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, 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....

  12. 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....

  13. 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....

  14. 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....

  15. 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...

  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. 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

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  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 1710.207 - 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...

  11. 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....

  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. 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.”

  8. 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.

  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_13");'>»</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_13");'>»</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('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/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('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://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/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://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_13");'>»</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_13");'>»</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://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/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://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_13");'>»</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_13");'>»</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/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/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://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/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.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://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/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/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/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://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/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://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_13");'>»</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_13");'>»</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://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://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://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://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_13");'>»</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_13");'>»</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/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> <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> </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_13");'>»</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_13");'>»</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://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/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://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://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/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://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://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/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://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://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://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_13");'>»</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_9");'>9</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_13");'>»</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://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/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://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://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://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://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/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://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://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/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/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/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://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/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> </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><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_13");'>»</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_9");'>9</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><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_13");'>»</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/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> <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://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://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://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/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> </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><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_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <center> <div class="footer-extlink text-muted"><small>Some links on this page may take you to non-federal websites. 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