate errors in wind analyses produced by major operational centers. The GADS impact on the Goddard Earth Observing <span class="hlt">System</span> Data Assimilation <span class="hlt">System</span> (GEOS DAS) analyses was investigated using GEOS-1 DAS version. Recently, a new Data Assimilation <span class="hlt">System</span> (fvDAS) has been developed at the Data Assimilation Office, NASA Goddard. Using fvDAS , the, GADS impact on analyses and <span class="hlt">forecasts</span> was investigated. It was shown the GADS data intensify wind speed analyses of jet streams for some cases. Five-day <span class="hlt">forecast</span> anomaly correlations and root mean squares were calculated for 300, 500 hPa and SLP for six different areas: Northern and Southern Hemispheres, North America, Europe, Asia, USA These scores were obtained as averages over 21 <span class="hlt">forecasts</span> from January 1998. Comparisons with scores for control experiments without GADS showed a positive impact of the GADS data on <span class="hlt">forecasts</span> beyond 2-3 days for all levels at the most areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN43B3686H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN43B3686H"><span id="translatedtitle">UQ -- Fast Surrogates Key to New Methodologies in an Operational and Research Volcanic Hazard <span class="hlt">Forecasting</span> <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hughes, C. G.; Stefanescu, R. E. R.; Patra, A. K.; Bursik, M. I.; Madankan, R.; Pouget, S.; Jones, M.; Singla, P.; Singh, T.; Pitman, E. B.; Morton, D.; Webley, P.</p> <p>2014-12-01</p> <p>As the decision to construct a hazard map is frequently precipitated by the sudden initiation of activity at a volcano that was previously considered dormant, timely completion of the map is imperative. This prohibits the calculation of probabilities through direct sampling of a numerical ash-transport and dispersion model. In developing a probabilistic <span class="hlt">forecast</span> for ash cloud locations following an explosive volcanic eruption, we construct a number of possible meta-models (a model of the simulator) to act as fast surrogates for the time-expensive model. We will illustrate the new fast surrogates based on both polynomial chaos and multilevel sparse representations that have allowed us to conduct the Uncertainty Quantification (UQ) in a timely fashion. These surrogates allow orders of magnitude improvement in cost associated with UQ, and are likely to have a major impact in many related domains.This work will be part of an operational and research volcanic <span class="hlt">forecasting</span> <span class="hlt">system</span> (see the Webley et al companion presentation) moving towards using ensembles of eruption source parameters and Numerical Weather Predictions (NWPs), rather than single deterministic <span class="hlt">forecasts</span>, to drive the ash cloud <span class="hlt">forecasting</span> <span class="hlt">systems</span>. This involves using an Ensemble Prediction <span class="hlt">System</span> (EPS) as input to an ash transport and dispersion model, such as PUFF, to produce ash cloud predictions, which will be supported by a Decision Support <span class="hlt">System</span>. Simulation ensembles with different input volcanic source parameters are intelligently chosen to predict the average and higher-order moments of the output correctly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JIEIB..95..369M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JIEIB..95..369M"><span id="translatedtitle">SCADA-based Operator Support <span class="hlt">System</span> for Power Plant Equipment Fault <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.</p> <p>2014-12-01</p> <p>Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid <span class="hlt">forecasting</span> techniques can be used to prevent plant equipment faults. A self learning rule-based expert <span class="hlt">system</span> is proposed in this paper for fault <span class="hlt">forecasting</span> in power plants controlled by supervisory control and data acquisition (SCADA) <span class="hlt">system</span>. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert <span class="hlt">system</span>. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series <span class="hlt">forecasting</span> model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed <span class="hlt">system</span>. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The <span class="hlt">forecasting</span> performance is evaluated by computing the mean absolute error and root mean square error of the predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33M..06T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33M..06T"><span id="translatedtitle">Development of an Operational Hydrological Monitoring and Seasonal <span class="hlt">Forecast</span> <span class="hlt">System</span> for China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, Q.; Zhang, X.</p> <p>2014-12-01</p> <p>Hydrological monitoring and <span class="hlt">forecast</span> are critical for disaster mitigation and water resources management. Although large investments have been made in climate <span class="hlt">forecasting</span> and in related monitoring of land surface conditions, the experimental streamflow monitoring and <span class="hlt">forecast</span> <span class="hlt">system</span> is yet to be developed for China. We propose a frame to collect near-real-time meteorological forcings from various sources, to apply land surface hydrological model to simulate hydrological states and fluxes, and to generate ensemble seasonal <span class="hlt">forecasts</span> of river discharge and soil moisture over China. A retrospective land surface hydrologic fluxes and states dataset with a 0.25° spatial resolution and a 3-hourly time step was developed using the Variable Infiltration Capacity (VIC) model as driven by gridded observation-based meteorological forcings in 1952-2012. The VIC simulations were carefully calibrated against the available streamflow observations and the simulated river discharge matched well with the observed monthly streamflow at the large river basins in China. The Tropical Rainfall Measuring Mission (TRMM) based near-real-time satellite precipitation product was adjusted at each grid to match the daily precipitation distribution with the ground observations during the period of 2000-2010. The adjusted satellite precipitation was used to simulate hydrological states and fluxes in a near-real-time manner and to provide initial hydrological conditions for seasonal <span class="hlt">forecast</span>. The performance of hydrological monitoring and skill of seasonal streamflow prediction were assessed. The potential and challenges of using the operational monitoring and <span class="hlt">forecast</span> <span class="hlt">system</span> for improved flooding and drought management are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5218J&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5218J&link_type=ABSTRACT"><span id="translatedtitle">Verification of WRC-KMA nowcasting <span class="hlt">systems</span> during summer: precipitation <span class="hlt">forecasting</span> skill</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jeong, Jong-Hoon; Nam, Kyung-Yeub; Ko, Jeong-Seok; Lee, Dong-In</p> <p>2016-04-01</p> <p>Radar based nowcasting <span class="hlt">systems</span> widely perform for short-term precipitation <span class="hlt">forecasting</span> for 1-6 hours by using extrapolation. In this time period, it is possible to <span class="hlt">forecast</span> of high-impact weather events such as flood-producing precipitation, hail, and snow with reasonable accuracy. For this purpose, Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has performed three different nowcasting <span class="hlt">systems</span>. These <span class="hlt">systems</span> are McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE; Germann and Zawadzki, 2002), Very Short Range <span class="hlt">Forecast</span> of precipitation (VSRF; JMA) merging with numerical weather prediction (NWP), and KOrea NOwcasting <span class="hlt">System</span> (KONOS; Jin et al. 2011) which was based on the MAPLE advection scheme. The primary focus of this study is the evaluation of the skill in predicting heavy rainfall events during two year warm season precipitation. The WRC-KMA nowcasting <span class="hlt">systems</span> verified using a variety of statistical techniques. Observational data was obtained from radar reflectivity (11 sites) and a network of rain gauge (694 points). Three nowcasting <span class="hlt">systems</span> successfully predicted the frequency of precipitation throughout the <span class="hlt">forecast</span> period, although most of predicted rainfall amount had underestimated. MAPLE and KONOS predicted better performance for advection field up to 2 hours. However, the skill of predicted precipitation decreased very quickly into the <span class="hlt">forecast</span> period. MAPLE and KONOS could not predict the precise location of heavy rainfall after 3 hours. VSRF blending with NWP tended to be more skillful than only extrapolation after 3 hours. This is because NWP could provide a convective initiation and growth.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H13K..06W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H13K..06W"><span id="translatedtitle">An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.</p> <p>2013-12-01</p> <p>One of the key recommendations of the WCRP Global Drought Information <span class="hlt">System</span> (GDIS) workshop is to develop an experimental real-time global monitoring and prediction <span class="hlt">system</span>. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought <span class="hlt">forecasting</span> has been stranded in part due to the limited skill both in climate <span class="hlt">forecast</span> models and global hydrologic predictions. Having been working on drought monitoring and <span class="hlt">forecasting</span> over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning <span class="hlt">system</span> that is based on multiple climate <span class="hlt">forecast</span> models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal <span class="hlt">forecasting</span> of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow <span class="hlt">forecasts</span> respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring <span class="hlt">system</span> and model hindcasts and real-time <span class="hlt">forecasts</span> from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature <span class="hlt">forecasts</span> from multiple climate <span class="hlt">forecast</span> models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought <span class="hlt">forecasts</span> at a 1-degree resolution. A parallel run using the ESP <span class="hlt">forecast</span> method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought <span class="hlt">forecast</span> does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=309670','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=309670"><span id="translatedtitle">Evaluation of a <span class="hlt">load</span> measurement <span class="hlt">system</span> for cotton harvesters</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>The objective of this work is to develop and characterize the performance of a <span class="hlt">system</span> used onboard a cotton harvester for obtaining seed cotton weight data. This <span class="hlt">system</span> can be used to measure seed cotton weight on a <span class="hlt">load</span> by <span class="hlt">load</span> basis, thereby enhancing the ability for a producer to conduct on-farm ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016GeoRL..43.6485R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016GeoRL..43.6485R&link_type=ABSTRACT"><span id="translatedtitle">Using climate regionalization to understand Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew</p> <p>2016-06-01</p> <p>Dynamically based seasonal <span class="hlt">forecasts</span> are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-<span class="hlt">forecast</span> skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> Version 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month <span class="hlt">forecasts</span> capture the general spatial character of warm season precipitation variability but that <span class="hlt">forecast</span> regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale <span class="hlt">forecast</span> in place of the local grid cell prediction.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100036254','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100036254"><span id="translatedtitle">Maintaining a Local Data Integration <span class="hlt">System</span> in Support of Weather <span class="hlt">Forecast</span> Operations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian</p> <p>2010-01-01</p> <p>Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) at Johnson Space Center in Houston, TX have used a local data integration <span class="hlt">system</span> (LDIS) as part of their <span class="hlt">forecast</span> and warning operations. The original LDIS was developed by NASA's Applied Meteorology Unit (AMU; Bauman et ai, 2004) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) analyses that are delivered to <span class="hlt">forecasters</span> every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather <span class="hlt">forecasts</span> issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction <span class="hlt">System</span> (ARPS) Data Analysis <span class="hlt">System</span> (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local <span class="hlt">forecasters</span> with a more comprehensive understanding of evolving fine-scale weather features</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN43C1750L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN43C1750L"><span id="translatedtitle">JPSS application in a near real time regional numerical <span class="hlt">forecast</span> <span class="hlt">system</span> at CIMSS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, J.; Wang, P.; Han, H.; Zhu, F.; Schmit, T. J.; Goldberg, M.</p> <p>2015-12-01</p> <p>Observations from next generation of environmental sensors onboard the Suomi National Polar-Orbiting Parnership (S-NPP) and its successor, the Joint Polar Satellite <span class="hlt">System</span> (JPSS), provide us the critical information for numerical weather <span class="hlt">forecast</span> (NWP). How to better represent these satellite observations and how to get value added information into NWP <span class="hlt">system</span> still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation <span class="hlt">system</span> for Tropical storm <span class="hlt">forecasts</span> (SDAT) (http://cimss.ssec.wisc.edu/sdat). The <span class="hlt">system</span> is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research <span class="hlt">Forecast</span> (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the <span class="hlt">system</span>. Using SDAT as a research testbed, studies have been conducted to show how to improve high impact weather <span class="hlt">forecast</span> by better handling cloud information in satellite data. Previously by collocating high spatial resolution MODIS data with hyperspectral resolution AIRS data, precise clear pixels of AIRS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity <span class="hlt">forecast</span>. We recently have extended these methodologies into processing CrIS/VIIRS data. We also tested similar ideas in microwave sounders by the collocation of AMSU/MODIS and ATMS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/865330','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/servlets/purl/865330"><span id="translatedtitle">Valve for fuel pin <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Christiansen, David W.</p> <p>1985-01-01</p> <p>A cyclone valve surrounds a wall opening through which cladding is projected. An axial valve inlet surrounds the cladding. Air is drawn through the inlet by a cyclone stream within the valve. An inflatable seal is included to physically engage a fuel pin subassembly during <span class="hlt">loading</span> of fuel pellets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5349701','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/biblio/5349701"><span id="translatedtitle">Valve for fuel pin <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Christiansen, D.W.</p> <p>1984-01-01</p> <p>A cyclone valve surrounds a wall opening through which cladding is projected. An axial valve inlet surrounds the cladding. Air is drawn through the inlet by a cyclone stream within the valve. An inflatable seal is included to physically engage a fuel pin subassembly during <span class="hlt">loading</span> of fuel pellets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JGRD..11910232S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JGRD..11910232S"><span id="translatedtitle">Impact of various observing <span class="hlt">systems</span> on weather analysis and <span class="hlt">forecast</span> over the Indian region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Randhir; Ojha, Satya P.; Kishtawal, C. M.; Pal, P. K.</p> <p>2014-09-01</p> <p>To investigate the potential impact of various types of data on weather <span class="hlt">forecast</span> over the Indian region, a set of data-denial experiments spanning the entire month of July 2012 is executed using the Weather Research and <span class="hlt">Forecasting</span> (WRF) model and its three-dimensional variational (3DVAR) data assimilation <span class="hlt">system</span>. The experiments are designed to allow the assessment of mass versus wind observations and terrestrial versus space-based instruments, to evaluate the relative importance of the classes of conventional instrument such as radiosonde, and finally to investigate the role of individual spaceborne instruments. The moist total energy norm is used for validation and <span class="hlt">forecast</span> skill assessment. The results show that the contribution of wind observations toward error reduction is larger than mass observations in the short range (48 h) <span class="hlt">forecast</span>. Terrestrial-based observations generally contribute more than space-based observations except for the moisture fields, where the role of the space-based instruments becomes more prevalent. Only about 50% of individual instruments are found to be beneficial in this experiment configuration, with the most important role played by radiosondes. Thereafter, Meteosat Atmospheric Motion Vectors (AMVs) (only for short range <span class="hlt">forecast</span>) and Special Sensor Microwave Imager (SSM/I) are second and third, followed by surface observations, Sounder for Probing Vertical Profiles of Humidity (SAPHIR) radiances and pilot observations. Results of the additional experiments of comparative performance of SSM/I total precipitable water (TPW), Microwave Humidity Sounder (MHS), and SAPHIR radiances indicate that SSM/I is the most important instrument followed by SAPHIR and MHS for improving the quality of the <span class="hlt">forecast</span> over the Indian region. Further, the impact of single SAPHIR instrument (onboard Megha-Tropiques) is significantly larger compared to three MHS instruments (onboard NOAA-18/19 and MetOp-A).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1414215F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1414215F"><span id="translatedtitle">Development of an Operational Typhoon Swell <span class="hlt">Forecasting</span> and Coastal Flooding Early Warning <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fan, Y. M.; Wu, L. C.; Doong, D. J.; Kao, C. C.; Wang, J. H.</p> <p>2012-04-01</p> <p>Coastal floods and typhoon swells are a consistent threat to oceanfront countries, causing major human suffering and substantial economic losses, such as wrecks, ship capsized, and marine construction failure, etc. Climate change is exacerbating the problem. An early warning <span class="hlt">system</span> is essential to mitigate the loss of life and property from coastal flooding and typhoon swells. The purpose of this study is to develop a typhoon swell <span class="hlt">forecasting</span> and coastal flooding early warning <span class="hlt">system</span> by integrating existing sea-state monitoring technology, numerical ocean <span class="hlt">forecasting</span> models, historical database and experiences, as well as computer science. The proposed <span class="hlt">system</span> has capability offering data for the past, information for the present, and for the future. The <span class="hlt">system</span> was developed for Taiwanese coast due to its frequent threat by typhoons. An operational <span class="hlt">system</span> without any manual work is the basic requirement of the <span class="hlt">system</span>. Integration of various data source is the <span class="hlt">system</span> kernel. Numerical ocean models play the important role within the <span class="hlt">system</span> because they provide data for assessment of possible typhoon swell and flooding. The <span class="hlt">system</span> includes regional wave model (SWAN) which nested with the large domain wave model (NWW III), is operationally set up for coastal waves <span class="hlt">forecasting</span>, especially typhoon swell <span class="hlt">forecasting</span> before typhoon coming, and the storm surge predicted by a POM model. Data assimilation technology is incorporated for enhanced accuracy. A warning signal is presented when the storm water level that accumulated from astronomical tide, storm surge, and wave-induced run-up exceeds the alarm sea level. This warning <span class="hlt">system</span> has been in practical use for coastal flooding damage mitigation in Taiwan for years. Example of the <span class="hlt">system</span> operation during Typhoon Haitung struck Taiwan in 2005 is illustrated in this study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016WRR....52.3815R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016WRR....52.3815R"><span id="translatedtitle">Valuing year-to-go hydrologic <span class="hlt">forecast</span> improvements for a peaking hydropower <span class="hlt">system</span> in the Sierra Nevada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rheinheimer, David E.; Bales, Roger C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.</p> <p>2016-05-01</p> <p>We assessed the potential value of hydrologic <span class="hlt">forecasting</span> improvements for a snow-dominated high-elevation hydropower <span class="hlt">system</span> in the Sierra Nevada of California, using a hydropower optimization model. To mimic different <span class="hlt">forecasting</span> skill levels for inflow time series, rest-of-year inflows from regression-based <span class="hlt">forecasts</span> were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical <span class="hlt">forecasting</span> approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic <span class="hlt">forecasts</span> increased annual hydropower revenue by 0.14 to 1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based <span class="hlt">forecast</span>, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect <span class="hlt">forecasts</span> was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of <span class="hlt">forecasts</span> was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose <span class="hlt">systems</span> to help guide investments in <span class="hlt">forecasting</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMNH21A1592P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMNH21A1592P"><span id="translatedtitle">a 24/7 High Resolution Storm Surge, Inundation and Circulation <span class="hlt">Forecasting</span> <span class="hlt">System</span> for Florida Coast</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Paramygin, V.; Davis, J. R.; Sheng, Y.</p> <p>2012-12-01</p> <p>A 24/7 <span class="hlt">forecasting</span> <span class="hlt">system</span> for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 <span class="hlt">forecasting</span> of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution <span class="hlt">forecasting</span> <span class="hlt">system</span> for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling <span class="hlt">System</span> (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The <span class="hlt">system</span> successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The <span class="hlt">system</span> uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3616S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3616S"><span id="translatedtitle"><span class="hlt">Forecasting</span> of Severe Weather in Austria and Hungary Using High-Resolution Ensemble Prediction <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Szucs, Mihaly; Simon, Andre; Szintai, Balazs; Suklitsch, Martin; Wang, Yong; Wastl, Clemens; Boloni, Gergely</p> <p>2015-04-01</p> <p>The study presents and compares several approaches in EPS (ensemble prediction <span class="hlt">system</span>) <span class="hlt">forecasting</span> based on the non-hydrostatic, high resolution AROME model. The PEARP (global ARPEGE model EPS) was used for coupling. Besides, AROME-EPS was also generated upon hydrostatic ALADIN-EPS <span class="hlt">forecasts</span> (LAEF), which were used as initial and lateral boundary conditions for each AROME-EPS run. The horizontal resolution of the AROME model is 2.5km and it uses 60 vertical levels for the vertical discretization. In most of the tests, the AROME-EPS run with 10+1 members in Hungarian and 16 members in Austrian implementation. The <span class="hlt">forecast</span> length was usually set to 30-36 hours. The use of high-resolution EPS has advantages in almost all situations with severe convection (mostly in <span class="hlt">forecasting</span> intense multicell thunderstorms or mesoscale convective <span class="hlt">systems</span> of non-frontal origin). The possibility of severe thunderstorm was indicated by several EPS runs even if the deterministic (reference) AROME model failed to <span class="hlt">forecast</span> the event. Similarly, it could be shown that the AROME-EPS can perform better than hydrostatic global or ALADIN-EPS models in situations with strong wind or heavy precipitation induced by large-scale circulation (mainly in mountain regions). Both EDA (Ensemble of Data Assimilation) and SPPT (Stochastically Perturbed Parameterized Tendencies) methods were tested as a potential perturbation generation method on limited area. The EDA method was able to improve the accuracy of single members through the reduction of the analysis error by applying local data assimilation. It was also able to increase the spread of the <span class="hlt">system</span> in the early hours due to the additional analysis perturbations. The impact of the SPPT scheme was proven to be smaller in comparison to the impact of this method in global ensemble <span class="hlt">systems</span>. Further possibilities of improving the assimilation methods and the setup of the AROME-EPS are also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814604F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814604F"><span id="translatedtitle">A model output statistics <span class="hlt">system</span> to <span class="hlt">forecast</span> the 2 metre temperature at the "Wettermast Hamburg" site</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Finn, Tobias Sebastian; Ament, Felix</p> <p>2016-04-01</p> <p>The model output statistics (MOS) method is frequently used to downscale and improve numerical weather models for specific measurement sites. One of these is the "Wettermast Hamburg" (http://wettermast-hamburg.zmaw.de/) in the south-east of Hamburg. It is operated by the Meteorological Institute of the University of Hamburg. The MOS approach was used to develop a not yet existing 2 metre temperature <span class="hlt">forecasting</span> <span class="hlt">system</span> for this site. The <span class="hlt">forecast</span> <span class="hlt">system</span> is based on the 0 UTC control run of the legacy "global ensemble <span class="hlt">forecast</span> <span class="hlt">system</span>". The multiple linear equations were calculated using a training period of 2 years (01.03.2012-28.02.2014), while the developed models were evaluated using the following year (01.03.2014-28.02.2015). During the development process it was found that a combination of forward and backward selection together with the "Bayesian information criterion", a warm-cold splitting and a five-fold cross-validation was the best automated method to minimize the risk of overfitting. To further reduce the risk, the number of predictors were limited to 6. Also the first 3 possible predictors were selected by hand. In comparison to the fully automated method, the error was not changed significantly through this restrictions for the evaluation period. The analysis of the importance of selected predictors shows that the global weather model has problems characterizing specific weather phenomena. Large model errors by misrepresenting the boundary layer were highlighted through the 10 metre wind speed, the surface temperature and the 1000 hPa temperature as frequently selected predictors. The final <span class="hlt">forecast</span> <span class="hlt">system</span> has a root-mean-square error minimum of 1.15 K for the initialization and a maximum 2.2 K at the 84 hour lead time. Compared to the direct model output this is a mean improvement of ˜ 22%. The main error reduction is achieved in the first 24 hours of the <span class="hlt">forecast</span>, especially at the initialization (up to 45% error reduction).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1215240T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1215240T"><span id="translatedtitle">A past discharges assimilation <span class="hlt">system</span> for ensemble streamflow <span class="hlt">forecasts</span> over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thirel, Guillaume; Martin, E.; Regimbeau, F.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Habets, F.</p> <p>2010-05-01</p> <p>The coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU (SIM) is developed at Météo-France for many years. This fully distributed catchment model is used in a pre-operational mode since 2005 for producing mid-range ensemble streamflow <span class="hlt">forecasts</span> based on the 51-member 10-day ECMWF EPS. A past discharges assimilation <span class="hlt">system</span> has been implemented in order to improve the initial states of these ensemble streamflow <span class="hlt">forecasts</span>. The daily observed discharges of a selection of 186 gauging stations distributed over France were used over a 19-month period. The analysis operator is the Best Linear Unbiased Operator (BLUE), and 3 configurations of the assimilation <span class="hlt">system</span> were tested, each one adjusting the soil moisture in a different way. An optional improvement of the physics of the model (the exponential profile of the hydraulic conductivity in the soil) was tested. The performance of the <span class="hlt">system</span> was assessed for a selection of 148 assimilated stations, as well as for a selection of 49 totally independent stations for each configuration. A global improvement of the simulated streamflows was found, and the modifications imposed by the BLUE remained low. Finally, the impact of the assimilation <span class="hlt">system</span> on the ensemble streamflow <span class="hlt">forecasts</span>, and the impact of the improved physics were assessed separately in comparison with the operational streamflow <span class="hlt">forecasts</span>. The results show a significant improvement of the <span class="hlt">forecasts</span>, and the best configuration demonstrate the benefit of the method along the 10-day range, even for very high flows and for stations where assimilation was not directly performed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730009297','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730009297"><span id="translatedtitle">Active transmission isolation/rotor <span class="hlt">loads</span> measurement <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kenigsberg, I. J.; Defelice, J. J.</p> <p>1973-01-01</p> <p>Modifications were incorporated into a helicopter active transmission isolation <span class="hlt">system</span> to provide the capability of utilizing the <span class="hlt">system</span> as a rotor force measuring device. These included; (1) isolator redesign to improve operation and minimize friction, (2) installation of pressure transducers in each isolator, and (3) <span class="hlt">load</span> cells in series with each torque restraint link. Full scale vibration tests performed during this study on a CH-53A helicopter airframe verified that these modifications do not degrade the <span class="hlt">systems</span> wide band isolation characteristics. Bench tests performed on each isolator unit indicated that steady and transient <span class="hlt">loads</span> can be measured to within 1 percent of applied <span class="hlt">load</span>. Individual isolator vibratory <span class="hlt">load</span> measurement accuracy was determined to be 4 percent. <span class="hlt">Load</span> measurement accuracy was found to be independent of variations in all basic isolator operating characteristics. Full scale <span class="hlt">system</span> <span class="hlt">load</span> calibration tests on the CH-53A airframe established the feasibility of simultaneously providing wide band vibration isolation and accurate measurement of rotor <span class="hlt">loads</span>. Principal rotor <span class="hlt">loads</span> (lift, propulsive force, and torque) were measured to within 2 percent of applied <span class="hlt">load</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614666L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614666L"><span id="translatedtitle">Major upgrade of the global Mercator Océan analysis and <span class="hlt">forecasting</span> high resolution <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lellouche, Jean-Michel; Legalloudec, Olivier; Bourdallé-Badie, Romain; Garric, Gilles; Greiner, Eric; Regnier, Charly; Bricaud, Clément; Testut, Charles-Emmanuel; Drevillon, Marie; Drillet, Yann; Dombrowsky, Eric</p> <p>2014-05-01</p> <p>Mercator Océan, the French ocean <span class="hlt">forecast</span> service provider, was setup about ten years ago by all the French organizations holding stakes in ocean <span class="hlt">forecasting</span>. It has been since then constantly developed and is currently operating operational ocean analysis and <span class="hlt">forecasting</span> <span class="hlt">systems</span> based on state-of-the-art Ocean General Circulation Models. The mandate of Mercator Océan is to cover the global ocean at eddy resolving resolution. To achieve this goal, Mercator Océan is strongly connected to the ocean modeling and data assimilation research communities, at French, European and international levels. Mercator Océan is engaged in the Global Monitoring for Environment and Security (GMES) European initiative and is currently coordinating a European consortium (~60 partners) gathering all European skills in ocean monitoring and <span class="hlt">forecasting</span> to build the Marine <span class="hlt">forecast</span> component of the GMES service. This is currently done in the MyOcean2 European funded project which started in 2012. In this context, we have recently performed a major upgrade of the global high resolution <span class="hlt">system</span> operated at Mercator Océan. This new <span class="hlt">system</span> now delivers weekly and daily services, and includes numerous improvements related to the ocean/sea-ice model and the assimilation scheme. The previous global <span class="hlt">system</span> did not benefit from these improvements that were implemented for most of them only in the regional <span class="hlt">system</span>. Consistency between Mercator Océan <span class="hlt">systems</span> is thereby ensured by the use of a common basis for all Mercator Océan analysis and <span class="hlt">forecasting</span> <span class="hlt">systems</span>. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the <span class="hlt">forecast</span> error. It includes an adaptive-error estimate and a localization algorithm. Altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean <span class="hlt">forecasting</span>. A 3D-Var scheme provides a correction</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC12C..07D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC12C..07D"><span id="translatedtitle">How can monthly to seasonal <span class="hlt">forecasts</span> help to better manage power <span class="hlt">systems</span>? (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubus, L.; Troccoli, A.</p> <p>2013-12-01</p> <p>The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate <span class="hlt">system</span> and in the ability to <span class="hlt">forecast</span> climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these <span class="hlt">forecasts</span> for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather <span class="hlt">forecasts</span>. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy <span class="hlt">systems</span> in which monthly to seasonal <span class="hlt">forecasts</span> can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcMod..96..103B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..96..103B"><span id="translatedtitle">An ensemble <span class="hlt">forecast</span> <span class="hlt">system</span> for prediction of Atlantic-UK wind waves</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bunney, Chris; Saulter, Andy</p> <p>2015-12-01</p> <p>Ensemble prediction <span class="hlt">systems</span> (EPS) provide a numerical method for determining uncertainty associated with <span class="hlt">forecasts</span> of environmental conditions. A <span class="hlt">system</span> is presented that has been designed to quantify uncertainties in short range (up to 7 days ahead) wave <span class="hlt">forecasts</span> for the Atlantic Ocean and shelf seas around the UK. Variability in the wave ensemble is primarily introduced via wind forcing taken from an Ensemble Transform Kalman Filter based atmospheric EPS. Restart files for each member in the wave ensemble use a short range <span class="hlt">forecast</span> from a previous run of the same member, in order to retain spread in initial conditions. Wave model run times were optimised through the choice of source term physics scheme and application of a spherical multi-cell grid. Verification of the wave-EPS shows good overall performance, since a substantial component of ensemble under-spread can be attributed to observation errors. Systematic biases, relating to the choice of source term, are noted when statistics are broken down regionally and have a major impact on the quality of the <span class="hlt">forecasts</span> at short lead times, when spread is limited.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC31A1007N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC31A1007N"><span id="translatedtitle">The use of seasonal <span class="hlt">forecasts</span> in a crop failure early warning <span class="hlt">system</span> for West Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nicklin, K. J.; Challinor, A.; Tompkins, A.</p> <p>2011-12-01</p> <p>Seasonal rainfall in semi-arid West Africa is highly variable. Farming <span class="hlt">systems</span> in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield <span class="hlt">forecasting</span> <span class="hlt">system</span> uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate <span class="hlt">forecasts</span> may be able to increase the lead-time of yield <span class="hlt">forecasts</span> and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning <span class="hlt">system</span>, which uses dynamic seasonal <span class="hlt">forecasts</span> and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather <span class="hlt">forecasts</span> (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the <span class="hlt">system</span> is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the <span class="hlt">forecast</span> date, followed by ECMWF <span class="hlt">system</span> 3 seasonal <span class="hlt">forecasts</span> until harvest. The variation of skill with <span class="hlt">forecast</span> date</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.B33C0416D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.B33C0416D"><span id="translatedtitle">A Remote Sensing-based Global Agricultural Drought Monitoring and <span class="hlt">Forecasting</span> <span class="hlt">System</span> for Supporting GEOSS (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>di, L.; Yu, G.; Han, W.; Deng, M.</p> <p>2010-12-01</p> <p>Group on Earth Observations (GEO) is a voluntary partnership of governments and international organizations. GEO is coordinating the implementation of the Global Earth Observation <span class="hlt">System</span> of <span class="hlt">Systems</span> (GEOSS), a worldwide effort to make Earth observation resources more useful to the society. As one of the important technical contributors to GEOSS, the Center for Spatial Information Science and <span class="hlt">Systems</span> (CSISS), George Mason University, is implementing a remote sensing-based global agricultural drought monitoring and <span class="hlt">forecasting</span> <span class="hlt">system</span> (GADMFS) as a GEOSS societal benefit areas (agriculture and water) prototype. The goals of the project are 1) to establish a <span class="hlt">system</span> as a component of GEOSS for providing global on-demand and systematic agriculture drought information to users worldwide, and 2) to support decision-making with improved monitoring, <span class="hlt">forecasting</span>, and analyses of agriculture drought. GADMFS has adopted the service-oriented architecture and is based on standard-compliant interoperable geospatial Web services to provide online on-demand drought conditions and <span class="hlt">forecasting</span> at ~1 km spatial and daily and weekly temporal resolutions for any part of the world to world-wide users through the Internet. Applicable GEOSS recommended open standards are followed in the <span class="hlt">system</span> implementation. The system’s drought monitoring relies on drought-related parameters, such as surface and root-zone soil moisture and NDVI time series derived from remote sensing data, to provide the current conditions of agricultural drought. The <span class="hlt">system</span> links to near real-time satellite remote sensing data sources from NASA and NOAA for the monitoring purpose. For drought <span class="hlt">forecasting</span>, the <span class="hlt">system</span> utilizes a neural-network based modeling algorithm. The algorithm is trained with inputs of current and historic vegetation-based and climate-based drought index data, biophysical characteristics of the environment, and time-series weather data. The trained algorithm will establish per-pixel model for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1211181L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1211181L"><span id="translatedtitle">Assessment of the hindcast, nowcast and <span class="hlt">forecast</span> capabilities of the Mercator-Ocean high resolution ocean <span class="hlt">forecasting</span> <span class="hlt">system</span> in the Global and Atlantic and Mediterranean basins.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lellouche, Jean-Michel; Tranchant, Benoît.; Bourdallé-Badie, Romain; Le Galloudec, Olivier; Greiner, Eric; Benkiran, Mounir; Derval, Corine; Testut, Charles-Emmanuel</p> <p>2010-05-01</p> <p>In the framework of the European project GMES/MyOcean, Mercator-Ocean has been designing a hierarchy of ocean analysis and <span class="hlt">forecasting</span> <span class="hlt">systems</span> based on numerical models of the ocean and data assimilation methods. Since April 2008, Mercator-Ocean runs an Atlantic and Mediterranean <span class="hlt">system</span> at 1/12° between 20°S and 80°N. Since a few months, a global <span class="hlt">system</span>, with the same horizontal and vertical resolution (50 levels on the vertical with a surface refinement), runs also in an operational mode. These two <span class="hlt">systems</span> are eddy resolving. The ocean and sea ice models are based on the NEMO code. The data assimilation algorithm is a reduced order Kalman filter using 3D multivariate modal decomposition of the <span class="hlt">forecast</span> error covariance. The <span class="hlt">system</span> assimilates conjointly altimeter data, SST and in situ observations (temperature and salinity profiles, including ARGO data) in order to provide the initial conditions required for numerical ocean prediction. The main characteristics of the assimilation <span class="hlt">system</span> are (i) the background error covariances calculated from a free oceanic simulation, (ii) the adaptive error variance, (iii) the use of the localization technique and (iv) the use of the IAU (Incremental Analysis Update) procedure where analysis increments are inserted at every time step over the same period as the data assimilation window. The real time operation of these <span class="hlt">systems</span> produce each week realistic 3-dimensional oceanic conditions (temperature, salinity, currents,…) two weeks back in time (hindcast and nowcast) and a one or two weeks <span class="hlt">forecast</span>, driven at the surface by atmospheric conditions from the European Center for Medium Range Weather <span class="hlt">Forecast</span> (ECMWF). Moreover, the Atlantic and Mediterranean <span class="hlt">system</span> is operated daily to produce 7 days ocean <span class="hlt">forecasts</span> with daily updates of the ECMWF atmospheric forcing. A new version of the regional <span class="hlt">system</span> is planned to replace soon the actual one with many improvements concerning the ocean model and the assimilation scheme</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.1600D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.1600D"><span id="translatedtitle">Perun: The <span class="hlt">System</span> For Seasonal Crop Yield <span class="hlt">Forecasting</span> Based On The Crop Model and Weather Generator</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubrovsky, M.; Zalud, Z.; Trnka, M.; Haberle, J.; Pesice, P.</p> <p></p> <p>The main purpose of the computer <span class="hlt">system</span> PERUN, which is now being developed, is the probabilistic seasonal crop yield <span class="hlt">forecasting</span>. The crop yields (winter wheat and spring barley in the first step) are simulated by crop model WOFOST. The input daily weather series consist of observed data, which are available in the date of <span class="hlt">forecast</span> issuance, and synthetic data, which follow up with the observed data till the end of the crop model simulation. The synthetic weather series are generated by stochastic generator Met&Roll conditionally on the seasonal weather <span class="hlt">forecast</span>. The probabilis- tic <span class="hlt">forecast</span> is based on multiple crop model runs. To provide the six daily weather characteristics required for crop model simulation (precipitation, solar radiation, max- imum and minimum temperatures, air humidity, wind speed), the previous WGEN- like four-variate version of Met&Roll generator was supplemented by a new module. This module adds wind speed and air humidity (necessary to calculate evapotranspi- ration) using the nearest neighbours resampling from the observed data. Because of the problems with availability and/or accuracy of wind and humidity data, the source code of the WOFOST model was modified and allows now to switch between Penman and Makkink methods of calculating the evapotranspiration (the daily values of wind speed and humidity are not required in the Makkink method). The contribution will address following items: 1) Structure of the PERUN <span class="hlt">system</span>: components and their inputs and outputs. Modifications to WOFOST crop model and Met&Roll generator will be discussed. 2) Validation of the WOFOST crop model. The accuracy obtained using the Penman and Makkink methods will be compared. 3) Demonstration of the <span class="hlt">forecast</span> accuracy in dependence on the date of issuance. Acknowledgement: The <span class="hlt">system</span> PERUN is being developed within the frame of project QC1316 sponsored by the Czech National Agency for Agricultural Research (NAZV).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012522','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012522"><span id="translatedtitle">Maintaining a Local Data Integration <span class="hlt">System</span> in Support of Weather <span class="hlt">Forecast</span> Operations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian</p> <p>2010-01-01</p> <p>Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration <span class="hlt">system</span> (LDIS) as part of their <span class="hlt">forecast</span> and warning operations. Each has benefited from 3-dimensional analyses that are delivered to <span class="hlt">forecasters</span> every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather <span class="hlt">forecasts</span> issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction <span class="hlt">System</span> (ARPS) Data Analysis <span class="hlt">System</span> (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local <span class="hlt">forecasters</span> with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the <span class="hlt">system</span>. A series of scripts run a complete modeling <span class="hlt">system</span> consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth <span class="hlt">System</span> Research Laboratory/Global <span class="hlt">Systems</span> Division</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvE..93c6202B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvE..93c6202B"><span id="translatedtitle">Reply to "Comment on `Nonparametric <span class="hlt">forecasting</span> of low-dimensional dynamical <span class="hlt">systems</span>' "</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berry, Tyrus; Giannakis, Dimitrios; Harlim, John</p> <p>2016-03-01</p> <p>In this Reply we provide additional results which allow a better comparison of the diffusion <span class="hlt">forecast</span> and the "past-noise" <span class="hlt">forecasting</span> (PNF) approach for the El Niño index. We remark on some qualitative differences between the diffusion <span class="hlt">forecast</span> and PNF, and we suggest an alternative use of the diffusion <span class="hlt">forecast</span> for the purposes of <span class="hlt">forecasting</span> the probabilities of extreme events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JGRD..11812061L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRD..11812061L"><span id="translatedtitle">A dynamical-statistical <span class="hlt">forecast</span> model for the annual frequency of western Pacific tropical cyclones based on the NCEP Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> version 2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Xun; Yang, Song; Wang, Hui; Jia, Xiaolong; Kumar, Arun</p> <p>2013-11-01</p> <p>A dynamical-statistical <span class="hlt">forecast</span> model for the annual tropical cyclones over the western North Pacific is developed based on the empirical relationship between the actual annual number of tropical cyclones (ANTCs) and the dynamical predictions of large-scale variables by the Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> version 2 of the National Centers for Environmental Prediction (NCEP). On interannual time scales, the ANTCs are significantly and negatively correlated with the July-October tropical North Atlantic sea surface temperature, tropical western Pacific vertical zonal wind shear (WPVZWS), and subtropical Pacific geopotential height at 500 hPa (HGT500). They are also positively correlated with the zonal wind at 850 hPa over the tropical Pacific Ocean. Skillful <span class="hlt">forecasts</span> of the above four potential predictors are made with the 24-member ensemble predictions by the NCEP model. The two-predictor model with the HGT500 and the WPVZWS shows the most skillful hindcasts at 0-2 month leads assessed by the leave-one-out cross validation for the ANTCs over the 31 year record between 1982 and 2012. The corresponding correlation coefficients and the root-mean-square errors (RMSEs) between the observed and hindcast ANTCs are in the ranges from 0.73 to 0.79 and from 3.11 to 2.75, respectively. Observed ANTCs during El Niño-Southern Oscillation events are generally well captured with RMSEs ranging from 3.12 to 3.04 during El Niño years and from 3.62 to 2.44 during La Niña years. The <span class="hlt">forecast</span> skill of the model for the past 10 years (2003-2012) is competitive with the current <span class="hlt">forecast</span> schemes. The <span class="hlt">forecast</span> model initialized in March, May, and June 2013 suggests an inactive season for 2013, with about 22 tropical cyclones.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015SPIE.9816E..1GA','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015SPIE.9816E..1GA"><span id="translatedtitle">Computer <span class="hlt">system</span> for <span class="hlt">forecasting</span> surgery on the eye muscles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Avrunin, Oleg G.; Kukharenko, Dmitriy V.; Romanyuk, Sergii O.; Kalizhanova, Aliya; Toygozhinova, Aynur; Gromaszek, Konrad</p> <p>2015-12-01</p> <p>For the successful surgery on the eye muscles it is recommended to use a computer <span class="hlt">system</span> of preoperative planning of the surgical correction of strabismus. With using the computer <span class="hlt">system</span> at surgery planning, ophthalmologist surgeon will be able to choose the best surgical treatment and surgery dosage for a particular patient.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA.....5927S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA.....5927S"><span id="translatedtitle">The Design and Implementation of a Real-Time Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> in Durban, South Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sinclair, Scott; Pegram, Geoff</p> <p>2003-04-01</p> <p>In South Africa, five flood events during the period 1994-1996 resulted in the loss of 173 lives, more than 7000 people requiring evacuation and/or emergency shelter and damages to the value of R680 million (White paper on Disaster Management 1998). The South African Disaster management bill provides for "...preventing or reducing the risk of disasters, mitigating the severity of disasters ...". To this end a pilot study funded by the Water Research Commission aims at providing flood <span class="hlt">forecasts</span> for the Mgeni and Mlazi catchments near the city of Durban in South Africa. The importance and usefulness of flood <span class="hlt">forecasting</span> is particularly evident in an urban context where the density of population and infrastructure provide great potential for disaster. A reliable flood warning or <span class="hlt">forecasting</span> <span class="hlt">system</span> cannot prevent the occurrence of floods, but provides a key tool that can allow decision makers to be proactive rather than reactive in their response to a flooding event. Taking preventative measures before the fact can significantly reduce the social and economic impacts associated with a disaster. The flood <span class="hlt">forecasting</span> <span class="hlt">system</span> described here makes use of a "best estimate" spatial rainfield (obtained by combining radar and telemetered rain gauge rainfall estimates) as input to a linear catchment model. The catchment model parameters are dynamically updated in response to measured streamflows using Kalman filtering techniques, allowing improved <span class="hlt">forecasts</span> of streamflow as the catchment conditions change. Precomputed flood lines and a graphical representation of the spatial rainfield are dynamically displayed on a GIS in the Durban disaster management control center enabling Disaster Managers to be proactive in times of impending floods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011476','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011476"><span id="translatedtitle">Anvil <span class="hlt">Forecast</span> Tool in the Advanced Weather Interactive Processing <span class="hlt">System</span> (AWIPS)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe H., III; Hood, Doris</p> <p>2009-01-01</p> <p>Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and <span class="hlt">forecasters</span> from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil <span class="hlt">forecasting</span> as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display <span class="hlt">System</span> (MIDDS, Short and Wheeler, 2002). The tool helps <span class="hlt">forecasters</span> estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing <span class="hlt">System</span> (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil <span class="hlt">Forecast</span> Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil <span class="hlt">Forecast</span> Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A31F0083A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A31F0083A"><span id="translatedtitle">Using the LAPS / WRF <span class="hlt">system</span> to Analyze and <span class="hlt">Forecast</span> Solar Radiation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Albers, S. C.; Xie, Y.; Jiang, H.; Toth, Z.</p> <p>2012-12-01</p> <p>The Local Analysis and Prediction <span class="hlt">System</span> (LAPS) is being used to produce rapid update, high resolution analyses and <span class="hlt">forecasts</span> of solar radiation (Global Horizontal Irradiance or GHI). LAPS is highly portable and can be run onsite, particularly when high-resolution and rapid updating is needed. This allows the user to assimilate their own observational data merged with centrally available observations and to set up the analysis/<span class="hlt">forecast</span> configuration to their liking. The cloud analysis uses satellite (including IR and 1-km resolution visible imagery, updated every 15-min), METARs, radar, aircraft and model first guess information to produce an hourly 3-D field of cloud fraction, cloud liquid, and cloud ice. The cloud analysis and satellite data together are used to produce a gridded analysis of total solar radiation. This is verified against a dense network of real-time solar radiation measurements that are independent (not used in the analysis). We are focusing mainly on a two nested domains covering the Southern Plains states that encompass networks of pyranometers located in Oklahoma and Texas. The GHI <span class="hlt">forecast</span> is being run on the outer domain, and is being initialized using the same cloud analysis package that drives the analysis fields mentioned above. The HWT domain initializes WRF every hour with 15-minute output. Real-time verification of the analyses (including images of the analysis), and <span class="hlt">forecasts</span> can be seen on our website, and updated results will be explored in this presentation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H41E0872Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41E0872Y"><span id="translatedtitle">Estimating Reservoir Inflow Using RADAR <span class="hlt">Forecasted</span> Precipitation and Adaptive Neuro Fuzzy Inference <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yi, J.; Choi, C.</p> <p>2014-12-01</p> <p>Rainfall observation and <span class="hlt">forecasting</span> using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference <span class="hlt">system</span>, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) <span class="hlt">forecasted</span> precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR <span class="hlt">forecasted</span> precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE <span class="hlt">forecasted</span> precipitation data showed better result for inflow estimation in the Chungju Reservoir.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H31A0824K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H31A0824K"><span id="translatedtitle">Real-Time Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> Using Channel Flow Routing Model with Updating by Particle Filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kudo, R.; Chikamori, H.; Nagai, A.</p> <p>2008-12-01</p> <p>A real-time flood <span class="hlt">forecasting</span> <span class="hlt">system</span> using channel flow routing model was developed for runoff <span class="hlt">forecasting</span> at water gauged and ungaged points along river channels. The <span class="hlt">system</span> is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream part models consist of a lumped rainfall-runoff model for hillslopes adjacent to a river channel and a kinematic flow routing model for a river channel. The flow <span class="hlt">forecast</span> of this model is updated by Particle filtering of the downstream part model as well as by the extended Kalman filtering of the upstream part model and the tributary part models. The Particle filtering is a simple and powerful updating algorithm for non-linear and non-gaussian <span class="hlt">system</span>, so that it can be easily applied to the downstream part model without complicated linearization. The presented flood runoff model has an advantage in simlecity of updating procedure to the grid-based distributed models, which is because of less number of state variables. This <span class="hlt">system</span> was applied to the Gono-kawa River Basin in Japan, and flood <span class="hlt">forecasting</span> accuracy of the <span class="hlt">system</span> with both Particle filtering and extended Kalman filtering and that of the <span class="hlt">system</span> with only extended Kalman filtering were compared. In this study, water gauging stations in the objective basin were divided into two types of stations, that is, reference stations and verification stations. Reference stations ware regarded as ordinary water gauging stations and observed data at these stations are used for calibration and updating of the model. Verification stations ware considered as ungaged or arbitrary points and observed data at these stations are used not for calibration nor updating but for only evaluation of <span class="hlt">forecasting</span> accuracy. The result confirms that Particle filtering of the downstream part model improves <span class="hlt">forecasting</span> accuracy of runoff at</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.7131S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7131S"><span id="translatedtitle">Decision-relevant early-warning thresholds for ensemble flood <span class="hlt">forecasting</span> <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stephens, Liz; Pappenberger, Florian; Cloke, Hannah; Alfieri, Lorenzo</p> <p>2014-05-01</p> <p>Over and under warning of potential future floods is problematic for decision-making, and could ultimately lead to trust being lost in the <span class="hlt">forecasts</span>. The use of ensemble flood <span class="hlt">forecasting</span> <span class="hlt">systems</span> for early warning therefore requires a consideration of how to determine and implement decision-relevant thresholds for flood magnitude and probability. This study uses a year's worth of hindcasts from the Global Flood Awareness <span class="hlt">System</span> (GloFAS) to explore the sensitivity of the warning <span class="hlt">system</span> to the choice of threshold. We use a number of different methods for choosing these thresholds, building on current approaches that use model climatologies to determine the critical flow magnitudes, to those that can provide 'first guesses' of potential impacts (through integration with global-scale inundation mapping), as well as methods that could incorporate resource limitations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/97193','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/97193"><span id="translatedtitle">Aggregate vehicle travel <span class="hlt">forecasting</span> model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Greene, D.L.; Chin, Shih-Miao; Gibson, R.</p> <p>1995-05-01</p> <p>This report describes a model for <span class="hlt">forecasting</span> total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for <span class="hlt">forecasting</span> through the year 2000, as well as a structural, scenario-based longer term module for <span class="hlt">forecasting</span> through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle <span class="hlt">load</span> factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management <span class="hlt">system</span> operating in the Windows environment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030112153','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030112153"><span id="translatedtitle">Single Vector Calibration <span class="hlt">System</span> for Multi-Axis <span class="hlt">Load</span> Cells and Method for Calibrating a Multi-Axis <span class="hlt">Load</span> Cell</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parker, Peter A. (Inventor)</p> <p>2003-01-01</p> <p>A single vector calibration <span class="hlt">system</span> is provided which facilitates the calibration of multi-axis <span class="hlt">load</span> cells, including wind tunnel force balances. The single vector <span class="hlt">system</span> provides the capability to calibrate a multi-axis <span class="hlt">load</span> cell using a single directional <span class="hlt">load</span>, for example <span class="hlt">loading</span> solely in the gravitational direction. The <span class="hlt">system</span> manipulates the <span class="hlt">load</span> cell in three-dimensional space, while keeping the uni-directional calibration <span class="hlt">load</span> aligned. The use of a single vector calibration <span class="hlt">load</span> reduces the set-up time for the multi-axis <span class="hlt">load</span> combinations needed to generate a complete calibration mathematical model. The <span class="hlt">system</span> also reduces <span class="hlt">load</span> application inaccuracies caused by the conventional requirement to generate multiple force vectors. The simplicity of the <span class="hlt">system</span> reduces calibration time and cost, while simultaneously increasing calibration accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E2211M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E2211M"><span id="translatedtitle">Near-Earth Radiation Environment: Operation Control and <span class="hlt">Forecast</span> <span class="hlt">System</span> at SINP MSU</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Myagkova, Irina; Bobrovnikov, Sergey; Kalegaev, Vladimir; Barinova, Vera; Dolenko, Sergey; Shiroky, Vladimir</p> <p></p> <p>Operational control and <span class="hlt">forecast</span> of the Earth’s radiation environment is very topical both for solving fundamental scientific problems of solar-terrestrial physics, and for providing safety of space missions and polar aviation. Therefore, data of experiments onboard LEO (low-altitudes polar) spacecraft are very important. Now, a lot of data of experiments are available, including measurements of LEO spacecraft like "Meteor-M No. 1" and POES NOAA series. In the nearest future, new Russian satellites RELEC and "Lomonosov" will be launched to LEO orbit. However, data transmitted from LEO spacecraft has specific character connected with the features of LEO orbit: a spacecraft consistently passes different areas of near-Earth space - polar caps, area of outer Earth’s radiations Belts (ERB), middle latitudes, inner ERB. No public <span class="hlt">systems</span> intended for analysis of radiation conditions at low altitudes, which could allow quick comparison of data obtained in L1 point with those from LEO and GEO, were created until now. The other important problem is <span class="hlt">forecasting</span> of the near-Earth radiation environment state which is of key importance for space weather. The described problems are solved by the operational <span class="hlt">system</span> of monitoring and <span class="hlt">forecasting</span> of the radiation state of near-Earth environment, created at SINP MSU. The <span class="hlt">system</span> of short-term (one hour ahead) <span class="hlt">forecasting</span> of solar energetic particles (SEP) and relativistic electron fluxes at GEO operates on the base of artificial neural networks. The <span class="hlt">system</span> also predicts the extreme location of SEP penetration boundary in the Earth’s magnetosphere at low altitudes and the high latitude boundary of outer ERB. Both predicted locations depend on Dst and Kp values, which, in turn, are predicted one hour ahead by artificial neural networks. The <span class="hlt">system</span> operates in the framework of Space monitoring data center of the Moscow State University - http://swx.sinp.msu.ru/radiastatus/currentStatus.php.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.A31A0844H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.A31A0844H"><span id="translatedtitle">Interest of assimilating simulated SMOS and AQUARIUS SSS data in the Mercator operational <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hernandez, F.</p> <p>2006-12-01</p> <p>The French MERCATOR project is developing several operational ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span> to take part in the Global Ocean Data Assimilation Experiment (GODAE). Prototype <span class="hlt">systems</span> are designed to simulate (1) the Atlantic and Mediterranean Sea (from 1/3° to 1/15°), and (2) the global ocean circulation (from 2° to 1/4°). In the context of a study undertaken by a consortium of European research centers, an OSSE has been performed to contribute to the development of the ground segment of the ESA SMOS (Soil Moisture and Ocean Salinity) mission. The OSSE used the new generation of fully multivariate assimilation <span class="hlt">system</span> referred to as SAM2v1 is being developed from the SEEK (Singular Evolutive Extended Kalman) algorithm. This scheme is a Reduced Order Kalman Filter using a 3D multivariate modal decomposition of the <span class="hlt">forecast</span> error covariance as well as an adaptive scheme to specify parameters of the <span class="hlt">forecast</span> error. Assimilation experiments of SSS (Sea Surface Salinity) with the SAM2v1 scheme using various observing <span class="hlt">systems</span> (SMOS Level 2, Aquarius Level 2, SMOS L2 + Aquarius L2) have been performed and inter-compared. In all the assimilation experiments, the operational observation baseline (along track sea level anomalies, SST, temperature and salinity in situ profiles and T/S climatology field in under 2000 meter depth) has been taken up. The OSSE enabled to show the positive impact of SSS assimilation on the MERCATOR operational <span class="hlt">forecasting</span> <span class="hlt">system</span> with a regional configuration at 1/3°, even when all other data set (altimetry, sea surface temperature, and in situ vertical profiles of temperature and salinity) are assimilated. This study has to be considered as a milestone. Further studies have to be conducted with other simulated data, other oceanic configurations and other improved assimilation schemes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.4013B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.4013B"><span id="translatedtitle">Development of A Real Time Physically-based Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> In The Piemonte Region, Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barbero, S. P.; Rabuffetti, D.; Buffo, M.; Graziadei, M.</p> <p></p> <p>The development and implementation of the Piemonte RegionSs real-time Flood Fore- casting <span class="hlt">System</span> is described. The area of interest is the Upper Po River basin (North- west Italy) of approximately 37000 km2 and its river network of about 3000 Km and 3 big lakes. FloodWatch, a GIS-based decision support <span class="hlt">system</span> for real-time flood fore- casting has been developed and operationally used since June 2000 at the Piemonte RegionSs Room for the Situation of Natural Hazards in Torino, Italy. FloodWatch is based on MIKE 11 modules which provide a continuos lumped hydrological model- ing of 187 tree-structured subcatchments connected by a 1D distributed hydrodynamic model. It is directly linked to the existing telemetric <span class="hlt">system</span>, which provides measured data from more than 270 meteorological stations (rainfall and temperature) and about 80 water level gauging stations. In addition, FloodWatch uses quantitative precipita- tion and temperature <span class="hlt">forecasts</span> daily issued by the Regional Meteorological Service on the 11 zones in which the study area is subdivided. At present, FloodWatch auto- matically supplies operational <span class="hlt">forecasts</span> of water-level and discharge at 73 locations for up to 48 hours. The development of a fast and reliable flow <span class="hlt">forecasting</span> <span class="hlt">system</span> for this large and heterogeneous river basin required careful balance between the need for rapid and accurate <span class="hlt">forecasts</span> and of a correct representation of run-off generation, flood propagation, baseflows, snow accumulation and melting. Strengths and limits of the <span class="hlt">system</span> are focused addressing the need for future development. Some results are presented with particular regard to the October 2000 flood event, when the northwest of Italy experienced one of the largest floods on record. Heavy and prolonged rainfall fell across the entire Po river basin. The flood inundated vast areas causing widespread damage and thousands of people were warned and alerted to evacuate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H51F1425P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H51F1425P&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Forecast</span>-Based Operations Support Tool for the New York City Water Supply <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pyke, G.; Porter, J.</p> <p>2012-12-01</p> <p>The NYC water supply <span class="hlt">system</span> serves 9 million people with over 1 BGD of water drawn from 19 reservoirs. To support operation of the <span class="hlt">system</span> to meet multiple objectives (e.g. supply reliability, water quality, environmental releases, hydropower, peak flow mitigation), the New York City Department of Environmental Protection (DEP) is developing an Operations Support Tool (OST), a <span class="hlt">forecast</span>-based decision support <span class="hlt">system</span> that provides a probabilistic foundation for water supply operations and planning. Key features of OST include: the ability to run both long-term simulations and short-term probabilistic simulations on the same model platform; automated processing of near-real-time (NRT) data sources; use of inflow <span class="hlt">forecasts</span> to support look-ahead operational simulations; and water supply-water quality model linkage to account for feedback and tradeoffs between supply and quality objectives. OST supports two types of simulations. Long-term runs execute the <span class="hlt">system</span> model over an extended historical record and are used to evaluate reservoir operating rules, infrastructure modifications, and climate change scenarios (with inflows derived from downscaled GCM data). Short-term runs for operational guidance consist of multiple (e.g. 80+) short (e.g. one year) runs, all starting from the same initial conditions (typically those of the current day). Ensemble reservoir inflow <span class="hlt">forecast</span> traces are used to drive the model for the duration of the simulation period. The result of these runs is a distribution of potential future <span class="hlt">system</span> states. DEP managers analyze the distributions for alternate scenarios and make operations decisions using risk-based metrics such as probability of refill or the likelihood of a water quality event. For operational simulations, the OST data <span class="hlt">system</span> acquires NRT data from DEP internal sources (SCADA operations data, keypoint water quality, in-stream/in-reservoir water quality, meteorological and snowpack monitoring sites). OST acquires streamflow data from</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4276118','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4276118"><span id="translatedtitle"><span class="hlt">Forecasting</span> Significant Societal Events Using The Embers Streaming Predictive Analytics <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren</p> <p>2014-01-01</p> <p>Abstract Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics <span class="hlt">system</span> for <span class="hlt">forecasting</span> significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the <span class="hlt">system</span>, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in <span class="hlt">forecasting</span> significant societal events in the past 2 years. PMID:25553271</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/160604','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/160604"><span id="translatedtitle">Demand <span class="hlt">forecasting</span> using fuzzy neural computation, with special emphasis on weekend and public holiday <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Srinivasan, D.; Chang, C.S.; Liew, A.C.</p> <p>1995-11-01</p> <p>This paper describes the implementation and <span class="hlt">forecasting</span> results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric <span class="hlt">load</span> <span class="hlt">forecasting</span>. The strengths of this powerful technique lie in its ability to <span class="hlt">forecast</span> accurately on weekdays, as well as, on weekends, public holidays, and days before and after public holidays. Furthermore, use of fuzzy logic effectively handles the <span class="hlt">load</span> variations due to special events. The Fuzzy-Neural Network (FNN) has been extensively tested on actual data obtained from a power <span class="hlt">system</span> for 24-hour ahead prediction based on <span class="hlt">forecast</span> weather information. Very impressive results, with an average error of 0.62% on weekdays, 0.83% on Saturdays and 1.17% on Sundays and public holidays have been obtained. This approach avoids complex mathematical calculations and training on many years of data, and is simple to implement on a personal computer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=47852&keyword=refrigeration&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=76661812&CFTOKEN=40093235','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=47852&keyword=refrigeration&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=76661812&CFTOKEN=40093235"><span id="translatedtitle">CONTROL OF HYDROCARBON EMISSIONS FROM GASOLINE <span class="hlt">LOADING</span> BY REFRIGERATION <span class="hlt">SYSTEMS</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The report gives results of a study of the capabilities of refrigeration <span class="hlt">systems</span>, operated at three temperatures, to control volatile organic compound (VOC) emissions from truck <span class="hlt">loading</span> at bulk gasoline terminals. Achievable VOC emission rates were calculated for refrigeration sy...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890001756','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890001756"><span id="translatedtitle">Power quality <span class="hlt">load</span> management for large spacecraft electrical power <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lollar, Louis F.</p> <p>1988-01-01</p> <p>In December, 1986, a Center Director's Discretionary Fund (CDDF) proposal was granted to study power <span class="hlt">system</span> control techniques in large space electrical power <span class="hlt">systems</span>. Presented are the accomplishments in the area of power <span class="hlt">system</span> control by power quality <span class="hlt">load</span> management. In addition, information concerning the distortion problems in a 20 kHz ac power <span class="hlt">system</span> is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007PhDT........96L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007PhDT........96L"><span id="translatedtitle">Development of fuzzy <span class="hlt">system</span> and nonlinear regression models for ozone and PM2.5 air quality <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, Yiqiu</p> <p>2007-12-01</p> <p>Ozone <span class="hlt">forecast</span> models using nonlinear regression (NLR) have been successfully applied to daily ozone <span class="hlt">forecast</span> for seven metro areas in Kentucky, including Ashland, Bowling Green, Covington, Lexington, Louisville, Owensboro, and Paducah. In this study, the updated 2005 NLR ozone <span class="hlt">forecast</span> models for these metro areas were evaluated on both the calibration data sets and independent data sets. These NLR ozone <span class="hlt">forecast</span> models explained at least 72% of the variance of the daily peak ozone. Using the models to predict the ozone concentrations during the 2005 ozone season, the metro area mean absolute errors (MAEs) of the model hindcasts ranged from 5.90 ppb to 7.20 ppb. For the model raw <span class="hlt">forecasts</span>, the metro area MAEs ranged from 7.90 ppb to 9.80 ppb. Based on previously developed NLR ozone <span class="hlt">forecast</span> models for those areas, Takagi-Sugeno fuzzy <span class="hlt">system</span> models were developed for the seven metro areas. The fuzzy "c-means" clustering technique coupled with an optimal output predefuzzification approach (least square method) was used to train the Takagi-Sugeno fuzzy <span class="hlt">system</span>. Two types of fuzzy models, basic fuzzy and NLR-fuzzy <span class="hlt">system</span> models, were developed. The basic fuzzy and NLR-fuzzy models exhibited essentially equivalent performance to the existing NLR models on 2004 ozone season hindcasts and <span class="hlt">forecasts</span>. Both types of fuzzy models had, on average, slightly lower metro area averaged MAEs than the NLR models. Among the seven Kentucky metro areas Ashland, Covington, and Louisville are currently designated nonattainment areas for both ground level O 3 and PM2.5. In this study, summer PM2.5 <span class="hlt">forecast</span> models were developed for providing daily average PM2.5 <span class="hlt">forecasts</span> for the seven metro areas. The performance of the PM2.5 <span class="hlt">forecast</span> models was generally not as good as that of the ozone <span class="hlt">forecast</span> models. For the summer 2004 model hindcasts, the metro-area average MAE was 5.33 mug/m 3. Exploratory research was conducted to find the relationship between the winter PM2.5 concentrations and</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25097265','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25097265"><span id="translatedtitle">Testing for ontological errors in probabilistic <span class="hlt">forecasting</span> models of natural <span class="hlt">systems</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Marzocchi, Warner; Jordan, Thomas H</p> <p>2014-08-19</p> <p>Probabilistic <span class="hlt">forecasting</span> models describe the aleatory variability of natural <span class="hlt">systems</span> as well as our epistemic uncertainty about how the <span class="hlt">systems</span> work. Testing a model against observations exposes ontological errors in the representation of a <span class="hlt">system</span> and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic <span class="hlt">forecasting</span> models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the <span class="hlt">forecasting</span> problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic <span class="hlt">forecasting</span>. Pessimistic views of <span class="hlt">system</span> predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4143071','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4143071"><span id="translatedtitle">Testing for ontological errors in probabilistic <span class="hlt">forecasting</span> models of natural <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Marzocchi, Warner; Jordan, Thomas H.</p> <p>2014-01-01</p> <p>Probabilistic <span class="hlt">forecasting</span> models describe the aleatory variability of natural <span class="hlt">systems</span> as well as our epistemic uncertainty about how the <span class="hlt">systems</span> work. Testing a model against observations exposes ontological errors in the representation of a <span class="hlt">system</span> and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic <span class="hlt">forecasting</span> models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the <span class="hlt">forecasting</span> problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic <span class="hlt">forecasting</span>. Pessimistic views of <span class="hlt">system</span> predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1111787H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1111787H"><span id="translatedtitle">The estimating of Curve Number from River Level for real-time flood <span class="hlt">forecasting</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, M.; Yoon, Kanghoon</p> <p>2009-04-01</p> <p>In the South Korea, the NRCS runoff curve number method is used to estimate the effective rainfall and the CN has much effect on the peak discharge and time for the real-time <span class="hlt">forecasting</span> <span class="hlt">system</span>. According to the experience and existing research about flooding <span class="hlt">forecasting</span> <span class="hlt">system</span>, the new method to estimate CN would be necessary, since it is very difficult to operate the flood <span class="hlt">forecasting</span> <span class="hlt">system</span> using the method which uses the AMC from 5-day antecedent rainfall developed by NRCS. It could be assumed that the maximum potential retention(S) will be related to the groundwater or groundwater levels; therefore, the relationship between water stage in river and maximum potential retention(S) would be investigated. In order to derive the relationship, the flooding data of 1980 through 2007 in Sulmachun and Pyungchang River is used, since this data is delicately constructed. Here, the CN is calculated using the total rainfall discharge and the total depth of runoff discharge at the flooding period and then water stage in river and maximum potential retention(S) would be determined. The relationship between water level in river and maximum potential retention(S) or CN has a higher correlation under the specific water stage of about 0.1m^3/sec/km^2; however, it shows relatively lower correlation above the specific water level. This result shows that NRCS method represents the relationship very well in the lower water stage as infiltration is actively occurred with relatively higher maximum potential retention(S). Keyword : CN, rela-time <span class="hlt">forecasting</span> <span class="hlt">system</span>, water stage</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005JMS....56...45O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005JMS....56...45O"><span id="translatedtitle">A rapid response nowcast/<span class="hlt">forecast</span> <span class="hlt">system</span> using multiply nested ocean models and distributed data <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Onken, Reiner; Robinson, Allan R.; Kantha, Lakshmi; Lozano, Carlos J.; Haley, Patrick J.; Carniel, Sandro</p> <p>2005-05-01</p> <p>Logistics and results of a real-time modeling effort, which took place in fall 2000 in the waters between Corsica and Italy in the Mediterranean Sea, are presented. The major objective was to nest a high-resolution local version of the Harvard Ocean Prediction <span class="hlt">System</span> (HOPS) into a coarse resolution Colorado University Princeton Ocean Model (CUPOM) covering the northern part of the Western Mediterranean. Due to the different designs of CUPOM and HOPS, traditional nesting methods were not successful. Therefore, a new method was developed that assimilated the CUPOM prognostic fields into HOPS instead of prescribing them only along the open boundaries. Another objective of the effort was to set up and test a data distribution <span class="hlt">system</span>, providing Internet-based rapid data transfer among the project partners being partly at sea and partly on land in different continents. It is shown that such a <span class="hlt">system</span> works, enabling turnaround times of less than a day from the time when measurements are taken to the release of the model <span class="hlt">forecast</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.3334F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.3334F"><span id="translatedtitle">Annual Rainfall <span class="hlt">Forecasting</span> by Using Mamdani Fuzzy Inference <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.</p> <p>2009-04-01</p> <p>Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent <span class="hlt">systems</span> that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference <span class="hlt">System</span> (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006ACPD....6.1867B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006ACPD....6.1867B"><span id="translatedtitle">Integrated <span class="hlt">systems</span> for <span class="hlt">forecasting</span> urban meteorology, air pollution and population exposure</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baklanov, A.; Hänninen, O.; Slørdal, L. H.; Kukkonen, J.; Bjergene, N.; Fay, B.; Finardi, S.; Hoe, S. C.; Jantunen, M.; Karppinen, A.; Rasmussen, A.; Skouloudis, A.; Sokhi, R. S.; Sørensen, J. H.</p> <p>2006-03-01</p> <p>Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement <span class="hlt">systems</span> require accurate <span class="hlt">forecasting</span> of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and <span class="hlt">Forecasting</span> <span class="hlt">Systems</span> (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the <span class="hlt">systems</span> in three Nordic cities: Helsinki and Oslo for assessment and <span class="hlt">forecasting</span> of urban air pollution and Copenhagen for urban emergency preparedness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007ACP.....7..855B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007ACP.....7..855B"><span id="translatedtitle">Integrated <span class="hlt">systems</span> for <span class="hlt">forecasting</span> urban meteorology, air pollution and population exposure</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baklanov, A.; Hänninen, O.; Slørdal, L. H.; Kukkonen, J.; Bjergene, N.; Fay, B.; Finardi, S.; Hoe, S. C.; Jantunen, M.; Karppinen, A.; Rasmussen, A.; Skouloudis, A.; Sokhi, R. S.; Sørensen, J. H.; Ødegaard, V.</p> <p>2007-02-01</p> <p>Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement <span class="hlt">systems</span> require accurate <span class="hlt">forecasting</span> of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and <span class="hlt">Forecasting</span> <span class="hlt">Systems</span> (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the <span class="hlt">systems</span> in three Nordic cities: Helsinki and Oslo for assessment and <span class="hlt">forecasting</span> of urban air pollution and Copenhagen for urban emergency preparedness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813516T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813516T&link_type=ABSTRACT"><span id="translatedtitle">The Use of Scale-Dependent Precision to Increase <span class="hlt">Forecast</span> Accuracy in Earth <span class="hlt">System</span> Modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thornes, Tobias; Duben, Peter; Palmer, Tim</p> <p>2016-04-01</p> <p>At the current pace of development, it may be decades before the 'exa-scale' computers needed to resolve individual convective clouds in weather and climate models become available to <span class="hlt">forecasters</span>, and such machines will incur very high power demands. But the resolution could be improved today by switching to more efficient, 'inexact' hardware with which variables can be represented in 'reduced precision'. Currently, all numbers in our models are represented as double-precision floating points - each requiring 64 bits of memory - to minimise rounding errors, regardless of spatial scale. Yet observational and modelling constraints mean that values of atmospheric variables are inevitably known less precisely on smaller scales, suggesting that this may be a waste of computer resources. More accurate <span class="hlt">forecasts</span> might therefore be obtained by taking a scale-selective approach whereby the precision of variables is gradually decreased at smaller spatial scales to optimise the overall efficiency of the model. To study the effect of reducing precision to different levels on multiple spatial scales, we here introduce a new model atmosphere developed by extending the Lorenz '96 idealised <span class="hlt">system</span> to encompass three tiers of variables - which represent large-, medium- and small-scale features - for the first time. In this chaotic but computationally tractable <span class="hlt">system</span>, the 'true' state can be defined by explicitly resolving all three tiers. The abilities of low resolution (single-tier) double-precision models and similar-cost high resolution (two-tier) models in mixed-precision to produce accurate <span class="hlt">forecasts</span> of this 'truth' are compared. The high resolution models outperform the low resolution ones even when small-scale variables are resolved in half-precision (16 bits). This suggests that using scale-dependent levels of precision in more complicated real-world Earth <span class="hlt">System</span> models could allow <span class="hlt">forecasts</span> to be made at higher resolution and with improved accuracy. If adopted, this new</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860009858','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860009858"><span id="translatedtitle">Improved memory <span class="hlt">loading</span> techniques for the TSRV display <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Easley, W. C.; Lynn, W. A.; Mcluer, D. G.</p> <p>1986-01-01</p> <p>A recent upgrade of the TSRV research flight <span class="hlt">system</span> at NASA Langley Research Center retained the original monochrome display <span class="hlt">system</span>. However, the display memory <span class="hlt">loading</span> equipment was replaced requiring design and development of new methods of performing this task. This paper describes the new techniques developed to <span class="hlt">load</span> memory in the display <span class="hlt">system</span>. An outdated paper tape method for <span class="hlt">loading</span> the BOOTSTRAP control program was replaced by EPROM storage of the characters contained on the tape. Rather than move a tape past an optical reader, a counter was implemented which steps sequentially through EPROM addresses and presents the same data to the loader circuitry. A cumbersome cassette tape method for <span class="hlt">loading</span> the applications software was replaced with a floppy disk method using a microprocessor terminal installed as part of the upgrade. The cassette memory image was transferred to disk and a specific software loader was written for the terminal which duplicates the function of the cassette loader.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/5052357','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/5052357"><span id="translatedtitle">Integrated <span class="hlt">loading</span> rate determination for wastewater infiltration <span class="hlt">system</span> sizing</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Jenssen, P.D. . Centre for Soil and Environmental Research); Siegrist, R.L. )</p> <p>1991-01-01</p> <p>One of the principal parameters used in wastewater <span class="hlt">system</span> design is the hydraulic <span class="hlt">loading</span> rate. Historically the determination of the <span class="hlt">loading</span> rate has been a straight forward process involving selection of a rate based on soil texture or water percolation rate. Research and experience over the past decade has provided additional insight into the complex processes occurring within wastewater-amended soil <span class="hlt">systems</span> and has suggested the fallacy of this approach. A mean grain size vs. sorting (MESO) diagram constitutes a new basis for soil classification for wastewater infiltration <span class="hlt">system</span> design. Crude characterization of the soil hydraulic properties is possible according to the MESO Diagram and <span class="hlt">loading</span> rate as well as certain purification aspects can be assessed from the diagram. In this paper, an approach is described based on the MESO Diagram that integrates soil properties and wastewater pretreatment to yield a <span class="hlt">loading</span> rate. 53 refs., 3 figs., 2 tabs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013SPIE.8878E..1BB','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013SPIE.8878E..1BB"><span id="translatedtitle">A novel <span class="hlt">load</span> balancing method for hierarchical federation simulation <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bin, Xiao; Xiao, Tian-yuan</p> <p>2013-07-01</p> <p>In contrast with single HLA federation framework, hierarchical federation framework can improve the performance of large-scale simulation <span class="hlt">system</span> in a certain degree by distributing <span class="hlt">load</span> on several RTI. However, in hierarchical federation framework, RTI is still the center of message exchange of federation, and it is still the bottleneck of performance of federation, the data explosion in a large-scale HLA federation may cause overload on RTI, It may suffer HLA federation performance reduction or even fatal error. Towards this problem, this paper proposes a <span class="hlt">load</span> balancing method for hierarchical federation simulation <span class="hlt">system</span> based on queuing theory, which is comprised of three main module: queue length predicting, <span class="hlt">load</span> controlling policy, and controller. The method promotes the usage of resources of federate nodes, and improves the performance of HLA simulation <span class="hlt">system</span> with balancing <span class="hlt">load</span> on RTIG and federates. Finally, the experiment results are presented to demonstrate the efficient control of the method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A11H0164L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A11H0164L"><span id="translatedtitle">A Two-Dimensional Gridded Solar <span class="hlt">Forecasting</span> <span class="hlt">System</span> using Situation-Dependent Blending of Multiple Weather Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, S.; Hwang, Y.; Shao, X.; Hamann, H.</p> <p>2015-12-01</p> <p>Previously, we reported the application of a "weather situation" dependent multi-model blending approach to improve the <span class="hlt">forecast</span> accuracy of solar irradiance and other atmospheric parameters. The approach uses machine-learning techniques to classify "weather situations" by a set of atmospheric parameters. The "weather situation" classification is location-dependent and each "weather situation" has characteristic <span class="hlt">forecast</span> errors from a set of individual input numerical weather prediction (NWP) models. The input models are thus corrected or combined differently for different "weather situations" to minimize the overall <span class="hlt">forecast</span> error. While the original implementation of the model-blending is applicable to only point-like locations having historical data of both measurements and <span class="hlt">forecasts</span>, here we extend the approach to provide two-dimensional (2D) gridded <span class="hlt">forecasts</span>. An experimental 2D <span class="hlt">forecasting</span> <span class="hlt">system</span> has been set up to provide gridded <span class="hlt">forecasts</span> of solar irradiance (global horizontal irradiance), temperature, wind speed, and humidity for the contiguous United States (CONUS). Validation results show around 30% enhancement of 0 to 48 hour ahead solar irradiance <span class="hlt">forecast</span> accuracy compared to the best input NWP model. The <span class="hlt">forecasting</span> <span class="hlt">system</span> may be leveraged by other site- or region-specific solar energy <span class="hlt">forecast</span> products. To enable the 2D <span class="hlt">forecasting</span> <span class="hlt">system</span>, historical solar irradiance measurements from around 1,600 selected sites of the remote automated weather stations (RAWS) network have been employed. The CONUS was divided into smaller sub-regions, each containing a group of 10 to 20 RAWS sites. A group of sites, as classified by statistical analysis, have similar "weather patterns", i.e. the NWPs have similar "weather situation" dependent <span class="hlt">forecast</span> errors for all sites in a group. The model-blending trained by the historical data from a group of sites is then applied for all locations in the corresponding sub-region. We discuss some key techniques developed for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015APS..SHK.W1025G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015APS..SHK.W1025G&link_type=ABSTRACT"><span id="translatedtitle">Gas <span class="hlt">loading</span> <span class="hlt">system</span> for LANL two-stage gas guns</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gibson, Lee; Bartram, Brian; Dattelbaum, Dana; Lang, John; Morris, John</p> <p>2015-06-01</p> <p>A novel gas <span class="hlt">loading</span> <span class="hlt">system</span> was designed for the specific application of remotely <span class="hlt">loading</span> high purity gases into targets for gas-gun driven plate impact experiments. The high purity gases are <span class="hlt">loaded</span> into well-defined target configurations to obtain Hugoniot states in the gas phase at greater than ambient pressures. The small volume of the gas samples is challenging, as slight changing in the ambient temperature result in measurable pressure changes. Therefore, the ability to <span class="hlt">load</span> a gas gun target and continually monitor the sample pressure prior to firing provides the most stable and reliable target fielding approach. We present the design and evaluation of a gas <span class="hlt">loading</span> <span class="hlt">system</span> built for the LANL 50 mm bore two-stage light gas gun. Targets for the gun are made of 6061 Al or OFHC Cu, and assembled to form a gas containment cell with a volume of approximately 1.38 cc. The compatibility of materials was a major consideration in the design of the <span class="hlt">system</span>, particularly for its use with corrosive gases. Piping and valves are stainless steel with wetted seals made from Kalrez and Teflon. Preliminary testing was completed to ensure proper flow rate and that the proper safety controls were in place. The <span class="hlt">system</span> has been used to successfully <span class="hlt">load</span> Ar, Kr, Xe, and anhydrous ammonia with purities of up to 99.999 percent. The design of the <span class="hlt">system</span>, and example data from the plate impact experiments will be shown. LA-UR-15-20521</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.2284M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.2284M"><span id="translatedtitle">Probabilistic Prediction of European Winter Temperature and Their Application Using The Ecmwf Seasonal <span class="hlt">Forecast</span> <span class="hlt">System</span> 1 and 2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Müller, W.; Appenzeller, Ch.</p> <p></p> <p>There is rising interest in economical applications of seasonal climate <span class="hlt">forecasts</span>, for example for weather risk and weather derivative markets. However seasonal <span class="hlt">forecast</span>- ing based on coupled atmosphere -ocean models is a complex task. As a consequence of the chaotic nature of the climate <span class="hlt">system</span> seasonal <span class="hlt">forecasts</span> can not be calculated in a deterministic sense. They need to be calculated in a probabilistic way by using an ensemble of model runs with slightly different initial conditions. Here we use the ECMWF experimental ensemble prediction <span class="hlt">system</span> 1 and 2 to explore the sensitivity of mid-latitude winter mean temperature <span class="hlt">forecasts</span> on different drift correction methods. The <span class="hlt">forecast</span> quality is quantified in a probabilistic framework using ranked proba- bility skill scores (RPSS). It is shown that a drift correction method that accounts for <span class="hlt">system</span> 1 decadal climate variability (such as the NAO) has a positive, but weak impact on the <span class="hlt">forecast</span> skill, especially over Europe. As an economic application we evaluate the skill of three month averaged heating degree days <span class="hlt">forecasts</span> over Europe.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhPro..24..832X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhPro..24..832X"><span id="translatedtitle">Power Grid Maintenance Scheduling Intelligence Arrangement Supporting <span class="hlt">System</span> Based on Power Flow <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xie, Chang; Wen, Jing; Liu, Wenying; Wang, Jiaming</p> <p></p> <p>With the development of intelligent dispatching, the intelligence level of network control center full-service urgent need to raise. As an important daily work of network control center, the application of maintenance scheduling intelligent arrangement to achieve high-quality and safety operation of power grid is very important. By analyzing the shortages of the traditional maintenance scheduling software, this paper designs a power grid maintenance scheduling intelligence arrangement supporting <span class="hlt">system</span> based on power flow <span class="hlt">forecasting</span>, which uses the advanced technologies in maintenance scheduling, such as artificial intelligence, online security checking, intelligent visualization techniques. It implements the online security checking of maintenance scheduling based on power flow <span class="hlt">forecasting</span> and power flow adjusting based on visualization, in order to make the maintenance scheduling arrangement moreintelligent and visual.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.819N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.819N"><span id="translatedtitle">Downscaling modelling <span class="hlt">system</span> for multi-scale air quality <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nuterman, R.; Baklanov, A.; Mahura, A.; Amstrup, B.; Weismann, J.</p> <p>2010-09-01</p> <p>Urban modelling for real meteorological situations, in general, considers only a small part of the urban area in a micro-meteorological model, and urban heterogeneities outside a modelling domain affect micro-scale processes. Therefore, it is important to build a chain of models of different scales with nesting of higher resolution models into larger scale lower resolution models. Usually, the up-scaled city- or meso-scale models consider parameterisations of urban effects or statistical descriptions of the urban morphology, whereas the micro-scale (street canyon) models are obstacle-resolved and they consider a detailed geometry of the buildings and the urban canopy. The developed <span class="hlt">system</span> consists of the meso-, urban- and street-scale models. First, it is the Numerical Weather Prediction (HIgh Resolution Limited Area Model) model combined with Atmospheric Chemistry Transport (the Comprehensive Air quality Model with extensions) model. Several levels of urban parameterisation are considered. They are chosen depending on selected scales and resolutions. For regional scale, the urban parameterisation is based on the roughness and flux corrections approach; for urban scale - building effects parameterisation. Modern methods of computational fluid dynamics allow solving environmental problems connected with atmospheric transport of pollutants within urban canopy in a presence of penetrable (vegetation) and impenetrable (buildings) obstacles. For local- and micro-scales nesting the Micro-scale Model for Urban Environment is applied. This is a comprehensive obstacle-resolved urban wind-flow and dispersion model based on the Reynolds averaged Navier-Stokes approach and several turbulent closures, i.e. k -ɛ linear eddy-viscosity model, k - ɛ non-linear eddy-viscosity model and Reynolds stress model. Boundary and initial conditions for the micro-scale model are used from the up-scaled models with corresponding interpolation conserving the mass. For the boundaries a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/6279558','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/6279558"><span id="translatedtitle">Measurements of individual parachute <span class="hlt">loads</span> in a clustered parachute <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Behr, V.L.</p> <p>1989-01-01</p> <p>When developing any parachute <span class="hlt">system</span>, it is necessary to know the <span class="hlt">loads</span> produced by the parachute throughout the deployment process. A major concern with a clustered parachute <span class="hlt">system</span> is the nonconcurrent inflation of the individual parachutes. If this nonconcurrent inflation produces sufficiently large asymmetric <span class="hlt">loads</span>, the design <span class="hlt">loads</span> may be exceeded. In the past, <span class="hlt">load</span> data have frequently been inferred from acceleration data of the parachute test vehicle. However, due to vehicle angle of attack and attitude relative to the longitudinal axis of the parachute, this method can give erroneous results. Thus, in the development of parachute <span class="hlt">systems</span>, it is desirable to have an instrumentation <span class="hlt">system</span> which can directly measure the <span class="hlt">load</span> produced by each parachute. Due to the environment, this <span class="hlt">system</span> must be very rugged and have minimal space requirements. Such a <span class="hlt">system</span> has been designed and incorporated into a test program at Sandia National Laboratories. The <span class="hlt">system</span> is described and representative test results are given to demonstrate the usefulness of such a <span class="hlt">system</span> in a development program. 3 refs., 6 figs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70047167','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70047167"><span id="translatedtitle">A prototype <span class="hlt">system</span> for <span class="hlt">forecasting</span> landslides in the Seattle, Washington, area</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Chleborad, Alan F.; Baum, Rex L.; Godt, Jonathan W.; Powers, Philip S.</p> <p>2008-01-01</p> <p>Empirical rainfall thresholds and related information form the basis of a prototype <span class="hlt">system</span> for <span class="hlt">forecasting</span> landslides in the Seattle area. The <span class="hlt">forecasts</span> are tied to four alert levels, and a decision tree guides the use of thresholds to determine the appropriate level. From analysis of historical landslide data, we developed a formula for a cumulative rainfall threshold (CT), P3  =  88.9 − 0.67P15, defined by rainfall amounts in millimeters during consecutive 3 d (72 h) periods, P3, and the 15 d (360 h) period before P3, P15. The variable CT captures more than 90% of historical events of three or more landslides in 1 d and 3 d periods recorded from 1978 to 2003. However, the low probability of landslide occurrence on a day when the CT is exceeded at one or more rain gauges (8.4%) justifies a low-level of alert for possible landslide occurrence, but it does trigger more vigilant monitoring of rainfall and soil wetness. Exceedance of a rainfall intensity-duration threshold I  =  82.73D−1.13, for intensity, I (mm/hr), and duration, D (hr), corresponds to a higher probability of landslide occurrence (30%) and forms the basis for issuing warnings of impending, widespread occurrence of landslides. Information about the area of exceedance and soil wetness can be used to increase the certainty of landslide <span class="hlt">forecasts</span> (probabilities as great as 71%). Automated analysis of real-time rainfall and subsurface water data and digital quantitative precipitation <span class="hlt">forecasts</span> are needed to fully implement a warning <span class="hlt">system</span> based on the two thresholds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.9667H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.9667H"><span id="translatedtitle">Intercomparison of Operational Ocean <span class="hlt">Forecasting</span> <span class="hlt">Systems</span> in the framework of GODAE</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hernandez, F.</p> <p>2009-04-01</p> <p>One of the main benefits of the GODAE 10-year activity is the implementation of ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span> in several countries. In 2008, several <span class="hlt">systems</span> are operated routinely, at global or basin scale. Among them, the BLUElink (Australia), HYCOM (USA), MOVE/MRI.COM (Japan), Mercator (France), FOAM (United Kingdom), TOPAZ (Norway) and C-NOOFS (Canada) <span class="hlt">systems</span> offered to demonstrate their operational feasibility by performing an intercomparison exercise during a three months period (February to April 2008). The objectives were: a) to show that operational ocean <span class="hlt">forecasting</span> <span class="hlt">systems</span> are operated routinely in different countries, and that they can interact; b) to perform in a similar way a scientific validation aimed to assess the quality of the ocean estimates, the performance, and <span class="hlt">forecasting</span> capabilities of each <span class="hlt">system</span>; and c) to learn from this intercomparison exercise to increase inter-operability and collaboration in real time. The intercomparison relies on the assessment strategy developed for the EU MERSEA project, where diagnostics over the global ocean have been revisited by the GODAE contributors. This approach, based on metrics, allow for each <span class="hlt">system</span>: a) to verify if ocean estimates are consistent with the current general knowledge of the dynamics; and b) to evaluate the accuracy of delivered products, compared to space and in-situ observations. Using the same diagnostics also allows one to intercompare the results from each <span class="hlt">system</span> consistently. Water masses and general circulation description by the different <span class="hlt">systems</span> are consistent with WOA05 Levitus climatology. The large scale dynamics (tropical, subtropical and subpolar gyres ) are also correctly reproduced. At short scales, benefit of high resolution <span class="hlt">systems</span> can be evidenced on the turbulent eddy field, in particular when compared to eddy kinetic energy deduced from satellite altimetry of drifter observations. Comparisons to high resolution SST products show some discrepancies on ocean surface</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12..835R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12..835R"><span id="translatedtitle">Assessment of a fuzzy based flood <span class="hlt">forecasting</span> <span class="hlt">system</span> optimized by simulated annealing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reyhani Masouleh, Aida; Pakosch, Sabine; Disse, Markus</p> <p>2010-05-01</p> <p>Flood <span class="hlt">forecasting</span> is an important tool to mitigate harmful effects of floods. Among the many different approaches for <span class="hlt">forecasting</span>, Fuzzy Logic (FL) is one that has been increasingly applied over the last decade. This method is principally based on the linguistic description of Rule <span class="hlt">Systems</span> (RS). A RS is a specific combination of membership functions of input and output variables. Setting up the RS can be implemented either automatically or manually, the choice of which can strongly influence the resulting rule <span class="hlt">systems</span>. It is therefore the objective of this study to assess the influence that the parameters of an automated rule generation based on Simulated Annealing (SA) have on the resulting RS. The study area is the upper Main River area, located in the northern part of Bavaria, Germany. The data of Mainleus gauge with area of 1165 km2 was investigated in the whole period of 1984 and 2004. The highest observed discharge of 357 m3/s was recorded in 1995. The input arguments of the FL model were daily precipitation, <span class="hlt">forecasted</span> precipitation, antecedent precipitation index, temperature and melting rate. The FL model of this study has one output variable, daily discharge and was independently set up for three different <span class="hlt">forecast</span> lead times, namely one-, two- and three-days ahead. In total, each RS comprised 55 rules and all input and output variables were represented by five sets of trapezoidal and triangular fuzzy numbers. Simulated Annealing, which is a converging optimum solution algorithm, was applied for optimizing the RSs in this study. In order to assess the influence of its parameters (number of iterations, temperature decrease rate, initial value for generating random numbers, initial temperature and two other parameters), they were individually varied while keeping the others fixed. With each of the resulting parameter sets, a full-automatic SA was applied to gain optimized fuzzy rule <span class="hlt">systems</span> for flood <span class="hlt">forecasting</span>. Evaluation of the performance of the</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17..730T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..730T"><span id="translatedtitle">A pan-African medium-range ensemble flood <span class="hlt">forecast</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta</p> <p>2015-04-01</p> <p>The African Flood <span class="hlt">Forecasting</span> <span class="hlt">System</span> (AFFS) is a probabilistic flood <span class="hlt">forecast</span> <span class="hlt">system</span> for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood <span class="hlt">forecasting</span> <span class="hlt">system</span> for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood <span class="hlt">forecasting</span> <span class="hlt">system</span>, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the <span class="hlt">system</span> showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009aame.conf..238K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009aame.conf..238K"><span id="translatedtitle">Towards a <span class="hlt">Load</span> Balancing Middleware for Automotive Infotainment <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khaluf, Yara; Rettberg, Achim</p> <p></p> <p>In this paper a middleware for distributed automotive <span class="hlt">systems</span> is developed. The goal of this middleware is to support the <span class="hlt">load</span> bal- ancing and service optimization in automotive infotainment and entertainment <span class="hlt">systems</span>. These <span class="hlt">systems</span> provide navigation, telecommunication, Internet, audio/video and many other services where a kind of dynamic <span class="hlt">load</span> balancing mechanisms in addition to service quality optimization mechanisms will be applied by the developed middleware in order to improve the <span class="hlt">system</span> performance and also at the same time improve the quality of services if possible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMOS23C1211B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMOS23C1211B"><span id="translatedtitle">Northeast Coastal Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> (NECOFS): A Multi-scale Global-Regional-Estuarine FVCOM Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beardsley, R. C.; Chen, C.</p> <p>2014-12-01</p> <p>The Northeast Coastal Ocean <span class="hlt">Forecast</span> <span class="hlt">System</span> (NECOFS) is a global-regional-estuarine integrated atmosphere/surface wave/ocean <span class="hlt">forecast</span> model <span class="hlt">system</span> designed for the northeast US coastal region covering a computational domain from central New Jersey to the eastern end of the Scotian Shelf. The present <span class="hlt">system</span> includes 1) the mesoscale meteorological model WRF (Weather Research and <span class="hlt">Forecasting</span>); 2) the regional-domain FVCOM covering the Gulf of Maine/Georges Bank/New England Shelf region (GOM-FVCOM); 3) the unstructured-grid surface wave model (FVCOM-SWAVE) modified from SWAN with the same domain as GOM-FVCOM; 3) the Mass coastal FVCOM with inclusion of inlets, estuaries and intertidal wetlands; and 4) three subdomain wave-current coupled inundation FVCOM <span class="hlt">systems</span> in Scituate, MA, Hampton River, NH and Mass Bay, MA. GOM-FVCOM grid features unstructured triangular meshes with horizontal resolution of ~ 0.3-25 km and a hybrid terrain-following vertical coordinate with a total of 45 layers. The Mass coastal FVCOM grid is configured with triangular meshes with horizontal resolution up to ~10 m, and 10 layers in the vertical. Scituate, Hampton River and Mass Bay inundation model grids include both water and land with horizontal resolution up to ~5-10 m and 10 vertical layers. GOM-FVCOM is driven by surface forcing from WRF model output configured for the region (with 9-km resolution), the COARE3 bulk air-sea flux algorithm, local river discharges, and tidal forcing constructed by eight constituents and subtidal forcing on the boundary nested to the Global-FVCOM. SWAVE is driven by the same WRF wind field with wave forcing at the boundary nested to Wave Watch III configured for the northwestern Atlantic region. The Mass coastal FVCOM and three inundation models are connected with GOM-FVCOM through one-way nesting in the common boundary zones. The Mass coastal FVCOM is driven by the same surface forcing as GOM-FVCOM. The nesting boundary conditions for the inundation models</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19670000490','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19670000490"><span id="translatedtitle">Material fatigue data obtained by card-programmed hydraulic <span class="hlt">loading</span> <span class="hlt">system</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Davis, W. T.</p> <p>1967-01-01</p> <p>Fatigue tests using <span class="hlt">load</span> distributions from actual <span class="hlt">loading</span> histories encountered in flight are programmed on punched electronic accounting machine cards. With this hydraulic <span class="hlt">loading</span> <span class="hlt">system</span>, airframe designers can apply up to 55 <span class="hlt">load</span> levels to a test specimen.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27078491','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27078491"><span id="translatedtitle">Reply to "Comment on 'Nonparametric <span class="hlt">forecasting</span> of low-dimensional dynamical <span class="hlt">systems</span>' ".</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Berry, Tyrus; Giannakis, Dimitrios; Harlim, John</p> <p>2016-03-01</p> <p>In this Reply we provide additional results which allow a better comparison of the diffusion <span class="hlt">forecast</span> and the "past-noise" <span class="hlt">forecasting</span> (PNF) approach for the El Niño index. We remark on some qualitative differences between the diffusion <span class="hlt">forecast</span> and PNF, and we suggest an alternative use of the diffusion <span class="hlt">forecast</span> for the purposes of <span class="hlt">forecasting</span> the probabilities of extreme events. PMID:27078491</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013OptEn..52e5003T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013OptEn..52e5003T"><span id="translatedtitle">Method of <span class="hlt">forecasting</span> energy center positions of laser beam spot images using a parallel hierarchical network for optical communication <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Timchenko, Leonid I.; Kokryatskaya, Natalia I.; Melnikov, Viktor V.; Kosenko, Galina L.</p> <p>2013-05-01</p> <p>A <span class="hlt">forecasting</span> method, based on the parallel-hierarchical (PH) network and hyperbolic smoothing of empirical data, is presented in this paper. Preceding values of the time series, hyperbolic smoothing, and PH network data are used for <span class="hlt">forecasting</span>. To determine a position of the next route fragment in relation to X and Y axes, hyperbola parameters are sent to the route parameter <span class="hlt">forecasting</span> <span class="hlt">system</span>. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the PH network. An average prediction error is 0.55% for the developed method and 1.62% for neural networks. That is why, due to the use of the PH network and hyperbolic smoothing, the developed method is more efficient for real-time <span class="hlt">systems</span> than traditional neural networks in <span class="hlt">forecasting</span> energy center positions of laser beam spot images for optical communication <span class="hlt">systems</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/23863443','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/23863443"><span id="translatedtitle">Comparison of short-term rainfall <span class="hlt">forecasts</span> for model-based flow prediction in urban drainage <span class="hlt">systems</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas; Borup, Morten; Ahm, Malte; Nielsen, Jesper Ellerbæk; Grum, Morten; Rasmussen, Michael R; Gill, Rasphall; Mikkelsen, Peter Steen</p> <p>2013-01-01</p> <p><span class="hlt">Forecast</span>-based flow prediction in drainage <span class="hlt">systems</span> can be used to implement real-time control of drainage <span class="hlt">systems</span>. This study compares two different types of rainfall <span class="hlt">forecast</span> - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible <span class="hlt">forecast</span>. Results show that it is possible to <span class="hlt">forecast</span> flows with a lead time of 24 h. The best performance of the <span class="hlt">system</span> is found using the radar nowcast for the short lead times and the weather model for larger lead times. PMID:23863443</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012amos.confE..37A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012amos.confE..37A"><span id="translatedtitle"><span class="hlt">Forecasting</span> of Optical Turbulence in Support of Realtime Optical Imaging and Communication <span class="hlt">Systems</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alliss, R.; Felton, B.</p> <p>2012-09-01</p> <p>Optical turbulence (OT) acts to distort light in the atmosphere, degrading imagery from large astronomical and imaging telescopes and possibly reducing data quality of free space optical communication (FSOC) links. Some of the degradation due to optical turbulence can be corrected by adaptive optics. However, the severity of optical turbulence, and thus the amount of correction required, is largely dependent upon the turbulence at the location of interest. In addition, clouds, precipitation, and inhomogeneities in atmospheric temperature and moisture all have the potential to disrupt imaging and communications through the atmosphere. However, there are strategies that can be employed to mitigate the atmospheric impacts. These strategies require an accurate characterization of the atmosphere through which the communications links travel. To date these strategies have been to climatological characterize OT and its properties. Recently efforts have been developed to employ a realtime <span class="hlt">forecasting</span> <span class="hlt">system</span> which provides planners useful information for maintaining links and link budgets. The strength of OT is characterized by the refractive index structure function Cn2, which in turn is used to calculate atmospheric seeing parameters. Atmospheric measurements provided by local instrumentation are valuable for link characterization, but provide an incomplete picture of the atmosphere. While attempts have been made to characterize Cn2 using empirical models, Cn2 can be calculated more directly from Numerical Weather Prediction (NWP) simulations using pressure, temperature, thermal stability, vertical wind shear, turbulent Prandtl number, and turbulence kinetic energy (TKE). During realtime FSOC demonstrations, in situ measurements are supplemented with NWP simulations, which provide near realtime characterizations and <span class="hlt">forecasts</span> of the Cn2, the Fried Coherence Length (ro), and time-varying, three-dimensional characterizations of the atmosphere. The three dimensional Weather</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19950048340&hterms=Atmospheric+pressure&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3D%2528Atmospheric%2Bpressure%2529','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19950048340&hterms=Atmospheric+pressure&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3D%2528Atmospheric%2Bpressure%2529"><span id="translatedtitle">Atmospheric pressure <span class="hlt">loading</span> effects on Global Positioning <span class="hlt">System</span> coordinate determinations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Vandam, Tonie M.; Blewitt, Geoffrey; Heflin, Michael B.</p> <p>1994-01-01</p> <p>Earth deformation signals caused by atmospheric pressure <span class="hlt">loading</span> are detected in vertical position estimates at Global Positioning <span class="hlt">System</span> (GPS) stations. Surface displacements due to changes in atmospheric pressure account for up to 24% of the total variance in the GPS height estimates. The detected <span class="hlt">loading</span> signals are larger at higher latitudes where pressure variations are greatest; the largest effect is observed at Fairbanks, Alaska (latitude 65 deg), with a signal root mean square (RMS) of 5 mm. Out of 19 continuously operating GPS sites (with a mean of 281 daily solutions per site), 18 show a positive correlation between the GPS vertical estimates and the modeled <span class="hlt">loading</span> displacements. Accounting for <span class="hlt">loading</span> reduces the variance of the vertical station positions on 12 of the 19 sites investigated. Removing the modeled pressure <span class="hlt">loading</span> from GPS determinations of baseline length for baselines longer than 6000 km reduces the variance on 73 of the 117 baselines investigated. The slight increase in variance for some of the sites and baselines is consistent with expected statistical fluctuations. The results from most stations are consistent with approximately 65% of the modeled pressure <span class="hlt">load</span> being found in the GPS vertical position measurements. Removing an annual signal from both the measured heights and the modeled <span class="hlt">load</span> time series leaves this value unchanged. The source of the remaining discrepancy between the modeled and observed <span class="hlt">loading</span> signal may be the result of (1) anisotropic effects in the Earth's <span class="hlt">loading</span> response, (2) errors in GPS estimates of tropospheric delay, (3) errors in the surface pressure data, or (4) annual signals in the time series of <span class="hlt">loading</span> and station heights. In addition, we find that using site dependent coefficients, determined by fitting local pressure to the modeled radial displacements, reduces the variance of the measured station heights as well as or better than using the global convolution sum.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/6325448','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/6325448"><span id="translatedtitle">Incremental cooling <span class="hlt">load</span> determination for passive direct gain heating <span class="hlt">systems</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Sullivan, P.W.; Mahone, D.; Fuller, W.; Gruber, J.; Kammerud, R.; Place, W.; Andersson, B.</p> <p>1981-05-01</p> <p>This paper examines the applicability of the National Association of Home Builders (NAHB) full <span class="hlt">load</span> compressor hour method for predicting the cooling <span class="hlt">load</span> increase in a residence, attributable to direct gain passive heating <span class="hlt">systems</span>. The NAHB method predictions are compared with the results of 200 hour-by-hour simulations using BLAST and the two methods show reasonable agreement. The degree of agreement and the limitations of the NAHB method are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030002829','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030002829"><span id="translatedtitle">Engine <span class="hlt">System</span> <span class="hlt">Loads</span> Analysis Compared to Hot-Fire Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Frady, Gregory P.; Jennings, John M.; Mims, Katherine; Brunty, Joseph; Christensen, Eric R.; McConnaughey, Paul R. (Technical Monitor)</p> <p>2002-01-01</p> <p>Early implementation of structural dynamics finite element analyses for calculation of design <span class="hlt">loads</span> is considered common design practice for high volume manufacturing industries such as automotive and aeronautical industries. However with the rarity of rocket engine development programs starts, these tools are relatively new to the design of rocket engines. In the NASA MC-1 engine program, the focus was to reduce the cost-to-weight ratio. The techniques for structural dynamics analysis practices, were tailored in this program to meet both production and structural design goals. Perturbation of rocket engine design parameters resulted in a number of MC-1 <span class="hlt">load</span> cycles necessary to characterize the impact due to mass and stiffness changes. Evolution of <span class="hlt">loads</span> and <span class="hlt">load</span> extraction methodologies, parametric considerations and a discussion of <span class="hlt">load</span> path sensitivities are important during the design and integration of a new engine <span class="hlt">system</span>. During the final stages of development, it is important to verify the results of an engine <span class="hlt">system</span> model to determine the validity of the results. During the final stages of the MC-1 program, hot-fire test results were obtained and compared to the structural design <span class="hlt">loads</span> calculated by the engine <span class="hlt">system</span> model. These comparisons are presented in this paper.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070021731','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070021731"><span id="translatedtitle">Deployable <span class="hlt">System</span> for Crash-<span class="hlt">Load</span> Attenuation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kellas, Sotiris; Jackson, Karen E.</p> <p>2007-01-01</p> <p>An externally deployable honeycomb structure is investigated with respect to crash energy management for light aircraft. The new concept utilizes an expandable honeycomb-like structure to absorb impact energy by crushing. Distinguished by flexible hinges between cell wall junctions that enable effortless deployment, the new energy absorber offers most of the desirable features of an external airbag <span class="hlt">system</span> without the limitations of poor shear stability, <span class="hlt">system</span> complexity, and timing sensitivity. Like conventional honeycomb, once expanded, the energy absorber is transformed into a crush efficient and stable cellular structure. Other advantages, afforded by the flexible hinge feature, include a variety of deployment options such as linear, radial, and/or hybrid deployment methods. Radial deployment is utilized when omnidirectional cushioning is required. Linear deployment offers better efficiency, which is preferred when the impact orientation is known in advance. Several energy absorbers utilizing different deployment modes could also be combined to optimize overall performance and/or improve <span class="hlt">system</span> reliability as outlined in the paper. Results from a series of component and full scale demonstration tests are presented as well as typical deployment techniques and mechanisms. LS-DYNA analytical simulations of selected tests are also presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...41..341P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...41..341P"><span id="translatedtitle">Influence of convective parameterization on the systematic errors of Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> (CFS) model over the Indian monsoon region from an extended range <span class="hlt">forecast</span> perspective</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pattnaik, S.; Abhilash, S.; De, S.; Sahai, A. K.; Phani, R.; Goswami, B. N.</p> <p>2013-07-01</p> <p>This study investigates the influence of Simplified Arakawa Schubert (SAS) and Relax Arakawa Schubert (RAS) cumulus parameterization schemes on coupled Climate <span class="hlt">Forecast</span> <span class="hlt">System</span> version.1 (CFS-1, T62L64) retrospective <span class="hlt">forecasts</span> over Indian monsoon region from an extended range <span class="hlt">forecast</span> perspective. The <span class="hlt">forecast</span> data sets comprise 45 days of model integrations based on 31 different initial conditions at pentad intervals starting from 1 May to 28 September for the years 2001 to 2007. It is found that mean climatological features of Indian summer monsoon months (JJAS) are reasonably simulated by both the versions (i.e. SAS and RAS) of the model; however strong cross equatorial flow and excess stratiform rainfall are noted in RAS compared to SAS. Both the versions of the model overestimated apparent heat source and moisture sink compared to NCEP/NCAR reanalysis. The prognosis evaluation of daily <span class="hlt">forecast</span> climatology reveals robust systematic warming (moistening) in RAS and cooling (drying) biases in SAS particularly at the middle and upper troposphere of the model respectively. Using error energy/variance and root mean square error methodology it is also established that major contribution to the model total error is coming from the systematic component of the model error. It is also found that the <span class="hlt">forecast</span> error growth of temperature in RAS is less than that of SAS; however, the scenario is reversed for moisture errors, although the difference of moisture errors between these two <span class="hlt">forecasts</span> is not very large compared to that of temperature errors. Broadly, it is found that both the versions of the model are underestimating (overestimating) the rainfall area and amount over the Indian land region (and neighborhood oceanic region). The rainfall <span class="hlt">forecast</span> results at pentad interval exhibited that, SAS and RAS have good prediction skills over the Indian monsoon core zone and Arabian Sea. There is less excess rainfall particularly over oceanic region in RAS up to 30 days of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/986554','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/servlets/purl/986554"><span id="translatedtitle">Parasitic <span class="hlt">load</span> control <span class="hlt">system</span> for exhaust temperature control</span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Strauser, Aaron D.; Coleman, Gerald N.; Coldren, Dana R.</p> <p>2009-04-28</p> <p>A parasitic <span class="hlt">load</span> control <span class="hlt">system</span> is provided. The <span class="hlt">system</span> may include an exhaust producing engine and a fuel pumping mechanism configured to pressurize fuel in a pressure chamber. The <span class="hlt">system</span> may also include an injection valve configured to cause fuel pressure to build within the pressure chamber when in a first position and allow injection of fuel from the pressure chamber into one or more combustion chambers of the engine when in a second position. The <span class="hlt">system</span> may further include a controller configured to independently regulate the pressure in the pressure chamber and the injection of fuel into the one or more combustion chambers, to increase a <span class="hlt">load</span> on the fuel pumping mechanism, increasing parasitic <span class="hlt">load</span> on the engine, thereby increasing a temperature of the exhaust produced by the engine.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1111238B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1111238B"><span id="translatedtitle"><span class="hlt">Forecasting</span> in an integrated surface water-ground water <span class="hlt">system</span>: The Big Cypress Basin, South Florida</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Butts, M. B.; Feng, K.; Klinting, A.; Stewart, K.; Nath, A.; Manning, P.; Hazlett, T.; Jacobsen, T.</p> <p>2009-04-01</p> <p>The South Florida Water Management District (SFWMD) manages and protects the state's water resources on behalf of 7.5 million South Floridians and is the lead agency in restoring America's Everglades - the largest environmental restoration project in US history. Many of the projects to restore and protect the Everglades ecosystem are part of the Comprehensive Everglades Restoration Plan (CERP). The region has a unique hydrological regime, with close connection between surface water and groundwater, and a complex managed drainage network with many structures. Added to the physical complexity are the conflicting needs of the ecosystem for protection and restoration, versus the substantial urban development with the accompanying water supply, water quality and flood control issues. In this paper a novel <span class="hlt">forecasting</span> and real-time modelling <span class="hlt">system</span> is presented for the Big Cypress Basin. The Big Cypress Basin includes 272 km of primary canals and 46 water control structures throughout the area that provide limited levels of flood protection, as well as water supply and environmental quality management. This <span class="hlt">system</span> is linked to the South Florida Water Management District's extensive real-time (SCADA) data monitoring and collection <span class="hlt">system</span>. Novel aspects of this <span class="hlt">system</span> include the use of a fully distributed and integrated modeling approach and a new filter-based updating approach for accurately <span class="hlt">forecasting</span> river levels. Because of the interaction between surface- and groundwater a fully integrated <span class="hlt">forecast</span> modeling approach is required. Indeed, results for the Tropical Storm Fay in 2008, the groundwater levels show an extremely rapid response to heavy rainfall. Analysis of this storm also shows that updating levels in the river <span class="hlt">system</span> can have a direct impact on groundwater levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012NHESS..12..485I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012NHESS..12..485I"><span id="translatedtitle">Implementation and validation of a coastal <span class="hlt">forecasting</span> <span class="hlt">system</span> for wind waves in the Mediterranean Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Inghilesi, R.; Catini, F.; Bellotti, G.; Franco, L.; Orasi, A.; Corsini, S.</p> <p>2012-02-01</p> <p>A coastal <span class="hlt">forecasting</span> <span class="hlt">system</span> was implemented to provide wind wave <span class="hlt">forecasts</span> over the whole Mediterranean Sea area, and with the added capability to focus on selected coastal areas. The goal of the <span class="hlt">system</span> was to achieve a representation of the small-scale coastal processes influencing the propagation of waves towards the coasts. The <span class="hlt">system</span> was based on a chain of nested wave models and adopted the WAve Model (WAM) to analyse the large-scale, deep-sea propagation of waves; and the Simulating WAves Nearshore (SWAN) to simulate waves in key coastal areas. Regional intermediate-scale WAM grids were introduced to bridge the gap between the large-scale and each coastal area. Even applying two consecutive nestings (Mediterranean grid → regional grid → coastal grid), a very high resolution was still required for the large scale WAM implementation in order to get a final resolution of about 400 m on the shores. In this study three regional areas in the Tyrrhenian Sea were selected, with a single coastal area embedded in each of them. The number of regional and coastal grids in the <span class="hlt">system</span> could easily be modified without significantly affecting the efficiency of the <span class="hlt">system</span>. The coastal <span class="hlt">system</span> was tested in three Italian coastal regions in order to optimize the numerical parameters and to check the results in orographically complex zones for which wave records were available. Fifteen storm events in the period 2004-2009 were considered.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMIN34A..07C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMIN34A..07C"><span id="translatedtitle">Development Of An Open <span class="hlt">System</span> For Integration Of Heterogeneous Models For Flood <span class="hlt">Forecasting</span> And Hazard Mitigation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chang, W.; Tsai, W.; Lin, F.; Lin, S.; Lien, H.; Chung, T.; Huang, L.; Lee, K.; Chang, C.</p> <p>2008-12-01</p> <p>During a typhoon or a heavy storm event, using various <span class="hlt">forecasting</span> models to predict rainfall intensity, and water level variation in rivers and flood situation in the urban area is able to reveal its capability technically. However, in practice, the following two causes tend to restrain the further application of these models as a decision support <span class="hlt">system</span> (DSS) for the hazard mitigation. The first one is due to the difficulty of integration of heterogeneous models. One has to take into consideration the different using format of models, such as input files, output files, computational requirements, and so on. The second one is that the development of DSS requires, due to the heterogeneity of models and <span class="hlt">systems</span>, a friendly user interface or platform to hide the complexity of various tools from users. It is expected that users can be governmental officials rather than professional experts, therefore the complicated interface of DSS is not acceptable. Based on the above considerations, in the present study, we develop an open <span class="hlt">system</span> for integration of several simulation models for flood <span class="hlt">forecasting</span> by adopting the FEWS (Flood Early Warning <span class="hlt">System</span>) platform developed by WL | Delft Hydraulics. It allows us to link heterogeneous models effectively and provides suitable display modules. In addition, FEWS also has been adopted by Water Resource Agency (WRA), Taiwan as the standard operational <span class="hlt">system</span> for river flooding management. That means this work can be much easily integrated with the use of practical cases. In the present study, based on FEWS platform, the basin rainfall-runoff model, SOBEK channel-routing model, and estuary tide <span class="hlt">forecasting</span> model are linked and integrated through the physical connection of model initial and boundary definitions. The work flow of the integrated processes of models is shown in Fig. 1. This differs from the typical single model linking used in FEWS, which only aims at data exchange but without much physical consideration. So it really</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H32C..04W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H32C..04W"><span id="translatedtitle">Development of an Experimental African Drought Monitoring and Seasonal <span class="hlt">Forecasting</span> <span class="hlt">System</span>: A First Step towards a Global Drought Information <span class="hlt">System</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.</p> <p>2012-12-01</p> <p>Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought <span class="hlt">forecasts</span> with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting <span class="hlt">systems</span>, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM <span class="hlt">system</span> has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM <span class="hlt">system</span> leverages off our U.S. drought monitoring and <span class="hlt">forecasting</span> <span class="hlt">system</span> (http://hydrology.princeton.edu/<span class="hlt">forecasting</span>) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal <span class="hlt">forecast</span> of drought, CFSv2 climate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5691V&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5691V&link_type=ABSTRACT"><span id="translatedtitle">Hourly <span class="hlt">forecasts</span> of renewable energy sources by an operating MOS-<span class="hlt">system</span> of the German Weather Service</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vogt, Gernot; Sebastian, Trepte</p> <p>2016-04-01</p> <p>Model Output Statistics (MOS) is a powerful tool for optimizing the direct output of numerical weather <span class="hlt">forecast</span> models. By developing multiple linear regressions with predictors, derived from observations and numerical weather prediction (NWP) at DWD (German Meteorological Service), a reduction of 50% of the error variance in the <span class="hlt">forecast</span> has been achieved. Moreover, statistical post-processing yields numerous advantages in <span class="hlt">forecasting</span>, e. g. down-scaling to point <span class="hlt">forecasts</span> at observation stations with specific topographic and climatologic characteristics, correction of biases and systematic errors produced by numerical models, the derivation of further predictands of interest (e. g. exceedance probabilities) and the combination of several models. In the German project EWeLiNE (Simultaneous improvement of weather and power <span class="hlt">forecasts</span> for the grid integration of renewable energies), which is carried out in collaboration by DWD and IWES (Fraunhofer Institute for Wind Energy and Energy <span class="hlt">System</span> Technology), one of the main goals is an adjustment of the DWD-<span class="hlt">system</span> MOSMIX (combining numerical <span class="hlt">forecasts</span> of the global models IFS and ICON) to the demands of transmission <span class="hlt">system</span> operators (TSO). This includes the implementation of new predictands like wind elements in altitudes > 10m or solar radiance. To meet the demands of the TSOs the temporal resolution of MOSMIX, currently delivering <span class="hlt">forecasts</span> in 3-hour time-steps, needs to be enhanced to 1-hour time-steps. This can be achieved by adjusting the statistical equations to take account of hourly SYNOP observations. Thus, diverse input parameters and internal processing schemes have to be re-specified for example in terms of precipitation. We show a comparative verification of 1-hour MOS and 3-hour MOS for different <span class="hlt">forecast</span> elements. Raw data comprising of acquired point measurements of wind observations have been converted and implemented into the MOS-<span class="hlt">system</span>. Sensitivity studies have then been conducted investigating the fit</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www