Science.gov

Sample records for load forecasting system

  1. Short term load forecasting of Taiwan power system using a knowledge-based expert system

    SciTech Connect

    Ho, K.L.; Hsu, Y.Y.; Chen, C.F.; Lee, T.E. . Dept. of Electrical Engineering); Liang, C.C.; Lai, T.S.; Chen, K.K. )

    1990-11-01

    A knowledge-based expert system is proposed for the short term load forecasting of Taiwan power system. The developed expert system, which was implemented on a personal computer, was written in PROLOG using a 5-year data base. To benefit from the expert knowledge and experience of the system operator, eleven different load shapes, each with different means of load calculations, are established. With these load shapes at hand, some peculiar load characteristics pertaining to Taiwan Power Company can be taken into account. The special load types considered by the expert system include the extremely low load levels during the week of the Chinese New Year, the special load characteristics of the days following a tropical storm or a typhoon, the partial shutdown of certain factories on Saturdays, and the special event caused by a holiday on Friday or on Tuesday, etc. A characteristic feature of the proposed knowledge-based expert system is that it is easy to add new information and new rules to the knowledge base. To illustrate the effectiveness of the presented expert system, short-term load forecasting is performed on Taiwan power system by using both the developed algorithm and the conventional Box-Jenkins statistical method. It is found that a mean absolute error of 2.52% for a year is achieved by the expert system approach as compared to an error of 3.86% by the statistical method.

  2. 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

  3. 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 GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

  4. Operation of Battery Energy Storage System in Demand Side using Local Load Forecasting

    NASA Astrophysics Data System (ADS)

    Hida, Yusuke; Yokoyama, Ryuichi; Shimizukawa, Jun; Iba, Kenji; Tanaka, Kouji; Seki, Tomomichi

    Recently, the various political movements, which reduce CO2-emission, have been proposed against global warming. Therefore, battery energy storage systems (BESSs) such as NAS (sodium and sulfur) battery are attracting attention around the world. The first purpose of BESS was the improvement of load factors. The second purpose is the improvement of power quality, especially against voltage-sag. The recent interest is oriented to utilize BESS to mitigate the intermittency of renewable energy. NAS battery has two operation modes. The first one is a fixed pattern operation, which is time-schedule in advance. The second mode is the load following operation. Although this mode can perform more the flexible operation by adjusting the change of load, it has the risks of shortage/surplus of battery energy. In this paper, an accurate demand forecasting method, which is based on multiple regression models, is proposed. Using this load forecasting, the more advanced control of load following operation for NAS battery is proposed.

  5. 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 features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

  6. 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 unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

  7. 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.

  8. 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.

  9. 7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... forecasts prepared under an approved load forecast work plan. In addition to the minimum requirements for... an approved load forecast work plan shall include the following: (1) Scope of the load forecast. The... management programs, supply resource planning, load flow studies, wholesale power marketing, retail...

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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. In both of these systems, we show that small event counts (the natural time domain) is an important component of a forecast system.

  17. 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.

  18. 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.

  19. 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)

  20. 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.

  1. 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.

  2. 7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 11 2010-01-01 2010-01-01 false Requirement to prepare a load forecast-distribution... TO ELECTRIC LOANS AND GUARANTEES Load Forecasts § 1710.203 Requirement to prepare a load forecast... utility plant of $500 million or more must maintain an approved load forecast that meets the...

  3. Residential Saudi load forecasting using analytical model and Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Al-Harbi, Ahmad Abdulaziz

    In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

  4. 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.

  5. 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.

  6. 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 features enable a degree of decentralization and distributed ownership that have helped other types of scientific information services succeed in recent years.

  7. 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.

  8. 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

  9. 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.

  10. 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...

  11. 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

  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.

    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 data, we extended the analyses by developing retrospective daily streamflow forecasts over the period 1979-2012 using reforecasts based on the K-NN resampling approach. Based on the coefficient of determination (R2Q-daily) of the daily streamflow forecasts, we computed the potential skill (R2TN-daily) in developing daily nutrient forecasts based on the R2 of the LOADEST model for each station. The analyses showed that the forecasting skills of TN loadings are relatively better in the winter and spring months, while skills are inferior during summer months. Despite these limitations, there is potential in utilizing the daily streamflow forecasts derived from real-time weather forecasts for developing daily nutrient forecasts, which could be employed for various adaptive nutrient management strategies for ensuring better water quality.

  13. Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system

    NASA Astrophysics Data System (ADS)

    Bourdin, Dominique R.; Nipen, Thomas N.; Stull, Roland B.

    2014-04-01

    This paper describes a probabilistic reservoir inflow forecasting system that explicitly attempts to sample from major sources of uncertainty in the modeling chain. Uncertainty in hydrologic forecasts arises due to errors in the hydrologic models themselves, their parameterizations, and in the initial and boundary conditions (e.g., meteorological observations or forecasts) used to drive the forecasts. The Member-to-Member (M2M) ensemble presented herein uses individual members of a numerical weather model ensemble to drive two different distributed hydrologic models, each of which is calibrated using three different objective functions. An ensemble of deterministic hydrologic states is generated by spinning up the daily simulated state using each model and parameterization. To produce probabilistic forecasts, uncertainty models are used to fit probability distribution functions (PDF) to the bias-corrected ensemble. The parameters of the distribution are estimated based on statistical properties of the ensemble and past verifying observations. The uncertainty model is able to produce reliable probability forecasts by matching the shape of the PDF to the shape of the empirical distribution of forecast errors. This shape is found to vary seasonally in the case-study watershed. We present an "intelligent" adaptation to a Probability Integral Transform (PIT)-based probability calibration scheme that relabels raw cumulative probabilities into calibrated cumulative probabilities based on recent past forecast performance. As expected, the intelligent scheme, which applies calibration corrections only when probability forecasts are deemed sufficiently unreliable, improves reliability without the inflation of ignorance exhibited in certain cases by the original PIT-based scheme.

  14. 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

  15. 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

  16. 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.

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

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 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, 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...

  19. 7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  20. 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...

  1. 7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 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...

  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, 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...

  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. 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...

  6. 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.

  7. An adaptive predictor for dynamic system forecasting

    NASA Astrophysics Data System (ADS)

    Wang, Wilson

    2007-02-01

    A reliable and real-time predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive predictor is developed based on the neuro-fuzzy approach to dynamic system forecasting. An adaptive training technique is proposed to improve forecasting performance, accommodate different operation conditions, and prevent possible trapping due to local minima. The viability of the developed predictor is evaluated by using both gear system condition monitoring and material fatigue testing. The investigation results show that the developed adaptive predictor is a reliable and robust forecasting tool. It can capture the system's dynamic behaviour quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.

  8. Matlab for Forecasting of Electric Power Load Based on BP Neural Network

    NASA Astrophysics Data System (ADS)

    Wang, Xi-Ping; Shi, Ming-Xi

    Modeling and predicting electricity consumption play a vital role both in developed and developing countries for policy makers and related organizations. Improve load forecasting technology level is not only beneficial to plan power management and make reasonable construction plan, but also good for saving energy and reducing power cost, and then, it can improve the economic benefits and social benefit for power system. BP neural network is one of the most widely used neural networks and it has many advantages in the power load forecasting. Matlab has become the best technology application software which has been internationally recognized, the software has many characteristics, such as data visualization function and neural network toolbox, for these, it is the essential software when we do some research on neural network.

  9. 7 CFR 1710.209 - Approval requirements for load forecast work plans.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ...) In addition to the approved load forecast required under §§ 1710.202 and 1710.203, any power supply... that are members of a power supply borrower with a total utility plant of $500 million or more must cooperate in the preparation of and submittal of the load forecast work plan of their power supply...

  10. Load sensing system

    DOEpatents

    Sohns, Carl W. (Oak Ridge, TN); Nodine, Robert N. (Knoxville, TN); Wallace, Steven Allen (Knoxville, TN)

    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

  11. 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.

  12. 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.

  13. 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 netCDF-CF, OpenDAP, WaterML2 and/or JSON REST as an interoperability experiment.

  14. Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

    NASA Astrophysics Data System (ADS)

    Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

    Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. The NASA GEOS-5 Aerosol Forecasting System

    NASA Technical Reports Server (NTRS)

    Colarco, Peter; daSilva, Arlindo; Darmenov, Anton

    2011-01-01

    The NASA Goddard Earth Observing System modeling and data assimilation environment (GEOS-5) is maintained by the Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center. Near-realtime meteorological forecasts are produced to support NASA satellite and field missions. We have implemented in this environment an aerosol module based on the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) model. This modeling system has previously been evaluated in the context of hindcasts based on assimilated meteorology. Here we focus on the development and evaluation of the near-realtime forecasting system. We present a description of recent efforts to implement near-realtime biomass burning emissions derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power products. We as well present a developing capability for improvement of aerosol forecasts by assimilation of aerosol information from MODIS.

  1. California Coastal Ocean Nowcast and Forecast System

    NASA Astrophysics Data System (ADS)

    Chao, Y.; Farrara, J. D.; Zhang, H.; Chai, F.

    2014-12-01

    This talk will describe the California coastal ocean nowcast and forecast system. The model is based on the Regional Ocean Modeling System (ROMS) with a horizontal resolution of 3 km and 40 vertical layers. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ and remotely sensed data from both satellite and land-based platforms. Model sensitivity experiments with different lateral boundary conditions and data sets being assimilated will be presented. An ensemble modeling methodology will be presented to estimate the forecast uncertainties. The hindcast/nowcast/forecast fields are validated against available observations. Preliminary results coupling the ocean circulation model with biogeochemistry and ecosystem models will be presented.

  2. 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.

  3. Operational data flow between hydrological forecasting systems

    NASA Astrophysics Data System (ADS)

    Davids, Femke; de Kleermaeker, Simone; van Loenen, Arnejan; Gijsbers, Peter; Bogaard, Tom; Twigt, Daniel; Heynert, Karel

    2014-05-01

    One of the major challenges in operational forecasting is organizing and controlling the flow of data. In the Water Management Centre for the Netherlands several FEWS (Flood Early Warning Systems) have been set up for operational use. The six systems specialize in different areas, namely i) fluvial flooding, ii) water distribution during droughts, iii) real time control of canal water levels and gauges, iv) coastal flooding, v) lake management and flooding and vi) water management in the delta area. These systems obtain data partly from the same but also from different data sources. Each individual system uses (different) models and pre and post processing steps that have been optimized for the most important parameters. It is crucial to exchange data and forecasts in an efficient way between the systems, for example to use as boundaries in model runs. This paper will describe the methods and challenges that we face in organizing the data flow between these systems.

  4. 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.

  5. UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM

    SciTech Connect

    Werth, D.; Garrett, A.

    2009-04-15

    We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts 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 forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast 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 forecasts 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.

  6. PC4CAST: A tool for DSN load forecasting and capacity planning

    NASA Technical Reports Server (NTRS)

    Loyola, S. J.

    1993-01-01

    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 load forecasting (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 System 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.

  7. Development of an oil spill forecast system for offshore China

    NASA Astrophysics Data System (ADS)

    Wang, Yonggang; Wei, Zexun; An, Wei

    2015-12-01

    An oil spill forecast system for offshore China was developed based on Visual C++. The oil spill forecast system includes an ocean environmental forecast model and an oil spill model. The ocean environmental forecast model was designed to include timesaving methods, and comprised a parametrical wind wave forecast model and a sea surface current forecast 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 forecast system, which contained fundamental information, such as the properties of oil, reserve of emergency equipment and distribution of marine petroleum platform. The oil spill forecast system 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.

  8. Seasonal streamflow forecasting with the global hydrological forecasting system FEWS-World

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.

    2011-12-01

    The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological forecasts and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. For these regions, a global forecasting system 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 system incorporates the global hydrological model PCR-GLOBWB and delivers streamflow forecasts on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast 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 forecasting mode. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from the European Center for Medium-Range Weather Forecasts (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 forecasts. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly anomalies and extreme events. The prospects for seasonal/monthly forecasting of hydrological extremes are therefore positive. Next, the true skill of the global hydrological forecasting system FEWS-World is assessed in retroactive forecasting mode, using seasonal meteorological forecasts subject to uncertainty from numerical weather prediction (NWP) models. The system is forced with ensemble seasonal meteorological forecasts from the seasonal forecast archives of ECMWF. We assess the skill of FEWS-World in forecasting monthly anomalies and extreme events on a range of different lead-times by applying verification measures for ensemble forecasts. Although forecasting skill decreases with increasing lead time, the value of forecasts does not necessarily do so. The real value of a forecast is to be determined on the basis of the benefits and costs of possible actions that can be taken in response to a forecast, provided that information on forecast reliability is properly communicated. A preliminary investigation of the forecast requirements and response options of several sectors over lead times from short-range through medium-range to monthly and seasonal show that most sectors benefit from seasonal forecasts to prepare for appropriate response.

  9. A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

    PubMed

    Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

    2015-01-01

    In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, 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 loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load 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 forecast with the measured channel loads, 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 load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

  10. A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

    PubMed Central

    Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

    2015-01-01

    In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, 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 loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load 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 forecast with the measured channel loads, 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 load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

  11. The Discriminant Analysis Flare Forecasting System (DAFFS)

    NASA Astrophysics Data System (ADS)

    Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

    2016-05-01

    The Discriminant Analysis Flare Forecasting System (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 forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system 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.

  12. 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

    ... 7 Agriculture 11 2010-01-01 2010-01-01 false RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an approved load forecast on an ongoing basis. 1710.207 Section 1710.207 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND...

  13. 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

    ... 7 Agriculture 11 2010-01-01 2010-01-01 false RUS criteria for approval of all load forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. 1710.208 Section 1710.208 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE,...

  14. The Mediterranean Forecasting System: recent developments

    NASA Astrophysics Data System (ADS)

    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

    2014-05-01

    Recent developments of the Mediterranean Monitoring and Forecasting Centre of the EU-Copernicus marine service, the Mediterranean Forecasting System (MFS), are presented. MFS provides forecast, analysis and reanalysis for the physical and biogeochemical parameters of the Mediterranean Sea. The different components of the system are continuously updated in order to provide to the users the best available product. This work is focus on the physical component of the system. 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 system 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 system 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) forcing provided by the COSMO-MED system (provided by the Italian Meteorological Office), have been performed and a pre-operational 3-day forecast production system has been developed. The comparison between this system and the official one forced by the ECMWF NWP data will be discussed.

  15. 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

    ... 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 loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...

  16. 7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 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 loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...

  17. 7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 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 loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...

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

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 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 loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate...

  19. Performance of fuzzy approach in Malaysia short-term electricity load forecasting

    NASA Astrophysics Data System (ADS)

    Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee

    2014-12-01

    Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. 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 forecasting electricity load 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 load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.

  20. The Red Sea Modeling and Forecasting System

    NASA Astrophysics Data System (ADS)

    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

    2015-04-01

    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 systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting 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 system 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 Forecasting (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 system (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 have also developed advanced visualization tools to interactively analyze the forecasts and their ensemble-based uncertainties.

  1. The Canadian coupled multi-seasonal forecasting system

    NASA Astrophysics Data System (ADS)

    Sebatian Fontecilla, Juan

    2013-04-01

    The Canadian coupled multi-seasonal forecasting system Since a year now, the Meteorological Service of Canada has its first coupled operational multi-seasonal forecasting system. The Canadian Meteorological Centre (CMC) in collaboration with the Canadian Centre for Climate Modeling and Analysis (CCCma) has implemented a one-tier climate prediction system which has replaced the old two-tier 4 model forecasting system used for forecasts of months 1 to 4, and the CCA statistical forecasting system used for forecasts of months 4 to 12. The coupled atmosphere-ocean-sea ice system combines ensemble forecasts 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 system, developed under the second Coupled Historical Forecasting Project (CHFP2) will be described briefly. Forecast skill improvements will be shown. The implementation of this new system permits the issuance of ENSO and arctic sea ice forecasts, which were not possible before. The predictive skill of NINO3.4 index from this new coupled system will compared against the skill from other centers.

  2. Offshore tanker loading system

    SciTech Connect

    Baan, J. de; Heijst, W.J. van.

    1994-01-04

    The present invention relates to an improved flexible loading system which provides fluid communication between a subsea pipeline and a surface vessel including a hose extending from the subsea pipeline to a first buoyancy tank, a second hose extending from the first buoyancy tank to a central buoyancy tank, a second buoyancy tank, means connecting said second buoyancy tank to the sea floor and to the central buoyancy tank whereby the forces exerted on said central buoyant tank by said second hose and said connecting means are balanced to cause said central buoyancy tank to maintain a preselected position, a riser section extending upwardly from said central buoyancy tank and means on the upper termination for engagement by a vessel on the surface to raise said upper termination onto the vessel to complete the communication for moving fluids between the subsea pipeline and the vessel. In one form the means for connecting between the sea floor to the second buoyancy tank includes an anchor on the sea floor and lines extending from the anchor to the second buoyancy tank and from the second buoyancy tank to the central buoyancy tank. In another form of the invention the means for connecting is a third hose extending from a second subsea pipeline to the second buoyancy tank and a fourth hose extending from the second buoyancy tank to the central buoyancy tank. The central buoyancy tank is preferred to be maintained at a level below the water surface which allows full movement of the vessel while connected to the riser section. A swivel may be positioned in the riser section and a pressure relief system may be included in the loading system to protect it from sudden excess pressures. 17 figs.

  3. Advances in Global Flood Forecasting Systems

    NASA Astrophysics Data System (ADS)

    Thielen-del Pozo, J.; Pappenberger, F.; Burek, P.; Alfieri, L.; Kreminski, B.; Muraro, D.

    2012-12-01

    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 forecasting systems already exist. The European Flood Awareness System (EFAS) has been developed in close collaboration with the National services and is going operational in 2012, enhancing the national forecasting 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 System (GloFAS) has now been developed jointly between researchers from the European Commission Joint Research Centre (JRC) and the European Centre for Medium-Range Weather Forecast (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 system 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 system performs quite well while in others the hydro-meteorological processes are not fully captured and calibration is necessary. Critical thresholds are computed from long-term simulations where the coupled HTESSEL/LISFLOOD model is driven with ERA-Interim data for a period of 21 years.From the longterm runs return periods are estimated against which each flood forecasts are compared. Results are displayed as maps and time series on a web-interface providing global overviews as well as local quantitative information. Major floods such as the ones in South East Asia in September-October 2010 in Thailand, Cambodia and Vietnam were well captured by the system: for the lower Mekong River, probabilistic forecasts from the global simulations on the 18th September 2011 showed a probability higher than 40% of exceeding the high alert level from 2nd-4th October, hence 14 days in advance. Collaborations exist between the EU and Brazil to further the system for Brazilian rivers. Next steps include further research and development, rigorous testing and adaptations. calibration of the system with available data, and work on selected case studies for quantitative improvements.

  4. Setup of the GLOWASIS seasonal global water scarcity forecasting system

    NASA Astrophysics Data System (ADS)

    Winsemius, H.; Weerts, A.; Candogan, N.; Dutra, E.; van Beek, R.; Wisser, D.; Westerhoff, R.

    2011-12-01

    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 forecast water scarcity at global scale. Here, we present first results of the GLOWASIS seasonal global water scarcity forecasting system. This forecasting system provides seasonal probabilistic forecasts of water scarcity indicators over the whole globe. The system is built within the data and model integration shell Delft-FEWS. The GLOWASIS system integrates reanalysis data from the European Centre for Medium-ranged Weather Forecasts (ECMWF), ECMWF seasonal probabilistic forecasts, information on water demand and use, the global hydrological model PCRGLOB-WB and user interfacing. The system can provide a forecast 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 forecast ensemble, the system is set up to run ensembles over multiple cores. A large number of hindcasts are made with the system. These hindcasts are used to demonstrate which water scarcity indicators are useful to forecast at seasonal time scales, where these indicators may provide satisfactory skill and with which lead time they can be meaningfully forecasted. 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

  5. Seasonal forecast quality of the West African monsoon rainfall regimes by multiple forecast systems

    NASA Astrophysics Data System (ADS)

    Rodrigues, Luis Ricardo Lage; García-Serrano, Javier; Doblas-Reyes, Francisco

    2014-07-01

    A targeted methodology to study the West African monsoon (WAM) rainfall variability is considered where monthly rainfall is averaged over 10°W-10°E to take into account the latitudinal migration and temporal distribution of the WAM summer rainfall. Two observational rainfall data sets and a large number of quasi-operational forecast systems, among them two systems from the European Seasonal to Interannual Prediction initiative and six systems from the North American Multi-model Ensemble project, are used in this research. The two leading modes of the WAM rainfall variability, namely, the Guinean and Sahelian regimes, are estimated by applying principal component analysis (PCA) on the longitudinally averaged precipitation. The PCA is performed upon the observations and each forecast system and lead time separately. A statistical model based on simple linear regression using sea surface temperature indices as predictors is considered both as a benchmark and an additional forecast system. The combination of the dynamical forecast systems and the statistical model is performed using different methods of combination. It is shown that most forecast systems capture the main features associated with the Guinean regime, that is, rainfall located mainly south of 10°N and the northward migration of rainfall over the season. On the other hand, only a fraction of the forecast systems capture the characteristics of the rainfall signal north of 10°N associated with the Sahelian regime. A simple statistical model proves to be of great value and outperforms most state-of-the-art dynamical forecast systems when predicting the principal components associated with the Guinean and Sahelian regimes. Combining all forecast systems do not lead to improved forecasts when compared to the best single forecast system, the European Centre for Medium-Range Weather Forecasts System 4 (S4). In fact, S4 is far better than any forecast system when predicting the variability of the WAM rainfall regimes several months ahead. This suggests that in some special occasions like this one, a multimodel approach is not necessarily better than an especially skillful model.

  6. Skill assessment for an operational algal bloom forecast system

    PubMed Central

    Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.

    2010-01-01

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting 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 forecasts 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 forecasts 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 forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts 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 forecast capabilities, and the need to match forecast and validation resolutions. PMID:20628532

  7. Skill assessment for an operational algal bloom forecast system.

    PubMed

    Stumpf, Richard P; Tomlinson, Michelle C; Calkins, Julie A; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T

    2009-02-20

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting 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 forecasts 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 forecasts 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 forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts 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 forecast capabilities, and the need to match forecast and validation resolutions. PMID:20628532

  8. Skill assessment for an operational algal bloom forecast system

    NASA Astrophysics Data System (ADS)

    Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.

    2009-02-01

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting 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 forecasts 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 forecasts 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 forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts 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 forecast capabilities, and the need to match forecast and validation resolutions.

  9. An Operational Environmental Meteorology Forecasting system for Eastern China

    NASA Astrophysics Data System (ADS)

    Zhou, Guangqiang; Xu, Jianming; Xie, Ying; Wu, Jianbin; Yu, Zhongqi; Chang, Luyu

    2015-04-01

    Since 2012 an operational environmental meteorology forecasting system was setup to provide daily forecasts of environmental meteorology pollutants for the Eastern China region. Initialized with 0.5 degree GFS meteorological fields, the system uses the WRF-Chem model to provide daily 96-hour forecasts. Model forecasts 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 system provides strong support for the daily AQI forecasting of Shanghai, and it also provides guidance products for other meteorological agencies in the Eastern China region. Here the modeling system design will be presented, together with long-term verification results for PM2.5 and O3forecasts.

  10. Short-term solar irradiance forecast for the efficiency assessment of photovoltaic systems in Poland.

    NASA Astrophysics Data System (ADS)

    Sobotka, K.; Struzewska, J.; Kaminski, J. W.

    2010-09-01

    Efficiency of solar based energy generation systems depend to a large extent on weather conditions. In Poland, the solar irradiance is often highly variable due to passages of frontal zones and extratropical cyclones. Consequently, electricity generation varies in time and often energy production pattern does not follow load demand. Efficient management of a solar electricity production system requires reliable short-term forecast of solar irradiance and energy yield. The existing methodologies are based on different approaches depending on the forecast length, e.g. satellite images, statistical models and numerical weather prediction models. Although the use of short-term meteorological forecast products to predict energy production from photovoltaic systems does not seem to be a complicated issue, the outcome from such experiments is very often inconclusive and in general less accurate than from statistical models. In Poland there is a growing effort to expand installations of photovoltaic systems; however, there is no foresting system for solar energy production and the existing maps of solar irradiance are based mainly on measurements from a sparse network of stations. An attempt will be made to develop a short-term solar electricity production forecast for Poland based on the environmental forecast prepared by EcoForecast.EU. The GEM-AQ meteorological and air quality model is used as a computational platform. We will present modeling results for solar irradiance and a comparison with available measurement and climatological data. In addition, the developed parameterization of the energy production using GEM-AQ forecast will be presented.

  11. Vancouver 2010 Winter Olympics Land Surface Forecast System

    NASA Astrophysics Data System (ADS)

    Bernier, N. B.; Belair, S.; Tong, L.; Abrahamowicz, M.; Mailhot, J.

    2009-04-01

    Environment Canada's land surface forecast system developed for the Vancouver 2010 Winter Olympics is presented together with an evaluation of its performance for winters 2007-2008 and 2008-2009. The motivation for this work is threefold: it is i) application driven for the 2010 Vancouver Olympics, ii) a testbed for the panCanadian operational land surface forecast model being developed, and iii) the precursor to the fully coupled land-surface model to come. The new high resolution (100m grid size), 2D, and novel imbedded point-based land surface forecast model used to predict hourly snow and surface temperature conditions at Olympic and Paralympic Competition Sites are described. The surface systems are driven by atmospheric forcing provided by the center's operational regional forecast model for the first 48 hours and by the operational global forecast model for hours 49 to 96. The forcing fields are corrected for elevation discrepancies over the rapidly changing and complex mountainous settings of the Vancouver Olympics that arise from resolution differences. Daily 96h land surface forecasts for 2 winters and snow depth and surface air temperature observations collected at several specially deployed competition sites are used to validate the land surface model. We show that the newly implemented surface forecast model refines and improves snow depth and surface temperature forecast issued by the operational weather forecast system throughout the forecast period.

  12. Skill of a global seasonal ensemble streamflow forecasting system

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

    2013-04-01

    Forecasting 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 forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system 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 forecasting 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 Forecasts (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 forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts 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.

  13. System Science approach to Space Weather forecast

    NASA Astrophysics Data System (ADS)

    Balikhin, Michael A.

    There are many dynamical systems 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 system scientists. A example that is closer to space physics is the terrestrial magnetosphere. The system approach has been developed to understand such complex objects from the observation of their dynamics. The systems approach employs advanced data analysis methodologies to identify patterns in the overall system behaviour and provides information regarding the linear and nonlinear processes involved in the dynamics of the system. 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 system. Advances and problems of systems science applications to provide a reliable forecasts of space weather phenomena such as geomagnetic storms, substorms and radiation belts particle fluxes are reviewed and compared with the physics based models.

  14. Forecasting the Performance of Agroforestry Systems

    NASA Astrophysics Data System (ADS)

    Luedeling, E.; Shepherd, K.

    2014-12-01

    Agroforestry has received considerable attention from scientists and development practitioners in recent years. It is recognized as a cornerstone of many traditional agricultural systems, as well as a new option for sustainable land management in currently treeless agricultural landscapes. Agroforestry systems 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 systems, projecting the outcomes of introducing new agroforestry systems, or forecasting system performance under changing environmental or climatic conditions, remains a substantial challenge. Due to the various interactions between system components, the multiple benefits produced by trees and crops, and the host of environmental, socioeconomic and cultural factors that shape agroforestry systems, mechanistic models of such systems 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 system complexity to an adequate degree, while taking full account of the fact that data on many system 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 system dynamics in a holistic manner and makes probabilistic projections about expected system performance. This approach avoids the temptation to take spuriously precise model results at face value, and it is able to make predictions even where data is scarce. It thus provides a rapid and honest assessment option that can quickly supply decision-makers with system performance estimates, offering an opportunity to improve the targeting of agroforestry interventions.

  15. A multi-step predictor for dynamic system property forecasting

    NASA Astrophysics Data System (ADS)

    Wang, Wilson; Vrbanek, Josip, Jr.

    2007-12-01

    A reliable multi-step predictor is very useful to a wide array of industries to forecast the behavior of dynamic systems. In this paper, an adaptive predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm to forecast properties of dynamic systems. An online training technique is proposed to improve forecasting convergence and accommodate different operating conditions. The viability of the developed predictor is firstly evaluated based on benchmark data sets, and then it is implemented for real-time machinery system monitoring. The monitoring index is derived from measurement based on a beta kurtosis reference function. The investigation results show that the developed adaptive predictor is a reliable forecasting tool and is able to accommodate different system conditions. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.

  16. Exercises for the VAST demonstration volcanic ash forecast system

    NASA Astrophysics Data System (ADS)

    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

    2014-05-01

    Within the ESA-funded international project VAST (Volcanic Ash Strategic Initiative Team) a demonstration service for volcanic ash forecasting 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 forecast 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 loads 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 system, 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 system and the ensemble results. The combination atmospheric dispersion model-meteorology used are: FLEXPART-ECMWF/GFS/WRF, WRF-Chem and SILAM.

  17. Value assessment of a global hydrological forecasting system

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, N.; Winsemius, H.; van Beek, L. P. H.; van Beek, E.; Bierkens, M. F. P.

    2012-04-01

    The inter-annual variability in streamflow presents risks and opportunities in the management of water resources systems. Reliable hydrological forecasts, 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 forecasting systems. A global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value for these regions. FEWS-World system is developed for this purpose. It is based on the Delft-FEWS (flood early warning system) 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 system to forecast 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 forecasting 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 Forecasts (ECMWF) was used. For the assessment in retroactive forecasting mode the model was forced with ensemble forecasts from the seasonal forecast archives of ECMWF. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts 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 forecasting skill decreases with increasing lead time, the value of forecasts does not necessarily decrease. The forecast requirements and response options of several water related sectors was investigated over lead times from short-range through medium-range to monthly and seasonal. These sectors are disaster management agencies and aid organizations, dam operators, hydro-power companies, irrigation agencies and agricultural sector, water supply companies, tourism sector and wildlife agencies, navigation sector, insurance companies. Most sectors benefit from seasonal forecasts in managing the risks and opportunities brought by the inter-annual variability of streamflow. Many sectors need forecasts on a lead time of several months to prepare for appropriate response, and others can make use of seasonal hydrological forecasts for planning purposes. A global forecasting system would be valuable for those parts of the world where there are no available local systems, provided that information on forecast reliability is properly communicated. The availability of a forecast along with an indication of its reliability offers water managers the choice of using the forecasts in decision-making. The real value of a forecast is specific to the user. It is to be determined on the basis of the the user's requirements, and by evaluating the benefits and costs of possible actions that can be taken in response to the forecast.

  18. Hybrid Intrusion Forecasting Framework for Early Warning System

    NASA Astrophysics Data System (ADS)

    Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

    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 systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

  19. Development of Load Balancing Systems in a Parallel MRP System

    NASA Astrophysics Data System (ADS)

    Tsukishima, Takahiro; Sato, Masahiro; Onari, Hisashi

    The application of parallel computing system to MRP (Material Requirements Planning) is essential to achieve a real-time demand forecasting for a whole Supply Chain which consists of Multiple enterprises near future. The MRP using loosely connected multi-computer system is examined here. New methods of synchronization, load balancing and data access are required to keep high parallel efficiency increasing PE’s(Processing Elements). In this paper load balancing and data access methods are proposed. The prototype system can keep 96% parallel efficiency for the MRP with 120, 000 items on the 6 PE’s structure and can be robust against unbalanced load. The processing speed increases in liner fashion.

  20. Satellite freeze forecast system: Executive summary

    NASA Technical Reports Server (NTRS)

    Martsolf, J. D. (Principal Investigator)

    1983-01-01

    A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (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.

  1. Evaluating the performance in the Swedish operational hydrological forecasting systems

    NASA Astrophysics Data System (ADS)

    Pechlivanidis, Ilias; Bosshard, Thomas; Spångmyr, Henrik; Lindström, Göran; Olsson, Jonas; Arheimer, Berit

    2014-05-01

    The production of hydrological forecasts generally involves the selection of model(s) and setup, calibration and initialization, verification and updating, generation and evaluation of forecasts. Although, field data are commonly used to calibrate and initiate hydrological models, technological advancements have allowed the use of additional information, i.e. remote sensing data and meteorological ensemble forecasts, to improve hydrological forecasts. However, the precision of hydrological forecasts is often subject to uncertainty related to various components of the production chain and data used. The Swedish Meteorological and Hydrological Institute (SMHI) operationally produces hydrological medium-range forecasts in Sweden using two modeling systems based on the HBV and S-HYPE hydrological models. The hydrological forecasts use both deterministic and ensemble (in total 51 ensemble members which are further reduced to 5 statistical members; 2, 25, 50, 75, 98% percentiles) meteorological forecasts from ECMWF to add information on the uncertainty of the predicted values. In this study, we evaluate the performance of the two operational hydrological forecasting systems and identify typical uncertainties in the forecasting production chain and ways to reduce them. In particular, we investigate the effect of autoregressive updating of the forecasted discharge, and of using the median of the ensemble instead of deterministic forecasts. Medium-range (10 days) hydrological forecasts across 71 selected indicator stations are used. The Kling-Gupta Efficiency and its decomposed terms are used to analyse the performance in different characteristics of the flow signal. Results show that the HBV and S-HYPE models with AR updating are both capable of producing adequate forecasts for a short lead time (1 to 2 days), and the performance steadily decreases in lead time. The autoregressive updating method can improve the performance of the two systems by 30 to 40% in terms of the KGE. This is mainly because the method has a significant impact on the improvement of discharge volume. S-HYPE seems to perform slightly better than HBV in the longer lead time, probably because the S-HYPE system is capable of updating the lake water level, which has an impact on the longer lead times. Moreover, the deterministic and ensemble HBV systems with AR updating perform fairly similar for all lead times. Keywords: Hydrological forecasting, S-HYPE, HBV, Operational production, Kling-Gupta Efficiency, Uncertainty.

  2. Observing System Forecast Experiments at the DAO

    NASA Technical Reports Server (NTRS)

    Atlas, Robert

    2001-01-01

    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 forecasts. 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 systems 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 systems and observing networks (Atlas and Emmitt, 1991; Rohaly and Krishnamurti, 1993), and to test new methodology for data assimilation (Atlas and Bloom, 1989).

  3. Forecasting front displacements with a satellite based ocean forecasting (SOFT) system

    NASA Astrophysics Data System (ADS)

    Alvarez, A.; Orfila, A.; Basterretxea, G.; Tintoré, J.; Vizoso, G.; Fornes, A.

    2007-03-01

    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 forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems 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 systems based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction systems. Instead, SOFT systems 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 systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The SOFT system 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 systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.

  4. Global crop production forecasting - A simulation analysis of the data system problems and their solutions

    NASA Technical Reports Server (NTRS)

    Golden, H.; Neiers, J. W.

    1978-01-01

    Alternative data systems for a global crop production forecasting system were studied with the aid of a unique simulation facility called the Data System Dynamic Simulator (DSDS). Information system requirements were determined and compared with existing and planned data systems, and deficiencies were identified and analyzed. A first step was to determine the data load for an operational global crop production forecasting system as a function of data frequency, crop types, biophases, cloud coverage, and number of satellites. The DSDS was used to correlate the interrelated influence of orbital parameters, crop calendars, and cloud conditions to generate global data loading profiles. Some of the more important conclusions and the main features of the simulation system are presented.

  5. Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

    NASA Astrophysics Data System (ADS)

    Biondi, D.; De Luca, D. L.

    2013-02-01

    SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty Processor.

  6. Forecasting systemic transitions in high dimensional stochastic complex systems

    NASA Astrophysics Data System (ADS)

    Piovani, Duccio; Grujic, Jelena; Jeldtoft Jensen, Henrik

    2015-09-01

    We briefly describe a new procedure to monitor and forecast transitions occurring in high dimensional complex systems. The method is illustrated by applications to two model systems: firstly to the Tangled Nature Model of evolutionary ecology and secondly to a stochastic replicator system. The quasi-stable configurations of the stochastic dynamics are taken as input for a stability analysis of the deterministic mean field approximation of the dynamics. We demonstrate that the largest overlap between the observed configuration and the unstable eigendirections serves as a precursor that allows us to forecast transitions with an efficiency of about 80% even if we only know the couplings matrix describing the dynamics to with 10% accuracy.

  7. Load Leveling Battery System Costs

    Energy Science and Technology Software Center (ESTSC)

    1994-10-12

    SYSPLAN evaluates capital investment in customer side of the meter load leveling battery systems. Such systems reduce the customer's monthly electrical demand charge by reducing the maximum power load supplied by the utility during the customer's peak demand. System 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 system. The system 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 load profile. Load shape requirements, utility rate structures, and battery equipment cost and performance data serve as bases for determining whether a load leveling battery system is economically feasible for a particular installation. Life-cycle costs for system 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 system 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 system sizing routine to help users identify load leveling system size requirements for their facilities. The optional XSIZE system sizing spreadsheet which is included can be used to identify a range of battery system sizes that might be economically attractive. XSIZE output consisting of system operating requirements can then be passed by the temporary file SIZE to the main SYSPLAN spreadsheet.« less

  8. Incorporating sources of uncertainty in forecasting peak power loads: A Monte Carlo analysis using the extreme value distribution

    SciTech Connect

    Belzer, D.B. ); Kellogg, M.A. )

    1993-05-01

    In this paper the extreme value distribution (EVD), in conjunction with Monte Carlo simulations, is used to analyze sources of uncertainty in forecasting annual peak power loads. The methodology is applied to 1984-1986 load and weather data for a public utility district near Spokane, Washington. The methodology embodies a four-step approach: (1) estimate a weather-sensitive daily peak load model, (2) simulate historical peak loads, (3) estimate the parameters of an extreme value distribution, and (4) predict the probability points associated with different forecast horizons. Monte Carlo analysis is used to incorporate the uncertainty of the disturbances in the estimated daily load model. A separate EVD is estimated for each Monte Carlo simulation, and then the estimated EVDs are used to derive a composite distribution. Corrections are made for the small sample bias in Maximum Likelihood estimates of the EVD parameters. A bootstrapping technique extends the procedure to examine the uncertainty of the daily load model's structural parameters.

  9. SOFT project: a new forecasting system based on satellite data

    NASA Astrophysics Data System (ADS)

    Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim

    2002-01-01

    The aim of the SOFT project is to develop a new ocean forecasting system 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 forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' systems 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 forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast 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 systems while the second set will be used to validate the hybrid and traditional numerical model results.

  10. Forecasting relativistic electron variation for alert system of satellite operating

    NASA Astrophysics Data System (ADS)

    Koga, K.; Obara, T.; Matsumoto, H.; Koshishi, H.; Goka, T.

    We have experimentally constructed the software in the SEES Space Environment and Effects System of JAXA Japan Aerospace Exploration Agency that forecasts the variation of the intensity of MeV electrons in the outer radiation belt for coming two days by using real time solar wind data In this talk we will present details of the system and also provide related information including the accuracy between the forecast result and the observation data of DRTS Data Relay Test Satellite geostationary orbit and MDS-1 Mission Demonstration test Satellite-1 geostationary transfer orbit This system is successfully working about two years and providing the useful data to satellite operator We also report the possibility of increasing the accuracy of forecast to use another index like ULF power Sigma Kp or Dst and extending the forecast region from GEO Geostationary Earth Orbit to low L-value

  11. Self-Organizing Maps-based ocean currents forecasting system

    NASA Astrophysics Data System (ADS)

    Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

    2016-03-01

    An ocean surface currents forecasting system, 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 systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

  12. Self-Organizing Maps-based ocean currents forecasting system

    PubMed Central

    Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

    2016-01-01

    An ocean surface currents forecasting system, 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 systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting 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

  13. Self-Organizing Maps-based ocean currents forecasting system.

    PubMed

    Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

    2016-01-01

    An ocean surface currents forecasting system, 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 systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting 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

  14. Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

    NASA Astrophysics Data System (ADS)

    Palchak, David

    Electrical load forecasting 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 load forecast 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 forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting 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 load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

  15. Science and Engineering of an Operational Tsunami Forecasting System

    SciTech Connect

    Gonzalez, Frank

    2009-04-06

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast 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 forecast models.

  16. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema

    Gonzalez, Frank

    2010-01-08

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast 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 forecast models.

  17. Load measurement system with load cell lock-out mechanism

    NASA Technical Reports Server (NTRS)

    Le, Thang; Carroll, Monty; Liu, Jonathan

    1995-01-01

    In the frame work of the project Shuttle Plume Impingement Flight Experiment (SPIFEX), a Load Measurement System was developed and fabricated to measure the impingement force of Shuttle Reaction Control System (RCS) jets. The Load Measurement System is a force sensing system that measures any combination of normal and shear forces up to 40 N (9 lbf) in the normal direction and 22 N (5 lbf) in the shear direction with an accuracy of +/- 0.04 N (+/- 0.01 lbf) Since high resolution is required for the force measurement, the Load Measurement System is built with highly sensitive load cells. To protect these fragile load cells in the non-operational mode from being damaged due to flight loads such as launch and landing loads of the Shuttle vehicle, a motor driven device known as the Load Cell Lock-Out Mechanism was built. This Lock-Out Mechanism isolates the load cells from flight loads and re-engages the load cells for the force measurement experiment once in space. With this highly effective protection system, the SPIFEX load measurement experiment was successfully conducted on STS-44 in September 1994 with all load cells operating properly and reading impingement forces as expected.

  18. Real time drought forecasting system for irrigation management

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

    2013-12-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas traditionally rich of water such as the Po Valley in northern Italy. In dry periods problems of water shortage can be enhanced by conflictual uses of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective about this issue is increasing due to possible impacts of climate change and global warming scenarios which come out from the fourth IPCC Report. 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 real-time drought forecasting system PRE.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction (20 members) at long-range (30 days) with hydrological simulations of water balance to forecast the soil water content over a maize field. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR probes. Reliability of the forecasting system and its benefits were assessed on the growing season of 2012. Obtained results show how the proposed drought forecasting system is able to have a high reliability of forecast at least for a fortnight as lead time.

  19. Verification and error sources of the California Seasonal Hydrologic Forecast (CaliForecast) System over the Feather River Basin

    NASA Astrophysics Data System (ADS)

    Park, G.; Imam, B.; Sorooshian, S.

    2008-12-01

    Operational water resource planning and management heavily rely on the seasonal streamflow forecasts of reservoir. The California Hydrologic Forecast System, a regional implementation of the West-Wide Seasonal Hydrologic forecast System over the state of California at the University of California-Irvine in a 1/8th degree resolution, provides probabilistic forecasts in the form of ensemble streamflow predictions (ESP) to facilitate our need in the state of California. Similar to any other hydrologic forecast systems, CaliForecast, however, contains significant forecast errors and uncertainties that are propagated from many sources. These within the CaliForecast system, 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 forecasts 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 forecast skills.

  20. Optimization of load shedding system

    SciTech Connect

    Halevi, Y. . Faculty of Mechanical Engineering); Kottick, D. . Research and Development Div.)

    1993-06-01

    An optimization algorithm for a load shedding system is presented. The Israeli power system is not interconnected with any neighboring system. Being a relatively small system, an outage of a single generating unit may cause blackouts. One way of preventing this is an underfrequency load shedding system, which is composed of several stages that are tripped at present frequencies. The paper considers the optimization of this system with respect to a cost function that includes a dynamic part, i.e. integral of the deviation from nominal frequency, and a static pare which is the total load shedding. The optimization is constrained by the requirement of minimum allowed frequency and limitation on the total load of the shedding system. A projected gradient method is used for the solution where analytic expressions for the partial derivatives are used to simplify the computation. Results concerning application of the optimization to a model of the actual Israeli power system are given together with a study of the cost parameters.

  1. Improving real time flood forecasting using fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Lohani, Anil Kumar; Goel, N. K.; Bhatia, K. K. S.

    2014-02-01

    In order to improve the real time forecasting of foods, this paper proposes a modified Takagi Sugeno (T-S) fuzzy inference system termed as threshold subtractive clustering based Takagi Sugeno (TSC-T-S) fuzzy inference system by introducing the concept of rare and frequent hydrological situations in fuzzy modeling system. The proposed modified fuzzy inference systems provide an option of analyzing and computing cluster centers and membership functions for two different hydrological situations, i.e. low to medium flows (frequent events) as well as high to very high flows (rare events) generally encountered in real time flood forecasting. The methodology has been applied for flood forecasting using the hourly rainfall and river flow data of upper Narmada basin, Central India. The available rainfall-runoff data has been classified in frequent and rare events and suitable TSC-T-S fuzzy model structures have been suggested for better forecasting of river flows. The performance of the model during calibration and validation is evaluated by performance indices such as root mean square error (RMSE), model efficiency and coefficient of correlation (R). In flood forecasting, it is very important to know the performance of flow forecasting model in predicting higher magnitude flows. The above described performance criteria do not express the prediction ability of the model precisely from higher to low flow region. Therefore, a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model. The developed model has been tested for different lead periods using hourly rainfall and discharge data. Further, the proposed fuzzy model results have been compared with artificial neural networks (ANN), ANN models for different classes identified by Self Organizing Map (SOM) and subtractive clustering based Takagi Sugeno fuzzy model (SC-T-S fuzzy model). It has been concluded from the study that the TSC-T-S fuzzy model provide reasonably accurate forecast with sufficient lead-time.

  2. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    SciTech Connect

    Not Available

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (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 forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts 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 forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  3. Operational monitoring and forecasting in the Aegean Sea: system limitations and forecasting skill evaluation.

    PubMed

    Nittis, K; Zervakis, V; Perivoliotis, L; Papadopoulos, A; Chronis, G

    2001-01-01

    The POSEIDON system, based on a network of 11 oceanographic buoys and a system of atmospheric/oceanic models, provides real-time observations and forecasts of the marine environmental conditions in the Aegean Sea. The buoy network collects meteorological, sea state and upper-ocean physical and biochemical data. The efficiency and functionality of the various system components are being evaluated during the present pre-operational phase and discussed in this paper. The problem of bio-fouling on optical and chemical sensors is found to be a main limitation factor on the quality of data. Possible solutions to this problem as well as quality control methods that are being developed are also described. Finally, an evaluation of the numerical models is presented through the estimation of their forecasting skill for selected periods. PMID:11760182

  4. Ensemble Bayesian forecasting system Part I: Theory and algorithms

    NASA Astrophysics Data System (ADS)

    Herr, Henry D.; Krzysztofowicz, Roman

    2015-05-01

    The ensemble Bayesian forecasting system (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 forecaster (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 forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). 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 forecasting system 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 forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

  5. Assessing Ozone Forecast Uncertainty with a Data Assimilation System

    NASA Astrophysics Data System (ADS)

    Coy, L.; Allen, D.; Eckermann, S. D.; McCormack, J.; Stajner, I.; Hogan, T.

    2006-12-01

    The increments or observation minus forecast (O-F) statistics produced by a data assimilation system provides a ready-made method of evaluating forecast skill. In this study, O-F statistics from the Global Ozone Assimilation Testing System (GOATS) are used to examine how ozone assimilation products and their associated O-F statistics depend on input data biases, ozone chemistry parameterizations, and forecast model variances. All the GOATS results shown are based on a 6-hour forecast and analysis cycle using observations from SBUV/2 (Solar Backscatter Ultraviolet Radiometer-2) during September--October~2002. Results show that using an ozone chemistry parameterization can provide a built-in standard for assessing observational bias that is lacking in a passive ozone advection model. This is because a passive tracer advection model is usually limited in how strongly it can change mean values, leaving global mean ozone to be data driven. These results answer the problem of why ozone assimilation with parameterized chemistry often have larger mean 0-Fs than passive ozone assimilation: the larger mean O-Fs with a chemistry parameterization reflects the ozone model's active contribution to the ozone assimilation. Results also show that including an ozone chemistry parameterization can lead to a significant reduction of the RMS O-Fs by both reducing the ozone forecast model variance as well as improving the covariance between the ozone forecast and observations. Understanding the O-F statistics from an ozone data assimilation system can lead to improved use of O-F statistics in diagnosing ozone and other tracer forecast skill and in monitoring operational ozone assimilation systems.

  6. Real-time drought forecasting system for irrigation management

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

    2014-09-01

    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 forecasting system; PREGI is an Italian acronym that means "hydro-meteorological forecast for irrigation management". The system, planned as a tool for irrigation optimization, is based on meteorological ensemble forecasts (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to forecast 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 forecasting system and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought forecasting system is able to have a high reliability of forecast at least for 7-10 days ahead of time.

  7. Marine loading vapor control systems

    SciTech Connect

    Babet, F.H.

    1996-09-01

    The EPA and State air quality control boards have mandated the collection and destruction or recovery of vapors generated by the loading of some hydrocarbons and chemicals into marine vessels. This is a brief overview of the main US Coast Guard requirements for marine vapor control systems. 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 loading vapor control system, 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 system. This should be done well up front in the design of the system to avoid costly mistakes which can occur as a result of lack of knowledge or misinterpretation of the regulations and guidelines.

  8. Research and Development for Technology Evolution Potential Forecasting System

    NASA Astrophysics Data System (ADS)

    Gao, Changqing; Cao, Shukun; Wang, Yuzeng; Ai, Changsheng; Ze, Xiangbo

    Technology forecasting 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 system for Technology Evolution Potential Forecasting (TEPF) with automatic radar plot drawing is introduced in this paper. The framework of the system 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 system is an effective tool during the technology strategy analyzing process with a referenced case study.

  9. Look-ahead voltage and load margin contingency selection functions for large-scale power systems

    SciTech Connect

    Chiang, H.D.; Wang, C.S.; Flueck, A.J.

    1997-02-01

    Given the current operating condition (obtained from the real-time data), the near-term load demand at each bus (obtained from short-term load forecast), and the generation dispatch (say, based on economic dispatch), the authors present in this paper a load margin measure (MW and/or MVAR) to assess the system`s ability to withstand the forecasted load and generation variations. The authors also present a method to predict near-term system voltage profiles. The proposed look-ahead measure and the proposed voltage prediction are then applied to contingency selections for the near-term power system in terms of load margins to collapse and of the bus voltage magnitudes. They evaluate the proposed load-ahead measure and the voltage profile prediction on several power systems including a 1169-bus power system with 53 contingencies with promising results.

  10. Signature-forecasting and early outbreak detection system

    PubMed Central

    Naumova, Elena N.; MacNeill, Ian B.

    2008-01-01

    SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

  11. Signature-forecasting and early outbreak detection system.

    PubMed

    Naumova, Elena N; Macneill, Ian B

    2005-01-01

    Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a 'signature' curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

  12. Forecast Impact of Components of the FGGE Observing System

    NASA Technical Reports Server (NTRS)

    Kalnay, E.; Atlas, R.; Baker, W.; Susskind, J.

    1984-01-01

    Experiments have been conducted to assess the summer and winter forecast impact of the FGGE system, and of its main observing components: temperature sounding data derived from the TIROS-N polar orbiting satellite, cloud track winds determined from geostationary satellite observations and drifting buoy data which were collected by satellite during FGGE. The Analysis/Forecast System used has a number of improvements upon the system utilized by Halen et al. (1982) for the FGGE Special Observing Period-1 (SOP-1). Several modifications were made in the analysis scheme, the most important being the interpolation of the analysis minus 6 h forecast deviations rather than of the analyzed fields themselves. The forecast model is still the 4 deg lat, 5 deg lon and 9 vertical levels GLAS Fourth Order GCM with several minor corrections implemented in the physics and numerics. The improved vertical interpolation in the analysis resulted in better assimilation of rawinsonde data, which has more vertical structure than satellite data. As a result, there was an improvement of the forecasts derived from conventional data only, and, consequently, a small reduction of the positive impact of satellite data from that obtained by Halem et al. (1982).

  13. Power system very short-term load prediction

    SciTech Connect

    Trudnowski, D.J.; Johnson, J.M.; Whitney, P.

    1997-02-01

    A fundamental objective of a power-system operating and control scheme is to maintain a match between the system`s overall real-power load and generation. To accurately maintain this match, modern energy management systems require estimates of the future total system load. Several strategies and tools are available for estimating system load. Nearly all of these estimate the future load in 1-hour steps over several hours (or time frames very close to this). While hourly load 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 load 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 load 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 load prediction algorithm and computer codes that automatically update and can reliably perform in a closed-loop controller for the BPA system. The requirements include accurate load 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 load prediction that incorporates information from a general hourly forecaster; specific algorithm parameters for implementing the predictor in the BPA system; performance and sensitivity studies of the algorithms on BPA-supplied data; an algorithm for filtering power system load samples as a precursor to inputting into the predictor; and FORTRAN 77 subroutines for implementing the algorithms.

  14. Implementing Probabilistic Climate Outlooks Within a Seasonal Hydrologic Forecast System

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Lettenmaier, D. P.

    2004-12-01

    We describe a method for implementing the Climate Prediction Center's probabilistic climate outlooks within an experimental ensemble hydrologic forecast system. The forecast system has been developed for the portion of the U.S. and the Canadian portion of the Columbia River basin west of the Continental Divide. Forecasts are made at lead times of six months to a year, using the Variable Infiltration Capacity (VIC) macroscale hydrology model implemented at 1/8 degree spatial resolution. Ensemble forecasts are derived from historical resampling, global climate model ensemble forecasts, and most recently the CPC Official Seasonal Outlooks, formulated as probability of exceedence (POE) distributions for temperature and precipitation, using the climate division as the spatial unit. Our current approach for downscaling the CPC POE forecasts involves a resampling step (called the "Schaake Shuffle", as described in a recent paper by M. Clark and others) that is applied to create a 13-member ensemble (of monthly timeseries of precipitation and temperature for each Climate Division) that exhibits plausible spatial and temporal structure. A subsequent resampling step is applied to disaggregate timeseries from a monthly to a daily timestep, and from the climate division to the 1/8 degree hydrologic model grid scale. In this talk, we address two questions: 1) whether the downscaling approach reproduces the observational mean and variability for streamflow, and for spatial temperature and precipitation fields; and 2) whether bias-correction of the climate division POE forecasts is a necessary step in the downscaling process. These questions were investigated through the application of the downscaling approach to the CPC retrospective observational climate division dataset, and via comparisons with a climate division unit dataset aggregated from the authors' PRISM-corrected retrospective climatology.

  15. EMISSIONS PROCESSING FOR THE ETA/ CMAQ AIR QUALITY FORECAST SYSTEM

    EPA Science Inventory

    NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system 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...

  16. METEOR - an artificial intelligence system for convective storm forecasting

    SciTech Connect

    Elio, R.; De haan, J.; Strong, G.S.

    1987-03-01

    An AI system called METEOR, which uses the meteorologist's heuristics, strategies, and statistical tools to forecast severe hailstorms in Alberta, is described, emphasizing the information and knowledge that METEOR uses to mimic the forecasting procedure of an expert meteorologist. METEOR is then discussed as an AI system, 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.

  17. Improvement of forecasting system with optimal interpolation focusing on korea

    NASA Astrophysics Data System (ADS)

    Kang, J.; Koo, Y. S.

    2014-12-01

    A system for forecasting future air quality can play an important role as part of an air quality management system 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-forecast conditions. It is found that the assimilation of PM10 observations significantly improves the one-day forecast of PM10, whereas the improvement is non significant for the tow-day forecast. 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 forecasts.

  18. Automated fuel pin loading system

    DOEpatents

    Christiansen, D.W.; Brown, W.F.; Steffen, J.M.

    An automated loading system 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 systems.

  19. Automated fuel pin loading system

    DOEpatents

    Christiansen, David W.; Brown, William F.; Steffen, Jim M.

    1985-01-01

    An automated loading system 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 systems.

  20. Forecast and virtual weather driven plant disease risk modeling system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We describe a system in use and development that leverages public weather station data, several spatialized weather forecast types, leaf wetness estimation, generic plant disease models, and online statistical evaluation. Convergent technological developments in all these areas allow, with funding f...

  1. An operational global ocean forecast system and its applications

    NASA Astrophysics Data System (ADS)

    Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

    2012-12-01

    A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system 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 forecast system is run once a day and produces a 6 day long forecast 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 forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (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 forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.

  2. Streamflow Forecasting Using Nuero-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Nanduri, U. V.; Swain, P. C.

    2005-12-01

    The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system 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 system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting 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 forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. 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 system 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 forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.

  3. Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

    NASA Astrophysics Data System (ADS)

    Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

    2015-12-01

    In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast 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 forecast 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 forecasts 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 forecasts 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 System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

  4. Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

    NASA Technical Reports Server (NTRS)

    Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

    2015-01-01

    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 system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system 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 system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

  5. Forecast Ensemble Reliability Assessment and Uncertainty Management for Reservoir Systems

    NASA Astrophysics Data System (ADS)

    Kistenmacher, M.; Georgakakos, A. P.

    2011-12-01

    Reservoir systems are subject to several uncertainties that are the result of imperfect knowledge about system behavior and inputs. A major source of uncertainty is the inability to precisely predict future inflows, especially as the management horizon increases. Fortunately, there often exists enough information from historical records and/or hydroclimatic models to generate probabilistic forecasts of inflow volumes in the form of probability density functions or ensembles. These inflow forecasts can be coupled with stochastic management models to help operate reservoir systems and provide stakeholders with probabilistic forecasts of system variables, such as reservoir storages, releases, hydropower production, water withdrawals, and water quality parameters. It is important for forecasts to provide dependable descriptions of the actual system variables that eventually occur since consistently misrepresenting these variables will provide inadequate or misleading information about expected benefits and risks. The first part of this study presents an assessment technique that can be used to determine the reliability of one and multi-dimensional ensemble forecasts through the use of relative frequency histograms and minimum spanning trees (MST). The technique is then used to assess the extended Gaussian linear quadrature (ELQG) management model and determine its ability to produce reliable ensemble forecast of reservoir system variables. Extensions of the existing model are discussed and evaluated. The second portion of this study concerns the management of the uncertainty distribution across a reservoir system. The large majority of stochastic management models only consider optimizing the expected benefits, thereby not allowing operators and stakeholders to explicitly choose or explore issues such as variable variability and risk avoidance. An iterative framework that allows stakeholders to trade-off risks and uncertainties is developed by using objective function reformulations that constrain variable variances. This framework can be used to derive different management policies that produce desired probabilistic behaviors of the entire reservoir system or its individual sub-components and objectives. The methods developed in this study are illustrated on real reservoir systems including a 7-reservoir multi-objective system in California's Central Valley.

  6. Automated Loads Analysis System (ATLAS)

    NASA Technical Reports Server (NTRS)

    Gardner, Stephen; Frere, Scot; O’Reilly, Patrick

    2013-01-01

    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 System (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 loads analysis simulating liftoff, landing, and all flight events between liftoff and landing. ATLAS utilizes efficient and numerically stable algorithms available in MSC/NASTRAN.

  7. Weather forecast-based optimization of integrated energy systems.

    SciTech Connect

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. 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 forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting 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.

  8. Using seasonal forecasts in a drought forecasting system for water management: case-study of the Arzal dam in Brittany

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Perrin, Charles; Penasso, Aldo

    2014-05-01

    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 system. 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 forecasts 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 forecasting system developed to forecast low flows upstream the Arzal dam and based on a lumped hydrological model. Medium-range meteorological forecasts from the ECMWF ensemble prediction system (51 scenarios up to 9 days ahead) are combined with seasonal meteorological forecasts also from ECMWF to provide extended streamflow forecasts for the summer period. The performance of the forecasts obtained by this method is compared with the performance of two benchmarks: (i) flow forecasts obtained using an ensemble of past observed precipitation series as precipitation scenarios, i.e. without any use of forecasts from meteorological models and (ii) flow forecasts obtained using the seasonal forecasts only, i.e. without medium-term information. First, the performance of ensemble forecasts is evaluated and compared by means of probabilistic scores. Then, a risk-based visualisation tool was set up to assess whether the different ensembles can forecast past drought risks. The tool is designed to support decision-making in situations that may potentially be a source of conflict between stakeholders and users by characterising future events in relation to past observed extremes. Results are presented and the capacity of the system to provide useful information in an operational context is discussed. This study is carried out within the Interreg IVB DROP Project (Benefit of governance in DROught adaPtation).

  9. Real-time drought forecasting system for irrigation managment

    NASA Astrophysics Data System (ADS)

    Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Masseroni, Daniele; Meucci, Stefania; Pala, Francesca; Salerno, Raffaele; Meazza, Giuseppe; Chiesa, Marco; Mancini, Marco

    2013-04-01

    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 forecasting system Pre.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction at long range (30 days) with hydrological simulation of water balance to forecast 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 forecasts 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 forecasting system and its benefits was assessed on some cases-study occurred in the recent years.

  10. On Improving the Operational Performance of the Cyprus Coastal Ocean Forecasting System

    NASA Astrophysics Data System (ADS)

    Radhakrishnan, H.; Moulitsas, I.; Hayes, D.; Zodiatis, G.; Georgiou, G.

    2012-04-01

    Modeling oceans is computationally expensive. Rising demands for speedier and higher resolution forecasts, better estimations of prediction uncertainty, and need for additional modules further increase the costs of computation. Parallel processing provides a viable solution to satisfy these demands without sacrificing accuracy or omitting any physical phenomena. Our objective is to develop and implement a parallel version of Cyprus Coastal Ocean Forecasting and Observing System (CYCOFOS) hydrodynamic model for the Eastern Mediterranean Levantine Sea using Message Passing Interface (MPI) that runs on commodity computing clusters running open source software. The parallel software is constructed in a modular fashion to make it easy to integrate end-user applications in the future. Parallelizing CYCOFOS also enables us to run multiple simulations using different parameters, and initial and boundary conditions to improve the accuracy of the model forecasts, and reduce uncertainty. The Cyprus Coastal Ocean Forecasting and Observing System (CYCOFOS) was developed within the broad frame of EuroGOOS (European GOOS) and MedGOOS (Mediterranean GOOS), to provide operational oceanographic forecast and monitoring on local and sub-regional scales in the Eastern Mediterranean Basin. The system has been operational since early 2002, consists of several forecasting, observing, and end-user modules, and has been enriched and improved in recent years. The system provides daily forecasting data to end-users, necessary for operational application in marine safety, such as the Mediterranean oil spill and trajectory modeling system. Like many coastal and sub-regional operational hydrodynamic forecasting systems in the Mediterranean, CYCOFOS is based on the Princeton Ocean Model (POM). There have been a number of attempts to parallelize the Princeton Ocean Model, on which the CYCOFOS is based, such as MP-POM. However, existing parallel code models rely on the use of specific outdated hardware architectures and associated software. Additionally, all reported works seem to be one-off attempts with no further development, the emphasis being given to the high performance computing aspect rather than to accurate ocean forecasting and end-user applications. The goal of producing a distributed memory parallel version of POM based on the Message Passing Interface (MPI) paradigm is done in three stages producing three versions of the code. In the first version, we take advantage of the Cartesian nature of the POM mesh, and use the built-in functionality of MPI routines to split the mesh uniformly along longitude and latitude among the processors. This version is the least efficient version because the processors whose meshes contain a lot of land regions will have a lower computational load. Therefore the overall computational load balance is poor and significant portion of the time is spent idling. The objective of this version is to produce a parallel version of the code that can replicate the results of the serial version of the POM code used in CYCOFOS for validation and verification purposes Results from the first parallel version of CYCOFOS will be presented during the conference, and speedup will be discussed. We will also present our parallelization strategies for the second and third versions which will improve the load balancing and speedup by using a weighted distribution of the grids among the processors.

  11. Satellite freeze forecast system. Operating/troubleshooting manual

    NASA Technical Reports Server (NTRS)

    Martsolf, J. D. (Principal Investigator)

    1983-01-01

    Examples of operational procedures are given to assist users of the satellites freeze forecasting system (SFFS) in logging in on to the computer, executing the programs in the menu, logging off the computer, and setting up the automatic system. 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.

  12. An Operational Flood Forecast System for the Indus Valley

    NASA Astrophysics Data System (ADS)

    Shrestha, K.; Webster, P. J.

    2012-12-01

    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 forecasts 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 forecasts can be used to assess flood risk at critical points in the basin. We describe a process for transforming these probabilities into an alert system for supporting flood mitigation and response decisions on a daily basis. We present a fully automated two-dimensional flood forecast methodology based on meteorological variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Ensemble Prediction System (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 forecast evolves with time.

  13. A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days

    NASA Astrophysics Data System (ADS)

    Bennett, James C.; Robertson, David E.; Shrestha, Durga Lal; Wang, Q. J.; Enever, David; Hapuarachchi, Prasantha; Tuteja, Narendra K.

    2014-11-01

    This study describes a System for Continuous Hydrological Ensemble Forecasting (SCHEF) designed to forecast 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 forecasts 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 forecast ensemble with the Schaake shuffle. The ensemble rainfall forecasts are then used as inputs to the GR4H hydrological model to produce streamflow forecasts. A hydrological error correction is applied to ensure forecasts transit smoothly from recent streamflow observations. SCHEF forecasts streamflows skilfully for a range of hydrological and climate conditions. Skill is particularly evident in forecasts of streamflows at lead times of 1-6 days. Forecasts perform best in temperate perennially flowing rivers, while forecasts are poorest in intermittently flowing rivers. The poor streamflow forecasts in intermittent rivers are primarily the result of poor rainfall forecasts, rather than an inadequate representation of hydrological processes. Forecast uncertainty becomes more reliably quantified at longer lead times; however there is considerable scope for improving the reliability of streamflow forecasts at all lead times. Additionally, we show that the choice of forecast time-step can influence forecast accuracy: forecasts 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 forecasts with more recent information, rather than the ability of GR4H to simulate hydrological processes better at shorter time-steps. SCHEF will form the basis of a streamflow forecast service for Australia to be operated by the Bureau of Meteorology.

  14. Enrollment Forecasting in a Large State System

    ERIC Educational Resources Information Center

    Render, Barry

    1977-01-01

    Using the Student Inventory File of the OBOR information system, detailed analyses were conducted on 1971-1975 enrollments in Ohio by institution, count, part-time versus full-time status, age, rank, day-evening, marital status, etc. Data on out-of-state enrollments, graduate students, and professional students were also tabulated. (LBH)

  15. Forecasting forecast skill

    NASA Technical Reports Server (NTRS)

    Kalnay, Eugenia; Dalcher, Amnon

    1987-01-01

    It is shown that it is possible to predict the skill of numerical weather forecasts - 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 forecasts 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 systems. 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 forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

  16. Observation impact analysis methods for storm surge forecasting systems

    NASA Astrophysics Data System (ADS)

    Verlaan, Martin; Sumihar, Julius

    2016-02-01

    This paper presents a simple method for estimating the impact of assimilating individual or group of observations on forecast 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 forecast 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 system, 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 systems 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 forecasting system based on the Dutch Continental Shelf Model version 5 (DCSMv5).

  17. Global crop production forecasting data system analysis

    NASA Technical Reports Server (NTRS)

    Castruccio, P. A. (Principal Investigator); Loats, H. L.; Lloyd, D. G.

    1978-01-01

    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 system: (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.

  18. System Measures Loads In Bolts

    NASA Technical Reports Server (NTRS)

    Allison, Sidney G.

    1994-01-01

    Improved technique for ultrasonic nondestructive measurement of loads in bolts involves use of pulsed phase-locked loop interferometer. Provides for correction of errors and for automatic readout of loads in bolts. Actual bolt load 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.

  19. Oceanic Stochastic Parameterizations in a Seasonal Forecast System

    NASA Astrophysics Data System (ADS)

    Andrejczuk, M.; Cooper, F. C.; Juricke, S.; Palmer, T. N.; Weisheimer, A.; Zanna, L.

    2016-05-01

    We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.

  20. Anvil Forecast Tool in the Advanced Weather Interactive Processing System

    NASA Technical Reports Server (NTRS)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting 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 System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast 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 forecasted 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 System (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.

  1. Ecological Forecasting: Microbial Contamination and Atmospheric Loadings of Nutrients to Land and Water

    EPA Science Inventory

    The development of ecological forecasts, 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...

  2. A multidisciplinary system for monitoring and forecasting Etna volcanic plumes

    NASA Astrophysics Data System (ADS)

    Coltelli, Mauro; Prestifilippo, Michele; Spata, Gaetano; Scollo, Simona; Andronico, Daniele

    2010-05-01

    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 system for monitoring and forecasting 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. Forecasting is carried out daily using automatic procedures which download weather forecast 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 system has been already tested successfully during several explosive events occurring at Etna in 2006, 2007 and 2008. These events produced eruption columns high up to several kilometers above sea level and, on the basis of parameters such as mass eruption rate and total grain-size distributions, showed different explosive style. The monitoring and forecasting system is going on developing through the installation of new instruments able to detect different features of the volcanic plumes (e.g. the dispersal and sedimentation processes) in order to reduce the uncertainty of the input parameters used in the modeling. This is crucial to perform a reliable forecasting. We show that multidisciplinary approaches can really give useful information on the presence of volcanic ash and consequently to prevent damages and airport disruptions.

  3. Forecasting system predicts presence of sea nettles in Chesapeake Bay

    NASA Astrophysics Data System (ADS)

    Brown, Christopher W.; Hood, Raleigh R.; Li, Zhen; Decker, Mary Beth; Gross, Thomas F.; Purcell, Jennifer E.; Wang, Harry V.

    Outbreaks of noxious biota, which occur in both aquatic and terrestrial systems, can have considerable negative economic impacts. For example, an increasing frequency of harmful algal blooms worldwide has negatively affected the tourism industry in many regions. Such impacts could be mitigated if the conditions that give rise to these outbreaks were known and could be monitored. Recent advances in technology and communications allow us to continuously measure and model many environmental factors that are responsible for outbreaks of certain noxious organisms. A new prototype ecological forecasting system predicts the likelihood of occurrence of the sea nettle (Chrysaora quinquecirrha), a stinging jellyfish, in the Chesapeake Bay.

  4. Road landslide information management and forecasting system base on GIS.

    PubMed

    Wang, Wei Dong; Du, Xiang Gang; Xie, Cui Ming

    2009-09-01

    Take account of the characters of road geological hazard and its supervision, it is very important to develop the Road Landslides Information Management and Forecasting System based on Geographic Information System (GIS). The paper presents the system objective, function, component modules and key techniques in the procedure of system development. The system, based on the spatial information and attribute information of road geological hazard, was developed and applied in Guizhou, a province of China where there are numerous and typical landslides. The manager of communication, using the system, can visually inquire all road landslides information based on regional road network or on the monitoring network of individual landslide. Furthermore, the system, integrated with mathematical prediction models and the GIS's strongpoint on spatial analyzing, can assess and predict landslide developing procedure according to the field monitoring data. Thus, it can efficiently assists the road construction or management units in making decision to control the landslides and to reduce human vulnerability. PMID:18704728

  5. Automated load management for spacecraft power systems

    NASA Technical Reports Server (NTRS)

    Lollar, Louis F.

    1987-01-01

    An account is given of the results of a study undertaken by NASA's Marshall Space Flight Center to design and implement the load management techniques for autonomous spacecraft power systems, such as the Autonomously Managed Power System Test Facility. Attention is given to four load-management criteria, which encompass power bus balancing on multichannel power systems, energy balancing in such systems, power quality matching of loads to buses, and contingency load shedding/adding. Full implementation of these criteria calls for the addition of a second power channel.

  6. GIM-TEC adaptive ionospheric weather assessment and forecast system

    NASA Astrophysics Data System (ADS)

    Gulyaeva, T. L.; Arikan, F.; Hernandez-Pajares, M.; Stanislawska, I.

    2013-09-01

    The Ionospheric Weather Assessment and Forecast (IWAF) system 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 system 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 forecast 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 system 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 system 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 forecast under different scenarios.

  7. AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

    NASA Technical Reports Server (NTRS)

    Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

    2009-01-01

    Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, 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 systems (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. Forecasts 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 forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.

  8. The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

    NASA Technical Reports Server (NTRS)

    Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

    2014-01-01

    The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System 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 Forecasting System (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 forecasting 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 Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting 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 Forecast 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 forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented.

  9. An Operational Coastal Forecasting System in Galicia (NW Spain)

    NASA Astrophysics Data System (ADS)

    Balseiro, C. F.; Carracedo, P.; Pérez, E.; Pérez, V.; Taboada, J.; Venacio, A.; Vilasa, L.

    2009-09-01

    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 Forecast System is the first step to the development of a more sophisticated Ocean Integrated Decision Support Tool. A regional oceanographic forecasting system in the Galician Coast has been developed by MeteoGalicia (the Galician regional meteorological agency) inside ESEOO project to provide forecasts on currents, sea level, water temperature and salinity. This system 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 system runs operationally daily, providing two days of forecast. 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 were validated against a CTDs profiles campaign carried out during an oil spill exercise in the Ria de Vigo in April 2007. During EROCIPS INTERREG IIIB and EASY INTERREG IVB projects, a Galician oceanographic observation network were built. Three stations located inside the Rias Baixas allow to collect meteorological and oceanographic data at different depths to calibrate and validate the modelization of the rias. To complete this network and to create a common data platform a new project emerged (RAIA INTERREG IVA). It will provide MeteoGalicia more scientific data to improve the study of the rias. Furthermore, MeteoGalicia is also involved in DRIFTER AMPERA project which allows to improve the capability of modelling and monitoring the trajectory of hazardous substances and inerts.

  10. Integrating Windblown Dust Forecasts with Public Safety and Health Systems

    NASA Astrophysics Data System (ADS)

    Sprigg, W. A.

    2014-12-01

    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 system 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 forecasts; 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.

  11. Design of a Forecasting Service System for Monitoring of Vulnerabilities of Sensor Networks

    NASA Astrophysics Data System (ADS)

    Song, Jae-Gu; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

    This study aims to reduce security vulnerabilities of sensor networks which transmit data in an open environment by developing a forecasting service system. The system 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 forecasting systems. Then, 5 steps of a forecasting service system are proposed in order to improve security responses.

  12. Traffic congestion forecasting model for the INFORM System. Final report

    SciTech Connect

    Azarm, A.; Mughabghab, S.; Stock, D.

    1995-05-01

    This report describes a computerized traffic forecasting 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 system 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 system. 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 forecasting 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 forecasted (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.

  13. A quality assessment of the MARS crop yield forecasting system for the European Union

    NASA Astrophysics Data System (ADS)

    van der Velde, Marijn; Bareuth, Bettina

    2015-04-01

    Timely information on crop production forecasts 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 forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting 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 forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast 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 forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

  14. Operational air quality forecasting system for Spain: CALIOPE

    NASA Astrophysics Data System (ADS)

    Baldasano, J. M.; Piot, M.; Jorba, O.; Goncalves, M.; Pay, M.; Pirez, C.; Lopez, E.; Gasso, S.; Martin, F.; García-Vivanco, M.; Palomino, I.; Querol, X.; Pandolfi, M.; Dieguez, J. J.; Padilla, L.

    2009-12-01

    The European Commission (EC) and the United States Environmental Protection Agency (US-EPA) have shown great concerns to understand the transport and dynamics of pollutants in the atmosphere. According to the European directives (1996/62/EC, 2002/3/EC, 2008/50/EC), air quality modeling, if accurately applied, is a useful tool to understand the dynamics of air pollutants, to analyze and forecast the air quality, and to develop programs reducing emissions and alert the population when health-related issues occur. The CALIOPE project, funded by the Spanish Ministry of the Environment, has the main objective to establish an air quality forecasting system for Spain. A partnership of four research institutions composes the CALIOPE project: the Barcelona Supercomputing Center (BSC), the center of investigation CIEMAT, the Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC) and the CEAM Foundation. CALIOPE will become the official Spanish air quality operational system. This contribution focuses on the recent developments and implementation of the integrated modelling system for the Iberian Peninsula (IP) and Canary Islands (CI) with a high spatial and temporal resolution (4x4 sq. km for IP and 2x2 sq. km for CI, 1 hour), namely WRF-ARW/HERMES04/CMAQ/BSC-DREAM. The HERMES04 emission model has been specifically developed as a high-resolution (1x1 sq. km, 1 hour) emission model for Spain. It includes biogenic and anthropogenic emissions such as on-road and paved-road resuspension production, power plant generation, ship and plane traffic, airports and ports activities, industrial and agricultural sectors as well as domestic and commercial emissions. The qualitative and quantitative evaluation of the model was performed for a reference year (2004) using data from ground-based measurement networks. The products of the CALIOPE system will provide 24h and 48h forecasts for O3, NO2, SO2, CO, PM10 and PM2.5 at surface level. An operational evaluation system has been developed to provide near real-time evaluation products for the Spanish territory. For this purpose, more than 130 surface stations, 2 ozonesondes and the OMI satellite retrieval information are introduced to the system on a daily basis. A web-based visualization system allows a straightforward access to all the evaluation products. The present contribution will describe the main characteristics of the operational system and results of the operational evaluation.

  15. Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

    NASA Astrophysics Data System (ADS)

    Drillet, Y.; Lellouche, J. M.; Levier, B.; Drévillon, M.; Le Galloudec, O.; Reffray, G.; Regnier, C.; Greiner, E.; Clavier, M.

    2014-12-01

    Operational systems operated by Mercator Ocean provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast 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 systems provide daily forecasts 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 forecast by the systems. Statistical scores and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are consistently better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4-day forecast. The impact of atmospheric forecast error, and for the wind field in particular (miss or time delay of a wind burst forecast), is also quantified in terms of occurrence and intensity of mixing or stratification events.

  16. WMOP: The SOCIB Western Mediterranean Sea OPerational forecasting system

    NASA Astrophysics Data System (ADS)

    Renault, Lionel; Juza, Mélanie; Garau, Bartolomé; Sayol, Juan Manuel; Orfila, Alejandro; Tintoré, Joaquín

    2013-04-01

    Development of science based ocean-forecasting systems 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 system) is the forecasting subsystem component of SOCIB, the new Balearic Islands Coastal Observing and Forecasting System. The WMOP system 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 systems 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 systems and remote sensing. The intrinsic three-dimensional variability of the coastal ocean and open-ocean exchanges imply the need of muti-plaform observing systems 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-comparison with products from different types of sources provides a good view of how well the model is performing and reproducing the dynamics of the basin. Additionally, this present study aims at assessing WMOP simulations quantitatively against complementary observational databases, i.e. to identify well-simulated physical features and to characterize the structure of model biases. The simulations are evaluated against hydrographic observations (temperature/salinity profiles from the ENACT-ENSEMBLES database), buoys, gliders and satellite data. We compare various simulations (WMOP, MFS, Mercator) to quantify the impact of the (sub)mesoscale on the large scale circulation.

  17. Assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems: Arctic Cap Nowcast/Forecast System (ACNFS) and the Global Ocean Forecast System (GOFS 3.1)

    NASA Astrophysics Data System (ADS)

    Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.

    2015-04-01

    This study presents the improvement in the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements to both of the Navy's sea ice forecasting systems.

  18. MOCASSIM - an operational forecast system for the Portuguese coastal waters.

    NASA Astrophysics Data System (ADS)

    Vitorino, J.; Soares, C.; Almeida, S.; Rusu, E.; Pinto, J.

    2003-04-01

    An operational system for the forecast of oceanographic conditions off the Portuguese coast is presently being implemented at Instituto Hidrográfico (IH), in the framework of project MOCASSIM. The system is planned to use a broad range of observations provided both from IH observational networks (wave buoys, tidal gauges) and programs (hydrographic surveys, moorings) as well as from external sources. The MOCASSIM system integrates several numerical models which, combined, are intended to cover the relevant physical processes observed in the geographical areas of interest. At the present stage of development the system integrates a circulation module and a wave module. The circulation module is based on the Harvard Ocean Prediction System (HOPS), a primitive equation model formulated under the rigid lid assumption, which includes a data assimilation module. The wave module is based on the WaveWatch3 (WW3) model, which provides wave conditions in the North Atlantic basin, and on the SWAN model which is used to improve the wave forecasts on coastal or other specific areas of interest. The models use the meteorological forcing fields of a limited area model (ALADIN model) covering the Portuguese area, which are being provided in the framework of a close colaboration with Instituto de Meteorologia. Although still under devellopment, the MOCASSIM system has already been used in several operationnal contexts. These included the operational environmental assessment during both national and NATO navy exercises and, more recently, the monitoring of the oceanographic conditions in the NW Iberian area affected by the oil spill of MV "Prestige". The system is also a key component of ongoing research on the oceanography of the Portuguese continental margin, which is presently being conducted at IH in the framework of national and European funded projects.

  19. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    NASA Astrophysics Data System (ADS)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system 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 system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast 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 system. 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 forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. 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 forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

  20. A Numerical Daily Air Quality Forecast System for The Pacific Northwest.

    NASA Astrophysics Data System (ADS)

    Vaughan, Joseph; Lamb, Brian; Frei, Chris; Wilson, Rob; Bowman, Clint; Figueroa-Kaminsky, Cristiana; Otterson, Sally; Boyer, Mike; Mass, Cliff; Albright, Mark; Koenig, Jane; Collingwood, Alice; Gilroy, Mike; Maykut, Naydene

    2004-04-01

    A real-time photochemical air quality forecast system has been implemented for the Puget Sound region to support public awareness of air quality issues. The Air Indicator Report for Public Access and Community Tracking (AIRPACT) forecast system uses daily numerical weather forecasts from the fifth-generation Pennsylvania State University (PSU) National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) to drive the California Meteorological Model (CALMET)/California photochemistry grid model (CALGRID) Eulerian photochemical modeling suite. Hourly forecasts of ozone and other pollutant concentrations, including primary particulate emissions from diesel sources, are produced for urban Seattle and environs within a gridded domain consisting of 62 × 67 grid cells (4 km × 4 km) with 13 vertical layers. Detailed gridded emission inventories are adjusted dynamically for time of day, day of the week, month, and gridded ambient temperatures to generate requisite emissions. The forecast system also uses hourly pollutant observation data, reported daily, to perform automated evaluations of forecast accuracy. Forecasts and verification results are provided on a daily basis via the Web (see www.airpact.wsu.edu). This paper describes the forecast system and presents preliminary forecast evaluation results from the Pacific Northwest 2001 (PNW2001) field program and from selected months using the routine monitoring network. AIRPACT is unique nationally as the sole numerical modeling system producing daily, year-round, high-resolution regional air quality forecasts with daily verification.

  1. Thirty Years of Improving the NCEP Global Forecast System

    NASA Astrophysics Data System (ADS)

    White, G. H.; Manikin, G.; Yang, F.

    2014-12-01

    Current eight day forecasts by the NCEP Global Forecast System are as accurate as five day forecasts 30 years ago. This revolution in weather forecasting 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 improvements to the assimilation. These changes significantly improve the tropospheric flow and reduce a tropical upper tropospheric warm bias. One important error remaining is the failure of the GFS to maintain deep convection over Indonesia and in the tropical west Pacific. This and other current systematic errors will be presented.

  2. A New Seasonal Forecasting System For The Ethiopian Summer Rains

    NASA Astrophysics Data System (ADS)

    Gissila, T.; Slingo, J. M.; Grimes, D. I. F.; Black, E. C. L.

    We present a new seasonal forecasting system for the June-September rains in Ethiopia. It has previously been found that the total June-September rainfall over the whole country is difficult to predict using statistical methods. Detailed study of all available data shows the rainfall seasonality varies greatly from one region to another, which would explain why the total June-September rainfall over all regions is a diffi- cult property to forecast. In addition, the correlation between rainfall and the Southern Oscillation Index varies spatially, with a strong teleconnection present only in some regions. This study accounts for the spatial variability in rainfall by grouping the rain gauge stations into 4 geographical clusters based on seasonality and cross-correlation of rainfall anomalies. Linear regression equations are then developed separately for each cluster. The variables we use for the regressions are SST anomalies in the pre- ceding March, April and May of: the tropical West Indian Ocean, tropical East Indian Ocean and Nino 3.4. Formal skill-testing of the equations shows that the new fore- casting scheme is more effective than either climatology or persistence - the methods currently used by the Ethiopian Meteorological Office.

  3. The Impact Of Calibration and Reforecasts of Ensemble Prediction System of Inputs on a Flood Forecast System

    NASA Astrophysics Data System (ADS)

    Trinh, B.; Pappenberger, F.

    2009-04-01

    The pre-operation European Flood Alerts System (EFAS) relies on reliable and accurate input by ensembles of Numerical Weather Predictions (NWPs). The usage of such inputs for Probabilistic Quantitative Hydrology Forecast is still difficult as a variety of uncertainties arise. For example, forecasts are biased and the ensemble spread of forecasts is often under- or overestimating observational variances. Moreover, anthropogenic effects (land use, urbanization and dykes), climate change, and tectonic/isostatic relief change, which affect return periods, significantly influence the quality of flood forecasts. In order to access to the hazard associated to the hydrological forecast issued by EFAS, we compare two methods to calibrate the ensemble forecast, especially Ensemble Prediction System (EPS) of ECMWF, as the inputs of EFAS: 1. Reforecast for regional meteorological forecast 2. Calibrating EPS for regional meteorological forecast. The results will be evaluated in terms of Talagrand diagram, Continuous Rank Probability Score (CRPS), spread skill relationship and Relative Operating Characteristic (ROC).

  4. A multi-step predictor with a variable input pattern for system state forecasting

    NASA Astrophysics Data System (ADS)

    Liu, Jie; Wang, Wilson; Golnaraghi, Farid

    2009-07-01

    A reliable predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive multi-step predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm for system state forecasting. A variable input pattern is proposed to improve the forecasting performance. A hybrid training algorithm, based on the recursive Levenberg-Marquardt algorithm and recursive least square estimate, is suggested to enhance forecasting convergence and to accommodate time-varying system conditions. The viability of the developed predictor is evaluated by simulations on both benchmark data sets and experimental data sets corresponding to machinery condition monitoring. The investigation results show that the developed adaptive predictor is a reliable and robust multi-step forecasting tool. It can capture and track system's response quickly and accurately. It outperforms other related classical forecasting schemes.

  5. Operational flood forecasting system of Umbria Region "Functional Centre

    NASA Astrophysics Data System (ADS)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    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 system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, 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 forecasting 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 system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according to the expected ground effects: ordinary, moderate and high. Particularly, hydrometric and rainfall thresholds for both floods and landslides alarms were assessed. Based on these thresholds, at the Umbria Region Functional Centre an automatic phone-call and SMS alert system is operating. For a real time flood forecasting system, at the CFD several hydrological and hydraulic models were developed. Three rainfall-runoff hydrological models, using different quantitative meteorological forecasts, are available: the event based models X-Nash (based on the Nash theory) and Mike-Drift coupled with the hydraulic model Mike-11 (developed by the Danish Hydraulic Institute - DHI); and the physically-based continuous model Mobidic (MOdello di Bilancio Idrologico DIstribuito e Continuo - Distributed and Continuous Model for the Hydrological Balance, developed by the University of Florence in cooperation with the Functional Centre of Tuscany Region). Other two hydrological models, using observed data of the real time hydrometeorological network, were implemented: the first one is the rainfall-runoff hydrological model Hec-Hms coupled with the hydraulic model Hec-Ras (United States Army Corps of Engineers - USACE). Moreover, Hec-Hms, is coupled also with a continuous soil moisture model for a more precise evaluation of the antecedent moisture condition of the basin, which is a key factor for a correct runoff volume evaluation. The second one is the routing hydrological model Stafom (STage FOrecasting Model, developed by the Italian Research Institute for Geo-Hydrological Protection of the National Research Council - IRPI-CNR). This model is an adaptive model for on-line stage forecasting for river branches where significant lateral inflow contributions occur and, up to now, it is implemented for the main Tiber River branch and it allows a forecasting lead time up to 10 hours for the downstream river section. Recently, during the period between December the 4th and the 16th 2008, Umbria region territory was interested by a severe rainfall event causing many floods and landslides. During the mainly critical phases the CFD furnished an immediate, significant 24h support for the decision support activities. The official web site (www.cfumbria.it), entirely developed with open source tools, represented a very useful device furnishing good performances for the monitoring and data dissemination to all the subjects involved, especially to the National/Regional Civil Protection offices and territorial presidium. Thresholds presented good accordance with non instrumental observations and automatic alert system was very effective. At last, during the flooding event a continuous link with the National Department, regional Civil Protection offices, territorial presidium and local public services, together with real time instrumental monitoring and now-casting hydrological activities performed by available models, represented a suitable junction between practice and science in CFD operational forecasting system at local, regional and national scale.

  6. Nonparametric forecasting of low-dimensional dynamical systems.

    PubMed

    Berry, Tyrus; Giannakis, Dimitrios; Harlim, John

    2015-03-01

    This paper presents a nonparametric modeling approach for forecasting stochastic dynamical systems 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 systems 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 Nio-3.4 data set which is used as a proxy of the El Nio Southern Oscillation. PMID:25871180

  7. Progress on a Real-Time Storm Surge Forecasting System

    NASA Astrophysics Data System (ADS)

    Weaver, R. J.; Slinn, D. N.

    2004-12-01

    As part of an ongoing NOPP project entitled Real-Time Forecasting System of Winds, Waves and Surge from Tropical Cyclones, we are developing a storm surge prediction scheme. 2004 has proven to be an excellent year for testing the model with one of the most active hurricane seasons on record. In developing our storm surge predictions, we seek to couple the wind, pressure and wave stress with the circulation model. We use the Advanced Circulation model, ADCIRC, to calculate the change in surface elevation. We began testing our system on hurricane Georges (1998). In these tests we found that including the wave stresses in a storm surge prediction scheme was important. The waves were found to contribute about 30% to the total water level increase. In some areas the waves can contribute significantly more on the order of 50%. Including the wave forcing reduced the normalized RMS error by 20 to 50%. For the case of hurricane Georges we are satisfied with the predictive results which yielded a MSE of less that \\( 0.10 m^{2} \\). We test our system next by running a hindcast of hurricane Isabel (2003). In this model scenario we hold all variables constant with those used in the run of hurricane Georges and only vary the wind, pressure and wave field. We are working for a model system that minimizes error without having to tune the model to specific storms. Our results for hurricane Isabel are compared to the measured sea level response. From a series of plots we see that with no tuning of the model formulations we achieve agreement with the recorded data. We will also present preliminary results from the 2004 hurricane season. This includes real-time model forecasts of Charlie, Frances and Ivan. These results are compared with the field data recorded at tidal stations.

  8. Satellite data assimilation in global forecast system in India

    NASA Astrophysics Data System (ADS)

    Basu, Swati

    2014-11-01

    Satellite data is very important for model initialization and verification. A large number of satellite observations are currently assimilated into the Numerical Weather Prediction (NWP) systems at the National Centre for Medium Range Weather Forecasting (NCMRWF). Apart from Global meteorological observations from GTS, near-real time satellite observations are received at NCMRWF from other operational centres like ISRO, NOAA/NESDIS, EUMETCAST, etc. Recently India has become member of Asia-Pacific Regional ATOVS Retransmission Service (APRARS) for faster access to high resolution global satellite data useful for high resolution regional models. Indian HRPT at Chennai covers the APRARS data gap region over South East Asia. A robust data monitoring system has been implemented at NCMRWF to assess the quantity and quality of the data as well as the satellite sensor strength, before getting assimilated in the models. Validation of new satellite observations, especially from Indian satellites are being carried out against insitu observations and similar space borne platforms. After establishing the quality of the data, Observation System Experiments (OSEs) are being conducted to study their impact in the assimilation and forecast systems. OSEs have been carried out with the Oceansat-2 scatterometer winds and radiance data from Megha-Tropiques SAPHIR sensor. Daily rainfall analysis dataset is being generated by merging satellite estimates and in-situ observations. ASCAT soil wetness measurements from METOP satellite is being assimilated into the global model. Land surface parameters (LuLc and albedo) retrieved from Indian satellites are being explored for its possible usage in the global and regional models. OLR from Indian satellites are used for validating model outputs. This paper reviews the efforts made at NCMRWF in (i) assimilating the data from Indian/International satellites and (ii) generating useful products from the satellite data.

  9. Towards an integrated forecasting system for pelagic fisheries

    NASA Astrophysics Data System (ADS)

    Christensen, A.; Butenschön, M.; Gürkan, Z.; Allen, I. J.

    2012-03-01

    First results of a coupled modeling and forecasting system for the pelagic fisheries are being presented. The system 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 forecasting system 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.

  10. Forecasting for the growth of fatigue cracks in rivetted panels at irregular loading

    NASA Astrophysics Data System (ADS)

    Kabakov, S. V.; Maksimenko, V. N.

    1994-01-01

    A method has been suggested to forecast for the durability of complicated panels reinforced with rivetted stringers. The method is based on the root-mean-square approach aimed at estimating the growth rate of fatigue cracks and on the method of singular integral equations. The last method gives the opportunity to make calculations less labor-intensive and to more accurately determine coefficients of stress intensity. The efficiency and reliability of the method suggested are illustrated by the results of numerical investigations and by the comparison of these results with experimental data.

  11. A Methodology To Allow Avalanche Forecasting on an Information Retrieval System.

    ERIC Educational Resources Information Center

    Purves, R. S.; Sanderson, M.

    1998-01-01

    Presents adaptations and tests undertaken to allow an information retrieval system to forecast the likelihood of avalanches on a particular day; the forecasting 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 system and…

  12. Camera-based forecasting of insolation for solar systems

    NASA Astrophysics Data System (ADS)

    Manger, Daniel; Pagel, Frank

    2015-02-01

    With the transition towards renewable energies, electricity suppliers are faced with huge challenges. Especially the increasing integration of solar power systems into the grid gets more and more complicated because of their dynamic feed-in capacity. To assist the stabilization of the grid, the feed-in capacity of a solar power system within the next hours, minutes and even seconds should be known in advance. In this work, we present a consumer camera-based system for forecasting the feed-in capacity of a solar system for a horizon of 10 seconds. A camera is targeted at the sky and clouds are segmented, detected and tracked. A quantitative prediction of the insolation is performed based on the tracked clouds. Image data as well as truth data for the feed-in capacity was synchronously collected at one Hz using a small solar panel, a resistor and a measuring device. Preliminary results demonstrate both the applicability and the limits of the proposed system.

  13. Statoil's offshore submerged turret loading system

    SciTech Connect

    Brevik, K. ); Smedal, S. )

    1993-01-01

    Statoil, the Norwegian state oil company, and Marine Consulting Group (MCG), with support from Norwegian research institutes, are jointly developing a new offshore shuttle tanker loading concept called the Submerged Turret Loading (STL) system. The STL comprises a spread-moored buoy and export line riser configured such that, when not in use, the buoy remains submerged. For shuttle tanker loading, the vessel moves over the buoy and pulls it into a compartment in the bottom of its hull. Mooring loads are then transferred into the vessel's hull; and the export riser is connected to the shuttle's tankage within the chamber, below waterline. Principal features of the innovative new system that allows operations in seastates well beyond present-system limits, increases safety and reduces pollution potential are outlined here.

  14. Load positioning system with gravity compensation

    NASA Technical Reports Server (NTRS)

    Hollow, R. H.

    1984-01-01

    A load positioning system 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 load. 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 load 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 load which thereby enables the positioning servomotor to see the load as weightless under both static and dynamic conditions. Positioning acceleration and velocity performance are therefore symmetrical.

  15. Faculty Teaching Loads in the UNC System

    ERIC Educational Resources Information Center

    Schalin, Jay

    2014-01-01

    This paper explores the teaching loads of faculty in the University of North Carolina (UNC) system. Salaries for faculty members are the single largest cost of higher education in the UNC system, accounting for approximately half of expenditures. The system's funding formula for its 16 college campuses is largely dependent upon the number of…

  16. Forecasting of Hourly Photovoltaic Energy in Canarian Electrical System

    NASA Astrophysics Data System (ADS)

    Henriquez, D.; Castaño, C.; Nebot, R.; Piernavieja, G.; Rodriguez, A.

    2010-09-01

    The Canarian Archipelago face similar problems as most insular region lacking of endogenous conventional energy resources and not connected to continental electrical grids. A consequence of the "insular fact" is the existence of isolated electrical systems that are very difficult to interconnect due to the considerable sea depths between the islands. Currently, the Canary Islands have six isolated electrical systems, only one utility generating most of the electricity (burning fuel), a recently arrived TSO (REE) and still a low implementation of Renewable Energy Resources (RES). The low level of RES deployment is a consequence of two main facts: the weakness of the stand-alone grids (from 12 MW in El Hierro up to only 1 GW in Gran Canaria) and the lack of space to install RES systems (more than 50% of the land protected due to environmental reasons). To increase the penetration of renewable energy generation, like solar or wind energy, is necessary to develop tools to manage them. The penetration of non manageable sources into weak grids like the Canarian ones causes a big problem to the grid operator. There are currently 104 MW of PV connected to the islands grids (Dec. 2009) and additional 150 MW under licensing. This power presents a serious challenge for the operation and stability of the electrical system. ITC, together with the local TSO (Red Eléctrica de España, REE) started in 2008 and R&D project to develop a PV energy prediction tool for the six Canarian Insular electrical systems. The objective is to supply reliable information for hourly forecast of the generation dispatch programme and to predict daily solar radiation patterns, in order to help program spinning reserves. ITC has approached the task of weather forecasting using different numerical model (MM5 and WRF) in combination with MSG (Meteosat Second Generation) images. From the online data recorded at several monitored PV plants and meteorological stations, PV nominal power and energy produced by every plant in Canary Islands are estimated using a series of theoretical and statistical energy models.

  17. Intelligent forecasting compensatory control system for profile machining

    NASA Astrophysics Data System (ADS)

    Fung, Eric H. K.; Chuen, C. W.; Lee, L. M.

    2000-10-01

    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 forecasting 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 system parameters which include the ARMAX orders, feed rate, depth of cut, material, and forgetting factor. An expert system has been successfully developed to implement the rules using the Prolog language for helping the users to select suitable parameters for the FCC system of the lathe machine. Based on the measurement data, it can be shown that the lathe machine, when equipped with the ARMAX-based FCC system, can yield a minimum value of average improvement of 26% under the testing conditions.

  18. A microphysical aerosol module in the ECMWF Integrated Forecasting System

    NASA Astrophysics Data System (ADS)

    Woodhouse, M. T.; Mann, G. W.; Carslaw, K. S.; Flemming, J.; Morcrette, J.-J.; Boucher, O.

    2012-04-01

    The Monitoring Atmospheric Composition and Climate II (MACC-II) project will provide a system for monitoring and predicting the characteristics of atmospheric constituents. Our contribution to this work is the incorporation and evaluation of the GLOMAP-mode microphysical aerosol scheme (Mann et al., 2010, GMD) within the ECMWF Integrated Forecasting System (IFS). The two-moment modal GLOMAP-mode scheme includes new particle formation, condensation, coagulation, cloud-processing, and wet and dry deposition. GLOMAP-mode is already incorporated as a module within the TOMCAT chemistry transport model and within the UK Met Office HadGEM3 general circulation model (where it is known as UKCA-mode). The use of a microphysical, process-based model allows a more realistic representation of the properties of the multi-component aerosol and will enable aerosol-cloud interactions to be robustly simulated within the IFS system. Presented here is an evaluation of the performance of the IFS-GLOMAP system in comparison to mass, number and aerosol optical depth measurements, using different chemical drivers to the aerosol scheme. Ongoing work explores the complexity needed to describe the aerosol size distribution such that the important characteristics of the aerosol are represented. A key driver of this latter work is the need to reduce the number of tracers and hence compute time required so that the aerosol scheme can be run in an operational environment, without greatly affecting model skill.

  19. Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.

    NASA Astrophysics Data System (ADS)

    Vislocky, Robert L.; Fritsch, J. Michael

    1997-12-01

    A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts 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 forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system 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 or better substitutes. On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.

  20. A Weather Analysis and Forecasting System for Baja California, Mexico

    NASA Astrophysics Data System (ADS)

    Farfan, L. M.

    2006-05-01

    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 systems 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 systems 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. Forecasts are derived from an operational model (GFS) and locally generated simulations based on a mesoscale model (MM5). Our analysis and forecasting system 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 systems 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 on storm location, expected track, and potential impact on weather conditions over Baja California. From late June through October, a set of more than 50 messages were issued daily and distributed to these users. This presentation focuses on providing an overview of the lessons learned from this experience, feedback from users, and our plans for the upcoming 2006 season.

  1. An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Velázquez, J. A.; Petit, T.; Lavoie, A.; Boucher, M.-A.; Turcotte, R.; Fortin, V.; Anctil, F.

    2009-11-01

    Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, 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 system 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. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast 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 forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts 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.

  2. An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Velázquez, J. A.; Petit, T.; Lavoie, A.; Boucher, M.-A.; Turcotte, R.; Fortin, V.; Anctil, F.

    2009-07-01

    Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, 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 system 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. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast 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 forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts 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.

  3. THE EMISSION PROCESSING SYSTEM FOR THE ETA/CMAQ AIR QUALITY FORECAST SYSTEM

    EPA Science Inventory

    NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system 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...

  4. Load control system. [for space shuttle external tank ground tests

    NASA Technical Reports Server (NTRS)

    Grosse, J. C.

    1977-01-01

    The load control system developed for the shuttle external structural tests is described. The system consists of a load programming/display module, and a load control module along with the following hydraulic system components: servo valves, dump valves, hydraulic system components, and servo valve manifold blocks. One load programming/display subsystem can support multiple load control subsystem modules.

  5. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE PAGESBeta

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting 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 forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  6. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting 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 forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  7. Short-term optimal operation of water systems using ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Raso, L.; Schwanenberg, D.; van de Giesen, N. C.; van Overloop, P. J.

    2014-09-01

    Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate. Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. 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 forecasts 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 forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts 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.

  8. A SIMPLE MODEL FOR FORECASTING THE EFFECTS OF NITROGEN LOADS ON CHESAPEAKE BAY HYPOXIA

    EPA Science Inventory

    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 load reductions are necessary to meet the water quality goals is ...

  9. Fuel cell stack compressive loading system

    DOEpatents

    Fahle, Ronald W.; Reiser, Carl A.

    1982-01-01

    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 system for providing a compressive load on the stack wherein the constraint system maintains the stack at a constant height (after thermal expansion) and allows the compressive load 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 system; however it can only be used with a stack having considerable resiliency and appropriate thermal expansion and creep characteristics.

  10. Satellite freeze forecast system. System configuration definition manual

    NASA Technical Reports Server (NTRS)

    Martsolf, J. D. (Principal Investigator)

    1983-01-01

    Equipment listings, interconnection information, and a basic overview is given of the hardware interaction of the Ruskin HP-100 computer system. A block diagram is included of the SFFS system at the National Weather Service Office in Ruskin, Florida. The generation answer file used to create the RTE-IVB operating system currently resident in Ruskin HP-1000 computer system is also described.

  11. An improved forecast system for relativistic electrons in Earth's outer radiation belt

    NASA Astrophysics Data System (ADS)

    Turner, D. L.; Li, X.

    2010-12-01

    As society becomes more dependent on satellite systems for navigation, communication, defense, and weather prediction, accurate forecasts of Earth's outer radiation belt electrons become ever more important to help mitigate risk to spacecraft in orbits that go through this hazardous region. Currently, outer radiation belt forecast models are only for daily-averaged fluxes (or fluence) at geosynchronous orbit (GEO), yet due to the nature of Earth's asymmetric magnetosphere, with its compressed dayside and stretched nightside, a spacecraft in GEO encounters a wide range of fluxes at different local times. Also, many of the existing forecasts are single-point dependent; that is, they rely on one spacecraft at the L1 point in the Sun-Earth system for upstream measurements of the solar wind. Here, we introduce a recently developed system of forecast models for outer radiation belt electrons that are currently running in real-time. This system includes several different forecast models, including one that is independent of solar wind measurements. It also incorporates a new technique to forecast fluxes at each local hour around GEO, and an innovative method to extend several of the forecasts out to +6 days. We conclude with a discussion of how data reanalysis techniques can be incorporated to extend forecasts to other L-shells.

  12. Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

    NASA Astrophysics Data System (ADS)

    Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

    2014-03-01

    To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting 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 forecasting 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 forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.

  13. Operational Solar Forecasting System for DNI and GHI for Horizons Covering 5 Minutes to 72 Hours

    NASA Astrophysics Data System (ADS)

    Coimbra, C. F.

    2014-12-01

    I will describe the methodology used to develop and deploy operationally a comprehensive solar forecasting system for both concentrated and non-concentrated solar technologies. This operational forecasting system 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 forecasts from 5 minutes out to 72 hours into the future. Each forecast is optimized with stochastic learning techniques that include input selection, model topology optimization, model output statistics, metric fitness optimization and machine learning. These forecasts are used by solar generators (plant managers), utilities and independent system operators for operations, scheduling, dispatching and market participation.

  14. Using ensemble NWP wind power forecasts to improve national power system management

    NASA Astrophysics Data System (ADS)

    Cannon, D.; Brayshaw, D.; Methven, J.; Coker, P.; Lenaghan, D.

    2014-12-01

    National power systems 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 system management. At this time horizon, power system operators plan transmission line outages for maintenance. In addition, forecast 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 system security standards, or in the worst instance, a shortfall in total electricity supply. On the other hand, overly conservative assumptions about the forecast uncertainty incur costs associated with the unnecessary holding of reserve power. Using the power system 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 forecasts. To validate the accuracy of these data sets, wind power reconstructions using reanalyses and forecast 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 forecasts from several national and international forecast centres (obtained through TIGGE). Firstly, the skill with which global ensemble forecasts can represent the uncertainty in the GB-total power output at up to 10 days ahead is quantified. Following this, novel ensemble forecast metrics are developed to improve estimates of forecast uncertainty within the context of power system 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 forecast, induce high stress scenarios that could threaten the security of the power system.

  15. Distributed rectifier loads in electric power systems

    SciTech Connect

    Heydt, G.T.; Grady, W.M.

    1984-09-01

    The level of rectifier and flourescent loads in electric power systems is often significant in localized areas. Typically, the nonlinear loads are distributed over many distribution and subtransmission busses. In this paper, the harmonic signal levels resulting from distributed rectifier loads are examined using a recently reported harmonic power flow study algorithm. Because the algorithm does not rely on superposition or sinusoidal bus voltage assumptions, it may be applied in cases of high harmonic content such as nearresonant conditions. A case history is presented and examination of alternative methods of harmonic attenuation is presented. Also, for six pulse, line commutated rectifiers, the effect of dc circuit load variation and dc circuit inductance on harmonic signal levels is examined.

  16. Hydro-economic assessment of hydrological forecasting systems

    NASA Astrophysics Data System (ADS)

    Boucher, M.-A.; Tremblay, D.; Delorme, L.; Perreault, L.; Anctil, F.

    2012-01-01

    SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

  17. A Global Hydrological Ensemble Forecasting System: Uncertainty Quantification and Data Assimilation

    NASA Astrophysics Data System (ADS)

    Hong, Y.; Zhang, Y.; Xue, X.; Wang, X.; Gourley, J. J.; Kirstetter, P.

    2012-12-01

    A Global Hydrological Ensemble Forecasting System (GHEFS) driven by TRMM Multi-satellite Prediction Analysis (TMPA) precipitation ensembles and Global Ensemble Forecast System (GEFS) Quantitative Precipitation Forecast (QPF) ensembles, via the Coupled Routing and Excess STorage (CREST) distributed hydrological model, provides deterministic and probabilistic (e.g. 95% confidence boundaries) simulations of streamflow. The TMPA inputs enable flood monitoring and short-term forecasts while the GEFS ensembles provide for forecasts up to a seven-day lead time. This talk will focus on a quantification of the system's uncertainty and streamflow ensemble prediction generation using the following three techniques: 1) an error model that first quantifies and then perturbs both temporal and spatial variability of the real-time, TMPA precipitation estimates by considering the version-7 research product as the reference rainfall product; 2) in forecast mode, utilization of the Ensemble Transform method to account for the uncertainty of GEFS forecasts from its initial condition errors; 3) a sequential data assimilation approach - the Ensemble Square Root Kalman Filter (EnSRF) applied to update the CREST model's internal states whenever observations (e.g. streamflow, soil moisture, and actual ET etc.) are available. The GHEFS is validated in several basins in the U.S. and other continents in terms of flood detection capability (e.g. CSI, NSCE, Peak, Timing), showing improved prognostic capability by offering more time for responding agencies and yielding unique uncertainty information about the magnitude of the forecast impacts.

  18. On the impact of stochastic parametrisations in the ECMWF seasonal forecasting system

    NASA Astrophysics Data System (ADS)

    Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

    2014-05-01

    Seasonal climate predictions several months ahead based on dynamical atmosphere-ocean GCMs are part of the routinely operational forecasts issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). Here, the seasonal forecasting system is a seamless extension of ECMWF's medium-range ensemble weather forecasting system for the atmosphere coupled to a state-of-the-art ocean model. Model uncertainty in the atmosphere is represented by two schemes, the Stochastically Perturbed Physical Tendency (SPPT) scheme and the Stochastic Kinetic Energy Backscatter (SKEB) scheme. This contributions looks at the impact of these two stochastic parametrisation schemes on the model performance for seasonal forecasts. It is found that these schemes reduce long-standing model biases in the Indonesian warm pool area dominated by intense convection. The simulation of MJO events in the seasonal forecasts has improved due to the stochastic parametrisations. Both schemes substantially increase the ensemble spread for El Niño SST forecasts and thus make the ensemble forecasting system better calibrated. In addition, the stochastic parametrisations also have a positive effect on the simulation of atmospheric quasi-stationary circulation regimes over the extratropical Pacific-North America region.

  19. The Space Weather Modeling System: An ESMF Compliant Solar Wind and Ionospheric Forecast System

    NASA Astrophysics Data System (ADS)

    Reich, J. P.; Fry, C. D.; Eccles, J. V.; Berman, L. M.; Sattler, M. P.

    2008-12-01

    Ionospheric storms can severely impact communications, navigation and surveillance systems. These ionospheric disturbances are driven by solar activity. A key challenge in space science is to understand the causes of the ionospheric response to solar forcing. Attempting to accurately forecast the time-dependent behavior of the ionosphere is the only way to truly test our understanding of the ionosphere. Space weather forecasters for the DoD face this challenge on a daily basis. The Air Force Weather Agency is meeting this challenge through the development of an operational Space Weather Modeling System (SWMS). The SWMS is a Battlespace Environments Institute (BEI) project that couples Earth system environmental models together under the Earth System Modeling Framework (ESMF). BEI is sponsored by the High Performance Computing (HPC) Modernization Office. The first two coupled components in SWMS are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and the Global Assimilation of Ionospheric Measurements (GAIM) model. The HAFv2 model produces quantitative forecasts of solar wind parameters at Earth and elsewhere in the inner heliosphere. The Ionosphere Forecast Model (IFM) is the physics-based ionosphere model within GAIM. IFM provides highly representative specifications of plasma conditions in the global ionosphere. The one-way coupling of HAFv2 to IFM links the solar storm drivers to the ionospheric response. Predicted solar wind quantities are fed as inputs to IFM, which computes the solar wind energy deposition into the high latitude ionosphere, enabling GAIM to provide multi- day forecasts of ionospheric electron density, currents and upper atmosphere dynamics. The SWMS development is a structured project, moving from partial to full ESMF compliance. Bringing the HAFv2 and IFM models into the ESMF allows significant improvements in computational efficiency and data throughput. Modifying these computer codes for the HPC environment opens the door for other new capabilities. These include the ability to: 1) ingest diverse data sets at higher resolution and cadence; 2) use denser computational grids; and 3) perform ensemble forecasts. The HAFv2-IFM coupling provides the first operational, physics-based forecasts of the near-earth space environment that anticipate solar storm effects. The SWMS effort will shed light on our understanding of the underlying physics and ultimately lead to more accurate ionospheric forecasts to better support DoD missions

  20. Systemic change increases forecast uncertainty of land use change models

    NASA Astrophysics Data System (ADS)

    Verstegen, J. A.; Karssenberg, D.; van der Hilst, F.; Faaij, A.

    2013-12-01

    Cellular Automaton (CA) models of land use change are based on the assumption that the relationship between land use change and its explanatory processes is stationary. This means that model structure and parameterization are usually kept constant over time, ignoring potential systemic changes in this relationship resulting from societal changes, thereby overlooking a source of uncertainty. Evaluation of the stationarity of the relationship between land use and a set of spatial attributes has been done by others (e.g., Bakker and Veldkamp, 2012). These studies, however, use logistic regression, separate from the land use change model. Therefore, they do not gain information on how to implement the spatial attributes into the model. In addition, they often compare observations for only two points in time and do not check whether the change is statistically significant. To overcome these restrictions, we assimilate a time series of observations of real land use into a land use change CA (Verstegen et al., 2012), using a Bayesian data assimilation technique, the particle filter. The particle filter was used to update the prior knowledge about the parameterization and model structure, i.e. the selection and relative importance of the drivers of location of land use change. In a case study of sugar cane expansion in Brazil, optimal model structure and parameterization were determined for each point in time for which observations were available (all years from 2004 to 2012). A systemic change, i.e. a statistically significant deviation in model structure, was detected for the period 2006 to 2008. In this period the influence on the location of sugar cane expansion of the driver sugar cane in the neighborhood doubled, while the influence of slope and potential yield decreased by 75% and 25% respectively. Allowing these systemic changes to occur in our CA in the future (up to 2022) resulted in an increase in model forecast uncertainty by a factor two compared to the assumption of a stationary system. This means that the assumption of a constant model structure is not adequate and largely underestimates uncertainty in the forecast. Non-stationarity in land use change projections is challenging to model, because it is difficult to determine when the system will change and how. We believe that, in sight of these findings, land use change modelers should be more aware, and communicate more clearly, that what they try to project is at the limits, and perhaps beyond the limits, of what is still projectable. References Bakker, M., Veldkamp, A., 2012. Changing relationships between land use and environmental characteristics and their consequences for spatially explicit land-use change prediction. Journal of Land Use Science 7, 407-424. Verstegen, J.A., Karssenberg, D., van der Hilst, F., Faaij, A.P.C., 2012. Spatio-Temporal Uncertainty in Spatial Decision Support Systems: a Case Study of Changing Land Availability for Bioenergy Crops in Mozambique. Computers , Environment and Urban Systems 36, 30-42.

  1. The Australian Air Quality Forecasting System: Exploring First Steps Towards Determining The Limits of Predictability For Short-Term Ozone Forecasting

    NASA Astrophysics Data System (ADS)

    Cope, M. E.; Hess, G. D.; Lee, S.; Tory, K. J.; Burgers, M.; Dewundege, P.; Johnson, M.

    2005-08-01

    Physical parameterisations of turbulent transfer processes in the atmospheric boundary layer, such as the stability parameterisations developed by Joost Businger, and recent advances in computing capabilities, have been important factors leading to the emergence of operational, numerical air quality forecasting systems. The present paper investigates the performance of the Australian Air Quality Forecasting System (AAQFS) in forecasting the peak 1 h ozone for the current or next day. These 24/36 h forecasts are generated for the Sydney and Melbourne regions and issued twice daily. Quantitative evidence is presented of the potential for the AAQFS to provide accurate numerical air quality forecasts. A second goal is to provide an initial benchmark for investigating the limits of predictability for air quality in the Sydney and Melbourne regions by looking at the dependence of the forecasts on the domain spatial scale (while maintaining the same model grid resolution), the starting time and length of the forecast (0000 UTC starts are 36-h forecasts and 1200 UTC starts are 24-h forecasts), and the sophistication of the photochemical mechanism (simple chemistry, Generic Reaction Set (GRS) and complex chemistry, Carbon Bond IV (CBIV)). The probability of detection by the forecast model is much better than persistence, showing considerable skill. The normalised bias, in general, decreases going from regional scale to sub-regional scale and becomes negative at the station scale. In Melbourne the gross error increases as the domain spatial scale decreases, but in Sydney there is a dip in the error at the sub-regional scale due to a sampling artifact. Better results are obtained at the smaller domain scales for 1200 UTC forecasts in Sydney. These are attributed to the shorter forecast period and secondarily to greater model spin-up effects at 0000 UTC. In Melbourne the results are ambiguous. Similar conclusions are derived from scatter plots of forecasts versus observations. Dividing the scatter plots into four sections by plotting vertical and horizontal lines (at 60 ppb) forms contingency tables for categorical forecasting. These plots show the increase in missed forecasts due to underprediction and the decrease in the number of extreme events detected as the spatial scale decreases. A comparison of the highly condensed GRS photochemical mechanism with the comprehensive CBIV mechanism indicates that, in general, GRS performs well for predicting ozone in urban situations provided that the background concentrations are appropriately specified. The potential to improve the forecasts at the smaller spatial scales, particularly for extreme events at high ozone concentrations, may require moving to a more complex mechanism as computer resources become available.

  2. Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

    NASA Technical Reports Server (NTRS)

    Prive, N. C.; Errico, Ronald M.

    2015-01-01

    The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

  3. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, S.; McNally, A.; Husak, G.; Funk, C.

    2014-10-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this forecast system for a region of equatorial EA (2° S-8° N, 36-46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM forecasts generated by this system on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM forecasts with start-of-season (SOS) (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the forecast was initialized with midseason (i.e., 5 April) SM conditions, the skill of forecasting SM estimates until the end-of-season improved (correlation > 0.5 over several grid cells). We also found these SM forecasts to be more skillful than the ones generated using the Ensemble Streamflow Prediction (ESP) method, which derives its hydrologic forecast skill solely from the knowledge of the initial hydrologic conditions. Finally, we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years (when standardized anomaly of MAM precipitation is below 0). This indicates that this system might be particularity useful for identifying drought events in this region and can support decision-making for mitigation or humanitarian assistance.

  4. Development and evaluation of an operational SDS forecasting system for East Asia: CUACE/DUST

    NASA Astrophysics Data System (ADS)

    Zhou, C. H.; Gong, S. L.; Zhang, X. Y.; Wang, Y. Q.; Niu, T.; Liu, H. L.; Zhao, T. L.; Yang, Y. Q.; Hou, Q.

    2007-06-01

    CUACE/Dust, an operational sand and dust storm (SDS) forecasting system for East Asia, was developed at CMA (China Meteorological Administration) by integrating a meso-scale dust aerosol model with a 3DVar data assimilation system that uses both surface network observation data and dust intensity data retrieved from the Chinese Geostationary Satellite FY-2C. For spring 2006, CUACE/Dust successfully forecasted most of the 31 SDS episodes in East Asia. A detailed comparison of the modeling predictions for the 8-12 March episode with surface network observations and lidar measurements revealed a robust forecasting ability of the system. The time series of the forecasted dust concentrations for a number of representative stations for the whole spring 2006 were also evaluated against surface PM10 monitoring data, showing a very good agreement in terms of the SDS timing and magnitudes near source regions where dust aerosols dominate. For the entire domain forecasts in spring 2006 (1 March-31 May), a TS (thread score) system evaluated the performance of the system against all available observations and rendered an averaged TS value of 0.31 for 24 h forecasts, 0.23 for 48 h and 0.21 for 72 h forecasts.

  5. 4 km Forecasting System to Support DISCOVER-AQ Campaigns: Model Configuration, Testing and Evaluation

    NASA Astrophysics Data System (ADS)

    Lee, P.; Pan, L.; Kim, H. C.; Chai, T.; Hu, Y.; Tong, D.; Ngan, F.; Wong, D.; Dornblaser, B.; Tanrikulu, S.; Pickering, K. E.

    2012-12-01

    This work presents the development and evaluation of a high-resolution air quality forecasting system to support two NASA Earth Venture campaigns (DISCOVER-AQ) in 2013. These campaigns aim to further understanding of column-integrated and vertically resolved observations in determining air pollution conditions near the surface (http://science.nasa.gov/missions/discover-aq/). The first one will be carried out in San Joaquin Valley (SJV) in winter and the second one in Houston (HOU) area in late summer. Accurate forecast of pollution plumes is critical for on-site deployment and co-ordination of the various observation platforms. We develop of a fine resolution forecasting system to provide dynamic prediction of the chemical fields over these regions. This system utilizes meteorology fields from the US National Centers for Environmental Prediction North American Model (NAM) that is equipped with an elaborative NAM Data Assimilation System (NDAS) for its Land Surface Model (LSM) and initialization processes. NAM output is used to drive the US EPA Community Multi-scale Air Quality Model (CMAQ) with identical horizontal resolution. The SJV campaign is believed to be subjected to rather high particulate matter loading and possible frequent occurrence of multiple-day fog. NDAS provides advanced methodology to constrain atmospheric stability and soil moisture characteristics. These meteorological parameters are critical for the winter campaign. Special attention is paid to emission modeling for agricultural dust aerosols, which were found important for the SJV area. In contrast to the winter campaign where strong atmospheric stability will likely be a challenge, the HOU campaign in September of 2013 will be challenged with strong atmospheric convection and rather rapid growth of and a sustained deep Planetary Boundary Layer (PBL) during mid-morning and afternoon, respectively. Convection often results in lightning. Wild-fires can contribute significantly to pollution. Therefore both climatology lightning NOx and real-time fires observed by the Hazardous Mapping System by National Environmental Satellite, Data and Information Service (NESDIS) will be included as emission sources. We have performed a real-time test for the HOU area. We will show model evaluation and post-campaign sensitivity modeling results to shed additional insight on processes responsible for the characteristics of the pollutant concentrations.

  6. Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

    NASA Astrophysics Data System (ADS)

    Engeland, Kolbjorn; Steinsland, Ingelin

    2014-05-01

    This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

  7. Transport aircraft loading and balancing system: Using a CLIPS expert system for military aircraft load planning

    NASA Technical Reports Server (NTRS)

    Richardson, J.; Labbe, M.; Belala, Y.; Leduc, Vincent

    1994-01-01

    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 load planning process for military aircraft cargo planes through the development of a computer-based system. We introduce TALBAS (Transport Aircraft Loading and BAlancing System), a knowledge-based system designed to assist personnel involved in preparing valid load plans for the C130 Hercules aircraft. The main features of this system 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 load plans and the automatic validation of such load plans.

  8. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  9. Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

    NASA Astrophysics Data System (ADS)

    Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.

    2015-08-01

    This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems.

  10. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

    PubMed

    Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-01-01

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. PMID:26910315

  11. Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

    NASA Astrophysics Data System (ADS)

    Keane, R. J.; Plant, R. S.; Tennant, W. J.

    2015-12-01

    The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

  12. A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

    PubMed Central

    Tokumitsu, Masahiro; Ishida, Yoshiteru

    2014-01-01

    This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

  13. Evaluation of an operational streamflow forecasting system driven by ensemble precipitation forecasts : a case study for the Gatineau watershed

    NASA Astrophysics Data System (ADS)

    Boucher, M.-A.; Perreault, L.; Tremblay, D.; Gaudet, J.; Minville, M.; Anctil, F.

    2009-04-01

    Among the various sources of uncertainty for hydrological forecasts, the uncertainty linked to meteorological inputs prevail. Precipitation is particularly difficult to forecast and observed values are often poor representation of the true precipitation field. In order to account for the uncertainty related to precipitation data, it can be interesting to produce ensemble streamflow forecasts by feeding a hydrological model with ensemble precipitation forecasts issued by atmospheric models. In this study, we use ensemble precipitation forecasts to drive Hydrotel, a distributed hydrological model. We concentrate on the Gatineau watershed, which serves as an experimental watershed for Hydro-Québec, the major hydropower producer in Quebec. The main goal of this study is to demonstrate that ensemble precipitation forecasts can improve streamflow forecasting for the watershed of interest. The ensemble precipitation forecasts were produced by Environnement Canada from march first of 2002 to december 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used with Hydrotel in order to compare ensemble streamflow forecasts with their deterministic counterparts. The quality of the precipitation forecasts is first assessed, using the continuous ranked probability score (CRPS), the logarithmic score, rank histograms and reliability diagrams. The performance of the corresponding streamflow forecasts obtained at the end of the process is also evaluated using the same quality assessment tools.

  14. Comparison of the economic impact of different wind power forecast systems for producers

    NASA Astrophysics Data System (ADS)

    Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

    2014-05-01

    Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

  15. On-line economic optimization of energy systems using weather forecast information.

    SciTech Connect

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-01-01

    We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.

  16. Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000

    SciTech Connect

    Das, S.

    1991-12-01

    The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

  17. AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

    NASA Technical Reports Server (NTRS)

    Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

    2011-01-01

    A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

  18. Verification of a probabilistic flood forecasting system for an Alpine Region of northern Italy

    NASA Astrophysics Data System (ADS)

    Laiolo, P.; Gabellani, S.; Rebora, N.; Rudari, R.; Ferraris, L.; Ratto, S.; Stevenin, H.

    2012-04-01

    Probabilistic hydrometeorological forecasting chains are increasingly becoming an operational tool used by civil protection centres for issuing flood alerts. One of the most important requests of decision makers is to have reliable systems, for this reason an accurate verification of their predictive performances become essential. The aim of this work is to validate a probabilistic flood forecasting system: Flood-PROOFS. The system works in real time, since 2008, in an alpine Region of northern Italy, Valle d'Aosta. It is used by the Civil Protection regional service to issue warnings and by the local water company to protect its facilities. Flood-PROOFS uses as input Quantitative Precipitation Forecast (QPF) derived from the Italian limited area model meteorological forecast (COSMO-I7) and forecasts issued by regional expert meteorologists. Furthermore the system manages and uses both real time meteorological and satellite data and real time data on the maneuvers performed by the water company on dams and river devices. The main outputs produced by the computational chain are deterministic and probabilistic discharge forecasts in different cross sections of the considered river network. The validation of the flood prediction system has been conducted on a 25 months period considering different statistical methods such as Brier score, Rank histograms and verification scores. The results highlight good performances of the system as support system for emitting warnings but there is a lack of statistics especially for huge discharge events.

  19. An integrated system for wind energy forecast using meteorological models and statistical post-processing

    NASA Astrophysics Data System (ADS)

    Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.

    2009-12-01

    With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric system implies a strong requirement for accurate wind forecasts, that can be used to forecast this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a system for 72h energy forecast in mainland Portugal was setup, using 6km resolution meteorological forecasts, forced by global GFS forecasts by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed system, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological forecast, and converts the forecasted wind fields into forecasted energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind forecasts, requiring fully developed meteorological models. In general, results from 2 full years of forecast indicate solid performance, in particular in what concerns the impact of synoptic scale systems going through the domain. However, there remain significant problems coming both from phasing errors in the evolution of synoptic systems and, more importantly, from limitations of the representation of surface and boundary layer processes in atmospheric models. Improvements in (quantitative) high-resolution meteorological forecasts may be a critical issue to support a sustained growth of the share on wind energy. The present paper presents a description of the developed system, results from the model evaluation exercise, and an analysis of the operational performance of the wind energy forecasts.

  20. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  1. Occupational Shortages Reporting System, Forecasting Model and Correlation Analysis.

    ERIC Educational Resources Information Center

    Park, Theresa

    The report presents a computer model for forecasting occupational shortages into the near future based on occupational data reported monthly by the Texas Employment Commission for the period January 1970 to July 1975 and on job openings listed in classified want ads from September 1974 to July 1975. The report describes the methodology of the…

  2. Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

    NASA Astrophysics Data System (ADS)

    Zhuang, Zhaorong; Yussouf, Nusrat; Gao, Jidong

    2016-05-01

    As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

  3. Study on Battery Capacity for Grid-connection Power Planning with Forecasts in Clustered Photovoltaic Systems

    NASA Astrophysics Data System (ADS)

    Shimada, Takae; Kawasaki, Norihiro; Ueda, Yuzuru; Sugihara, Hiroyuki; Kurokawa, Kosuke

    This paper aims to clarify the battery capacity required by a residential area with densely grid-connected photovoltaic (PV) systems. This paper proposes a planning method of tomorrow's grid-connection power from/to the external electric power system by using demand power forecasting and insolation forecasting for PV power predictions, and defines a operation method of the electricity storage device to control the grid-connection power as planned. A residential area consisting of 389 houses consuming 2390 MWh/year of electricity with 2390kW PV systems is simulated based on measured data and actual forecasts. The simulation results show that 8.3MWh of battery capacity is required in the conditions of half-hour planning and 1% or less of planning error ratio and PV output limiting loss ratio. The results also show that existing technologies of forecasting reduce required battery capacity to 49%, and increase the allowable installing PV amount to 210%.

  4. Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

    NASA Astrophysics Data System (ADS)

    ÁLvarez, A.; Orfila, A.; Tintoré, J.

    2004-03-01

    Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

  5. A Real-Time Eulerian Photochemical Model Forecast System: Overview and Initial Ozone Forecast Performance in the Northeast U.S. Corridor.

    NASA Astrophysics Data System (ADS)

    McHenry, John N.; Ryan, William F.; Seaman, Nelson L.; Coats, Carlie J., Jr.; Pudykiewicz, Janusz; Arunachalam, Sarav; Vukovich, Jeffery M.

    2004-04-01

    This article reports on the first implementation of a real-time Eulerian photochemical model forecast system in the United States. The forecast system consists of a tripartite set of one-way coupled models that run routinely on a parallel microprocessor supercomputer. The component models are the fifth-generation Pennsylvania State University (PSU) NCAR Mesoscale Model (MM5), the Sparse-Matrix Operator Kernel for Emissions (SMOKE) model, and the Multiscale Air Quality Simulation Platform—Real Time (MAQSIP-RT) photochemical model. Though the system has been run in real time since the summer of 1998, forecast results obtained during August of 2001 at 15-km grid spacing over New England and the northern mid-Atlantic—conducted as part of an “early start” NOAA air quality forecasting initiative—are described in this article.The development and deployment of a real-time numerical air quality prediction (NAQP) system is technically challenging. MAQSIP-RT contains a full pho-tochemical oxidant gas-phase chemical mechanism together with transport, dry deposition, and sophisticated cloud treatment. To enable the NAQP system to run fast enough to meet operational forecast deadlines, significant work was devoted to data flow design and software engineering of the models and control codes. The result is a turnkey system now in use by a number of agencies concerned with operational ozone forecasting.Results of the chosen episode are compared against three other models/modeling techniques: a traditional statistical model used routinely in the metropolitan Philadelphia, Pennsylvania, area, a set of publicly issued forecasts in the northeastern United States, and the operational Canadian Hemispheric and Regional Ozone and NOx System (CHRONOS) model. For the test period it is shown that the NAQP system performs as well or better than all of these operational approaches. Implications for the impending development of an operational U.S. ozone forecasting capability are discussed in light of these results.

  6. Semi-distributed flood forecasting system for the Middle Vistula reach

    NASA Astrophysics Data System (ADS)

    Romanowicz, Renata; Karamuz, Emilia; Osuch, Marzena

    2014-05-01

    The aim of this study is the development of an integrated forecasting system for the middle reach of the River Vistula. The system consists of combined in series lumped parameter Stochastic Transfer Function models. In order to prolong the forecast lead-time, the system was extended to include gauging stations situated upstream of Zawichost. There is a number of tributaries located along the studied reach. The largest are Kamienna, Pilica and Wieprz. Therefore apart from Single- Input -Single-Output models (SISO), multiple input models were also developed (MISO). The system is based on water levels instead of flows, in order to avoid errors related to rating curve transformation. The problem of the nonlinear transformation of system inputs in order to separate the nonlinearity of the flow process to obtain the linear model dynamics is equally important for the accuracy of forecasts. The possibility of linearizing the flow routing process was investigated using a State Dependent Parameter approach. The nonparametric relationship was parameterised using a power function. This procedure allowed the application of a model with a nonlinear transformation of input in the forecasting mode. It is important to note that the applied methods are stochastic in nature and the structure of the models and their parameters are estimated from available observations, taking into account inherent observation and model approximation errors. As a result, forecasts are estimated together with uncertainty bands. We apply a Kalman filter updating of model predictions as a data assimilation procedure. The procedure involves formulating the forecasting problem in a state space form. Validation of the developed forecasting system shows that the quality of forecasts obtained using a semi-distributed lumped parameter model is comparable with the forecasts obtained using a distributed model with the advantage of obtaining forecast uncertainty by the former. This work was supported by the project "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" carried by the Institute of Geophysics, Polish Academy of Sciences on the order of the National Science Centre (contract No. 2011/01/B/ST10/06866). The water level data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

  7. Seasonal forecasting of global hydrologic extremes using the North American Multi-model Ensemble system

    NASA Astrophysics Data System (ADS)

    Wood, Eric F.; Yuan, Xing; Roundy, Joshua K.; Sheffield, Justin

    2015-04-01

    Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving our understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales, are among the grand challenges proposed by the World Climate Research Programme (WCRP), and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Exchanges Project (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed, which is based on coupled climate forecast models participating in the North American Multi-Model Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX/RHP river basins by comparing with Ensemble Streamflow Prediction (ESP). The multi-model seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high flow ensemble forecasts, and better "real-time" prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast, a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX/RHP basins, and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).

  8. On the assessment of Argo float trajectory assimilation in the Mediterranean Forecasting System

    NASA Astrophysics Data System (ADS)

    Nilsson, Jenny A. U.; Dobricic, Srdjan; Pinardi, Nadia; Taillandier, Vincent; Poulain, Pierre-Marie

    2011-10-01

    The Mediterranean Forecasting System (MFS) has been operational for a decade, and is continuously providing forecasts and analyses for the region. These forecasts comprise local- and basin-scale information of the environmental state of the sea and can be useful for tracking oil spills and supporting search-and-rescue missions. Data assimilation is a widely used method to improve the forecast skill of operational models and, in this study, the three-dimensional variational (OceanVar) scheme has been extended to include Argo float trajectories, with the objective of constraining and ameliorating the numerical output primarily in terms of the intermediate velocity fields at 350 m depth. When adding new datasets, it is furthermore crucial to ensure that the extended OceanVar scheme does not decrease the performance of the assimilation of other observations, e.g., sea-level anomalies, temperature, and salinity. Numerical experiments were undertaken for a 3-year period (2005-2007), and it was concluded that the Argo float trajectory assimilation improves the quality of the forecasted trajectories with ~15%, thus, increasing the realism of the model. Furthermore, the MFS proved to maintain the forecast quality of the sea-surface height and mass fields after the extended assimilation scheme had been introduced. A comparison between the modeled velocity fields and independent surface drifter observations suggested that assimilating trajectories at intermediate depth could yield improved forecasts of the upper ocean currents.

  9. Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

    NASA Astrophysics Data System (ADS)

    Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

    2015-04-01

    The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful between the 5th and the 8th day of the prediction. The information obtained is then included in an early warning system, designed in the framework of the European project iCoast (ECHO/SUB/2013/661009) with the aim of set alarms in coastal areas depending on the wave conditions, the sea level, the flooding and the run up in the coast.

  10. Assessment of the hindcast, nowcast and forecast capabilities of the Mercator-Ocean high resolution ocean forecasting system in the Atlantic and Mediterranean basins

    NASA Astrophysics Data System (ADS)

    Lellouche, J.; Benkiran, M.; L'Hvder, B.; Mortier, L.; Testor, P.; Dombrowsky, E.

    2009-12-01

    In the framework of the European project GMES/MyOcean, Mercator-Ocean has been designing a hierarchy of ocean analysis and forecasting systems based on numerical models of the ocean and data assimilation methods. Since April 2008, Mercator-Ocean runs an Atlantic and Mediterranean system between 20S and 80N. It is eddy resolving as its horizontal resolution is 1/12 and it has 50 levels on the vertical with a surface refinement. The ocean and sea ice models are based on the NEMO code. The data assimilation algorithm is a reduced order Kalman filter using 3D multivariate modal decomposition of the forecast error covariance. The system assimilates conjointly altimeter data, SST and in situ observations (temperature and salinity profiles, including ARGO data). The real time operation of this system produces each week realistic 3-dimensional oceanic conditions (temperature, salinity, currents,) two weeks back in time (hindcast and nowcast) and a two weeks forecast, driven at the surface by atmospheric conditions from the European Center for Medium Range Weather Forecast (ECMWF). Moreover, the system is operated daily to produce 7 days ocean forecasts with daily updates of the ECMWF atmospheric forcing. After a brief description of the system, we will present recent validation results. The first one will consist of a comparison between a glider and Mercator-Ocean fields along a particular section in Mediterranean Sea. The second one will consist of a study of forecast validity showing the impact of daily updates of the atmospheric forcing.

  11. Assessment of the hindcast, nowcast and forecast capabilities of the Mercator-Ocean high resolution ocean forecasting system in the Atlantic and Mediterranean basins.

    NASA Astrophysics Data System (ADS)

    Lellouche, J.-M.; Benkiran, M.; Dombrowsky, E.; L'Hvder, B.; Mortier, L.; Testor, P.

    2009-04-01

    In the framework of the European project GMES/MyOcean, Mercator-Ocean has been designing a hierarchy of ocean analysis and forecasting systems based on numerical models of the ocean and data assimilation methods. Since April 2008, Mercator-Ocean runs an Atlantic and Mediterranean system between 20S and 80N. It is eddy resolving as its horizontal resolution is 1/12 and it has 50 levels on the vertical with a surface refinement. The ocean and sea ice models are based on the NEMO code. The data assimilation algorithm is a reduced order Kalman filter using 3D multivariate modal decomposition of the forecast error covariance. The system assimilates conjointly altimeter data, SST and in situ observations (temperature and salinity profiles, including ARGO data). The real time operation of this system produces each week realistic 3-dimensional oceanic conditions (temperature, salinity, currents,) two weeks back in time (hindcast and nowcast) and a two weeks forecast, driven at the surface by atmospheric conditions from the European Center for Medium Range Weather Forecast (ECMWF). Moreover, the system is operated daily to produce 7 days ocean forecasts with daily updates of the ECMWF atmospheric forcing. After a brief description of the system, we will present recent validation results. The first one will consist of a comparison between a glider and Mercator-Ocean fields along a particular section in Mediterranean Sea. The second one will consist of a study of forecast validity showing the impact of daily updates of the atmospheric forcing.

  12. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    NASA Astrophysics Data System (ADS)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  13. Short-term energy outlook. Volume II. Methodology. [STIFS: Short-Term Integrated Forecasting System

    SciTech Connect

    Not Available

    1980-08-01

    In this series, Volume I discusses the forecasts and projections of each major energy source or fuel. Volume II analyzes special issues or problems and includes descriptive materials on the analytical techniques or methodologies used in preparing short-term forecasts. Volume II includes sections by J. Philip Childress, developer of the Short-Term Integrated Forecasting System (STIFS), on Factors Affecting the Level of Primary Petroleum Inventories and on the Forecasting Methodology for Petroleum Balances in STIFS. Scott Sitzer writes on natural gas and residual fuel oil pricing, and Derriel Cato, Cecelia Chu, Neil Gamson, and John Hartmann, on modeling of petroleum-product and electricity demands. The Appendix provdes a table of the conversion factors used in the STIFS system.

  14. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    NASA Astrophysics Data System (ADS)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2015-11-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  15. Flood forecasting model based on geographical information system

    NASA Astrophysics Data System (ADS)

    Dong, A.; Zhi-Jia, L.; Yong-Tuo, W.; Cheng, Y.; Yi-Heng, D.

    2015-05-01

    In this paper, the Antecedent Precipitation Index Model (API) combined with Nash's Instantaneous Unit Curve Method is adopted for flood forecasting. The parameters n and k of Nash's Method is obtained by setting up the mathematic relation between these two parameters and topographic characteristics. Based on the DEM information, ArcGIS software is used to get the topographic characteristics and the topographic parameters. The Tunxi basin in the humid region was taken as an example for analysis. Through comparison with the simulation results of the Xinanjiang model, the detailed analysis of our simulation results is carried out giving a Nash-Sutcliffe efficiency 0.80 for the combined model and 0.94 for the Xinanjiang model. This indicates that the combined model as well as the Xinanjiang Model has a good performance in the simulation process. The combined model has great potential as a new efficient approach for flood forecasting in similar basins.

  16. Decadal Prediction Skill in the GEOS-5 Forecast System

    NASA Technical Reports Server (NTRS)

    Ham, Yoo-Geun; Rienecker, Michele M.; Suarez, Max J.; Vikhliaev, Yury; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

    2013-01-01

    A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

  17. Decadal Prediction Skill in the GEOS-5 Forecast System

    NASA Technical Reports Server (NTRS)

    Ham, Yoo-Geun; Rienecker, Michael M.; Suarez, M.; Vikhliaev, Yury V.; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

    2012-01-01

    A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office?s GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). The hindcasts are initialized every December from 1959 to 2010 following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from the atmospheric reanalysis (MERRA, the Modern-Era Retrospective Analysis for Research and Applications) generated using the GEOS-5 atmospheric model. The forecast skill of a three-member-ensemble mean is compared to that of an experiment without initialization but forced with observed CO2. The results show that initialization acts to increase the forecast skill of Northern Atlantic SST compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. The annual-mean Atlantic Meridional Overturning Circulation (AMOC) index is predictable up to a 5-year lead time, consistent with the predictable signal in upper ocean heat content over the Northern Atlantic. While the skill measured by Mean Squared Skill Score (MSSS) shows 50% improvement up to 10-year lead forecast over the subtropical and mid-latitude Atlantic, however, prediction skill is relatively low in the subpolar gyre, due in part to the fact that the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region appears to be unrealistic. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

  18. A Novel Hydro-information System for Improving National Weather Service River Forecast System

    NASA Astrophysics Data System (ADS)

    Nan, Z.; Wang, S.; Liang, X.; Adams, T. E.; Teng, W. L.; Liang, Y.

    2009-12-01

    A novel hydro-information system has been developed to improve the forecast accuracy of the NOAA National Weather Service River Forecast System (NWSRFS). An MKF-based (Multiscale Kalman Filter) spatial data assimilation framework, together with the NOAH land surface model, is employed in our system to assimilate satellite surface soil moisture data to yield improved evapotranspiration. The latter are then integrated into the distributed version of the NWSRFS to improve its forecasting skills, especially for droughts, but also for disaster management in general. Our system supports an automated flow into the NWSRFS of daily satellite surface soil moisture data, derived from the TRMM Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and the forcing information of the North American Land Data Assimilation System (NLDAS). All data are custom processed, archived, and supported by the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). An optional data fusing component is available in our system, which fuses NEXRAD Stage III precipitation data with the NLDAS precipitation data, using the MKF-based framework, to provide improved precipitation inputs. Our system employs a plug-in, structured framework and has a user-friendly, graphical interface, which can display, in real-time, the spatial distributions of assimilated state variables and other model-simulated information, as well as their behaviors in time series. The interface can also display watershed maps, as a result of the integration of the QGIS library into our system. Extendibility and flexibility of our system are achieved through the plug-in design and by an extensive use of XML-based configuration files. Furthermore, our system can be extended to support multiple land surface models and multiple data assimilation schemes, which would further increase its capabilities. Testing of the integration of the current system into the NWSRFS is ongoing.

  19. National Launch System cycle 1 loads and models data book

    NASA Technical Reports Server (NTRS)

    Bugg, F.; Brunty, J.; Ernsberger, G.; Mcghee, D.; Gagliano, L.; Harrington, F.; Meyer, D.; Blades, E.

    1992-01-01

    This document contains preliminary cycle 1 loads for the National Launch System (NLS) 1 and 2 vehicles. The loads provided and recommended as design loads represent the maximum load expected during prelaunch and flight regimes, i.e., limit loads, except that propellant tank ullage pressure has not been included. Ullage pressure should be added to the loads book values for cases where the addition results in higher loads. The loads must be multiplied by the appropriate factors of safety to determine the ultimate loads for which the structure must be capable.

  20. Anvil Forecast Tool in the Advanced Weather Interactive Processing System, Phase II

    NASA Technical Reports Server (NTRS)

    Barrett, Joe H., III

    2008-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display Systems (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input.

  1. Optimal Planning Strategy for Large PV/Battery System Based on Long-Term Insolation Forecasting

    NASA Astrophysics Data System (ADS)

    Yona, Atsushi; Uchida, Kosuke; Senjyu, Tomonobu; Funabashi, Toshihisa

    Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. Usually the power output of PV system fluctuates depending on weather conditions. In order to control the fluctuating power output for PV system, it requires control method of energy storage system. This paper proposes an optimization approach to determine the operational planning of power output for PV system with battery energy storage system (BESS). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output for PV system. The optimization method applies genetic algorithm (GA) considering PV power output forecast error. The forecast error is based on our previous works with the insolation forecasting at one day ahead by using weather reported data, fuzzy theory and neural network(NN). The validity of the proposed method is confirmed by the computer simulations.

  2. Adaptive load sharing in heterogeneous distributed systems

    NASA Astrophysics Data System (ADS)

    Mirchandaney, Ravi; Towsley, Don; Stankovic, John A.

    1990-08-01

    In this paper, we study the performance characteristics of simple load sharing algorithms for heterogeneous distributed systems. We assume that nonnegligible delays are encountered in transferring jobs from one node to another. We analyze the effects of these delays on the performance of two threshold-based algorithms called Forward and Reverse. We formulate queuing theoretic models for each of the algorithms operating in heterogeneous systems under the assumption that the job arrival process at each node in Poisson and the service times and job transfer times are exponentially distributed. The models are solved using the Matrix-Geometric solution technique. These models are used to study the effects of different parameters and algorithm variations on the mean job response time: e.g., the effects of varying the thresholds, the impact of changing the probe limit, the impact of biasing the probing, and the optimal response times over a large range of loads and delays. Wherever relevant, the results of the models are compared with the M/M/ 1 model, representing no load balancing (hereafter referred to as NLB), and the M/M/K model, which is an achievable lower bound (hereafter referred to as LB).

  3. Load-dependent regulation of neuromuscular system

    NASA Technical Reports Server (NTRS)

    Ohira, Yoshinobu; Kawano, Fuminori; Stevens, James L.; Wang, Xiao D.; Ishihara, Akihiko

    2004-01-01

    Roles of gravitational loading, sarcomere length, and/or tension development on the electromyogram (EMG) of soleus and afferent neurogram recorded at the L5 segmental level of spinal cord were investigated during parabolic flight of a jet airplane or hindlimb suspension in conscious rats. Both EMG and neurogram levels were increased when the gravity levels were elevated from 1-G to 2-G during the parabolic flight. They were decreased when the hindlimbs were unloaded by exposure to actual microgravity or by suspension. These phenomena were related to passive shortening of muscle fibers and/or sarcomeres. Unloading-related decrease in sarcomere length was greater at the central rather than the proximal and distal regions of fibers. These activities and tension development were not detected when the mean sarcomere length was less than 2.03 micrometers. It is suggested that load-dependent regulation of neuromuscular system is related to the tension development which is influenced by sarcomere length.

  4. The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

    NASA Astrophysics Data System (ADS)

    Rossa, Andrea; Liechti, Katharina; Zappa, Massimiliano; Bruen, Michael; Germann, Urs; Haase, Günther; Keil, Christian; Krahe, Peter

    2011-05-01

    Quantifying uncertainty in flood forecasting is a difficult task, given the multiple and strongly non-linear model components involved in such a system. Much effort has been and is being invested in the quest of dealing with uncertain precipitation observations and forecasts and the propagation of such uncertainties through hydrological and hydraulic models predicting river discharges and risk for inundation. The COST 731 Action is one of these and constitutes a European initiative which deals with the quantification of forecast uncertainty in hydro-meteorological forecast systems. COST 731 addresses three major lines of development: (1) combining meteorological and hydrological models to form a forecast chain, (2) propagating uncertainty information through this chain and make it available to end users in a suitable form, (3) advancing high-resolution numerical weather prediction precipitation forecasts by using non-conventional observations from, for instance, radar to determine details in the initial conditions on scales smaller than what can be resolved by conventional observing systems. Recognizing the interdisciplinarity of the challenge COST 731 has organized its work forming Working Groups at the interfaces between the different scientific disciplines involved, i.e. between observation and atmospheric (and hydrological) modelling (WG-1), between atmospheric and hydrologic modelling (WG-2) and between hydrologic modelling and end-users (WG-3). This paper summarizes the COST 731 activities and its context, provides a review of the recent progress made in dealing with uncertainties in flood forecasting, and sets the scene for the papers of this Thematic Issue. In particular, a bibliometric analysis highlights the strong recent increase in addressing the uncertainty analysis in flood forecasting from an integrated perspective. Such a perspective necessarily involves the area of meteorology, hydrology, and decision making in order to take operational advantage of the scientific progress, an aspect in which COST 731 is successfully contributing to furthering the flood damage mitigation capabilities in Europe.

  5. An Advanced Buffet Load Alleviation System

    NASA Technical Reports Server (NTRS)

    Burnham, Jay K.; Pitt, Dale M.; White, Edward V.; Henderson, Douglas A.; Moses, Robert W.

    2001-01-01

    This paper describes the development of an advanced buffet load alleviation (BLA) system that utilizes distributed piezoelectric actuators in conjunction with an active rudder to reduce the structural dynamic response of the F/A-18 aircraft vertical tails to buffet loads. The BLA system was defined analytically with a detailed finite-element-model of the tail structure and piezoelectric actuators. Oscillatory aerodynamics were included along with a buffet forcing function to complete the aeroservoelastic model of the tail with rudder control surface. Two single-input-single-output (SISO) controllers were designed, one for the active rudder and one for the active piezoelectric actuators. The results from the analytical open and closed loop simulations were used to predict the system performance. The objective of this BLA system is to extend the life of vertical tail structures and decrease their life-cycle costs. This system can be applied to other aircraft designs to address suppression of structural vibrations on military and commercial aircraft.

  6. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    USGS Publications Warehouse

    Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

    2014-01-01

     The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

  7. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, S.; McNally, A.; Husak, G.; Funk, C.

    2014-03-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993-2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

  8. Verification of an ensemble prediction system for storm surge forecast in the Adriatic Sea

    NASA Astrophysics Data System (ADS)

    Mel, Riccardo; Lionello, Piero

    2014-12-01

    In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level forecast is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction system) for operational forecasting of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather Forecasts) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. Forecast uncertainty increases with the predicted value of the storm surge and with the forecast lead time. The EMF (ensemble mean forecast) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic forecast), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the forecast and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the forecast. For large lead times the EPS spread and the forecast error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the forecast and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability forecast) of the EPS has a clear skill in predicting the actual probability distribution of sea level, and it outperforms simple "dressed" PF methods. A probability estimate based on the single DF is shown to be inadequate. However, a PF obtained with a prescribed Gaussian distribution and centered on the DF value performs very similarly to the EPS-based PF.

  9. Climate informed long term seasonal forecasts of hydroenergy inflow for the Brazilian hydropower system

    NASA Astrophysics Data System (ADS)

    Lima, Carlos H. R.; Lall, Upmanu

    2010-02-01

    SummaryEfficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time.

  10. A Novel Forecasting System for Solar Particle Events and Flares (FORSPEF)

    NASA Astrophysics Data System (ADS)

    Papaioannou, A.; Anastasiadis, A.; Sandberg, I.; Georgoulis, M. K.; Tsiropoula, G.; Tziotziou, K.; Jiggens, P.; Hilgers, A.

    2015-08-01

    Solar Energetic Particles (SEPs) result from intense solar eruptive events such as solar flares and coronal mass ejections (CMEs) and pose a significant threat for both personnel and infrastructure in space conditions. In this work, we present FORSPEF (Forecasting Solar Particle Events and Flares), a novel dual system, designed to perform forecasting of SEPs based on forecasting of solar flares, as well as independent SEP nowcasting. An overview of flare and SEP forecasting methods of choice is presented. Concerning SEP events, we make use for the first time of the newly re-calibrated GOES proton data within the energy range 6.0-243 MeV and we build our statistics on an extensive time interval that includes roughly 3 solar cycles (1984-2013). A new comprehensive catalogue of SEP events based on these data has been compiled including solar associations in terms of flare (magnitude, location) and CME (width, velocity) characteristics.

  11. The Mediterranean Monitoring and Forecasting Centre, a component of the MyOcean System

    NASA Astrophysics Data System (ADS)

    Tonani, Marina; Salon, Stefano; Korres, Gerasimos; Bolzon, Giorgio; Clementi, Emanuela; Cossarini, Giampiero; Crise, Alessandro; Drudi, Massimiliano; Fratianni, Claudia; Girardi, Giacomo; Guarnieri, Antonio; Marino, Stefano; Oddo, Paolo; Pinardi, Nadia; Simoncelli, Simona; Solidoro, Cosimo; Teruzzi, Anna

    2013-04-01

    Within the Global Monitoring for Environment and Security program (GMES) and its Marine Service Fast Track, MyOcean consolidates the past efforts in pre-operational ocean monitoring and forecasting capacity in Europe. The Mediterranean Monitoring and Forecasting Centre (Med-MFC) is one of the regional production centre of the MyOcean system (www.myocean.eu). The Med-MFC is therefore the operational centre for the provision of basic modelling data sets that are at the basis of the continuous monitoring and forecasting of the marine environment for this region. This system is composed by two key element off line coupled: Med-currents for the physical system, and Med-biogeochemistry, for the biogeochemical component. The system produces ten day forecast daily for Med-currents and bi-weekly for Med-biogeochemistry. The MyOcean system is operational since December 2009 and is periodically updated. The development of the components of the MyOcean Med-MFC has started more than ten years ago in the frame of several EU-projects and since year 2009 is part of the MyOcean and MyOcean2 system. All the Med-MFC products are available via the MyOcean catalog and MyOcean download facilities. The products available on the MyOcean 2 catalogue will be described as well as the ongoing research activities related to the future update of the forecasting system.

  12. High resolution operational air quality forecast for Poland and Central Europe with the GEM-AQ model - EcoForecast System

    NASA Astrophysics Data System (ADS)

    Kaminski, Jacek W.; Struzewska, Joanna

    2013-04-01

    The air quality forecast is an important component of the environmental assessment system. The "Clean Air for Europe" (CAFE) Directive 2008/50/EC stipulates a need for numerical modelling in order to support public information services to interpret measurements of pollutants concentrations and to prepare and evaluate air quality plans. Most European countries have developed model-based air quality modelling and information services. We will present the design strategy, development and implementation of a regional high resolution forecasting system that was implemented in Poland. The new national high resolution air quality forecasting system has evolved from a semi-operational chemical weather system EcoForecast.EU which is based on the GEM-AQ model (Kaminski et al., 2008). GEM-AQ is a comprehensive chemical weather model where air quality processes (chemistry and aerosols), troposphere and stratospheric chemistry are implemented on-line in the operational weather prediction model, the Global Environmental Multiscale (GEM) model (Cote et al, 1998), developed at Environment Canada. For these applications, the model is run on a global variable resolution grid with horizontal spacing of 15 km over Europe. In the vertical there are 28 hybrid levels, with the top at 10 hPa. A high resolution nested forecast at 5 km resolution over Poland (and surrounding countries) was implemented in December 2012. The forecast is published once a day at www.EcoForecast.EU. The air quality forecast is presented for ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, PM10 and PM2.5 as maps of daily maxima and daily averages. We will present results from the on-going model evaluation study over Central Europe (2010-2012). Modelling results were evaluated and compared with available observation of ozone and primary pollutants from air quality monitoring stations and from meteorological synoptic stations. Ozone exposure indices, as defined in the CAFE Directive, will be shown for the high resolution regional configuration.

  13. EC-EARTH: an Earth System Model based on the ECWMF Integrated Forecasting System

    NASA Astrophysics Data System (ADS)

    Selten, F.; Bintanja, R.; Yang, S.; Severijns, C.; Semmler, T.; Wyser, K.; Wang, X.; Hazeleger, W.

    2009-04-01

    EC-EARTH is the name of an Earth system model that is being developed by a number of institutes in Europe. It is based on the Integrated Forecast System of the European Centre for Medium Range Weather Forecasts (ECWMF). The ECMWF model delivers the best weather forecasts in the world by an objective measure. However, when applied to climate time scales, the performance is not better than the state-of-art climate models by an objective metrics. In the Numerical Weather Prediction version, the top of the atmosphere fluxes (TOA) are not balanced with observed sea surface temperatures as a lower boundary condition. After consultation of experts at ECMWF, a set of parameters was identified that could be used to reduce the model biases and close the TOA heat budget. We describe a set of tuning experiments and show the subsequent improvements in the simulated climate by an objective metrics. The adjusted model at T159L62 resolution coupled to the NEMO2/ORCA1 ocean model outperforms the mean CMIP3 model using this metrics. Additional transient integrations show the extent to which 'fast processes' contribute to the errors in the mean state and variance.

  14. Comparison of Observation Impacts in Two Forecast Systems using Adjoint Methods

    NASA Technical Reports Server (NTRS)

    Gelaro, Ronald; Langland, Rolf; Todling, Ricardo

    2009-01-01

    An experiment is being conducted to compare directly the impact of all assimilated observations on short-range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.

  15. Digital data processing system dynamic loading analysis

    NASA Technical Reports Server (NTRS)

    Lagas, J. J.; Peterka, J. J.; Tucker, A. E.

    1976-01-01

    Simulation and analysis of the Space Shuttle Orbiter Digital Data Processing System (DDPS) are reported. The mated flight and postseparation flight phases of the space shuttle's approach and landing test configuration were modeled utilizing the Information Management System Interpretative Model (IMSIM) in a computerized simulation modeling of the ALT hardware, software, and workload. System requirements simulated for the ALT configuration were defined. Sensitivity analyses determined areas of potential data flow problems in DDPS operation. Based on the defined system requirements and the sensitivity analyses, a test design is described for adapting, parameterizing, and executing the IMSIM. Varying load and stress conditions for the model execution are given. The analyses of the computer simulation runs were documented as results, conclusions, and recommendations for DDPS improvements.

  16. The impact of a new simple SST slab model on a monthly forecasting system.

    NASA Astrophysics Data System (ADS)

    Rendina, C.; Buzzi, A.; Malguzzi, P.; Mastrangelo, D.; Drofa, O.

    2012-04-01

    Atmospheric monthly forecast is intermediate between medium range forecast, an initial value problem, and seasonal forecast, a boundary value problem. The influence of sea surface temperature (SST) on the atmospheric dynamics in the time range of 10-40 days is still not well understood. As a consequence, there is no common approach for the representation of the SST in a monthly prediction system. At ISAC-CNR in Bologna a monthly ensemble forecasting system is run experimentally once a month, based on the GLOBO model. GLOBO is an atmospheric general circulation model developed at ISAC. The evolution of SST is represented by a simple slab ocean model based on surface flux balance with a relaxation term to climatological SST. Recently, a new definition of the slab ocean model which includes a flux correction term has been implemented to improve the SST simulation. It has been tested in parallel with the operational forecast for some months of 2011. The results show that the globally averaged root mean square forecast error of the SST simulated with the new model is slightly larger than the operational one. However, the ensemble spread of the SST predicted with the new model increases significantly and becomes very similar to the observed SST variability, in particular in the Northern Hemisphere. The atmospheric field differences between the new and operational forecasts show that SST has an impact in the second part of the month, especially in the Southern Hemisphere. The ensemble spread of atmospheric parameters shows a slight increase using the new slab model. However, its impact on the anomaly forecast fields is small.

  17. GCLAS: a graphical constituent loading analysis system

    USGS Publications Warehouse

    McKallip, T.E.; Koltun, G.F.; Gray, J.R.; Glysson, G.D.

    2001-01-01

    The U. S. Geological Survey has developed a program called GCLAS (Graphical Constituent Loading Analysis System) to aid in the computation of daily constituent loads transported in stream flow. Due to the relative paucity with which most water-quality data are collected, computation of daily constituent loads is moderately to highly dependent on human interpretation of the relation between stream hydraulics and constituent transport. GCLAS provides a visual environment for evaluating the relation between hydraulic and other covariate time series and the constituent chemograph. GCLAS replaces the computer program Sedcalc, which is the most recent USGS sanctioned tool for constructing sediment chemographs and computing suspended-sediment loads. Written in a portable language, GCLAS has an interactive graphical interface that permits easy entry of estimated values and provides new tools to aid in making those estimates. The use of a portable language for program development imparts a degree of computer platform independence that was difficult to obtain in the past, making implementation more straightforward within the USGS' s diverse computing environment. Some of the improvements introduced in GCLAS include (1) the ability to directly handle periods of zero or reverse flow, (2) the ability to analyze and apply coefficient adjustments to concentrations as a function of time, streamflow, or both, (3) the ability to compute discharges of constituents other than suspended sediment, (4) the ability to easily view data related to the chemograph at different levels of detail, and (5) the ability to readily display covariate time series data to provide enhanced visual cues for drawing the constituent chemograph.

  18. Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

    NASA Astrophysics Data System (ADS)

    Manukalo, V.

    2012-12-01

    Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of current information, forecasts and warnings to consumers automatically. Besides scientific and technical issues the implementation of these objectives requires solution of a number of organizational issues. Thus, as a result of the increased complexity of types of hydrometeorological data and in order to develop forecasting methods, a reconsideration of meteorological and hydrological measurement networks should be carried out. The "optimal density of measuring networks" is proposed taking into account principal terms: a) minimizing an uncertainty in characterizing the spacial distribution of hydrometeorological parameters; b) minimizing the Total Life Cycle Cost of creation and maintenance of measurement networks. Much attention will be given to training Ukrainian disaster management authorities from the Ministry of Emergencies and the Water Management Agency to identify the flood hazard risk level and to indicate the best protection measures on the basis of continuous monitoring and forecasts of evolution of meteorological and hydrological conditions in the river basin.

  19. Comment on "Nonparametric forecasting of low-dimensional dynamical systems "

    NASA Astrophysics Data System (ADS)

    Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

    2016-03-01

    The comparison performed in Berry et al. [Phys. Rev. E 91, 032915 (2015), 10.1103/PhysRevE.91.032915] between the skill in predicting the El Niño-Southern Oscillation climate phenomenon by the prediction method of Berry et al. and the "past-noise" forecasting method of Chekroun et al. [Proc. Natl. Acad. Sci. USA 108, 11766 (2011), 10.1073/pnas.1015753108] is flawed. Three specific misunderstandings in Berry et al. are pointed out and corrected.

  20. Using Self-Organizing Maps in Creation of an Ocean Forecasting System

    NASA Astrophysics Data System (ADS)

    Vilibic, I.; Zagar, N.; Cosoli, S.; Dadic, V.; Ivankovic, D.; Jesenko, B.; Kalinic, H.; Mihanovic, H.; Sepic, J.; Tudor, M.

    2014-12-01

    We present the first results of the NEURAL project (www.izor.hr/neural), which is dedicated to creation of an efficient and reliable ocean surface current forecasting system. This system is based on high-frequency (HF) radar measurements, numerical weather prediction (NWP) models and neural network algorithms (Self-Organizing Maps, SOM). Joint mapping of mesoscale ground winds and HF radars in a coastal area points to a high correlation between two sets, indicating that wind forecast may be used as a basis for forecasting ocean surface currents. NEURAL project consists of three modules: (i) the technological module which covers installation of new HF radars in the coastal area of the middle Adriatic, and implementation of data management procedures; (ii) the research module which deals with an assessment of different combinations of input variables (radial vs. Cartesian vectors, original vs. detided vs. filtered series, WRF-ARW vs. Aladin meteorological model), all in order to get the best hindcasted surface currents; and finally (iii) the operational module in which NWP operational products will be used for short-term forecasting of ocean surface currents. Both historical and newly observed HF radar data, as well as reanalysis and operational NWP model runs will be used within the (ii) and (iii) modules of the project. Finally, the observed, hindcasted and forecasted ocean current will be compared to the operational ROMS model outputs to compare skill reliability of the forecasting system based on neural network approach to the skill and reliability of numerical ocean models. We expect the forecasting system based on neural network approach to be more reliable than the one based on numerical ocean model as it is more exclusively based on measurements. Disadvantages of such a system are that it can be applied only in areas where long series surface currents measurements exist and where the recognized patterns can be properly ascribed to a forcing field.

  1. Improvements to the NOS Experimental Nowcast/Forecast System for Galveston Bay

    NASA Astrophysics Data System (ADS)

    Schmalz, R. A.

    2002-05-01

    The National Ocean Service (NOS) has developed an experimental nowcast/forecast system in conjunction with its Physical Oceanographic Real Time System (PORTS) for Galveston Bay. Both a Bay model and a one-way coupled, fine resolution Houston Ship Channel model (Schmalz, 1998) based on the Mellor-Blumberg (1987) three-dimensional sigma coordinate formulation have been used to provide daily 24 hour nowcasts and 36 hour forecasts for water levels, currents, salinity, and temperature on an experimental basis over the past three years. During the nowcast, PORTS station derived wind and sea level pressure, USGS streamflow data, and Galveston Pleasure Pier water level station derived nontidal signals are used to provide the meteorological, freshwater inflow, and Gulf of Mexico subtidal water level forcings, respectively. During the forecast, the National Weather Service's Aviation Atmospheric, Western Gulf River Forecast Center forecast model river flows, and Extratropical Storm Surge Forecast Models are used to provide these forcings. Nowcast and forecast results have been evaluated using the NOS (1999) statistical measures for water levels, currents, salinity, and temperature over the one year period April 2000 through March 2001. Based on the evaluation, an initial plan for incorporating the following improvements is outlined: 1) a rainfall-runoff model for the Houston metroplex, 2) statistical nowcasting of missing PORTS data, 3) the incorporation of overland flow and marsh storage, 4) alternate vertical coordinates, and 5) wave-current interaction. It is anticipated that water level response will be improved by 1 and 3, current response by 1 and 5, salinity and temperature stratification by 4, and system robustness by 2, respectively. Plans for implementing these improvements with respect to operational system considerations will be highlighted.

  2. An operational real-time flood forecasting system in Southern Italy

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio

    2015-04-01

    A real-time flood forecasting system has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood Forecasting System (SFFS) has been built within the FEWS (Flood Early Warning System) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for forecasting the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal forecast, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the forecast lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the forecast lead time and the distribution of the time occurrence of the flood peak, starting from one or more model forecasts. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management system.

  3. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    SciTech Connect

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  4. Right model, wrong prediction: Model-free vs Mechanistic forecasting for nonlinear ecological systems

    NASA Astrophysics Data System (ADS)

    Perretti, C.; Munch, S. B.; Deyle, E. R.; Ye, H.; Sugihara, G.

    2012-12-01

    As environmental time series have grown, and computer-intensive statistical methods have become more convenient, fitting mechanistic models that incorporate both process and observation error (i.e. state-space models) has become increasingly popular. It has been suggested that such models are more robust to noise due to their inclusion of a process-error term, however their out-of-sample forecast ability remains largely untested. Therefore, it is important to determine how various forecasting strategies perform under realistic levels of noise and forcing. We compared the forecast accuracy of a model-free forecasting approach based on nonlinear state-space reconstruction (SSR) against a suite of mechanistic models fit to their own time series with realistic levels of noise added. To further favor the mechanistic approach, these models were fit using a Bayesian adaptive MCMC algorithm actually initiated on the correct parameter values. Surprisingly, we found that the SSR forecasts were more accurate than the correct mechanistic models despite being fit to only one time series of a multivariate system. This was true for four different ecological models, and for experimental data from a series of flour beetle experiments. Our results suggest that for forecasting real ecosystems, where the correct model is never known, a robust model-free approach such as SSR may be a more practical alternative to complex fitted models containing many free parameters.

  5. Operational perspective of remote sensing-based forest fire danger forecasting systems

    NASA Astrophysics Data System (ADS)

    Chowdhury, Ehsan H.; Hassan, Quazi K.

    2015-06-01

    Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is the forecasting of fire danger conditions. Here, our aim was to critically analyse the following issues, (i) current operational forest fire danger forecasting systems and their limitations; (ii) remote sensing-based fire danger monitoring systems and usefulness in operational perspective; (iii) remote sensing-based fire danger forecasting systems and their functional implications; and (iv) synergy between operational forecasting systems and remote sensing-based methods. In general, the operational systems use point-based measurements of meteorological variables (e.g., temperature, wind speed and direction, relative humidity, precipitations, cloudiness, solar radiation, etc.) and generate danger maps upon employing interpolation techniques. Theoretically, it is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. During the last several decades, efforts were given to develop fire danger condition systems, which could be broadly classified into two major groups: fire danger monitoring and forecasting systems. Most of the monitoring systems focused on determining the danger during and/or after the period of image acquisition. A limited number of studies were conducted to forecast fire danger conditions, which could be adaptable. Synergy between the operational systems and remote sensing-based methods were investigated in the past but too much complex in nature. Thus, the elaborated understanding about these developments would be worthwhile to advance research in the area of fire danger in the context of making them operational.

  6. SASS wind forecast impact studies using the GLAS and NEPRF systems: Preliminary conclusions

    NASA Technical Reports Server (NTRS)

    Kalnay, E.; Atlas, R.; Baker, W. E.; Duffy, D.; Halem, M.; Helfand, M.

    1984-01-01

    For this project, a version of the GLAS Analysis/Forecast System was developed that includes an objective dealiasing scheme as an integral part of the analysis cycle. With this system the (100 sq km) binned SASS wind data generated by S. Peteherych of AER, Canada corresponding of the period 0000 GMT 7 September 1978 to 1200 GMT 13 September 1978 was objectively dealiased. The dealiased wind fields have been requested and received by JPL, NMC and the British Meteorological Office. The first 3.5 days of objectively dealiased fields were subjectively enhanced on the McIDAS system. Approximately 20% of the wind directions were modified, and of these, about 70% were changed by less than 90 deg. Two SASS forecast impact studies, were performed using the dealiased fields, with the GLAS and the NEPRF (Navy Environmental Prediction Research Facility) analysis/forecast systems.

  7. Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

    NASA Technical Reports Server (NTRS)

    Kehrer, Kristen C.; Graf, Brian; Roeder, William

    2006-01-01

    This paper evaluates the use of precipitable water (PW) from Global Positioning System (GPS) in lightning prediction. Additional independent verification of an earlier model is performed. This earlier model used binary logistic regression with the following four predictor variables optimally selected from a candidate list of 23 candidate predictors: the current precipitable water value for a given time of the day, the change in GPS-PW over the past 9 hours, the KIndex, and the electric field mill value. This earlier model was not optimized for any specific forecast interval, but showed promise for 6 hour and 1.5 hour forecasts. Two new models were developed and verified. These new models were optimized for two operationally significant forecast intervals. The first model was optimized for the 0.5 hour lightning advisories issued by the 45th Weather Squadron. An additional 1.5 hours was allowed for sensor dwell, communication, calculation, analysis, and advisory decision by the forecaster. Therefore the 0.5 hour advisory model became a 2 hour forecast model for lightning within the 45th Weather Squadron advisory areas. The second model was optimized for major ground processing operations supported by the 45th Weather Squadron, which can require lightning forecasts with a lead-time of up to 7.5 hours. Using the same 1.5 lag as in the other new model, this became a 9 hour forecast model for lightning within 37 km (20 NM)) of the 45th Weather Squadron advisory areas. The two new models were built using binary logistic regression from a list of 26 candidate predictor variables: the current GPS-PW value, the change of GPS-PW over 0.5 hour increments from 0.5 to 12 hours, and the K-index. The new 2 hour model found the following for predictors to be statistically significant, listed in decreasing order of contribution to the forecast: the 0.5 hour change in GPS-PW, the 7.5 hour change in GPS-PW, the current GPS-PW value, and the KIndex. The new 9 hour forecast model found the following five independent variables to be statistically significant, listed in decreasing order of contribution to the forecast: the current GPSPW value, the 8.5 hour change in GPS-PW, the 3.5 hour change in GPS-PW, the 12 hour change in GPS-PW, and the K-Index. In both models, the GPS-PW parameters had better correlation to the lightning forecast than the K-Index, a widely used thunderstorm index. Possible future improvements to this study are discussed.

  8. Assessment of the Aviation Weather Center Global Forecasts of Mesoscale Convective Systems(.

    NASA Astrophysics Data System (ADS)

    Ziv, Baruch; Yair, Yoav; Presman, Karin; Füllekrug, Martin

    2004-05-01

    This paper examines the precision of location and top height of mesoscale convective systems, as forecast by the Aviation Weather Center (AWC). The examination was motivated by the Mediterranean Israeli Dust Experiment (MEIDEX) on the space shuttle Columbia, aimed to image transient luminous events (TLEs), such as sprites, jets, and elves, from orbit. Mesoscale convective systems offer a high probability for the occurrence of TLEs above active thunderstorms. Because the operational methodology was planned around a 24-h cycle, there was a need for a global forecast of areas with a high probability of massive thunderstorms that are prone to exhibit TLE activity. The forecast was based on the high-level significant weather (SIGWX) maps, commonly used for civil aviation, provided by the AWC on the Internet. To estimate the operational skill of this forecast for successfully detecting clouds with a high probability for producing TLEs, predictions for selected dates were compared with satellite observations. The locations of 66 mesoscale cloud systems on Significant Weather Maps, produced for eight different dates in August 2001, were compared with satellite global IR images for these dates. Operational skill was determined as the percentage of observed cloud systems found within a 5° range in the regions that appeared on the forecast maps as having the potential to contain thunderclouds and was found to be 92%. No consistent error was found in location. The predicted size of the convective system was typically larger than the observed size. Cloud-top heights of 53 systems were examined on four dates in October November 2001, using IR radiances converted to brightness temperatures. For each convective system, the coldest cloud-top temperature was converted to height, using the NCEP NCAR reanalysis data for the respective location and time. The standard error in the forecast heights was 2516 m. Because the purpose was to get true alerts of potential TLE occurrences, operational forecast skill was defined as the percentage of forecasts that were accurate within 1000 m or higher than observed. The 1000-m tolerance was allowed because of inevitable uncertainties underlying this method of analysis. Operational skill was found to be only 43%. During the “STS-107” mission flown in January 2003, the forecasted areas of main convective centers were transmitted daily to the crew and helped them in pointing the cameras and targeting thunderstorms. This ensured the success of the MEIDEX sprite observations that recorded numerous events in many different geographical locations.


  9. Plutonium Immobilization Project System Design Description for Can Loading System

    SciTech Connect

    Kriikku, E.

    2001-02-15

    The purpose of this System Design Description (SDD) is to specify the system and component functions and requirements for the Can Loading System and provide a complete description of the system (design features, boundaries, and interfaces), principles of operation (including upsets and recovery), and the system maintenance approach. The Plutonium Immobilization Project (PIP) will immobilize up to 13 metric tons (MT) of U.S. surplus weapons usable plutonium materials.

  10. A stochastic operational forecasting system of the Black Sea: Technique and validation

    NASA Astrophysics Data System (ADS)

    Vandenbulcke, Luc; Barth, Alexander

    2015-09-01

    In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well.

  11. Forecasting Electric Power Generation of Photovoltaic Power System for Energy Network

    NASA Astrophysics Data System (ADS)

    Kudo, Mitsuru; Takeuchi, Akira; Nozaki, Yousuke; Endo, Hisahito; Sumita, Jiro

    Recently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electric power and heat are controlled with a communications network. We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan. As a result of comparing measurements with a prediction values, the average prediction error per day was about 26% of the measured power.

  12. Seasonal drought forecast system for food-insecure regions of East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, Shraddhanand; McNally, Amy; Husak, Greg; Funk, Chris

    2014-05-01

    In East Africa, agriculture is mostly rainfed and hence sensitive to interannual rainfall variability, and the increasing food and water demands of a growing population place further stresses on the water resources of this region. Skillful seasonal agricultural drought forecasts for this region can inform timely water and agricultural management decisions, support the proper allocation of the region's water resources, and help mitigate socio-economic losses. Here we describe the development and implementation of a seasonal drought forecast system that is being used for providing seasonal outlooks of agricultural drought in East Africa. We present a test case of the evaluation and applicability of this system for March-April-May growing season over equatorial East Africa (latitude 20 south to 80 North and 360 E to 460E) that encompasses one of the most food insecure and climatically and socio-economically vulnerable regions in East Africa. This region experienced famine as recently as in 2011. The system described here combines advanced satellite and re-analysis as well as station-based long term and real-time observations (e.g. NASA's TRMM, Infra-red remote sensing, Climate Forecast System Reanalysis), state-of-the-art dynamical climate forecast system (NCEP's Climate Forecast System Verison-2) and large scale land surface models (e.g. Variable Infiltration Capacity, NASA's Land Information System) to provide forecasts of seasonal rainfall, soil moisture and Water Requirement Satisfaction Index (WRSI) throughout the season - with an emphasis on times when water is the most critical: start of season/planting and the mid-season/crop reproductive phase. Based on the hindcast assessment of this system, we demonstrate the value of this approach to the US Agency for International Development (USAID)'s efforts to mitigate future losses of lives and economic losses by allowing a proactive approach of drought management that includes early warning and timely action.

  13. Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

    NASA Astrophysics Data System (ADS)

    Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

    2013-04-01

    The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of flood preparedness and crisis management for basins greater than 1.000 km2.

  14. 14 CFR 25.397 - Control system loads.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Control system loads. 25.397 Section 25.397 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION AIRCRAFT AIRWORTHINESS STANDARDS: TRANSPORT CATEGORY AIRPLANES Structure Control Surface and System Loads § 25.397 Control system loads. (a) General. The maximum...

  15. 14 CFR 25.397 - Control system loads.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Control system loads. 25.397 Section 25.397 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION AIRCRAFT AIRWORTHINESS STANDARDS: TRANSPORT CATEGORY AIRPLANES Structure Control Surface and System Loads § 25.397 Control system loads. (a) General. The maximum...

  16. Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

    NASA Astrophysics Data System (ADS)

    Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

    2014-05-01

    The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

  17. VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

    EPA Science Inventory

    An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...

  18. WRF forecast skill of the Great Plains low level jet and its correlation to forecast skill of mesoscale convective system precipitation

    NASA Astrophysics Data System (ADS)

    Squitieri, Brian Joseph

    One of the primary mechanisms for supporting summer nocturnal precipitation across the central United States is the Great Plains low-level Jet (LLJ). Mesoscale Convective Systems (MCSs) are organized storm complexes that can be supported from the upward vertical motion supplied at the terminus of the LLJ, which bring beneficial rains to farmers. As such, a need for forecasting these storm complexes exists. Correlating forecast skills of the LLJ and MCS precipitation in high spatial resolution modeling was the main goal of this research. STAGE IV data was used as observations for MCS precipitation and the 00-hr 13 km RUC analysis was employed for evaluation of the LLJ. The 4 km WRF was used for high resolution forecast simulations, with 2 microphysics and 3 planetary boundary layer schemes selected for a sensitivity study to see which model run best simulated reality. It was found that the forecast skill of the potential temperature and directional components of the geostrophic and ageostrophic winds within the LLJ correlated well with MCS precipitation, especially early during LLJ evolution. Since the 20 real cases sampled consisted of three LLJ types (synoptic, inertial oscillation and transition), forecast skill in other parameters such as deep layer and low level shear, convergence, frontogenesis and stability parameters were compared to MCS forecast skill to see if consistent signals outside of the LLJ influenced MCS evolution in forecasts. No correlations were found among these additional parameters. Given the variety of synoptic setups present, the lack of forecast skill correlations between several variables and MCSs resulted as different synoptic or mesoscale mechanisms played varying roles if importance in different cases.

  19. A Weather Forecasting System using concept of Soft Computing: a new approach

    NASA Astrophysics Data System (ADS)

    Sharma, A.; Datta, U.

    2007-07-01

    Weather forecasting and warnings are the major services provided by the meteorological profession. Many government and private agencies are working on its behavior but still it is challenging and incomplete. We propose a new technique to construct the learning set of images, which represents the actual data. We relate this data to the forthcoming weather events based on their previous records and history or whatever recognized by our system. Our work presents a new approach where the data explanation is performed with soft computing technique i.e. a neuro-fuzzy system is used to predict meteorological position on the basis of measurements by a weather system designed by us. This model can help us in making forecast of different weather conditions like rain and thunderstorm, sunshine and dry day, and perhaps a cloudy weather i.e. purpose of this model is to represent a warning system for likely adverse conditions. Our model is designed with a concept of Adaptive Forecasting Model(AFD) in the mind. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain weather conditions. The results are compared with actual working of meteorological department and these results confirm that our model which is based on soft computing have the potential for successful application to weather forecasting. We considered atmospheric pressure a primary key parameter and atmospheric temperature and relative humidity secondary type. We will, however, examine temperature as signature of weather conditions in some typical cases where we could observe the effect of temperature. As an example, the estimations produced by the proposed methodology were applied on different weather forecasting data provided by the Gwalior meteorology center to make the result more practical and believable. The forecasts are always current are based on neuro-fuzzy systems that utilize the concept of soft computing. Real time processing of weather data indicate that the neuro-fuzzy based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.

  20. Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

    PubMed

    Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

    2014-06-28

    The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific-North America region. PMID:24842026

  1. Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

    NASA Astrophysics Data System (ADS)

    Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

    2013-12-01

    An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.

  2. Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

    PubMed Central

    Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

    2014-01-01

    The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region. PMID:24842026

  3. Investigation into a displacement bias in numerical weather prediction models' forecasts of mesoscale convective systems

    NASA Astrophysics Data System (ADS)

    Yost, Charles

    Although often hard to correctly forecast, mesoscale convective systems (MCSs) are responsible for a majority of warm-season, localized extreme rain events. This study investigates displacement errors often observed by forecasters and researchers in the Global Forecast System (GFS) and the North American Mesoscale (NAM) models, in addition to the European Centre for Medium Range Weather Forecasts (ECMWF) and the 4-km convection allowing NSSL-WRF models. Using archived radar data and Stage IV precipitation data from April to August of 2009 to 2011, MCSs were recorded and sorted into unique six-hour intervals. The locations of these MCSs were compared to the associated predicted precipitation field in all models using the Method for Object-Based Diagnostic Evaluation (MODE) tool, produced by the Developmental Testbed Center and verified through manual analysis. A northward bias exists in the location of the forecasts in all lead times of the GFS, NAM, and ECMWF models. The MODE tool found that 74%, 68%, and 65% of the forecasts were too far to the north of the observed rainfall in the GFS, NAM and ECMWF models respectively. The higher-resolution NSSL-WRF model produced a near neutral location forecast error with 52% of the cases too far to the south. The GFS model consistently moved the MCSs too quickly with 65% of the cases located to the east of the observed MCS. The mean forecast displacement error from the GFS and NAM were on average 266 km and 249 km, respectively, while the ECMWF and NSSL-WRF produced a much lower average of 179 km and 158 km. A case study of the Dubuque, IA MCS on 28 July 2011 was analyzed to identify the root cause of this bias. This MCS shattered several rainfall records and required over 50 people to be rescued from mobile home parks from around the area. This devastating MCS, which was a classic Training Line/Adjoining Stratiform archetype, had numerous northward-biased forecasts from all models, which are examined here. As common with this archetype, the MCS was triggered by the low-level jet impinging on a stationary front, with the heaviest precipitation totals in this case centered along the tri-state area of Iowa, Illinois, and Wisconsin. Low-level boundaries were objectively analyzed, using the gradient of equivalent potential temperature, for all forecasts and the NAM analysis. In the six forecasts that forecasted precipitation too far to the north, the predicted stationary front was located too far to the north of the observed front, and therefore convection was predicted to initiate too far to the north. Forecasts associated with a northern bias had a stationary front that was too far to the north, and neutral forecasts' frontal locations were closer to the observed location.

  4. Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

    NASA Technical Reports Server (NTRS)

    Hailperin, Max

    1993-01-01

    This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.

  5. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    NASA Astrophysics Data System (ADS)

    Zulkepli, Jafri; Fong, Chan Hwa; Abidin, Norhaslinda Zainal

    2015-12-01

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  6. Forecasting drought risks for a water supply storage system using bootstrap position analysis

    USGS Publications Warehouse

    Tasker, Gary; Dunne, Paul

    1997-01-01

    Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

  7. Post-processing of a low-flow forecasting system in the Thur basin (Switzerland)

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Joerg-Hess, Stefanie; Bernhard, Luzi; Zappa, Massimiliano

    2015-04-01

    Low-flows and droughts are natural hazards with potentially severe impacts and economic loss or damage in a number of environmental and socio-economic sectors. As droughts develop slowly there is time to prepare and pre-empt some of these impacts. Real-time information and forecasting of a drought situation can therefore be an effective component of drought management. Although Switzerland has traditionally been more concerned with problems related to floods, in recent years some unprecedented low-flow situations have been experienced. Driven by the climate change debate a drought information platform has been developed to guide water resources management during situations where water resources drop below critical low-flow levels characterised by the indices duration (time between onset and offset), severity (cumulative water deficit) and magnitude (severity/duration). However to gain maximum benefit from such an information system it is essential to remove the bias from the meteorological forecast, to derive optimal estimates of the initial conditions, and to post-process the stream-flow forecasts. Quantile mapping methods for pre-processing the meteorological forecasts and improved data assimilation methods of snow measurements, which accounts for much of the seasonal stream-flow predictability for the majority of the basins in Switzerland, have been tested previously. The objective of this study is the testing of post-processing methods in order to remove bias and dispersion errors and to derive the predictive uncertainty of a calibrated low-flow forecast system. Therefore various stream-flow error correction methods with different degrees of complexity have been applied and combined with the Hydrological Uncertainty Processor (HUP) in order to minimise the differences between the observations and model predictions and to derive posterior probabilities. The complexity of the analysed error correction methods ranges from simple AR(1) models to methods including wavelet transformations and support vector machines. These methods have been combined with forecasts driven by Numerical Weather Prediction (NWP) systems with different temporal and spatial resolutions, lead-times and different numbers of ensembles covering short to medium to extended range forecasts (COSMO-LEPS, 10-15 days, monthly and seasonal ENS) as well as climatological forecasts. Additionally the suitability of various skill scores and efficiency measures regarding low-flow predictions will be tested. Amongst others the novel 2afc (2 alternatives forced choices) score and the quantile skill score and its decompositions will be applied to evaluate the probabilistic forecasts and the effects of post-processing. First results of the performance of the low-flow predictions of the hydrological model PREVAH initialised with different NWP's will be shown.

  8. Model documentation report: Short-term Integrated Forecasting System demand model 1985. [(STIFS)

    SciTech Connect

    Not Available

    1985-07-01

    The Short-Term Integrated Forecasting System (STIFS) Demand Model consists of a set of energy demand and price models that are used to forecast monthly demand and prices of various energy products up to eight quarters in the future. The STIFS demand model is based on monthly data (unless otherwise noted), but the forecast is published on a quarterly basis. All of the forecasts are presented at the national level, and no regional detail is available. The model discussed in this report is the April 1985 version of the STIFS demand model. The relationships described by this model include: the specification of retail energy prices as a function of input prices, seasonal factors, and other significant variables; and the specification of energy demand by product as a function of price, a measure of economic activity, and other appropriate variables. The STIFS demand model is actually a collection of 18 individual models representing the demand for each type of fuel. The individual fuel models are listed below: motor gasoline; nonutility distillate fuel oil, (a) diesel, (b) nondiesel; nonutility residual fuel oil; jet fuel, kerosene-type and naphtha-type; liquefied petroleum gases; petrochemical feedstocks and ethane; kerosene; road oil and asphalt; still gas; petroleum coke; miscellaneous products; coking coal; electric utility coal; retail and general industry coal; electricity generation; nonutility natural gas; and utility petroleum. The demand estimates produced by these models are used in the STIFS integrating model to produce a full energy balance of energy supply, demand, and stock change. These forecasts are published quarterly in the Outlook. Details of the major changes in the forecasting methodology and an evaluation of previous forecast errors are presented once a year in Volume 2 of the Outlook, the Methodology publication.

  9. Multivariate Climate-Weather Forecasting System: An Integrated Approach for Mitigating Agricultural Risks in India

    NASA Astrophysics Data System (ADS)

    Devineni, N.; Lall, U.; Perveen, S.

    2012-12-01

    While India has a long history of prediction of the All India Monsoon, work on spatially specific attributes of the monsoon, as well as monsoon break periods has only recently emerged. However, from a risk management context, prognostic information of a single variable such as total precipitation or average temperature will be of less utility especially for specific operational purposes. An integrated regional climate-weather forecast system covering precipitation, temperature and humidity etc. over the year will benefit the farmers in the context of a specific decision time table for irrigation scheduling as well as for pre-season crop choices. Hence, contrary to the existing forecasting methods that develop multi time scale information of a single variable at a time, in this paper, we introduce an integrated regional multivariate climate-weather forecasting system that directly relates to agricultural decision making and risk mitigation. These multi-scale risk attributes include mutually dependent, spatially disaggregated statistics such as total rainfall, average temperature, growing degree days, relative humidity, total number of rainfall days/dry spell length, and cumulative water deficits that inform the potential irrigation water requirements for crops etc. Given that these attributes exhibit mutual dependence across space and time, we propose to explore common ocean-atmospheric conditions from the observations and the state of the art Global Circulation Models (GCMs) that can be utilized as the predictor variables for the forecasting system. Non parametric bootstrap resampling methods and Hierarchical Bayesian methods that can easily handle the high dimensionality of such problems will be used to develop the integrated forecast system. The developed multivariate forecasts will be adapted and disseminated as decision tools for the farmers under the Columbia Water Center's pilot project in Punjab region of India.

  10. Error discrimination of an operational hydrological forecasting system at a national scale

    NASA Astrophysics Data System (ADS)

    Jordan, F.; Brauchli, T.

    2010-09-01

    The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation pour la réduction des débits de crue, thèse de doctorat n° 3711, Ecole Polytechnique Fédérale, Lausanne [3] Keller, R. 2009. "Le débit des rivières au peigne fin", Revue Technique Suisse, N°7/8 2009, Swiss engineering RTS, UTS SA, Lausanne, p. 11 [4] Kaufmann, P., Schubiger, F. & Binder, P. 2003. Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model : eight years of experience, Hydrology and Earth System

  11. North American Ensemble Forecasting System (NAEFS): Bias Removal and Multi-Model Ensemble

    NASA Astrophysics Data System (ADS)

    Beauregard, S.; Candille, G.; Gagnon, N.

    2009-05-01

    The North American Forecasting System (NAEFS) currently combines the U.S. National Weather Service (NWS) and Meteorological Service of Canada (MSC) ensemble forecast systems. Previous studies show clear benefits to merging the two ensembles. A running mean bias removal scheme is applied on the NAEFS ensemble for a number of variables and pressure levels. Here we present verification results against upper air observations comparing the NAEFS, NWS and MSC raw ensemble forecasts with the respective bias corrected version of each. The bias, dispersion and Continuous Ranked Probability Scores (CRPS) of both raw and bias corrected systems are compared. A bootstrap method is used to compute 90% confidence intervals on the differences in scores for both systems. It is found that the bias removal scheme improves the bias and the CRPS of both the NWS and MSC ensemble systems when compared to upper air observations. However the effect on the combined NAEFS system is relatively small in CRPS term. In fact, it is found that the raw NAEFS combined forecasts (40 members) have similar scores than the bias corrected ones. This may highlight the limits of the current simple bias removal framework and/or the efficiency of the multi-model approach.

  12. Tests of oceanic stochastic parameterisation in a seasonal forecast system.

    NASA Astrophysics Data System (ADS)

    Cooper, Fenwick; Andrejczuk, Miroslaw; Juricke, Stephan; Zanna, Laure; Palmer, Tim

    2015-04-01

    Over seasonal time scales, our aim is to compare the relative impact of ocean initial condition and model uncertainty, upon the ocean forecast skill and reliability. Over seasonal timescales we compare four oceanic stochastic parameterisation schemes applied in a 1x1 degree ocean model (NEMO) with a fully coupled T159 atmosphere (ECMWF IFS). The relative impacts upon the ocean of the resulting eddy induced activity, wind forcing and typical initial condition perturbations are quantified. Following the historical success of stochastic parameterisation in the atmosphere, two of the parameterisations tested were multiplicitave in nature: A stochastic variation of the Gent-McWilliams scheme and a stochastic diffusion scheme. We also consider a surface flux parameterisation (similar to that introduced by Williams, 2012), and stochastic perturbation of the equation of state (similar to that introduced by Brankart, 2013). The amplitude of the stochastic term in the Williams (2012) scheme was set to the physically reasonable amplitude considered in that paper. The amplitude of the stochastic term in each of the other schemes was increased to the limits of model stability. As expected, variability was increased. Up to 1 month after initialisation, ensemble spread induced by stochastic parameterisation is greater than that induced by the atmosphere, whilst being smaller than the initial condition perturbations currently used at ECMWF. After 1 month, the wind forcing becomes the dominant source of model ocean variability, even at depth.

  13. The assimilation of satellite soundings, winds and satellite products in a mesoscale analysis/forecast system

    NASA Technical Reports Server (NTRS)

    Diak, G. R.; Smith, W. L.

    1985-01-01

    Investigations in FY-85 were centered on three case study days in 1982. Two of these, March 6 and April 24, were Atmospheric Variability Experiment/Verical Atmospheric Sounder (AVE/VAS) days for which high spatial and temporal resolution RAOB and Vertical Atmospheric Sounder (VAS) data sets were available. The third investigation day, April 26, was a day of interesting severe weather. In the last part of FY-84 and early FY-85 we were able to demonstrate most importantly the complimentary nature of satellite soundings and winds in a forecast/analysis system. In our variational analysis scheme, cloud drift and water vapor winds enter into the height field as gradient information. The cloud drift winds especially, have the character of supplying information in cloudy areas where satellite soundings are not possible. In the April 26 experiments, analyses and forecasts using the combination satellite winds and soundings were superior to those using only soundings. Good consistency was shown between independent satellite forecasts from different initialization times run to the same verification time. A significant accomplishment in FY-85 was expanding experiments on April 26 to include quasi-continuous initialization inserting satellite soundings and winds from several different times into an analysis/forecast. Contrary to the first set of experiments on April 26, here forecast initialization fields were not independent, but contained satellite information from two data times.

  14. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

    PubMed Central

    Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-01-01

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

  15. Using the LAPS / WRF system to Analyze and Forecast Solar Radiation

    NASA Astrophysics Data System (ADS)

    Albers, S. C.; Jankov, I.

    2011-12-01

    The LAPS system is being used to produce rapid update, high resolution analyses and forecasts of solar radiation. The cloud analysis uses satellite, METARs, radar, aircraft and model first guess information to produce an hourly 3-D field of cloud fraction, cloud liquid, and cloud ice. The cloud analysis and satellite data together are used to produce a gridded analysis of total solar radiation. This is verified against solar radiation measurements that are independent (not used in the analysis). Two domains are being run and verified at present. The one with the most stations covers the Oklahoma mesonet with about 100 pyranometers. The total solar radiation forecast is being run on two domains, and is being initialized using the same cloud analysis package that drives the analysis fields mentioned above. The Colorado domain produces hourly forecasts, initialized every 6 hours. It is verified with about 20 Oklahoma mesonet stations. The HWT domain initializes WRF every 2 hours, with 15-minute output. Forecasts are being compared with the Oklahoma mesonet. Real-time verification of the analyses (including images of the analysis), and forecasts can be seen on our website 'laps.noaa.gov', and will be explored in this presentation.

  16. A space weather forecasting system with multiple satellites based on a self-recognizing network.

    PubMed

    Tokumitsu, Masahiro; Ishida, Yoshiteru

    2014-01-01

    This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

  17. Final Report on California Regional Wind Energy Forecasting Project:Application of NARAC Wind Prediction System

    SciTech Connect

    Chin, H S

    2005-07-26

    Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and direction, using its wind tunnel facility at the windmill farm at the Altamont Pass. The main objective of LLNL's involvement is to provide UC-Davis with improved wind forecasts to drive the parameterization scheme of turbine power curves developed from the wind tunnel facility. Another objective of LLNL's effort is to support the windmill farm operation with real-time wind forecasts for the effective energy management. The forecast skill in capturing the situation to meet the cut-in and cutout speed of given turbines would help reduce the operation cost in low and strong wind scenarios, respectively. The main focus of this report is to evaluate the wind forecast errors of LLNL's three-dimensional real-time weather forecast model at the location with the complex terrain. The assessment of weather forecast accuracy would help quantify the source of wind energy forecast errors from the atmospheric forecast model and/or wind-tunnel module for further improvement in the wind energy forecasting system.

  18. A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts

    NASA Astrophysics Data System (ADS)

    Xu, Wei; Zhang, Chi; Peng, Yong; Fu, Guangtao; Zhou, Huicheng

    2014-12-01

    This paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process.

  19. Aquaculture techniques: a production forecasting model for aquaculture systems. Technical completion report

    SciTech Connect

    Downey, P.C.; Klontz, G.W.

    1983-03-01

    Computer implementation of the mathematical models of quantitative relationships in aquaculture systems is a dynamic process which provides a conceptual framework for understanding systems behavior. These models can provide useful information on variable significance to systems functioning. This computer-implemented mathematical model addresses one of the significant limitations of aquaculture systems management, namely, production forecasting, by providing a method of using current technology to predict Allowable Growth Rate (AGR).

  20. Analysis of data systems requirements for global crop production forecasting in the 1985 time frame

    NASA Technical Reports Server (NTRS)

    Downs, S. W.; Larsen, P. A.; Gerstner, D. A.

    1978-01-01

    Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process.

  1. System Approach to the Forecast of Electron Fluxes iAT Geostationary Orbit

    NASA Astrophysics Data System (ADS)

    Balikhin, M. A.; Boynton, R. J.; Billings, S. A.; Pakhotin, I. P.

    2012-12-01

    A black box input-output NARMAX approach has been used to develop a one day ahead forecast of the electron fluxes at geostationary orbit. ACE measurements are used as the system inputs and GOES spacecraft data as outputs. An online real time forecasting tool has been developed as the result to this approach, which is available on the Sheffield R. Boynton www page http://www.acse.shef.ac.uk/~cop08rjb/2MeV_EF.html. This tool provides more accurate forecast in comparison to presently available online tools based on local wave acceleration models. As NARMAX provides physically interpretable results, the model terms are analysed in order to improve understanding of underlying physical processes. The identified NARMAX model indicates a significance of solar wind density in the control of high energy electron fluxes. Such a density dependence can be explained as a consequence of ULF pulsations. The alternative explanation relates it to the dynamics of EMIC waves.

  2. Development of an Operational Hydrological Monitoring and Seasonal Forecast System for China

    NASA Astrophysics Data System (ADS)

    Tang, Q.; Zhang, X.

    2014-12-01

    Hydrological monitoring and forecast are critical for disaster mitigation and water resources management. Although large investments have been made in climate forecasting and in related monitoring of land surface conditions, the experimental streamflow monitoring and forecast system is yet to be developed for China. We propose a frame to collect near-real-time meteorological forcings from various sources, to apply land surface hydrological model to simulate hydrological states and fluxes, and to generate ensemble seasonal forecasts of river discharge and soil moisture over China. A retrospective land surface hydrologic fluxes and states dataset with a 0.25° spatial resolution and a 3-hourly time step was developed using the Variable Infiltration Capacity (VIC) model as driven by gridded observation-based meteorological forcings in 1952-2012. The VIC simulations were carefully calibrated against the available streamflow observations and the simulated river discharge matched well with the observed monthly streamflow at the large river basins in China. The Tropical Rainfall Measuring Mission (TRMM) based near-real-time satellite precipitation product was adjusted at each grid to match the daily precipitation distribution with the ground observations during the period of 2000-2010. The adjusted satellite precipitation was used to simulate hydrological states and fluxes in a near-real-time manner and to provide initial hydrological conditions for seasonal forecast. The performance of hydrological monitoring and skill of seasonal streamflow prediction were assessed. The potential and challenges of using the operational monitoring and forecast system for improved flooding and drought management are discussed.

  3. UQ -- Fast Surrogates Key to New Methodologies in an Operational and Research Volcanic Hazard Forecasting System

    NASA Astrophysics Data System (ADS)

    Hughes, C. G.; Stefanescu, R. E. R.; Patra, A. K.; Bursik, M. I.; Madankan, R.; Pouget, S.; Jones, M.; Singla, P.; Singh, T.; Pitman, E. B.; Morton, D.; Webley, P.

    2014-12-01

    As the decision to construct a hazard map is frequently precipitated by the sudden initiation of activity at a volcano that was previously considered dormant, timely completion of the map is imperative. This prohibits the calculation of probabilities through direct sampling of a numerical ash-transport and dispersion model. In developing a probabilistic forecast for ash cloud locations following an explosive volcanic eruption, we construct a number of possible meta-models (a model of the simulator) to act as fast surrogates for the time-expensive model. We will illustrate the new fast surrogates based on both polynomial chaos and multilevel sparse representations that have allowed us to conduct the Uncertainty Quantification (UQ) in a timely fashion. These surrogates allow orders of magnitude improvement in cost associated with UQ, and are likely to have a major impact in many related domains.This work will be part of an operational and research volcanic forecasting system (see the Webley et al companion presentation) moving towards using ensembles of eruption source parameters and Numerical Weather Predictions (NWPs), rather than single deterministic forecasts, to drive the ash cloud forecasting systems. This involves using an Ensemble Prediction System (EPS) as input to an ash transport and dispersion model, such as PUFF, to produce ash cloud predictions, which will be supported by a Decision Support System. Simulation ensembles with different input volcanic source parameters are intelligently chosen to predict the average and higher-order moments of the output correctly.

  4. The Impact of British Airways Wind Observations on the Goddard Earth Observing System Analyses and Forecasts

    NASA Technical Reports Server (NTRS)

    Rukhovets, Leonid; Sienkiewicz, M.; Tenenbaum, J.; Kondratyeva, Y.; Owens, T.; Oztunali, M.; Atlas, Robert (Technical Monitor)

    2001-01-01

    British Airways flight data recorders can provide valuable meteorological information, but they are not available in real-time on the Global Telecommunication System. Information from the flight recorders was used in the Global Aircraft Data Set (GADS) experiment as independent observations to estimate errors in wind analyses produced by major operational centers. The GADS impact on the Goddard Earth Observing System Data Assimilation System (GEOS DAS) analyses was investigated using GEOS-1 DAS version. Recently, a new Data Assimilation System (fvDAS) has been developed at the Data Assimilation Office, NASA Goddard. Using fvDAS , the, GADS impact on analyses and forecasts was investigated. It was shown the GADS data intensify wind speed analyses of jet streams for some cases. Five-day forecast anomaly correlations and root mean squares were calculated for 300, 500 hPa and SLP for six different areas: Northern and Southern Hemispheres, North America, Europe, Asia, USA These scores were obtained as averages over 21 forecasts from January 1998. Comparisons with scores for control experiments without GADS showed a positive impact of the GADS data on forecasts beyond 2-3 days for all levels at the most areas.

  5. Validation of reactive gases and aerosols in the MACC global analysis and forecast system

    NASA Astrophysics Data System (ADS)

    Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

    2015-11-01

    The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

  6. SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

    NASA Astrophysics Data System (ADS)

    Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

    2014-12-01

    Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

  7. Linkage methodology of the Intermediate Future Forecasting System/Gas Analysis Modeling System interface for the Annual Energy Outlook, 1983

    SciTech Connect

    Not Available

    1984-09-01

    The Energy Information Administration (EIA) developed the Intermediate Future Forecasting System (IFFS) and the Gas Analysis Modeling System (GAMS) as independent projects during the 1981 to 1983 time period. Both modeling systems were designed to be partial equilibrium representations of the energy markets, with, however, quite striking differences in scopes and methodologies. Despite the differences between the two systems, their results overlap in an extremely sensitive and visible area - the pricing, supply, and consumption of natural gas. In an effort to produce consistent forecasts, the two models were linked forming one modeling system referred to as IFFS/GAMS. This report describes the process by which IFFS and GAMS were integrated to produce a single energy forecast for the Annual Energy Outlook, 1983 (AEO). A general description of both models is presented with an emphasis on the areas where the models interact with one another. Finally, a more specific description of the interactions between the models is provided. 21 references, 7 figures.

  8. The effect of load distribution within military load carriage systems on the kinetics of human gait.

    PubMed

    Birrell, Stewart A; Haslam, Roger A

    2010-07-01

    Military personnel carry their equipment in load carriage systems (LCS) which consists of webbing and a Bergen (aka backpack). In scientific terms it is most efficient to carry load as close to the body's centre of mass (CoM) as possible, this has been shown extensively with physiological studies. However, less is known regarding the kinetic effects of load distribution. Twelve experienced load carriers carried four different loads (8, 16, 24 and 32 kg) in three LCS (backpack, standard and AirMesh). The three LCS represented a gradual shift to a more even load distribution around the CoM. Results from the study suggest that shifting the CoM posteriorly by carrying load solely in a backpack significantly reduced the force produced at toe-off, whilst also decreasing stance time at the heavier loads. Conversely, distributing load evenly on the trunk significantly decreased the maximum braking force by 10%. No other interactions between LCS and kinetic parameters were observed. Despite this important findings were established, in particular the effect of heavy load carriage on maximum braking force. Although the total load carried is the major cause of changes to gait patterns, the scientific testing of, and development of, future LCS can modify these risks. PMID:20060096

  9. Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

    NASA Technical Reports Server (NTRS)

    Hailperin, M.

    1993-01-01

    This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.

  10. Maintaining a Local Data Integration System in Support of Weather Forecast Operations

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

    2010-01-01

    Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) at Johnson Space Center in Houston, TX have used a local data integration system (LDIS) as part of their forecast and warning operations. The original LDIS was developed by NASA's Applied Meteorology Unit (AMU; Bauman et ai, 2004) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive understanding of evolving fine-scale weather features

  11. a system approach to the long term forecasting of the climat data in baikal region

    NASA Astrophysics Data System (ADS)

    Abasov, N.; Berezhnykh, T.

    2003-04-01

    The Angara river running from Baikal with a cascade of hydropower plants built on it plays a peculiar role in economy of the region. With view of high variability of water inflow into the rivers and lakes (long-term low water periods and catastrophic floods) that is due to climatic peculiarities of the water resource formation, a long-term forecasting is developed and applied for risk decreasing at hydropower plants. Methodology and methods of long-term forecasting of natural-climatic processes employs some ideas of the research schools by Academician I.P.Druzhinin and Prof. A.P.Reznikhov and consists in detailed investigation of cause-effect relations, finding out physical analogs and their application to formalized methods of long-term forecasting. They are divided into qualitative (background method; method of analogs based on solar activity), probabilistic and approximative methods (analog-similarity relations; discrete-continuous model). These forecasting methods have been implemented in the form of analytical aids of the information-forecasting software "GIPSAR" that provides for some elements of artificial intelligence. Background forecasts of the runoff of the Ob, the Yenisei, the Angara Rivers in the south of Siberia are based on space-time regularities that were revealed on taking account of the phase shifts in occurrence of secular maxima and minima on integral-difference curves of many-year hydrological processes in objects compared. Solar activity plays an essential role in investigations of global variations of climatic processes. Its consideration in the method of superimposed epochs has allowed a conclusion to be made on the higher probability of the low-water period in the actual inflow to Lake Baikal that takes place on the increasing branch of solar activity of its 11-year cycle. The higher probability of a high-water period is observed on the decreasing branch of solar activity from the 2nd to the 5th year after its maximum. Probabilistic method of forecasting (with a year in advance) is based on the property of alternation of series of years with increase and decrease in the observed indicators (characteristic indices) of natural processes. Most of the series (98.4-99.6%) are represented by series of one to three years. The problem of forecasting is divided into two parts: 1) qualitative forecast of the probability that the started series will either continue or be replaced by a new series during the next year that is based on the frequency characteristics of series of years with increase or decrease of the forecasted sequence); 2) quantitative estimate of the forecasted value in the form of a curve of conditional frequencies is made on the base of intra-sequence interrelations among hydrometeorological elements by their differentiation with respect to series of years of increase or decrease, by construction of particular curves of conditional frequencies of the runoff for each expected variant of series development and by subsequent construction a generalized curve. Approximative learning methods form forecasted trajectories of the studied process indices for a long-term perspective. The method of analog-similarity relations is based on the fact that long periods of observations reveal some similarities in the character of variability of indices for some fragments of the sequence x (t) by definite criteria. The idea of the method is to estimate similarity of such fragments of the sequence that have been called the analogs. The method applies multistage optimization of both external parameters (e.g. the number of iterations of the sliding averaging needed to decompose the sequence into two components: the smoothed one with isolated periodic oscillations and the residual or random one). The method is applicable to current terms of forecasts and ending with the double solar cycle. Using a special procedure of integration, it separates terms with the best results for the given optimization subsample. Several optimal vectors of parameters obtained are tested on the examination (verifying) subsample. If the procedure is successful, the forecast is immediately made by integration of several best solutions. Peculiarities of forecasting extreme processes. Methods of long-term forecasting allow the sufficiently reliable forecasts to be made within the interval of xmin+Δ_1, xmax - Δ_2 (i.e. in the interval of medium values of indices). Meanwhile, in the intervals close to extreme ones, reliability of forecasts is substantially lower. While for medium values the statistics of the100-year sequence gives acceptable results owing to a sufficiently large number of revealed analogs that correspond to prognostic samples, for extreme values the situation is quite different, first of all by virtue of poverty of statistical data. Decreasing the values of Δ_1,Δ_2: Δ_1,Δ_2 rightarrow 0 (by including them into optimization parameters of the considered forecasting methods) could be one of the ways to improve reliability of forecasts. Partially, such an approach has been realized in the method of analog-similarity relations, giving the possibility to form a range of possible forecasted trajectories in two variants - from the minimum possible trajectory to the maximum possible one. Reliability of long-term forecasts. Both the methodology and the methods considered above have been realized as the information-forecasting system "GIPSAR". The system includes some tools implementing several methods of forecasting, analysis of initial and forecasted information, a developed database, a set of tools for verification of algorithms, additional information on the algorithms of statistical processing of sequences (sliding averaging, integral-difference curves, etc.), aids to organize input of initial information (in its various forms) as well as aids to draw up output prognostic documents. Risk management. The normal functioning of the Angara cascade is periodically interrupted by risks of two types that take place in the Baikal, the Bratsk and Ust-Ilimsk reservoirs: long low-water periods and sudden periods of extremely high water levels. For example, low-water periods, observed in the reservoirs of the Angara cascade can be classified under four risk categories : 1 - acceptable (negligible reduction of electric power generation by hydropower plants; certain difficulty in meeting environmental and navigation requirements); 2 - significant (substantial reduction of electric power generation by hydropower plants; certain restriction on water releases for navigation; violation of environmental requirements in some years); 3 - emergency (big losses in electric power generation; limited electricity supply to large consumers; significant restriction of water releases for navigation; threat of exposure of drinkable water intake works; violation of environmental requirements for a number of years); 4 - catastrophic (energy crisis; social crisis exposure of drinkable water intake works; termination of navigation; environmental catastrophe). Management of energy systems consists in operative, many-year regulation and perspective planning and has to take into account the analysis of operative data (water reserves in reservoirs), long-term statistics and relations among natural processes and also forecasts - short-term (for a day, week, decade), long-term and/or super-long-term (from a month to several decades). Such natural processes as water inflow to reservoirs, air temperatures during heating periods depend in turn on external factors: prevailing types of atmospheric circulation, intensity of the 11- and 22-year cycles of solar activity, volcanic activity, interaction between the ocean and atmosphere, etc. Until recently despite the formed scientific schools on long-term forecasting (I.P.Druzhinin, A.P.Reznikhov) the energy system management has been based on specially drawn dispatching schedules and long-term hydrometeorological forecasts only without attraction of perspective forecasted indices. Insertion of a parallel block of forecast (based on the analysis of data on natural processes and special methods of forecasting) into the scheme can largely smooth unfavorable consequences from the impact of natural processes on sustainable development of energy systems and especially on its safe operation. However, the requirements to reliability and accuracy of long-term forecasts significantly increase. The considered approach to long term forecasting can be used for prediction: mean winter and summer air temperatures, droughts and wood fires.

  12. Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms

    NASA Astrophysics Data System (ADS)

    Chaudhuri, Sutapa; Middey, Anirban

    2011-11-01

    The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April-May) over Kolkata (22°32'N, 88°20'E), India. The pre-monsoon thunderstorms during 1997-2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12 h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.

  13. Development of an Operational Typhoon Swell Forecasting and Coastal Flooding Early Warning System

    NASA Astrophysics Data System (ADS)

    Fan, Y. M.; Wu, L. C.; Doong, D. J.; Kao, C. C.; Wang, J. H.

    2012-04-01

    Coastal floods and typhoon swells are a consistent threat to oceanfront countries, causing major human suffering and substantial economic losses, such as wrecks, ship capsized, and marine construction failure, etc. Climate change is exacerbating the problem. An early warning system is essential to mitigate the loss of life and property from coastal flooding and typhoon swells. The purpose of this study is to develop a typhoon swell forecasting and coastal flooding early warning system by integrating existing sea-state monitoring technology, numerical ocean forecasting models, historical database and experiences, as well as computer science. The proposed system has capability offering data for the past, information for the present, and for the future. The system was developed for Taiwanese coast due to its frequent threat by typhoons. An operational system without any manual work is the basic requirement of the system. Integration of various data source is the system kernel. Numerical ocean models play the important role within the system because they provide data for assessment of possible typhoon swell and flooding. The system includes regional wave model (SWAN) which nested with the large domain wave model (NWW III), is operationally set up for coastal waves forecasting, especially typhoon swell forecasting before typhoon coming, and the storm surge predicted by a POM model. Data assimilation technology is incorporated for enhanced accuracy. A warning signal is presented when the storm water level that accumulated from astronomical tide, storm surge, and wave-induced run-up exceeds the alarm sea level. This warning system has been in practical use for coastal flooding damage mitigation in Taiwan for years. Example of the system operation during Typhoon Haitung struck Taiwan in 2005 is illustrated in this study.

  14. a 24/7 High Resolution Storm Surge, Inundation and Circulation Forecasting System for Florida Coast

    NASA Astrophysics Data System (ADS)

    Paramygin, V.; Davis, J. R.; Sheng, Y.

    2012-12-01

    A 24/7 forecasting system for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 forecasting of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution forecasting system for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling System (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The system successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The system uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and East, which continues north to the Florida/Georgia border. The system has a data acquisition and processing module that is used to collect data for model runs (e.g. wind, river flow, precipitation). Depending on the domain, forecasts runs can take ~1-18 hours to complete on a single CPU (8-core) system (1-2 hrs for 2D setup and up to 18 hrs for a 3D setup) with 4 forecasts generated per day. All data is archived / catalogued and model forecast skill is continuously being evaluated. In addition to the baseline forecasts, additional forecasts are being perform using various options for wind forcing (GFS, GFDL, WRF, and parametric hurricane models), model configurations (2D/ 3D), and open boundary conditions by coupling with large scale models (ROMS, NCOM, HYCOM), as well as incorporating real-time and forecast river flow and precipitation data to better understand how to improve model skill. In addition, new forecast products (e.g. more informative inundation maps) are being developed to targeted stakeholders. To support modern data standards, CH3D-SSMS results are available online via a THREDDS server in CF-Compliant NetCDF format as well as other stakeholder-friendly (e.g. GIS) formats. The SECOORA website provides visualization of the model via GODIVA-THREDDS interface.

  15. Forecasting of Severe Weather in Austria and Hungary Using High-Resolution Ensemble Prediction System

    NASA Astrophysics Data System (ADS)

    Szucs, Mihaly; Simon, Andre; Szintai, Balazs; Suklitsch, Martin; Wang, Yong; Wastl, Clemens; Boloni, Gergely

    2015-04-01

    The study presents and compares several approaches in EPS (ensemble prediction system) forecasting based on the non-hydrostatic, high resolution AROME model. The PEARP (global ARPEGE model EPS) was used for coupling. Besides, AROME-EPS was also generated upon hydrostatic ALADIN-EPS forecasts (LAEF), which were used as initial and lateral boundary conditions for each AROME-EPS run. The horizontal resolution of the AROME model is 2.5km and it uses 60 vertical levels for the vertical discretization. In most of the tests, the AROME-EPS run with 10+1 members in Hungarian and 16 members in Austrian implementation. The forecast length was usually set to 30-36 hours. The use of high-resolution EPS has advantages in almost all situations with severe convection (mostly in forecasting intense multicell thunderstorms or mesoscale convective systems of non-frontal origin). The possibility of severe thunderstorm was indicated by several EPS runs even if the deterministic (reference) AROME model failed to forecast the event. Similarly, it could be shown that the AROME-EPS can perform better than hydrostatic global or ALADIN-EPS models in situations with strong wind or heavy precipitation induced by large-scale circulation (mainly in mountain regions). Both EDA (Ensemble of Data Assimilation) and SPPT (Stochastically Perturbed Parameterized Tendencies) methods were tested as a potential perturbation generation method on limited area. The EDA method was able to improve the accuracy of single members through the reduction of the analysis error by applying local data assimilation. It was also able to increase the spread of the system in the early hours due to the additional analysis perturbations. The impact of the SPPT scheme was proven to be smaller in comparison to the impact of this method in global ensemble systems. Further possibilities of improving the assimilation methods and the setup of the AROME-EPS are also discussed.

  16. Assessment of the hindcast, nowcast and forecast capabilities of the Mercator-Ocean high resolution ocean forecasting system in the Global and Atlantic and Mediterranean basins.

    NASA Astrophysics Data System (ADS)

    Lellouche, Jean-Michel; Tranchant, Benot.; Bourdall-Badie, Romain; Le Galloudec, Olivier; Greiner, Eric; Benkiran, Mounir; Derval, Corine; Testut, Charles-Emmanuel

    2010-05-01

    In the framework of the European project GMES/MyOcean, Mercator-Ocean has been designing a hierarchy of ocean analysis and forecasting systems based on numerical models of the ocean and data assimilation methods. Since April 2008, Mercator-Ocean runs an Atlantic and Mediterranean system at 1/12 between 20S and 80N. Since a few months, a global system, with the same horizontal and vertical resolution (50 levels on the vertical with a surface refinement), runs also in an operational mode. These two systems are eddy resolving. The ocean and sea ice models are based on the NEMO code. The data assimilation algorithm is a reduced order Kalman filter using 3D multivariate modal decomposition of the forecast error covariance. The system assimilates conjointly altimeter data, SST and in situ observations (temperature and salinity profiles, including ARGO data) in order to provide the initial conditions required for numerical ocean prediction. The main characteristics of the assimilation system are (i) the background error covariances calculated from a free oceanic simulation, (ii) the adaptive error variance, (iii) the use of the localization technique and (iv) the use of the IAU (Incremental Analysis Update) procedure where analysis increments are inserted at every time step over the same period as the data assimilation window. The real time operation of these systems produce each week realistic 3-dimensional oceanic conditions (temperature, salinity, currents,) two weeks back in time (hindcast and nowcast) and a one or two weeks forecast, driven at the surface by atmospheric conditions from the European Center for Medium Range Weather Forecast (ECMWF). Moreover, the Atlantic and Mediterranean system is operated daily to produce 7 days ocean forecasts with daily updates of the ECMWF atmospheric forcing. A new version of the regional system is planned to replace soon the actual one with many improvements concerning the ocean model and the assimilation scheme. Improvement, assessment and validation of the high resolution regional system is of crucial importance because it represents a benchmark for the future global high resolution forecasting system which will be the reference at the end of the European MyOcean project in 2012. After a brief description of the systems, we will present recent validation results. Different comparisons with other systems or independent data as well as the impact of daily updates of the atmospheric forcing will be shown.

  17. How can monthly to seasonal forecasts help to better manage power systems? (Invited)

    NASA Astrophysics Data System (ADS)

    Dubus, L.; Troccoli, A.

    2013-12-01

    The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate system and in the ability to forecast climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these forecasts for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather forecasts. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy systems in which monthly to seasonal forecasts can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.

  18. An ensemble forecast system for prediction of Atlantic-UK wind waves

    NASA Astrophysics Data System (ADS)

    Bunney, Chris; Saulter, Andy

    2015-12-01

    Ensemble prediction systems (EPS) provide a numerical method for determining uncertainty associated with forecasts of environmental conditions. A system is presented that has been designed to quantify uncertainties in short range (up to 7 days ahead) wave forecasts for the Atlantic Ocean and shelf seas around the UK. Variability in the wave ensemble is primarily introduced via wind forcing taken from an Ensemble Transform Kalman Filter based atmospheric EPS. Restart files for each member in the wave ensemble use a short range forecast from a previous run of the same member, in order to retain spread in initial conditions. Wave model run times were optimised through the choice of source term physics scheme and application of a spherical multi-cell grid. Verification of the wave-EPS shows good overall performance, since a substantial component of ensemble under-spread can be attributed to observation errors. Systematic biases, relating to the choice of source term, are noted when statistics are broken down regionally and have a major impact on the quality of the forecasts at short lead times, when spread is limited.

  19. The use of seasonal forecasts in a crop failure early warning system for West Africa

    NASA Astrophysics Data System (ADS)

    Nicklin, K. J.; Challinor, A.; Tompkins, A.

    2011-12-01

    Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

  20. Major upgrade of the global Mercator Océan analysis and forecasting high resolution system

    NASA Astrophysics Data System (ADS)

    Lellouche, Jean-Michel; Legalloudec, Olivier; Bourdallé-Badie, Romain; Garric, Gilles; Greiner, Eric; Regnier, Charly; Bricaud, Clément; Testut, Charles-Emmanuel; Drevillon, Marie; Drillet, Yann; Dombrowsky, Eric

    2014-05-01

    Mercator Océan, the French ocean forecast service provider, was setup about ten years ago by all the French organizations holding stakes in ocean forecasting. It has been since then constantly developed and is currently operating operational ocean analysis and forecasting systems based on state-of-the-art Ocean General Circulation Models. The mandate of Mercator Océan is to cover the global ocean at eddy resolving resolution. To achieve this goal, Mercator Océan is strongly connected to the ocean modeling and data assimilation research communities, at French, European and international levels. Mercator Océan is engaged in the Global Monitoring for Environment and Security (GMES) European initiative and is currently coordinating a European consortium (~60 partners) gathering all European skills in ocean monitoring and forecasting to build the Marine forecast component of the GMES service. This is currently done in the MyOcean2 European funded project which started in 2012. In this context, we have recently performed a major upgrade of the global high resolution system operated at Mercator Océan. This new system now delivers weekly and daily services, and includes numerous improvements related to the ocean/sea-ice model and the assimilation scheme. The previous global system did not benefit from these improvements that were implemented for most of them only in the regional system. Consistency between Mercator Océan systems is thereby ensured by the use of a common basis for all Mercator Océan analysis and forecasting systems. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimize the risk of erroneous observed profiles being assimilated in the model. The presentation is focused on product quality improvements, highlighting the high level of performance and the stability of the new system compared to the previous one. The new system is closer to altimetric observations with a forecast RMS difference of 6 cm. The update of the Mean Dynamic Topography corrects local biases in the Indonesian throughflow and in the western Tropical Pacific. This improves also the subsurface currents at the Equator. The new system gives a more accurate description of water masses almost everywhere. Between 0 and 500 m, departures from in situ observations rarely exceed 1 °C and 0.2 psu. Lastly, the assimilation of an improved Sea Surface Temperature product aims to better represent the sea-ice edge during summertime.

  1. A Remote Sensing-based Global Agricultural Drought Monitoring and Forecasting System for Supporting GEOSS (Invited)

    NASA Astrophysics Data System (ADS)

    di, L.; Yu, G.; Han, W.; Deng, M.

    2010-12-01

    Group on Earth Observations (GEO) is a voluntary partnership of governments and international organizations. GEO is coordinating the implementation of the Global Earth Observation System of Systems (GEOSS), a worldwide effort to make Earth observation resources more useful to the society. As one of the important technical contributors to GEOSS, the Center for Spatial Information Science and Systems (CSISS), George Mason University, is implementing a remote sensing-based global agricultural drought monitoring and forecasting system (GADMFS) as a GEOSS societal benefit areas (agriculture and water) prototype. The goals of the project are 1) to establish a system as a component of GEOSS for providing global on-demand and systematic agriculture drought information to users worldwide, and 2) to support decision-making with improved monitoring, forecasting, and analyses of agriculture drought. GADMFS has adopted the service-oriented architecture and is based on standard-compliant interoperable geospatial Web services to provide online on-demand drought conditions and forecasting at ~1 km spatial and daily and weekly temporal resolutions for any part of the world to world-wide users through the Internet. Applicable GEOSS recommended open standards are followed in the system implementation. The system’s drought monitoring relies on drought-related parameters, such as surface and root-zone soil moisture and NDVI time series derived from remote sensing data, to provide the current conditions of agricultural drought. The system links to near real-time satellite remote sensing data sources from NASA and NOAA for the monitoring purpose. For drought forecasting, the system utilizes a neural-network based modeling algorithm. The algorithm is trained with inputs of current and historic vegetation-based and climate-based drought index data, biophysical characteristics of the environment, and time-series weather data. The trained algorithm will establish per-pixel model for drought forecasting. The model will produce on-demand drought prediction in ~1km or higher spatial resolution, covering whole world by using weather forecasting data as the input. The system will be implemented in multiple phases. Phase I is concentrated only on NDVI-based drought monitoring to demonstrate the concept and feasibility. In phase I, 30-year calibrated global weekly NDVI composites from AVHRR and MODIS are used to establish the baseline and dynamics of vegetation conditions for each co-registered pixel. Multiple NDVI based agricultural drought indices will be computed (e.g., normalized agricultural drought index (NADI), SVI, VegDRI) from the baseline and dynamics for drought monitoring. Phase I prototype will be demonstrated in December 2010.

  2. Reply to "Comment on `Nonparametric forecasting of low-dimensional dynamical systems' "

    NASA Astrophysics Data System (ADS)

    Berry, Tyrus; Giannakis, Dimitrios; Harlim, John

    2016-03-01

    In this Reply we provide additional results which allow a better comparison of the diffusion forecast and the "past-noise" forecasting (PNF) approach for the El Niño index. We remark on some qualitative differences between the diffusion forecast and PNF, and we suggest an alternative use of the diffusion forecast for the purposes of forecasting the probabilities of extreme events.

  3. Nanogel Aerogel as Load Bearing Insulation for Cryogenic Systems

    NASA Astrophysics Data System (ADS)

    Koravos, J. J.; Miller, T. M.; Fesmire, J. E.; Coffman, B. E.

    2010-04-01

    Load support structures in cryogenic storage, transport and processing systems are large contributors to the total heat leak of the system. Conventional insulation systems require the use of these support members in order to stabilize the process fluid enclosure and prevent degradation of insulation performance due to compression. Removal of these support structures would substantially improve system efficiency. Nanogel aerogel insulation performance is tested at vacuum pressures ranging from high vacuum to atmospheric pressure and under loads from loosely packed to greater than 10,000 Pa. Insulation performance is determined using boil-off calorimetry with liquid nitrogen as the latent heat recipient. Two properties of the aerogel insulation material suit it to act as a load bearing "structure" in a process vessel: (1) Ability to maintain thermal performance under load; (2) Elasticity when subjected to load. Results of testing provide positive preliminary indication that these properties allow Nanogel aerogel to effectively be used as a load bearing insulation in cryogenic systems.

  4. Maintaining a Local Data Integration System in Support of Weather Forecast Operations

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

    2010-01-01

    Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.

  5. Two examples of expert knowledge based system for avalanche forecasting and protection

    NASA Astrophysics Data System (ADS)

    Buisson, Laurent; Giraud, Gérald

    1995-11-01

    In avalanche modelling and control and in avalanche forecasting, most of the knowledge is based on scientific theory but the experience of specialists (field practitioners, forecasters...) plays a large role. This paper presents two French computer-based systems dedicated to avalanche modelling and control and to avalanche forecasting. They are both based on expert knowledge. ELSA (Etude et Limites de Sites d'Avalanches), is a computer system dedicated to the modelling of the knowledge of avalanche experts and to the integration of new symbolic computer models with classical numerical models. The basic aim of integration is to build a unique computer system incorporating all these models. After a description of the terrain representation, we present the different scenarios that ELSA takes into account. Then, the methods which deal with some phenomena occurring in avalanches are described. The problems involved in the integration of these methods close this first part. MEPRA is an expert system built to create an objective tool in avalanche risk forecasting. This development allowed us to imagine a processing system for 2 of the most important problems in avalanche risk forecasting: representation of the present snow cover characteristics and evaluation of avalanche instability and risk. In this way, mechanics and thermodynamics play a major role in the system. After a punctual validation at the location of a snow weather station and in order to describe the great variability of the snow pack and the avalanche risk in a massif, the MEPRA expert system was connected with a meteorological analysis system, SAFRAN and a numerical model to simulate the snow cover CROCUS. Then, every day, a MEPRA expert analysis is carried out in different locations with different orientations, slopes and altitudes. Its results were used successfully during the Winter Olympic Games of Albertville and by avalanche forecasters during the 92/93 winter season. The daily avalanche risks estimated by MEPRA are also compared with the observed avalanche activity during the 10 last winters. For a better description of local phenomena like wind slab or snow accumulation, a local version of this tool should be developed to integrate field characteristics.

  6. Managing Forecast Uncertainty and Risk in Multireservoir, Multiobjective, and Multistakeholder Systems

    NASA Astrophysics Data System (ADS)

    Kistenmacher, M.; Georgakakos, A. P.

    2012-12-01

    Hydroclimatic forecasts are often issued in probabilistic form to capture the uncertainties that remain in forecasting inflow magnitude and timing. These forecasts can then be used as input to stochastic management models to help operate reservoir systems and provide stakeholders with probabilistic forecasts of system variables, such as reservoir storages, releases, hydropower production, water withdrawals, and water quality parameters. However, due to high computational requirements, many stochastic management models only evaluate one management policy and are therefore not capable of exploring risk management options. Furthermore, such models usually neglect elements of the real-life water resources systems that they are supposed to represent. This presentation focuses on several advances designed to address these shortcoming and shows their application to a real-life reservoir system in California's Central Valley. This first part of the study focuses on exploring and managing the risks and uncertainties in multi-objective and multi-stakeholder systems. Traditional management models only find management policies that optimize the expected values of system benefits or costs, thereby not allowing operators and stakeholders to explicitly consider issues related to uncertainty and risk management. A technique that can be used to explore different risk management options was developed. The technique allows users to derive policies that produce desired probabilistic distributions of reservoir system outputs reflecting stakeholder preferences by imposing variance constraints on relevant system variables. The second part of this study highlights the expansion of an existing management model, previously only considering monthly water quantity fluxes, to incorporate water quality and flood control objectives. An existing water temperature model is analyzed and used to produce a reduced order model while the flood control objectives are considered through the development of routing models. Both of these models are directly incorporated into the existing management model and therefore allow for the exploration of management policies that can explicitly meet water quality and quantity objectives on several different time scales.

  7. Computer system for forecasting surgery on the eye muscles

    NASA Astrophysics Data System (ADS)

    Avrunin, Oleg G.; Kukharenko, Dmitriy V.; Romanyuk, Sergii O.; Kalizhanova, Aliya; Toygozhinova, Aynur; Gromaszek, Konrad

    2015-12-01

    For the successful surgery on the eye muscles it is recommended to use a computer system of preoperative planning of the surgical correction of strabismus. With using the computer system at surgery planning, ophthalmologist surgeon will be able to choose the best surgical treatment and surgery dosage for a particular patient.

  8. The Extra Load Index as a method for comparing the relative economy of load carriage systems.

    PubMed

    Lloyd, Ray; Hind, Karen; Parr, Bridget; Davies, Simeon; Cooke, Carlton

    2010-12-01

    The Extra Load Index (ELI) has been proposed as a suitable method of assessing the relative economy of load carriage systems. The purpose of this study was to determine, based on empirical evidence, that the ELI can accommodate variations in both body composition and added load. In total, 30 women walked carrying loads of up to 70% body mass at self-selected walking speeds whilst expired air was collected. In addition, each of the women had body composition assessed via dual energy X-ray absorptiometry. Results show that the ELI is independent of body composition variables, the magnitude of additional loads and the speed of progression. Consequently, it is suggested that it represents an appropriate method of comparing load carriage systems in both scientific and commercial arenas. STATEMENT OF RELEVANCE: This paper demonstrates that ELI is independent of body composition, added load and speed and is therefore an appropriate method to generalise comparisons of load carriage systems. It has the advantage of being easily understood by manufacturers and consumers whilst retaining appropriate scientific precision. PMID:21108086

  9. Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

    NASA Technical Reports Server (NTRS)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

  10. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  11. Decision-relevant early-warning thresholds for ensemble flood forecasting systems

    NASA Astrophysics Data System (ADS)

    Stephens, Liz; Pappenberger, Florian; Cloke, Hannah; Alfieri, Lorenzo

    2014-05-01

    Over and under warning of potential future floods is problematic for decision-making, and could ultimately lead to trust being lost in the forecasts. The use of ensemble flood forecasting systems for early warning therefore requires a consideration of how to determine and implement decision-relevant thresholds for flood magnitude and probability. This study uses a year's worth of hindcasts from the Global Flood Awareness System (GloFAS) to explore the sensitivity of the warning system to the choice of threshold. We use a number of different methods for choosing these thresholds, building on current approaches that use model climatologies to determine the critical flow magnitudes, to those that can provide 'first guesses' of potential impacts (through integration with global-scale inundation mapping), as well as methods that could incorporate resource limitations.

  12. Aggregate vehicle travel forecasting model

    SciTech Connect

    Greene, D.L.; Chin, Shih-Miao; Gibson, R.

    1995-05-01

    This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.

  13. Near-Earth Radiation Environment: Operation Control and Forecast System at SINP MSU

    NASA Astrophysics Data System (ADS)

    Myagkova, Irina; Bobrovnikov, Sergey; Kalegaev, Vladimir; Barinova, Vera; Dolenko, Sergey; Shiroky, Vladimir

    Operational control and forecast of the Earth’s radiation environment is very topical both for solving fundamental scientific problems of solar-terrestrial physics, and for providing safety of space missions and polar aviation. Therefore, data of experiments onboard LEO (low-altitudes polar) spacecraft are very important. Now, a lot of data of experiments are available, including measurements of LEO spacecraft like "Meteor-M No. 1" and POES NOAA series. In the nearest future, new Russian satellites RELEC and "Lomonosov" will be launched to LEO orbit. However, data transmitted from LEO spacecraft has specific character connected with the features of LEO orbit: a spacecraft consistently passes different areas of near-Earth space - polar caps, area of outer Earth’s radiations Belts (ERB), middle latitudes, inner ERB. No public systems intended for analysis of radiation conditions at low altitudes, which could allow quick comparison of data obtained in L1 point with those from LEO and GEO, were created until now. The other important problem is forecasting of the near-Earth radiation environment state which is of key importance for space weather. The described problems are solved by the operational system of monitoring and forecasting of the radiation state of near-Earth environment, created at SINP MSU. The system of short-term (one hour ahead) forecasting of solar energetic particles (SEP) and relativistic electron fluxes at GEO operates on the base of artificial neural networks. The system also predicts the extreme location of SEP penetration boundary in the Earth’s magnetosphere at low altitudes and the high latitude boundary of outer ERB. Both predicted locations depend on Dst and Kp values, which, in turn, are predicted one hour ahead by artificial neural networks. The system operates in the framework of Space monitoring data center of the Moscow State University - http://swx.sinp.msu.ru/radiastatus/currentStatus.php.

  14. Evaluation of a load measurement system for cotton harvesters

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The objective of this work is to develop and characterize the performance of a system used onboard a cotton harvester for obtaining seed cotton weight data. This system can be used to measure seed cotton weight on a load by load basis, thereby enhancing the ability for a producer to conduct on-farm ...

  15. Forecasting of cyclone Viyaru and Phailin by NWP-based cyclone prediction system (CPS) of IMD - an evaluation

    NASA Astrophysics Data System (ADS)

    Kotal, S. D.; Bhattacharya, S. K.; Roy Bhowmik, S. K.; Kundu, P. K.

    2014-10-01

    An objective NWP-based cyclone prediction system (CPS) was implemented for the operational cyclone forecasting work over the Indian seas. The method comprises of five forecast components, namely (a) Cyclone Genesis Potential Parameter (GPP), (b) Multi-Model Ensemble (MME) technique for cyclone track prediction, (c) cyclone intensity prediction, (d) rapid intensification, and (e) predicting decaying intensity after the landfall. GPP is derived based on dynamical and thermodynamical parameters from the model output of IMD operational Global Forecast System. The MME technique for the cyclone track prediction is based on multiple linear regression technique. The predictor selected for the MME are forecast latitude and longitude positions of cyclone at 12-hr intervals up to 120 hours forecasts from five NWP models namely, IMD-GFS, IMD-WRF, NCEP-GFS, UKMO, and JMA. A statistical cyclone intensity prediction (SCIP) model for predicting 12 hourly cyclone intensity (up to 72 hours) is developed applying multiple linear regression technique. Various dynamical and thermodynamical parameters as predictors are derived from the model outputs of IMD operational Global Forecast System and these parameters are also used for the prediction of rapid intensification. For forecast of inland wind after the landfall of a cyclone, an empirical technique is developed. This paper briefly describes the forecast system CPS and evaluates the performance skill for two recent cyclones Viyaru (non-intensifying) and Phailin (rapid intensifying), converse in nature in terms of track and intensity formed over Bay of Bengal in 2013. The evaluation of performance shows that the GPP analysis at early stages of development of a low pressure system indicated the potential of the system for further intensification. The 12-hourly track forecast by MME, intensity forecast by SCIP model and rapid intensification forecasts are found to be consistent and very useful to the operational forecasters. The error statistics of the decay model shows that the model was able to predict the decaying intensity after landfall with reasonable accuracy. The performance statistics demonstrates the potential of the system for improving operational cyclone forecast service over the Indian seas.

  16. Validation of reactive gases and aerosols in the MACC global analysis and forecast system

    NASA Astrophysics Data System (ADS)

    Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

    2015-02-01

    The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in-situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols and greenhouse gases, and is based on the Integrated Forecast System of the ECMWF. The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past three years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

  17. A Real-Time Data-Assimilative Nowcast/Forecast System for the Gulf of Mexico

    NASA Astrophysics Data System (ADS)

    Farrara, J.; Chao, Y.; Li, Z.; Wang, X.; Zhang, H.; He, R.; Qian, H.

    2012-12-01

    Based on the Regional Ocean Modeling System (ROMS), this talk will describe a real-time data-assimilative ocean nowcast/forecast system that has been developed as part of the GOMEX-Pilot Prediction Project funded by Department of Energy. This project seeks to evaluate and demonstrate numerical model-based operational predictions of the circulation of the Gulf of Mexico with a particular focus on the Loop Current and associated eddies. The data assimilation component of this system uses a multi-scale 3-dimensional variational (3DVAR) methodology that is capable of assimilating different types of observational data and is also computationally efficient enough for near real-time operations. In order to demonstrate the model performance and prediction skills, only the routine observations (e.g., satellite observations) are used for data assimilation. A set of real-time 3-month forecasts was conducted during the Fall of 2011 to evaluate the potential of the system to predict variations in the Loop Current and associated eddies, including eddy-shedding events. The skill of these forecasts will be analyzed with an emphasis on an eddy-shedding event that took place in November 2011.

  18. Evaluation of a new microphysical aerosol module in the ECMWF Integrated Forecasting System

    NASA Astrophysics Data System (ADS)

    Woodhouse, Matthew; Mann, Graham; Carslaw, Ken; Morcrette, Jean-Jacques; Schulz, Michael; Kinne, Stefan; Boucher, Olivier

    2013-04-01

    The Monitoring Atmospheric Composition and Climate II (MACC-II) project will provide a system for monitoring and predicting atmospheric composition. As part of the first phase of MACC, the GLOMAP-mode microphysical aerosol scheme (Mann et al., 2010, GMD) was incorporated within the ECMWF Integrated Forecasting System (IFS). The two-moment modal GLOMAP-mode scheme includes new particle formation, condensation, coagulation, cloud-processing, and wet and dry deposition. GLOMAP-mode is already incorporated as a module within the TOMCAT chemistry transport model and within the UK Met Office HadGEM3 general circulation model. The microphysical, process-based GLOMAP-mode scheme allows an improved representation of aerosol size and composition and can simulate aerosol evolution in the troposphere and stratosphere. The new aerosol forecasting and re-analysis system (known as IFS-GLOMAP) will also provide improved boundary conditions for regional air quality forecasts, and will benefit from assimilation of observed aerosol optical depths in near real time. Presented here is an evaluation of the performance of the IFS-GLOMAP system in comparison to in situ aerosol mass and number measurements, and remotely-sensed aerosol optical depth measurements. Future development will provide a fully-coupled chemistry-aerosol scheme, and the capability to resolve nitrate aerosol.

  19. Valve for fuel pin loading system

    DOEpatents

    Christiansen, D.W.

    1984-01-01

    A cyclone valve surrounds a wall opening through which cladding is projected. An axial valve inlet surrounds the cladding. Air is drawn through the inlet by a cyclone stream within the valve. An inflatable seal is included to physically engage a fuel pin subassembly during loading of fuel pellets.

  20. Valve for fuel pin loading system

    DOEpatents

    Christiansen, David W.

    1985-01-01

    A cyclone valve surrounds a wall opening through which cladding is projected. An axial valve inlet surrounds the cladding. Air is drawn through the inlet by a cyclone stream within the valve. An inflatable seal is included to physically engage a fuel pin subassembly during loading of fuel pellets.

  1. Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System

    PubMed Central

    Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren

    2014-01-01

    Abstract Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years. PMID:25553271

  2. Forecast-Based Operations Support Tool for the New York City Water Supply System

    NASA Astrophysics Data System (ADS)

    Pyke, G.; Porter, J.

    2012-12-01

    The NYC water supply system serves 9 million people with over 1 BGD of water drawn from 19 reservoirs. To support operation of the system to meet multiple objectives (e.g. supply reliability, water quality, environmental releases, hydropower, peak flow mitigation), the New York City Department of Environmental Protection (DEP) is developing an Operations Support Tool (OST), a forecast-based decision support system that provides a probabilistic foundation for water supply operations and planning. Key features of OST include: the ability to run both long-term simulations and short-term probabilistic simulations on the same model platform; automated processing of near-real-time (NRT) data sources; use of inflow forecasts to support look-ahead operational simulations; and water supply-water quality model linkage to account for feedback and tradeoffs between supply and quality objectives. OST supports two types of simulations. Long-term runs execute the system model over an extended historical record and are used to evaluate reservoir operating rules, infrastructure modifications, and climate change scenarios (with inflows derived from downscaled GCM data). Short-term runs for operational guidance consist of multiple (e.g. 80+) short (e.g. one year) runs, all starting from the same initial conditions (typically those of the current day). Ensemble reservoir inflow forecast traces are used to drive the model for the duration of the simulation period. The result of these runs is a distribution of potential future system states. DEP managers analyze the distributions for alternate scenarios and make operations decisions using risk-based metrics such as probability of refill or the likelihood of a water quality event. For operational simulations, the OST data system acquires NRT data from DEP internal sources (SCADA operations data, keypoint water quality, in-stream/in-reservoir water quality, meteorological and snowpack monitoring sites). OST acquires streamflow data from USGS and ensemble inflow forecasts from the National Weather Service (NWS). Incoming data passes through an automated flagging/filling process, and data is presented to operators for approval prior to use as model input. OST allows the user to drive operational runs with two types of ensemble inflow forecasts. Statistical forecasts are based on historical inflows that are conditioned on antecedent hydrology. The statistical algorithm is relatively simple and versatile and is useful for longer-term projections. For improved short-term skill, OST will rely on NWS meteorologically-based ensemble forecasts. A post-processor within OST will provide bias correction for the NWS ensembles. OST applications to date have included routine short-term operational projections to support release decisions, analysis of tradeoffs between water supply and water quality during turbidity events, facility outage planning, development of operating rules and release policies, long-term water supply planning, and climate change assessment. The structure and capabilities of OST are expected to be a useful template for drinking water utilities and water system managers seeking to integrate forecasts into system operations and balance tradeoffs between competing objectives in both near-term operations and long-term planning.

  3. The Impact of TRMM Data on Numerical Forecast of Mesoscale Systems

    NASA Technical Reports Server (NTRS)

    Pu, Zhao-Xia; Tao, Wei-Kuo

    2002-01-01

    The impact of surface rainfall data derived from the TRMM Microwave Image (TMI) on the numerical forecast of mesoscale systems is evaluated. A series of numerical experiments are performed that assimilate TMI rainfall data into the Penn State University/National Centers for Atmospheric Research (PSU/NCAR) Mesoscale Model version 5 (MM5) using a four-dimensional variational data assimilation (4DVAR) technique. Experiments are conducted incorporating TMI rainfall data into the mesoscale model to improve hurricane initialization. It is found that assimilation of rainfall data into the model is beneficial in producing a more realistic eye and rain bands and also helps to improve the intensity forecast for the hurricane. Further 4DVAR experiments are performed on mesoscale convective systems (MCSs). Detailed results and related issues will be presented during the conference.

  4. Reliability Constrained Priority Load Shedding for Aerospace Power System Automation

    NASA Technical Reports Server (NTRS)

    Momoh, James A.; Zhu, Jizhong; Kaddah, Sahar S.; Dolce, James L. (Technical Monitor)

    2000-01-01

    The need for improving load shedding on board the space station is one of the goals of aerospace power system automation. To accelerate the optimum load-shedding functions, several constraints must be involved. These constraints include congestion margin determined by weighted probability contingency, component/system reliability index, generation rescheduling. The impact of different faults and indices for computing reliability were defined before optimization. The optimum load schedule is done based on priority, value and location of loads. An optimization strategy capable of handling discrete decision making, such as Everett optimization, is proposed. We extended Everett method to handle expected congestion margin and reliability index as constraints. To make it effective for real time load dispatch process, a rule-based scheme is presented in the optimization method. It assists in selecting which feeder load to be shed, the location of the load, the value, priority of the load and cost benefit analysis of the load profile is included in the scheme. The scheme is tested using a benchmark NASA system consisting of generators, loads and network.

  5. Prototype of a drought monitoring and forecasting system for the Tuscany region

    NASA Astrophysics Data System (ADS)

    Magno, R.; Angeli, L.; Chiesi, M.; Pasqui, M.

    2014-05-01

    A system for drought monitoring and medium-long time forecasting in the Tuscany region (central Italy) is briefly introduced, which is based on ground and satellite data (1 km spatial resolution and 16-day temporal resolution). It is also shown how information about current conditions and future evolution of a drought event is periodically delivered on the LaMMA Consortium website, in collaboration with the Institute of Biometeorology (IBIMET-CNR).

  6. Testing for ontological errors in probabilistic forecasting models of natural systems

    PubMed Central

    Marzocchi, Warner; Jordan, Thomas H.

    2014-01-01

    Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265

  7. Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

    2009-04-01

    Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

  8. Active transmission isolation/rotor loads measurement system

    NASA Technical Reports Server (NTRS)

    Kenigsberg, I. J.; Defelice, J. J.

    1973-01-01

    Modifications were incorporated into a helicopter active transmission isolation system to provide the capability of utilizing the system as a rotor force measuring device. These included; (1) isolator redesign to improve operation and minimize friction, (2) installation of pressure transducers in each isolator, and (3) load cells in series with each torque restraint link. Full scale vibration tests performed during this study on a CH-53A helicopter airframe verified that these modifications do not degrade the systems wide band isolation characteristics. Bench tests performed on each isolator unit indicated that steady and transient loads can be measured to within 1 percent of applied load. Individual isolator vibratory load measurement accuracy was determined to be 4 percent. Load measurement accuracy was found to be independent of variations in all basic isolator operating characteristics. Full scale system load calibration tests on the CH-53A airframe established the feasibility of simultaneously providing wide band vibration isolation and accurate measurement of rotor loads. Principal rotor loads (lift, propulsive force, and torque) were measured to within 2 percent of applied load.

  9. Dynamical Downscaling NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS)

    NASA Astrophysics Data System (ADS)

    Lu, L.; Zheng, Y.; Pielke, R. A.

    2009-12-01

    As part of the NOAA CPPA-sponsored MRED project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively downscale NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The first set of RCM prediction experiment focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Our first suite of numerical experiment includes one ensemble member for each year from 1982 through 2008, with all the simulations starting on December 1 and ending on April 30. Driven by the same atmospheric and SST forcings, RAMS will be compared with other RCMs, and evaluated against observations and reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions. The overall strengths and weaknesses of the modeling systems will be identified, as well as the consistent model biases. In addition, we will analyze the changes in kinetic energy spectra before and after the spectral nudging algorithm is implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales.

  10. An operational coupled wave-current forecasting system for the northern Adriatic Sea

    NASA Astrophysics Data System (ADS)

    Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

    2012-04-01

    Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave heights. Part of the activity has been funded by the EU FP VII program (project "MICORE", contract n. 202798) and by the Regione Veneto regional law 15/2007 (Progetto "MARINA").

  11. An objective, statistical system for short-term probabilistic forecasts of thunderstorms

    NASA Astrophysics Data System (ADS)

    Hilliker, Joby Lee

    A major dilemma facing the aviation industry early in the 21st century is the consequence of high consumer demand for air travel. With passenger numbers at record numbers, the United States air-traffic system has become strained. Inclement weather, such as low clouds, reduced visibility, and thunderstorms, compounds the problem. Currently, the aviation industry does not have a nationwide system for forecasting high-impact weather. The reason is that the guidance needed by the aviation industry is much different that the guidance that has traditionally been disseminated. Forecasts need to made for the short-term (<6 h), be timely, and have rapid-update capabilities. Moreover, the guidance needs to take into account the inherent uncertainty in weather prediction. Hence, the requirement for objective and reliable probabilistic guidance. This thesis presents a prototype forecast system that possess these attributes. Specifically, a system is constructed to generate short-term forecasts of thunderstorms. Thunderstorms are chosen because they are the greatest contributing cause of air-traffic delays. In fact, the aviation industry spends $3 billion a year from thunderstorm impacts. The system makes uses of archives of high-resolution (both in time and space) observational datasets. These observations are: (a) WSR-88D radar data, (b) profiler data, and (c) surface data from the Oklahoma mesonetwork. After the datasets have been analyzed for bad/missing data, and then replaced with suitable estimates, the most powerful thunderstorm predictors are ascertained. Results show that radar data have the greatest contribution to skill. There is an increasing contribution from surface, then upper-air data, for longer lead times. The upstream percent areal coverage of reflectivities is the most powerful predictor of thunderstorms for all lead times. The absolute convergence and climatological departure of relative humidity are the most powerful predictors from the surface data. From upper-air data, the most popular predictors are relative humidity and stability observations from the closest radiosonde site. The system has skill scores of 0.09-0.39 relative to persistence climatology (alternatively, a 9-39% improvement in MSE) through a 360-min lead time. Its performance is highest for lead times ≤30 min, with a gradually drop thereafter. Yet, even for the 360-min lead time, forecast performance relative to persistence climatology remains superior to a statistically significant degree.

  12. Downscaling modelling system for multi-scale air quality forecasting

    NASA Astrophysics Data System (ADS)

    Nuterman, R.; Baklanov, A.; Mahura, A.; Amstrup, B.; Weismann, J.

    2010-09-01

    Urban modelling for real meteorological situations, in general, considers only a small part of the urban area in a micro-meteorological model, and urban heterogeneities outside a modelling domain affect micro-scale processes. Therefore, it is important to build a chain of models of different scales with nesting of higher resolution models into larger scale lower resolution models. Usually, the up-scaled city- or meso-scale models consider parameterisations of urban effects or statistical descriptions of the urban morphology, whereas the micro-scale (street canyon) models are obstacle-resolved and they consider a detailed geometry of the buildings and the urban canopy. The developed system consists of the meso-, urban- and street-scale models. First, it is the Numerical Weather Prediction (HIgh Resolution Limited Area Model) model combined with Atmospheric Chemistry Transport (the Comprehensive Air quality Model with extensions) model. Several levels of urban parameterisation are considered. They are chosen depending on selected scales and resolutions. For regional scale, the urban parameterisation is based on the roughness and flux corrections approach; for urban scale - building effects parameterisation. Modern methods of computational fluid dynamics allow solving environmental problems connected with atmospheric transport of pollutants within urban canopy in a presence of penetrable (vegetation) and impenetrable (buildings) obstacles. For local- and micro-scales nesting the Micro-scale Model for Urban Environment is applied. This is a comprehensive obstacle-resolved urban wind-flow and dispersion model based on the Reynolds averaged Navier-Stokes approach and several turbulent closures, i.e. k -ɛ linear eddy-viscosity model, k - ɛ non-linear eddy-viscosity model and Reynolds stress model. Boundary and initial conditions for the micro-scale model are used from the up-scaled models with corresponding interpolation conserving the mass. For the boundaries a kind of Dirichlet condition is chosen to provide the values based on interpolation from the coarse to the fine grid. When the roughness approach is changed to the obstacle-resolved one in the nested model, the interpolation procedure will increase the computational time (due to additional iterations) for meteorological/ chemical fields inside the urban sub-layer. In such situations, as a possible alternative, the perturbation approach can be applied. Here, the effects of main meteorological variables and chemical species are considered as a sum of two components: background (large-scale) values, described by the coarse-resolution model, and perturbations (micro-scale) features, obtained from the nested fine resolution model.

  13. Probabilistic Prediction of European Winter Temperature and Their Application Using The Ecmwf Seasonal Forecast System 1 and 2

    NASA Astrophysics Data System (ADS)

    Müller, W.; Appenzeller, Ch.

    There is rising interest in economical applications of seasonal climate forecasts, for example for weather risk and weather derivative markets. However seasonal forecast- ing based on coupled atmosphere -ocean models is a complex task. As a consequence of the chaotic nature of the climate system seasonal forecasts can not be calculated in a deterministic sense. They need to be calculated in a probabilistic way by using an ensemble of model runs with slightly different initial conditions. Here we use the ECMWF experimental ensemble prediction system 1 and 2 to explore the sensitivity of mid-latitude winter mean temperature forecasts on different drift correction methods. The forecast quality is quantified in a probabilistic framework using ranked proba- bility skill scores (RPSS). It is shown that a drift correction method that accounts for system 1 decadal climate variability (such as the NAO) has a positive, but weak impact on the forecast skill, especially over Europe. As an economic application we evaluate the skill of three month averaged heating degree days forecasts over Europe.

  14. Numerical simulation of birch pollen dispersion with an operational weather forecast system.

    PubMed

    Vogel, Heike; Pauling, Andreas; Vogel, Bernhard

    2008-11-01

    We included a parameterisation of the emissions of pollen grains into the comprehensive model system COSMO-ART. In addition, a detailed density distribution of birch trees within Switzerland was derived. Based on these new developments, we carried out numerical simulations of the dispersion of pollen grains for an episode that occurred in April 2006 over Switzerland and the adjacent regions. Since COSMO-ART is based on the operational forecast model of the German Weather Service, we are presenting a feasibility study of daily pollen forecast based on methods which have been developed during the last two decades for the treatment of anthropogenic aerosol. A comparison of the model results and very detailed pollen counts documents the current possibilities and the shortcomings of the method and gives hints for necessary improvements. PMID:18651182

  15. Technical Note: The Normal Quantile Transformation and its application in a flood forecasting system

    NASA Astrophysics Data System (ADS)

    Bogner, K.; Pappenberger, F.; Cloke, H. L.

    2011-10-01

    The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Density Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as Normal-Score Transform. In this paper some possible problems caused by small sample sizes for the applicability in flood forecasting systems will be discussed and illustrated by examples. For the practical implementation commands and examples from the freely available and widely used statistical computing language R (R Development Core Team, 2011) will be given (represented in Courier font) and possible solutions are suggested by combining extreme value analysis and non-parametric regression methods.

  16. Validation of reactive gases and aerosols in the MACC global analysis and forecast system

    NASA Astrophysics Data System (ADS)

    Eskes, Henk; Arola, Antti; Blechschmidt, Anne; Botek, Edith; Cuevas, Emilio; Gaudel, Audrey; Kapsomenakis, John; Katragkou, Eleni; Razinger, Miha; Schulz, Michael; Sudarchikova, Natalia; Wagner, Annette

    2015-04-01

    The European MACC (Monitoring Atmospheric Composition and Climate) project is building the pre-operational Atmosphere Monitoring Service of the European Copernicus Programme. In this project data assimilation techniques are applied to combine in-situ and remote sensing observations with global and European-scale models of atmospheric reactive gases, aerosols and greenhouse gases. The global component is based on the Integrated Forecast System of the ECMWF. This global service has a dedicated validation activity to document the quality of the atmospheric composition products. In our contribution we discuss the validation approach as has been developed over the past years during MACC-II and MACC-III, including the validation requirements, the operational aspects, the upgrade procedure, the validation reports and scoring methods, the measurement data sets, and the model configurations and assimilation systems validated. Of special concern are the forecasts of high pollution concentration events. A brief summary is provided of the validation results for the MACC daily global analysis and forecasts.

  17. Convective systems over North America and their role in European forecast busts

    NASA Astrophysics Data System (ADS)

    Rodwell, M.; Ecmwf Working Group On Diagnostics

    2011-12-01

    During Northern Spring, intense convective events develop over the United States (Fig. 1). During this convection, data assimilation systems work hard to correct background errors. At the same time, weather forecasts produced by the World's leading global NWP centers exhibit strong 'busts' over Europe. European spatial anomaly correlation scores for 500 hPa geopotential at a lead-time of 6 days can drop from typical values of over 80% to as low as -20% (Fig. 2). It is thought that these two issues may be connected, with initial errors over the United States growing in the forecast as they cross the Atlantic. Here the ECMWF data assimilation system is investigated during some of these convective events. This investigation includes the availability and use of conventional and satellite observations, and the role of model process error. In addition, the strength of the projection of any initial errors on the upper-tropospheric Rossby waves is quantified in order to assess the proposed link to European forecast busts.

  18. A prototype system for forecasting landslides in the Seattle, Washington, area

    USGS Publications Warehouse

    Chleborad, Alan F.; Baum, Rex L.; Godt, Jonathan W.; Powers, Philip S.

    2008-01-01

    Empirical rainfall thresholds and related information form the basis of a prototype system for forecasting landslides in the Seattle area. The forecasts are tied to four alert levels, and a decision tree guides the use of thresholds to determine the appropriate level. From analysis of historical landslide data, we developed a formula for a cumulative rainfall threshold (CT), P3  =  88.9 − 0.67P15, defined by rainfall amounts in millimeters during consecutive 3 d (72 h) periods, P3, and the 15 d (360 h) period before P3, P15. The variable CT captures more than 90% of historical events of three or more landslides in 1 d and 3 d periods recorded from 1978 to 2003. However, the low probability of landslide occurrence on a day when the CT is exceeded at one or more rain gauges (8.4%) justifies a low-level of alert for possible landslide occurrence, but it does trigger more vigilant monitoring of rainfall and soil wetness. Exceedance of a rainfall intensity-duration threshold I  =  82.73D−1.13, for intensity, I (mm/hr), and duration, D (hr), corresponds to a higher probability of landslide occurrence (30%) and forms the basis for issuing warnings of impending, widespread occurrence of landslides. Information about the area of exceedance and soil wetness can be used to increase the certainty of landslide forecasts (probabilities as great as 71%). Automated analysis of real-time rainfall and subsurface water data and digital quantitative precipitation forecasts are needed to fully implement a warning system based on the two thresholds.

  19. Intercomparison of Operational Ocean Forecasting Systems in the framework of GODAE

    NASA Astrophysics Data System (ADS)

    Hernandez, F.

    2009-04-01

    One of the main benefits of the GODAE 10-year activity is the implementation of ocean forecasting systems in several countries. In 2008, several systems are operated routinely, at global or basin scale. Among them, the BLUElink (Australia), HYCOM (USA), MOVE/MRI.COM (Japan), Mercator (France), FOAM (United Kingdom), TOPAZ (Norway) and C-NOOFS (Canada) systems offered to demonstrate their operational feasibility by performing an intercomparison exercise during a three months period (February to April 2008). The objectives were: a) to show that operational ocean forecasting systems are operated routinely in different countries, and that they can interact; b) to perform in a similar way a scientific validation aimed to assess the quality of the ocean estimates, the performance, and forecasting capabilities of each system; and c) to learn from this intercomparison exercise to increase inter-operability and collaboration in real time. The intercomparison relies on the assessment strategy developed for the EU MERSEA project, where diagnostics over the global ocean have been revisited by the GODAE contributors. This approach, based on metrics, allow for each system: a) to verify if ocean estimates are consistent with the current general knowledge of the dynamics; and b) to evaluate the accuracy of delivered products, compared to space and in-situ observations. Using the same diagnostics also allows one to intercompare the results from each system consistently. Water masses and general circulation description by the different systems are consistent with WOA05 Levitus climatology. The large scale dynamics (tropical, subtropical and subpolar gyres ) are also correctly reproduced. At short scales, benefit of high resolution systems can be evidenced on the turbulent eddy field, in particular when compared to eddy kinetic energy deduced from satellite altimetry of drifter observations. Comparisons to high resolution SST products show some discrepancies on ocean surface representation, either due to model and forcing fields errors, or assimilation scheme efficiency. Comparisons to sea-ice satellite products also evidence discrepancies linked to model, forcing and assimilation strategies of each forecasting system. Key words: Intercomparison, ocean analysis, operational oceanography, system assessment, metrics, validation GODAE Intercomparison Team: L. Bertino (NERSC/Norway), G. Brassington (BMRC/Australia), E. Chassignet (FSU/USA), J. Cummings (NRL/USA), F. Davidson (DFO/Canda), M. Drévillon (CERFACS/France), P. Hacker (IPRC/USA), M. Kamachi (MRI/Japan), J.-M. Lellouche (CERFACS/France), K. A. Lisæter (NERSC/Norway), R. Mahdon (UKMO/UK), M. Martin (UKMO/UK), A. Ratsimandresy (DFO/Canada), and C. Regnier (Mercator Ocean/France)

  20. Assessment of a fuzzy based flood forecasting system optimized by simulated annealing

    NASA Astrophysics Data System (ADS)

    Reyhani Masouleh, Aida; Pakosch, Sabine; Disse, Markus

    2010-05-01

    Flood forecasting is an important tool to mitigate harmful effects of floods. Among the many different approaches for forecasting, Fuzzy Logic (FL) is one that has been increasingly applied over the last decade. This method is principally based on the linguistic description of Rule Systems (RS). A RS is a specific combination of membership functions of input and output variables. Setting up the RS can be implemented either automatically or manually, the choice of which can strongly influence the resulting rule systems. It is therefore the objective of this study to assess the influence that the parameters of an automated rule generation based on Simulated Annealing (SA) have on the resulting RS. The study area is the upper Main River area, located in the northern part of Bavaria, Germany. The data of Mainleus gauge with area of 1165 km2 was investigated in the whole period of 1984 and 2004. The highest observed discharge of 357 m3/s was recorded in 1995. The input arguments of the FL model were daily precipitation, forecasted precipitation, antecedent precipitation index, temperature and melting rate. The FL model of this study has one output variable, daily discharge and was independently set up for three different forecast lead times, namely one-, two- and three-days ahead. In total, each RS comprised 55 rules and all input and output variables were represented by five sets of trapezoidal and triangular fuzzy numbers. Simulated Annealing, which is a converging optimum solution algorithm, was applied for optimizing the RSs in this study. In order to assess the influence of its parameters (number of iterations, temperature decrease rate, initial value for generating random numbers, initial temperature and two other parameters), they were individually varied while keeping the others fixed. With each of the resulting parameter sets, a full-automatic SA was applied to gain optimized fuzzy rule systems for flood forecasting. Evaluation of the performance of the resulting fuzzy rule forecasting systems (with the intention to draw conclusions on the best SA parameters) was carried out in two steps: a) Evaluation of objective functions such as Nash-Sutcliffe and RMSE for all RSs. b) Manual evaluation of the preselected results from the first step. The evaluation was based on visual inspection (scatter plots, time-series and Degree Of Fulfilment (DOF) graphs) as well as logical interpretation of the rule systems. Comparing the results showed that there were SA parameter sets which lead to forecast systems of equally high quality (with respect to objective criteria such as Nash-Sutcliffe), however the underlying rule systems significantly varied from each other. Therefore, manual inspection played a key role in finding the overall best results. In the presentation, the procedure of preparing different sets of SA parameters, the evaluation process of different results and the performance of the optimal RS will be explained and presented by an example.

  1. A pan-African medium-range ensemble flood forecast system

    NASA Astrophysics Data System (ADS)

    Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

    2015-04-01

    The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. Also the forecasts showed on average a good reliability, and the CRPSS helped identifying regions to focus on for future improvements. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe and Mozambique) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a good prospective as an operational system, as it has demonstrated its significant potential to contribute to the reduction of flood-related losses in Africa by providing national and international aid organizations timely with medium-range flood forecast information. However, issues related to the practical implication will still need to be investigated.

  2. Foil system fatigue load environments for commercial hydrofoil operation

    NASA Technical Reports Server (NTRS)

    Graves, D. L.

    1979-01-01

    The hydrofoil fatigue loads environment in the open sea is examined. The random nature of wave orbital velocities, periods and heights plus boat heading, speed and control system design are considered in the assessment of structural fatigue requirements. Major nonlinear load events such as hull slamming and foil unwetting are included in the fatigue environment. Full scale rough water load tests, field experience plus analytical loads work on the model 929 Jetfoil commercial hydrofoil are discussed. The problem of developing an overall sea environment for design is defined. State of the art analytical approaches are examined.

  3. Northeast Coastal Ocean Forecast System (NECOFS): A Multi-scale Global-Regional-Estuarine FVCOM Model

    NASA Astrophysics Data System (ADS)

    Beardsley, R. C.; Chen, C.

    2014-12-01

    The Northeast Coastal Ocean Forecast System (NECOFS) is a global-regional-estuarine integrated atmosphere/surface wave/ocean forecast model system designed for the northeast US coastal region covering a computational domain from central New Jersey to the eastern end of the Scotian Shelf. The present system includes 1) the mesoscale meteorological model WRF (Weather Research and Forecasting); 2) the regional-domain FVCOM covering the Gulf of Maine/Georges Bank/New England Shelf region (GOM-FVCOM); 3) the unstructured-grid surface wave model (FVCOM-SWAVE) modified from SWAN with the same domain as GOM-FVCOM; 3) the Mass coastal FVCOM with inclusion of inlets, estuaries and intertidal wetlands; and 4) three subdomain wave-current coupled inundation FVCOM systems in Scituate, MA, Hampton River, NH and Mass Bay, MA. GOM-FVCOM grid features unstructured triangular meshes with horizontal resolution of ~ 0.3-25 km and a hybrid terrain-following vertical coordinate with a total of 45 layers. The Mass coastal FVCOM grid is configured with triangular meshes with horizontal resolution up to ~10 m, and 10 layers in the vertical. Scituate, Hampton River and Mass Bay inundation model grids include both water and land with horizontal resolution up to ~5-10 m and 10 vertical layers. GOM-FVCOM is driven by surface forcing from WRF model output configured for the region (with 9-km resolution), the COARE3 bulk air-sea flux algorithm, local river discharges, and tidal forcing constructed by eight constituents and subtidal forcing on the boundary nested to the Global-FVCOM. SWAVE is driven by the same WRF wind field with wave forcing at the boundary nested to Wave Watch III configured for the northwestern Atlantic region. The Mass coastal FVCOM and three inundation models are connected with GOM-FVCOM through one-way nesting in the common boundary zones. The Mass coastal FVCOM is driven by the same surface forcing as GOM-FVCOM. The nesting boundary conditions for the inundation models include both hydrodynamics and waves provided by GOM-FVCOM and SWAVE. NECOFS was placed in experimental forecast operation in late 2007 and the daily 3-day forecast product can be accessed and viewed on the NECOFS web map server (http://porpoise1.smast.umassd.edu:8080/fvcomwms/).

  4. An integrated weather and sea-state forecasting system for the Arabian Peninsula (WASSF)

    NASA Astrophysics Data System (ADS)

    Kallos, George; Galanis, George; Spyrou, Christos; Mitsakou, Christina; Solomos, Stavros; Bartsotas, Nikolaos; Kalogrei, Christina; Athanaselis, Ioannis; Sofianos, Sarantis; Vervatis, Vassios; Axaopoulos, Panagiotis; Papapostolou, Alexandros; Qahtani, Jumaan Al; Alaa, Elyas; Alexiou, Ioannis; Beard, Daniel

    2013-04-01

    Nowadays, large industrial conglomerates such as the Saudi ARAMCO, require a series of weather and sea state forecasting products that cannot be found in state meteorological offices or even commercial data providers. The two major objectives of the system is prevention and mitigation of environmental problems and of course early warning of local conditions associated with extreme weather events. The management and operations part is related to early warning of weather and sea-state events that affect operations of various facilities. The environmental part is related to air quality and especially the desert dust levels in the atmosphere. The components of the integrated system include: (i) a weather and desert dust prediction system with forecasting horizon of 5 days, (ii) a wave analysis and prediction component for Red Sea and Arabian Gulf, (iii) an ocean circulation and tidal analysis and prediction of both Red Sea and Arabian Gulf and (iv) an Aviation part specializing in the vertical structure of the atmosphere and extreme events that affect air transport and other operations. Specialized data sets required for on/offshore operations are provided ate regular basis. State of the art modeling components are integrated to a unique system that distributes the produced analysis and forecasts to each department. The weather and dust prediction system is SKIRON/Dust, the wave analysis and prediction system is based on WAM cycle 4 model from ECMWF, the ocean circulation model is MICOM while the tidal analysis and prediction is a development of the Ocean Physics and Modeling Group of University of Athens, incorporating the Tidal Model Driver. A nowcasting subsystem is included. An interactive system based on Google Maps gives the capability to extract and display the necessary information for any location of the Arabian Peninsula, the Red Sea and Arabian Gulf.

  5. The impact of observation errors on analysis error and forecast skill investigated with an Observing System Simulation Experiment

    NASA Astrophysics Data System (ADS)

    Prive, N.; Errico, R. M.; Tai, K.

    2012-12-01

    A global observing system simulation experiment (OSSE) has been developed at the NASA Global Modeling and Assimilation Office using the Global Earth Observing System (GEOS-5) forecast model and Gridpoint Statistical Interpolation data assimilation. A 13-month integration of the European Centre for Medium-Range Weather Forecasts operational forecast model is used as the Nature Run. Synthetic observations for conventional and radiance data types are interpolated from the Nature Run, with calibrated observation errors added to reproduce realistic statistics of analysis increment and observation innovation. It is found that correlated observation errors are necessary in order to replicate the statistics of analysis increment and observation innovation found with real data. The impact of these observation errors is explored in a series of OSSE experiments in which the magnitude of the applied observation error is varied from zero to double the calibrated values while the observation error covariances of the GSI are held fixed. Increased observation error has a strong effect on the variance of the analysis increment and observation innovation fields, but a much weaker impact on the root mean square (RMS) analysis error. For the 120 hour forecast, only slight degradation of forecast skill in terms of anomaly correlation and RMS forecast error is observed in the midlatitudes, and there is no appreciable impact of observation error on forecast skill in the tropics.

  6. Comparison of short-term rainfall forecasts for model-based flow prediction in urban drainage systems.

    PubMed

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas; Borup, Morten; Ahm, Malte; Nielsen, Jesper Ellerbæk; Grum, Morten; Rasmussen, Michael R; Gill, Rasphall; Mikkelsen, Peter Steen

    2013-01-01

    Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times. PMID:23863443

  7. Forecasting of Optical Turbulence in Support of Realtime Optical Imaging and Communication Systems

    NASA Astrophysics Data System (ADS)

    Alliss, R.; Felton, B.

    2012-09-01

    Optical turbulence (OT) acts to distort light in the atmosphere, degrading imagery from large astronomical and imaging telescopes and possibly reducing data quality of free space optical communication (FSOC) links. Some of the degradation due to optical turbulence can be corrected by adaptive optics. However, the severity of optical turbulence, and thus the amount of correction required, is largely dependent upon the turbulence at the location of interest. In addition, clouds, precipitation, and inhomogeneities in atmospheric temperature and moisture all have the potential to disrupt imaging and communications through the atmosphere. However, there are strategies that can be employed to mitigate the atmospheric impacts. These strategies require an accurate characterization of the atmosphere through which the communications links travel. To date these strategies have been to climatological characterize OT and its properties. Recently efforts have been developed to employ a realtime forecasting system which provides planners useful information for maintaining links and link budgets. The strength of OT is characterized by the refractive index structure function Cn2, which in turn is used to calculate atmospheric seeing parameters. Atmospheric measurements provided by local instrumentation are valuable for link characterization, but provide an incomplete picture of the atmosphere. While attempts have been made to characterize Cn2 using empirical models, Cn2 can be calculated more directly from Numerical Weather Prediction (NWP) simulations using pressure, temperature, thermal stability, vertical wind shear, turbulent Prandtl number, and turbulence kinetic energy (TKE). During realtime FSOC demonstrations, in situ measurements are supplemented with NWP simulations, which provide near realtime characterizations and forecasts of the Cn2, the Fried Coherence Length (ro), and time-varying, three-dimensional characterizations of the atmosphere. The three dimensional Weather Research and Forecasting (WRF) model is used to produce characterizations and forecasts of OT. These forecasts are used for planning FSOC experiments in the 3-24 hour range. The WRF model is configured to run at up to 300 meters horizontal resolution over a 250km by 250km horizontal domain. The vertical resolution varies from 25 meters in the boundary layer to 500 meters in the stratosphere with over 130 vertical levels. The model top is 20 km in altitude. The model is run up to twice per day and generates forecasts out to 27 hours. The WRF model has proven to be a valuable tool for link characterization and forecasting, since it can identify thin relatively layers of optical turbulence that are not represented by standard empirically derived Cn2 profiles. Results show that WRF simulations can accurately predict upcoming turbulence events that may degrade system performance. Demonstrations of these forecasts will be shown at the conference. The near realtime simulations of OT are performed using the Maui High Performance Computing Centers (MHPCC) Mana cluster.

  8. Updating real-time flood forecasts via the dynamic system response curve method

    NASA Astrophysics Data System (ADS)

    Si, Wei; Bao, Weimin; Gupta, Hoshin V.

    2015-07-01

    The accuracy of flood forecasts generated using spatially lumped hydrological models can be severely affected by errors in the estimates of areal mean rainfall. The quality of the latter depends both on the size and type of errors in point-based rainfall measurements, and on the density and spatial arrangement of rain gauges in the basin. Here we use error feedback correction, based on the dynamic system response curve (DSRC) method, to compute updated estimates of the rainfall inputs. The method is evaluated via synthetic and real-data cases, showing that the method works as theoretically expected. The ability of the method to improve the accuracy of real-time flood forecasts is then demonstrated using 20 basins of different sizes and having different rain gauge densities. We find that the degree of forecast improvement is more significant for larger basins and for basins with lower rain gauge density. The method is relatively simple to apply and can improve the accuracy and stability of real-time model predictions without increasing either model complexity and/or the number of model parameters.

  9. Single Vector Calibration System for Multi-Axis Load Cells and Method for Calibrating a Multi-Axis Load Cell

    NASA Technical Reports Server (NTRS)

    Parker, Peter A. (Inventor)

    2003-01-01

    A single vector calibration system is provided which facilitates the calibration of multi-axis load cells, including wind tunnel force balances. The single vector system provides the capability to calibrate a multi-axis load cell using a single directional load, for example loading solely in the gravitational direction. The system manipulates the load cell in three-dimensional space, while keeping the uni-directional calibration load aligned. The use of a single vector calibration load reduces the set-up time for the multi-axis load combinations needed to generate a complete calibration mathematical model. The system also reduces load application inaccuracies caused by the conventional requirement to generate multiple force vectors. The simplicity of the system reduces calibration time and cost, while simultaneously increasing calibration accuracy.

  10. Forecasting in an integrated surface water-ground water system: The Big Cypress Basin, South Florida

    NASA Astrophysics Data System (ADS)

    Butts, M. B.; Feng, K.; Klinting, A.; Stewart, K.; Nath, A.; Manning, P.; Hazlett, T.; Jacobsen, T.

    2009-04-01

    The South Florida Water Management District (SFWMD) manages and protects the state's water resources on behalf of 7.5 million South Floridians and is the lead agency in restoring America's Everglades - the largest environmental restoration project in US history. Many of the projects to restore and protect the Everglades ecosystem are part of the Comprehensive Everglades Restoration Plan (CERP). The region has a unique hydrological regime, with close connection between surface water and groundwater, and a complex managed drainage network with many structures. Added to the physical complexity are the conflicting needs of the ecosystem for protection and restoration, versus the substantial urban development with the accompanying water supply, water quality and flood control issues. In this paper a novel forecasting and real-time modelling system is presented for the Big Cypress Basin. The Big Cypress Basin includes 272 km of primary canals and 46 water control structures throughout the area that provide limited levels of flood protection, as well as water supply and environmental quality management. This system is linked to the South Florida Water Management District's extensive real-time (SCADA) data monitoring and collection system. Novel aspects of this system include the use of a fully distributed and integrated modeling approach and a new filter-based updating approach for accurately forecasting river levels. Because of the interaction between surface- and groundwater a fully integrated forecast modeling approach is required. Indeed, results for the Tropical Storm Fay in 2008, the groundwater levels show an extremely rapid response to heavy rainfall. Analysis of this storm also shows that updating levels in the river system can have a direct impact on groundwater levels.

  11. Development of the Brazilian Operational Ocean Forecast System with the OOFɛ Python engine for model ROMS

    NASA Astrophysics Data System (ADS)

    Marta-Almeida, Martinho; Cirano, Mauro; Pereira, Janini; Ruiz-Villarreal, Manuel

    2010-05-01

    The first implementation of an automatic operational ocean modeling system of the brazilian oceanic region was created and is under continuous development. The operational system is a joint effort between a group of institutions in a research and development consortium called Oceanographic Modelling and Research Network (with Portuguese acronym REMO). Among the objectives of this network is the contribution for a better understanding of the ocean, including mesoscale, shelf and tidal circulation, and to provide oceanographic forecasts for the Brazilian shelf/slope as support of the activities of the oil industry. The model underwent through a 9.5 years spinup being forced at the boundaries with climatological data from global simulations of the model OCCAM1-4, and at surface with data from NCEP (first 9 years) and GFS 1°. The operational stage started at the 1st of July 2009 and is producing daily analysis and 5 days forecasts. Currently the model uses OCCAM1-12 boundary climatologies and GFS 0.5° surface forcings. The ocean model being used is the Regional Ocean Modeling System, ROMS, an advanced and robust rapidly evolving comunity-code model. ROMS has been applied in deterministic simulations in a wide range of space and time scales and oceanic systems types. In terms of technical operations, the task needed for the operational ocean model to run, like the creation of inputs files, extraction of atmospheric data, as well as the control of the successfulness of the simulations and all the operational flow, is done with OOFɛ (Operational Ocean Forecast Engine), a collection of Python modules prepared to perform all the work required for the operational modeling system, including data visualisation. Due to its design, OOFɛ requires almost no human intervention, and except for some initial refinements and performance issues, OOFɛ is now working in a totally automatic manner.

  12. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  13. Developing a Heatwave Early Warning System for Sweden: Evaluating Sensitivity of Different Epidemiological Modelling Approaches to Forecast Temperatures

    PubMed Central

    Åström, Christofer; Ebi, Kristie L.; Langner, Joakim; Forsberg, Bertil

    2014-01-01

    Over the last two decades a number of heatwaves have brought the need for heatwave early warning systems (HEWS) to the attention of many European governments. The HEWS in Europe are operating under the assumption that there is a high correlation between observed and forecasted temperatures. We investigated the sensitivity of different temperature mortality relationships when using forecast temperatures. We modelled mortality in Stockholm using observed temperatures and made predictions using forecast temperatures from the European Centre for Medium-range Weather Forecasts to assess the sensitivity. We found that the forecast will alter the expected future risk differently for different temperature mortality relationships. The more complex models seemed more sensitive to inaccurate forecasts. Despite the difference between models, there was a high agreement between models when identifying risk-days. We find that considerations of the accuracy in temperature forecasts should be part of the design of a HEWS. Currently operating HEWS do evaluate their predictive performance; this information should also be part of the evaluation of the epidemiological models that are the foundation in the HEWS. The most accurate description of the relationship between high temperature and mortality might not be the most suitable or practical when incorporated into a HEWS. PMID:25546283

  14. CONTROL OF HYDROCARBON EMISSIONS FROM GASOLINE LOADING BY REFRIGERATION SYSTEMS

    EPA Science Inventory

    The report gives results of a study of the capabilities of refrigeration systems, operated at three temperatures, to control volatile organic compound (VOC) emissions from truck loading at bulk gasoline terminals. Achievable VOC emission rates were calculated for refrigeration sy...

  15. Power quality load management for large spacecraft electrical power systems

    NASA Technical Reports Server (NTRS)

    Lollar, Louis F.

    1988-01-01

    In December, 1986, a Center Director's Discretionary Fund (CDDF) proposal was granted to study power system control techniques in large space electrical power systems. Presented are the accomplishments in the area of power system control by power quality load management. In addition, information concerning the distortion problems in a 20 kHz ac power system is presented.

  16. Development of an Experimental African Drought Monitoring and Seasonal Forecasting System: A First Step towards a Global Drought Information System

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.

    2012-12-01

    Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought forecasts with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM system has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM system leverages off our U.S. drought monitoring and forecasting system (http://hydrology.princeton.edu/forecasting) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal forecast of drought, CFSv2 climate forecasts are bias corrected, downscaled and used as inputs to the VIC LSM as well as forecasts based on ESP and CPC official seasonal outlook. For Africa, data from a combination of remote sensing (TMPA-based precipitation, land cover characteristics) and GFS analysis fields (temperature and wind) are used to monitor drought using our soil moisture drought index as well as 1, 3 and and 6-month SPI. River discharge is also estimated at over 900 locations. Seasonal forecasts have been developed using CFSv2 climate forecasts following the approaches used over CONUS. We will discuss the performance of the system to evaluate the depiction of drought over various scales, from regional to the African continent, and over a number of years to capture multiple drought events. Furthermore, the hindcasts from the seasonal drought forecast system are analyzed to assess the ability of seasonal climate models to detect drought on-set and its recovery. Finally, we will discuss whether our ADM provides a pathway to a Global Drought Information System, a goal of the WCRP Drought Task Force.

  17. Rapid Retrieval and Assimilation of Ground Based GPS-Met Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts

    NASA Astrophysics Data System (ADS)

    Gutman, S.

    2003-04-01

    This year, 2003, marks the tenth anniversary of ground-based Global Positioning System meteorology. GPS-Met as we now know it started in 1992 with the definition of the essential techniques to retrieve integrated (total column) precipitable water vapor (IPW) from zenith-scaled neutral atmospheric signal delays (Bevis et al., 1992). It culminated with the GPS/Storm experiment in 1993, which demonstrated the ability to make IPW measurements with about the predicted accuracy under warm-weather conditions (Rocken et al., 1995). Since then, most of the major advances in GPS-Met data processing have been in the form of improved mapping functions (Niell, 1996), the estimation of GPS signal delays in an absolute (Duan et al., 1996) versus a relative sense (Rocken et al., 1993), and improved GPS satellite orbit accuracy with reduced latency (Fang et al., 1998). Experiments with other GPS-Met data processing techniques, such as the estimation of line-of-sight GPS signal delays using a double-difference to zero-difference technique described by Alber et al. (2000) and Braun et al. (2001) are noted, but lingering questions about the validity of this approach (Gutman, 2002), and not the potential value of a slant-path measurements per se, (as enumerated by MacDonald and Xie, 2001 or Ha et al., 2002) have thus far precluded its routine implementation at the National Oceanic and Atmospheric Administration Forecast Systems Laboratory (NOAA/FSL). Since 1994, NOAA/FSL has concentrated on evaluating the scientific and engineering bases of ground-based GPS-Met and assessing its utility for operational weather forecasting, climate monitoring, satellite calibration and validation, and improved differential GPS positioning and navigation. The term “rapid” in the title of this paper is defined as “available in time to be used for a specific application.” The requirement for high accuracy GPS-Met retrievals with lower latency is primarily driven by two factors: the trend toward shorter forecast cycles and higher spatial resolution in mesoscale numerical weather prediction (NWP) models, and the use by weather forecasters in subjective forecasting and/or model verification. GPS and ancillary surface meteorological observations, and improved satellite orbits, must be available on demand. Data processing hardware and techniques must provide GPS-Met retrievals in sufficient time to be assimilated into the current model cycle. Model data assimilation techniques must minimize the errors in estimating the initial state of a numerical forecast that come from spatial and temporal aliasing when interpolating discrete observations into an "analysis increment" field. While more GPS-Met retrievals can minimize horizontal aliasing, they can do little to minimize vertical aliasing that comes from assimilating any vertically integrated quantity (e.g. satellite radiances, zenith tropospheric signal delays, or GPS-IPW retrievals) into an NWP model. This is primarily because the forecast background error at a discrete vertical level must be estimated from the difference between observed and forecast integrated quantities. Absent the development of a new observing system or measurement technique, we must rely on improved data assimilation techniques, coupled with the more efficient use of complementary observing systems, to improve the three-dimensional description of moisture in the atmosphere. NOAA/FSL has conducted data denial experiments since 1998 to determine the statistical impact that GPS-IPW retrievals have on 3-hour moisture and precipitation forecasts in the central United States. Results from 5-years of experiments indicate more or less continuous improvement in forecast skill as the GPS-Met network expands. Improvements are observed in relative humidity forecast accuracy at all levels below 500 hPa, and all precipitation levels above “trace”. The impact steadily decreases with the length of the forecast; it is usually substantial from 0-3 hours, and negligible from 6-12 hours. The largest impacts usually occur during active weather, and usually manifest themselves as changes in the locations of boundaries such as fronts and dry lines.

  18. A Near Real-Time Monitoring System for NWP Forecast and Satellite-Based Products

    NASA Astrophysics Data System (ADS)

    Vasquez, L.; Peng, G.; Urzen, M.; Hankins, B.; Zhang, H.

    2012-12-01

    A data repository for collocated model forecasts and simulations and in-situ observations for Surface Fluxes Analysis (SurFA) project has been established at the US National Climatic Data Center (NCDC; www.ncdc.noaa.gov/thredds/surfa.html), which is also one of the data centers for the National Oceanic and Atmospheric Administration (NOAA)'s satellite and other climate data. The SurFA project is initiated by the World Climate Research Program (WCRP) Working Groups on Numerical Experimentation and on the Surface Fluxes (WGNE and WGSF), Observation & Assimilation Panel (WOAP), and the Ocean Observation Panel for Climate (OOPC) to evaluate the performance of high-resolution global Numerical Weather Prediction (NWP) forecast variables and satellite-based products used in computing surface fluxes such as surface winds against high quality in-situ observations. SPEC is a satellite product evaluation system that is developed at NCDC and designed to facilitate the integrated operational monitoring of NOAA's satellite and modeling systems and products. Currently, it has the capability of ingesting data via various types of data servers, co- locating fields in time and space of various data formats, and providing users with various data output formats including NetCDF format and basic analysis and visualization functionality. A prototype has been developed utilizing the existing capability and functionality of SPEC and the SurFA datasets to monitor the performance of NWP short-range forecast winds and satellite-derived winds by identifying potential outliers in near real-time and in an automatic fashion. An end-to-end system design and data flow will be outlined along with examples of its applications and future improvement. Such a system can be readily applied to other model variables.

  19. Synoptic scale forecast skill and systematic errors in the MASS 2.0 model. [Mesoscale Atmospheric Simulation System

    NASA Technical Reports Server (NTRS)

    Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K. F.

    1985-01-01

    The synoptic scale performance characteristics of MASS 2.0 are determined by comparing filtered 12-24 hr model forecasts to same-case forecasts made by the National Meteorological Center's synoptic-scale Limited-area Fine Mesh model. Characteristics of the two systems are contrasted, and the analysis methodology used to determine statistical skill scores and systematic errors is described. The overall relative performance of the two models in the sample is documented, and important systematic errors uncovered are presented.

  20. An assessment of the ECMWF tropical cyclone ensemble forecasting system and its use for insurance loss predictions

    NASA Astrophysics Data System (ADS)

    Aemisegger, F.; Martius, O.; Wüest, M.

    2010-09-01

    Tropical cyclones (TC) are amongst the most impressive and destructive weather systems of Earth's atmosphere. The costs related to such intense natural disasters have been rising in recent years and may potentially continue to increase in the near future due to changes in magnitude, timing, duration or location of tropical storms. This is a challenging situation for numerical weather prediction, which should provide a decision basis for short term protective measures through high quality medium range forecasts on the one hand. On the other hand, the insurance system bears great responsibility in elaborating proactive plans in order to face these extreme events that individuals cannot manage independently. Real-time prediction and early warning systems are needed in the insurance sector in order to face an imminent hazard and minimise losses. Early loss estimates are important in order to allocate capital and to communicate to investors. The ECMWF TC identification algorithm delivers information on the track and intensity of storms based on the ensemble forecasting system. This provides a physically based framework to assess the uncertainty in the forecast of a specific event. The performance of the ECMWF TC ensemble forecasts is evaluated in terms of cyclone intensity and location in this study and the value of such a physically-based quantification of uncertainty in the meteorological forecast for the estimation of insurance losses is assessed. An evaluation of track and intensity forecasts of hurricanes in the North Atlantic during the years 2005 to 2009 is carried out. Various effects are studied like the differences in forecasts over land or sea, as well as links between storm intensity and forecast error statistics. The value of the ECMWF TC forecasting system for the global re-insurer Swiss Re was assessed by performing insurance loss predictions using their in-house loss model for several case studies of particularly devastating events. The generally known problem of the intensity bias in tropical cyclone forecasts is a very critical aspect, when using the meteorological forecasts for insurance loss prediction in such a chain modelling approach.

  1. Forecasting recreational water quality standard violations with a linked hydrologic-hydronamic modeling system

    NASA Astrophysics Data System (ADS)

    Gronewold, A. D.; Ritzenthaler, A.; Fry, L. M.; Anderson, E. J.

    2012-12-01

    There is a clear need in the water resource and public health management communities to develop and test modeling systems which provide robust predictions of water quality and water quality standard violations, particularly in coastal communities. These predictions have the potential to supplement, or even replace, conventional human health protection strategies which (in the case of controlling public access to beaches, for example) are often based on day-old fecal indicator bacteria monitoring results. Here, we present a coupled modeling system which builds upon recent advancements in watershed-scale hydrological modeling and coastal hydrodynamic modeling, including the evolution of the Huron-Erie Connecting Waterways Forecasting System (HECWFS), developed through a partnership between NOAA's Great Lakes Environmental Research Laboratory (GLERL) and the University of Michigan Cooperative Institute for Limnology and Ecosystems Research (CILER). Our study is based on applying the modeling system to a popular beach in the metro-Detroit (Michigan, USA) area and implementing a routine shoreline monitoring program to help assess model forecasting skill. This research presents an important stepping stone towards the application of similar modeling systems in frequently-closed beaches throughout the Great Lakes region.

  2. Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

    NASA Astrophysics Data System (ADS)

    Lahmiri, Salim; Boukadoum, Mounir

    2015-08-01

    We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

  3. Accuracy of the Operational NOAA-EPA National Air Quality Forecasting System during Summer 2005 and 2006 in Philadelphia

    NASA Astrophysics Data System (ADS)

    Huff, A. K.; Ryan, W. F.; Bahrmann, C. P.

    2007-12-01

    The NOAA-EPA National Air Quality Forecasting System (NAQFS) is a numerical ozone forecast model that consists of a coupled version of NOAA's North American Mesoscale (NAM)-12 model and EPA's Community Multiscale Air Quality model (CMAQ). NAQFS runs twice daily at 0600 UTC and 1200 UTC and predicts hourly ozone abundances as mixing ratios in units of parts per billion (ppb). Model output is typically processed to provide 1-hour and 8-hour average ozone forecasts that correspond to the national ambient air quality standards (NAAQS) for ozone. Now in its third season as an operational model, NAQFS is designed to assist air quality meteorologists by providing accurate and dependable forecast guidance. Before air quality meteorologists are willing to rely upon a new tool like NAQFS to assist them in preparing forecasts for the public, however, the model must be evaluated in the typical operational setting for air quality forecasting - the metropolitan scale. To address this need, results will be presented from a pilot statistical study of Summer 2005 and 2006 NAQFS performance in the Philadelphia forecast area. The accuracy, bias, and skill of the model has been determined by comparing 8-hour average ozone forecasts from the 1200 UTC run of NAQFS to corresponding observed ozone values. Overall, the model shows the best skill in suburban areas, where ozone levels tend to peak across the metropolitan region. NAQFS was not reliable in 2005 during Code Orange and Red events, when ozone levels were greater than 84 ppb. In 2006, NAQFS accuracy increased during these cases, which was most likely a result of the NAM's changeover from the Eta model to the Weather and Forecasting (WRF) model. The most probable sources of error that impacted NAQFS performance in 2005 and 2006 will be discussed, including shortcomings in emissions databases and poorly characterized atmospheric chemistry. Preliminary results from the 2007 summer ozone season will also be provided.

  4. A novel load balancing method for hierarchical federation simulation system

    NASA Astrophysics Data System (ADS)

    Bin, Xiao; Xiao, Tian-yuan

    2013-07-01

    In contrast with single HLA federation framework, hierarchical federation framework can improve the performance of large-scale simulation system in a certain degree by distributing load on several RTI. However, in hierarchical federation framework, RTI is still the center of message exchange of federation, and it is still the bottleneck of performance of federation, the data explosion in a large-scale HLA federation may cause overload on RTI, It may suffer HLA federation performance reduction or even fatal error. Towards this problem, this paper proposes a load balancing method for hierarchical federation simulation system based on queuing theory, which is comprised of three main module: queue length predicting, load controlling policy, and controller. The method promotes the usage of resources of federate nodes, and improves the performance of HLA simulation system with balancing load on RTIG and federates. Finally, the experiment results are presented to demonstrate the efficient control of the method.

  5. Fuzzy Modeling for Analysis of Load Curve in Power System

    NASA Astrophysics Data System (ADS)

    Huang, Pei-Hwa; Tseng, Ta-Hsiu; Liu, Chien-Heng; Fan, Guang-Zhong

    The main purpose of this paper is to study the use of fuzzy modeling for the analysis of customer load characteristics in power system. A fuzzy model is a collection of fuzzy IF-THEN rules for describing the features or behaviors of the data set or system under study. In view of the nonlinear characteristics of customer load demand with respect to time, the method of fuzzy modeling is adopted for analyzing the studied daily load curves. Based on the Sugeno-type fuzzy model, various models with different numbers of modeling rules have been constructed for describing the investigated power curve. Sample results are demonstrated for illustrating the effectiveness of the fuzzy model in the study of power system load curves.

  6. Integrated loading rate determination for wastewater infiltration system sizing

    SciTech Connect

    Jenssen, P.D. . Centre for Soil and Environmental Research); Siegrist, R.L. )

    1991-01-01

    One of the principal parameters used in wastewater system design is the hydraulic loading rate. Historically the determination of the loading rate has been a straight forward process involving selection of a rate based on soil texture or water percolation rate. Research and experience over the past decade has provided additional insight into the complex processes occurring within wastewater-amended soil systems and has suggested the fallacy of this approach. A mean grain size vs. sorting (MESO) diagram constitutes a new basis for soil classification for wastewater infiltration system design. Crude characterization of the soil hydraulic properties is possible according to the MESO Diagram and loading rate as well as certain purification aspects can be assessed from the diagram. In this paper, an approach is described based on the MESO Diagram that integrates soil properties and wastewater pretreatment to yield a loading rate. 53 refs., 3 figs., 2 tabs.

  7. Strain Measurement System Developed for Biaxially Loaded Cruciform Specimens

    NASA Technical Reports Server (NTRS)

    Krause, David L.

    2000-01-01

    A new extensometer system developed at the NASA Glenn Research Center at Lewis Field measures test area strains along two orthogonal axes in flat cruciform specimens. This system incorporates standard axial contact extensometers to provide a cost-effective high-precision instrument. The device was validated for use by extensive testing of a stainless steel specimen, with specimen temperatures ranging from room temperature to 1100 F. In-plane loading conditions included several static biaxial load ratios, plus cyclic loadings of various waveform shapes, frequencies, magnitudes, and durations. The extensometer system measurements were compared with strain gauge data at room temperature and with calculated strain values for elevated-temperature measurements. All testing was performed in house in Glenn's Benchmark Test Facility in-plane biaxial load frame.

  8. Improved memory loading techniques for the TSRV display system

    NASA Technical Reports Server (NTRS)

    Easley, W. C.; Lynn, W. A.; Mcluer, D. G.

    1986-01-01

    A recent upgrade of the TSRV research flight system at NASA Langley Research Center retained the original monochrome display system. However, the display memory loading equipment was replaced requiring design and development of new methods of performing this task. This paper describes the new techniques developed to load memory in the display system. An outdated paper tape method for loading the BOOTSTRAP control program was replaced by EPROM storage of the characters contained on the tape. Rather than move a tape past an optical reader, a counter was implemented which steps sequentially through EPROM addresses and presents the same data to the loader circuitry. A cumbersome cassette tape method for loading the applications software was replaced with a floppy disk method using a microprocessor terminal installed as part of the upgrade. The cassette memory image was transferred to disk and a specific software loader was written for the terminal which duplicates the function of the cassette loader.

  9. Assimilating remote sensing observations across the terrestrial water cycle in a drought forecasting system

    NASA Astrophysics Data System (ADS)

    Andreadis, K.; Behrangi, A.; Das, N. N.; Fisher, J. B.; Granger, S. L.; Landerer, F. W.; Painter, T. H.; Turk, F. J.

    2013-12-01

    The application of data assimilation techniques in hydrologic studies has been gaining traction in the last 10-15 years. Most of these studies have focused on a single water cycle component, while few studies have examined methods of assimilating multiple observations from different sensors and of different hydrologic variables. The latter is challenging since any potential disparities in the observations could lead to suboptimal estimates after assimilation. The optimal estimates of hydrologic states, such as soil moisture, can be used as initial conditions for hydrologic forecasting systems. A multi-sensor and multi-variable data assimilation forecast system has been developed at JPL (RHEAS, Regional Hydrologic Extremes Assessment System) with an initial focus on forecasting drought characteristics. The core of the flexible RHEAS framework is the VIC hydrology model, while a number of satellite data products are used to drive or constrain RHEAS that correspond to different components of the water cycle. Precipitation is obtained from TRMM, CMORPH, NRLgeo, REFAME, and PERSIANN-CCS; ET from PT-JPL and PM-MOD16; snow covered area and dust radiative forcing in snow from MODSCAG and MODDRFS, respectively; soil moisture (where possible) from AMSR-E and SMOS; and groundwater change derived from GRACE. The initial study area includes two basins: the Sacramento-San Joaqin and the Upper Colorado River basins. The benefit of implementing RHEAS in these basins is the existence of in-situ measurements of rainfall, soil moisture and streamflow. The experiment starts with a baseline simulation using only in-situ measurements to drive the model, while a hindcast experiment is performed by predicting the hydrologic fluxes with a model driven by an ensemble of meteorological forcings. Additionally, initial results are presented from a hindcast experiment of the summer 2012 drought over the Midwest United States.

  10. A bracing system subjected to dynamic loading

    SciTech Connect

    Keller, M.D.; Khan, P.K.

    1996-09-01

    As part of the seismic upgrade at the Los Alamos National Laboratory (LANL), it was necessary to provide horizontal support at the second floor level of the Chemistry and Metallurgy Research (CMR) building. A steel strut connected these points to an anchor at the ground line. Two methods of anchoring the strut at the ground were considered in this initial study (AGRA, 1994). One solution was a 12 foot cube of concrete, equal in weight to the vertical upward force. That solution provided acceptable Demand/Capacity (D/C) ratios. The other method considered was a 4 foot diameter drilled caisson, 30 feet deep. The deflection at the second story support was too large, using this method, and second floor concrete members had D/C ratios greater than unity (overstressed). Using the cube as an anchor would cost in excess of $100,000 more than the estimated cost for the caissons. The caisson design is the subject of this paper. Although neither of the authors are geotechnical engineers, the senior author has 30 years experience working with the volcanic tuff in the Los Alamos area. Whenever unusual situations arose, the senior author worked in concert with geotechnical firms to arrive at reasonable engineering solutions. In the case of lateral loading on caissons in volcanic tuff, there was a steep learning curve for both the authors and for their geotechnical counterparts to measure and apply the known properties of the volcanic tuff. In this paper the knowledge gained during the development of the initial report and that learned to date will be shared.

  11. Gas loading system for LANL two-stage gas guns

    NASA Astrophysics Data System (ADS)

    Gibson, Lee; Bartram, Brian; Dattelbaum, Dana; Lang, John; Morris, John

    2015-06-01

    A novel gas loading system was designed for the specific application of remotely loading high purity gases into targets for gas-gun driven plate impact experiments. The high purity gases are loaded into well-defined target configurations to obtain Hugoniot states in the gas phase at greater than ambient pressures. The small volume of the gas samples is challenging, as slight changing in the ambient temperature result in measurable pressure changes. Therefore, the ability to load a gas gun target and continually monitor the sample pressure prior to firing provides the most stable and reliable target fielding approach. We present the design and evaluation of a gas loading system built for the LANL 50 mm bore two-stage light gas gun. Targets for the gun are made of 6061 Al or OFHC Cu, and assembled to form a gas containment cell with a volume of approximately 1.38 cc. The compatibility of materials was a major consideration in the design of the system, particularly for its use with corrosive gases. Piping and valves are stainless steel with wetted seals made from Kalrez and Teflon. Preliminary testing was completed to ensure proper flow rate and that the proper safety controls were in place. The system has been used to successfully load Ar, Kr, Xe, and anhydrous ammonia with purities of up to 99.999 percent. The design of the system, and example data from the plate impact experiments will be shown. LA-UR-15-20521

  12. Early warning system to forecast rainfall-induced landslides in Italy (SANF)

    NASA Astrophysics Data System (ADS)

    Rossi, Mauro

    2010-05-01

    Harmful landslide events are frequent in Italy. In this Country, in 2009 rainfall-induced landslides have caused at least 208 casualties, in multiple landslide events. In the period 1950-2009, the average yearly number of harmful landslide events has exceeded 35, most of which rainfall-induced landslide events. These figures indicate the impact that rainfall-induced landslides have on the population of Italy. The Italian national Department for Civil Protection (DPC), an Office of the Prime Minister, and the Research Institute for Geo-Hydrological Protection (IRPI), of the Italian National Research Council (CNR), are designing and implementing a prototype system to forecast the possible occurrence of rainfall-induced landslides in Italy. The system is based on two components. The first component consists of: (i) a set of national, regional, and local rainfall thresholds (of the intensity-duration (ID) type) for possible landslide imitation, (ii) a database of sub-hourly rainfall measurements obtained by a network of 1950 rain gauges in Italy, and (iii) quantitative rainfall forecasts acquired through numerical modelling. Every day, and for each individual rain gauge, the system compares the measured and the forecasted rainfall amounts against pre-defined thresholds, and assigns to each rain gauge a probability for possible landslide occurrence. This information is used to prepare synoptic-scale maps showing where rainfall-induced landslides are expected, in a period of time. The second component of the system consists of synoptic assessments of landslide hazard and risk in Italy, including small-scale zoning maps. The assessments are obtained through statistical modelling of thematic and environmental information, including national catalogues of historical landslides and of historical landslides with human consequences in Italy, in the period 1900-2005. Combination of the hazard and risk zonations with the daily forecasts for possible landslide occurrence, allows the Italian national Department for Civil Protection for an improved management of potential landslide risk conditions in Italy. To further facilitate this challenging task, the different maps are displayed using Web-GIS technology.

  13. Forecasting Data from Periodic Systems of Molecules, and Other Systematics, Using Regression and Neural Networks

    NASA Astrophysics Data System (ADS)

    Hefferlin, Ray

    2006-06-01

    Early interpolations of intensity constants led to constructing a periodic system of diatomic molecules, analogous to the element periodic chart, and testing it against data of many properties. The axes of this system (the period and group numbers of the two atoms) have been employed to forecast various data using regression and neural networks. Kong has constructed a periodic system for triatomic molecules. Periodic behavior is also seen in many-atom species and data forecasts have resulted. The axes employed are the number of substituents (e.g. of halogens substituted in benzenes) or of added ligands (e.g. of oxides of transition metals); and the period number of the central, substituted, or ligand species. It is hoped that the spectroscopists will suggest planetary, stellar, and interstellar molecules for which estimated data, obtainable with these methods, are of interest to their community.R. Hefferlin and L. A. Kuznetsova, “Systematics of Diatomic Molecular Transition Moments,” J. Quant. Spectr. Radiative Transf. 62 (1999) 765-774.Ray Hefferlin, W. Bradford Davis, and Jason Ileto, “An Atlas of Forecasted Molecular Data I: Internuclear Separations of Main-Group and Transition-Metal Neutral Gas-Phase Diatomic Molecules in the Ground State,” J. Chem. Inf. Comput. Sci. 43 (2002) 622-628.W. Bradford Davis and Ray Hefferlin, “An Atlas of Forecasted Molecular Data II: Vibration frequencies of Main-Group and Transition-Metal Neutral Gas-Phase Diatomic Molecules in the Ground State,” J. Chem. Inf. Comput. Sci., in press.F.-A. Kong, in R. Hefferlin, Periodic Systems of Molecules and their Relation to the Systematic Analysis of Molecular Data, Edwin Mellen Press, Lewiston, New York, 1989, Chapter 11.Ken Luk, Ray Hefferlin, and Gabriel Johnson, “How Deep in Molecular Space can Periodicity be Found?” World Science and Engineering Academy and Society 9th WSEAS CSCC Multiconference, Vouliagmeni, Athens, Greece, July 13, 2005.

  14. Measurements of individual parachute loads in a clustered parachute system

    SciTech Connect

    Behr, V.L.

    1989-01-01

    When developing any parachute system, it is necessary to know the loads produced by the parachute throughout the deployment process. A major concern with a clustered parachute system is the nonconcurrent inflation of the individual parachutes. If this nonconcurrent inflation produces sufficiently large asymmetric loads, the design loads may be exceeded. In the past, load data have frequently been inferred from acceleration data of the parachute test vehicle. However, due to vehicle angle of attack and attitude relative to the longitudinal axis of the parachute, this method can give erroneous results. Thus, in the development of parachute systems, it is desirable to have an instrumentation system which can directly measure the load produced by each parachute. Due to the environment, this system must be very rugged and have minimal space requirements. Such a system has been designed and incorporated into a test program at Sandia National Laboratories. The system is described and representative test results are given to demonstrate the usefulness of such a system in a development program. 3 refs., 6 figs.

  15. Forecasting the mixed layer depth in the north east Atlantic: an ensemble approach, with uncertainties based on data from operational oceanic systems

    NASA Astrophysics Data System (ADS)

    Drillet, Y.; Lellouche, J. M.; Levier, B.; Drévillon, M.; Le Galloudec, O.; Reffray, G.; Regnier, C.; Greiner, E.; Clavier, M.

    2014-06-01

    Operational systems operated by Mercator Océan provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast and uncertainty estimates. This study focuses on the mixed layer depth in the North East Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Océan operational systems provide daily forecasts 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 forecasted by the systems. A statistical approach and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are in any case better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4 day forecast. The impact of atmospheric forecast error, and for the wind field in particular, is also quantified in terms of the forecast time delay and the intensity of mixing or stratification events.

  16. Impact of Interactive Aerosol on the African Easterly Jet in the NASA GEOS-5 Global Forecasting System

    NASA Technical Reports Server (NTRS)

    Reale, O.; Lau, K. M.; da Silva, A.

    2010-01-01

    The real-time treatment of interactive realistically varying aerosol in a global operational forecasting system, as opposed to prescribed (fixed or climatologically varying) aerosols, is a very difficult challenge that only recently begins to be addressed. Experiment results from a recent version of the NASA GEOS-5 forecasting system, inclusive of interactive aerosol treatment, are presented in this work. Four sets of 30 5-day forecasts are initialized from a high quality set of analyses previously produced and documented to cover the period from 15 August to 16 September 2006, which corresponds to the NASA African Monsoon Multidisciplinary Analysis (NAMMA) observing campaign. The four forecast sets are at two different horizontal resolutions and with and without interactive aerosol treatment. The net impact of aerosol, at times in which there is a strong dust outbreak, is a temperature increase at the dust level and decrease in the near-surface levels, in complete agreement with previous observational and modeling studies. Moreover, forecasts in which interactive aerosols are included depict an African Easterly (AEJ) at slightly higher elevation, and slightly displace northward, with respect to the forecasts in which aerosols are not include. The shift in the AEJ position goes in the direction of observations and agrees with previous results.

  17. The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

    NASA Technical Reports Server (NTRS)

    Prive, Nikki C.; Errico, Ronald M.

    2013-01-01

    A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

  18. Towards a flash flood early warning system through hydrological simulation of probabilistic ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Alfieri, Lorenzo; Thielen Del Pozo, Jutta

    2010-05-01

    In this work we test a flash flood early warning system based on state-of-the-art probabilistic weather forecasting input data. We make use of the Limited area Ensemble Prediction System (LEPS) provided by the Consortium for Small scale Modeling (COSMO). COSMO-LEPS ensembles are fed into a distributed hydrological model, to obtain discharge estimates. Likewise, discharge climatology is created from a continuous meteorological dataset based on 30-year COSMO-LEPS hindcasts, and used as reference to detect threshold exceedance in the operational ensemble hydrographs. Coherent reference climatology is particularly useful for flash flood events, as they often take place in small watersheds, where no gauge measurements are available. The concept of persistence of meteorological forecasts is also tested as a method to improve the detection of severe events. Starting from the operational 5-km simulation at the European scale, when a signal for possible flash flooding is detected a regional catchment-scale simulation is activated on a finer spatial scale (1 km grid). Two targeted analyses are carried out to investigate: a) an automatic rule to activate the fine-scale analysis, and b) the influence of initial conditions on the estimated hydrographs, and in turn on threshold exceedances. The Gardon d'Anduze catchment, in the south of France, is chosen as a case study. A number of simulations are performed and results are analyzed and discussed. Our findings show that flash floods can sometimes be detected with a considerable lead time, especially if compared to the response time of the catchments where these phenomena take place. However, the amount of uncertainty related to the forecast is considerable, therefore the choice of appropriate thresholds for flash flood detection is of crucial importance and should account for the maximum acceptable number of either false alarms and undetected events.

  19. Towards a Load Balancing Middleware for Automotive Infotainment Systems

    NASA Astrophysics Data System (ADS)

    Khaluf, Yara; Rettberg, Achim

    In this paper a middleware for distributed automotive systems is developed. The goal of this middleware is to support the load bal- ancing and service optimization in automotive infotainment and entertainment systems. These systems provide navigation, telecommunication, Internet, audio/video and many other services where a kind of dynamic load balancing mechanisms in addition to service quality optimization mechanisms will be applied by the developed middleware in order to improve the system performance and also at the same time improve the quality of services if possible.

  20. Forecast experiments with the NASA/GLA stratospheric/tropospheric data assimilation system

    NASA Technical Reports Server (NTRS)

    Takano, Kenji; Baker, Wayman E.; Kalnay, Eugenia; Lamich, David J.; Rosenfield, Joan E.

    1987-01-01

    For the first time, a four-dimensional stratospheric/tropospheric data assimilation system with a top analysis level at 0.4 mb has been developed and used to produce physically consistent gridded analyses for the stratosphere as well as the troposphere for a period during the First GARP Global Experiment (FGGE) and Limb Infrared Monitor of the Stratosphere (LIMS) (November 1978-May 1979). The system consists of a two-dimensional optimum interpolation analysis with 18 mandatory pressure levels and a 19-level fourth order stratospheric/tropospheric general circulation model with a horizontal resolution of 4 (latitude) by 5 deg (longitude) and a top at 0.3 mb. The system allows the utilization of stratospheric data including LIMS, Tiros-N retrievals, rocketsondes and vertical temperature profile radiometer soundings in addition to the other FGGE level 2b data. These data are analyzed every six hours. In order to examine the quality of the analyzed data, forecast experiments starting from different analyses are performed for the period of the stratospheric sudden warming of late February 1979. The results indicate that by employing the present four-dimensional assimilation approach, the medium-range forecast skill for this event is improved.

  1. Forecasting and visualization of wildfires in a 3D geographical information system

    NASA Astrophysics Data System (ADS)

    Castrillón, M.; Jorge, P. A.; López, I. J.; Macías, A.; Martín, D.; Nebot, R. J.; Sabbagh, I.; Quintana, F. M.; Sánchez, J.; Sánchez, A. J.; Suárez, J. P.; Trujillo, A.

    2011-03-01

    This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open-source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connections of several users for monitoring a real wildfire event. The system is able to make a realistic composition of what is really happening in the area of the wildfire with dynamic 3D objects and location of human and material resources in real time, providing a new perspective to analyze the wildfire information. The user is enabled to simulate and visualize the propagation of a fire on the terrain integrating at the same time spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-to-date meteorological data. The client application implements a realistic 3D visualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system.

  2. Coordination of Economic Load Dispatch and Load Frequency Control for Interconnected Power System

    NASA Astrophysics Data System (ADS)

    Shankar, R.; Chatterjee, K.; Chatterjee, T. K.

    2015-03-01

    This paper deals with the coordination of economic load dispatch and load frequency control concepts of the interconnected power system. The total change in the particular control area is shared by each unit according to their participation factor obtained from the calculation of the economic load dispatch. In this work, two control areas are considered, the first control area contains the combination of hydro, thermal and gas generating unit and in the second control area, it contains the combination of the thermal and hydro generating units. Integral controller is used for secondary controller for load frequency control mechanism. A digital simulation is used in conjunction with the genetic algorithm (GA) technique to determine the optimum parameters of the individual gain of integral controller. Three different types of performance indices are considered to measure the appropriateness of the proposed controller. The optimum values of the gains improve the dynamic performance of the controller and reduce the overshoot and maximum frequency deviation and net tie-line power flow deviation error for a particular load change. To show the effectiveness of the proposed controller, simulation result is shown in result and discussion section.

  3. Material fatigue data obtained by card-programmed hydraulic loading system

    NASA Technical Reports Server (NTRS)

    Davis, W. T.

    1967-01-01

    Fatigue tests using load distributions from actual loading histories encountered in flight are programmed on punched electronic accounting machine cards. With this hydraulic loading system, airframe designers can apply up to 55 load levels to a test specimen.

  4. TRAVEL FORECASTER

    NASA Technical Reports Server (NTRS)

    Mauldin, L. E.

    1994-01-01

    Business travel planning within an organization is often a time-consuming task. Travel Forecaster is a menu-driven, easy-to-use program which plans, forecasts cost, and tracks actual vs. planned cost for business-related travel of a division or branch of an organization and compiles this information into a database to aid the travel planner. The program's ability to handle multiple trip entries makes it a valuable time-saving device. Travel Forecaster takes full advantage of relational data base properties so that information that remains constant, such as per diem rates and airline fares (which are unique for each city), needs entering only once. A typical entry would include selection with the mouse of the traveler's name and destination city from pop-up lists, and typed entries for number of travel days and purpose of the trip. Multiple persons can be selected from the pop-up lists and multiple trips are accommodated by entering the number of days by each appropriate month on the entry form. An estimated travel cost is not required of the user as it is calculated by a Fourth Dimension formula. With this information, the program can produce output of trips by month with subtotal and total cost for either organization or sub-entity of an organization; or produce outputs of trips by month with subtotal and total cost for international-only travel. It will also provide monthly and cumulative formats of planned vs. actual outputs in data or graph form. Travel Forecaster users can do custom queries to search and sort information in the database, and it can create custom reports with the user-friendly report generator. Travel Forecaster 1.1 is a database program for use with Fourth Dimension Runtime 2.1.1. It requires a Macintosh Plus running System 6.0.3 or later, 2Mb of RAM and a hard disk. The standard distribution medium for this package is one 3.5 inch 800K Macintosh format diskette. Travel Forecaster was developed in 1991. Macintosh is a registered trademark of Apple Computer, Inc. Fourth Dimension is a registered trademark of Acius, Inc.

  5. Atmospheric pressure loading effects on Global Positioning System coordinate determinations

    NASA Technical Reports Server (NTRS)

    Vandam, Tonie M.; Blewitt, Geoffrey; Heflin, Michael B.

    1994-01-01

    Earth deformation signals caused by atmospheric pressure loading are detected in vertical position estimates at Global Positioning System (GPS) stations. Surface displacements due to changes in atmospheric pressure account for up to 24% of the total variance in the GPS height estimates. The detected loading signals are larger at higher latitudes where pressure variations are greatest; the largest effect is observed at Fairbanks, Alaska (latitude 65 deg), with a signal root mean square (RMS) of 5 mm. Out of 19 continuously operating GPS sites (with a mean of 281 daily solutions per site), 18 show a positive correlation between the GPS vertical estimates and the modeled loading displacements. Accounting for loading reduces the variance of the vertical station positions on 12 of the 19 sites investigated. Removing the modeled pressure loading from GPS determinations of baseline length for baselines longer than 6000 km reduces the variance on 73 of the 117 baselines investigated. The slight increase in variance for some of the sites and baselines is consistent with expected statistical fluctuations. The results from most stations are consistent with approximately 65% of the modeled pressure load being found in the GPS vertical position measurements. Removing an annual signal from both the measured heights and the modeled load time series leaves this value unchanged. The source of the remaining discrepancy between the modeled and observed loading signal may be the result of (1) anisotropic effects in the Earth's loading response, (2) errors in GPS estimates of tropospheric delay, (3) errors in the surface pressure data, or (4) annual signals in the time series of loading and station heights. In addition, we find that using site dependent coefficients, determined by fitting local pressure to the modeled radial displacements, reduces the variance of the measured station heights as well as or better than using the global convolution sum.

  6. 1994 Pacific Northwest Loads and Resources Study.

    SciTech Connect

    United States. Bonneville Power Administration.

    1994-12-01

    The 1994 Pacific Northwest Loads and Resources Study presented herein establishes a picture of how the agency is positioned today in its loads and resources balance. It is a snapshot of expected resource operation, contractual obligations, and rights. This study does not attempt to present or analyze future conservation or generation resource scenarios. What it does provide are base case assumptions from which scenarios encompassing a wide range of uncertainties about BPA`s future may be evaluated. The Loads and Resources Study is presented in two documents: (1) this summary of Federal system and Pacific Northwest region loads and resources and (2) a technical appendix detailing the loads and resources for each major Pacific Northwest generating utility. This analysis updates the 1993 Pacific Northwest Loads and Resources Study, published in December 1993. In this loads and resources study, resource availability is compared with a range of forecasted electricity consumption. The Federal system and regional analyses for medium load forecast are presented.

  7. Deployable System for Crash-Load Attenuation

    NASA Technical Reports Server (NTRS)

    Kellas, Sotiris; Jackson, Karen E.

    2007-01-01

    An externally deployable honeycomb structure is investigated with respect to crash energy management for light aircraft. The new concept utilizes an expandable honeycomb-like structure to absorb impact energy by crushing. Distinguished by flexible hinges between cell wall junctions that enable effortless deployment, the new energy absorber offers most of the desirable features of an external airbag system without the limitations of poor shear stability, system complexity, and timing sensitivity. Like conventional honeycomb, once expanded, the energy absorber is transformed into a crush efficient and stable cellular structure. Other advantages, afforded by the flexible hinge feature, include a variety of deployment options such as linear, radial, and/or hybrid deployment methods. Radial deployment is utilized when omnidirectional cushioning is required. Linear deployment offers better efficiency, which is preferred when the impact orientation is known in advance. Several energy absorbers utilizing different deployment modes could also be combined to optimize overall performance and/or improve system reliability as outlined in the paper. Results from a series of component and full scale demonstration tests are presented as well as typical deployment techniques and mechanisms. LS-DYNA analytical simulations of selected tests are also presented.

  8. A Real-time, Two-way, Coupled, Refined, Forecasting System to Predict Coastal Storm Impacts

    NASA Astrophysics Data System (ADS)

    Armstrong, B. N.; Warner, J. C.; Signell, R. P.

    2012-12-01

    Storms are one of the primary environmental forces causing coastal change. These discrete events often produce large waves, storm surges, and flooding, resulting in coastal erosion. In addition, strong storm-generated currents may pose threats to life, property, and navigation. The ability to predict these events, their location, duration, and magnitude allows resource managers to better prepare for the storm impacts as well as guide post-storm survey assessments and recovery efforts. As a step towards increasing our event prediction capability we have developed an automated system to run a daily forecast of the Coupled Ocean - Atmosphere - Wave - Sediment Transport (COAWST) Modeling System, which includes the ocean model ROMS and the wave model SWAN. Management of the system is controlled on a high-performance computing cluster. Data required to drive the modeling system include wave, wind, atmospheric surface inputs, and climatology fields obtained from other modeling products. The Unidata Internet Data Distribution/Local Data Manager, nctoolbox and other NetCDF tools are used to access large data sets from the National Centers for Environmental Prediction (NCEP) and the Nomads http://nomads.ncep.noaa.gov site. The data are used to create forcings and boundary conditions for the ROMS and SWAN models that run on both a 5 km US east coast and a 1 km nested region in the Gulf of Maine. Improvements in the modeling system, data acquisition, and visualization methods required for the forecasting system are described. Results of the newly coupled and refined system show improvement for the prediction of the free surface due to the increased resolution from the grid refinement in the Bay of Fundy. The surface currents of the refined system are more consistent with climatology. The surface waves are permitted to interact with the surface currents and show tidal oscillations at certain locations. Additionally, wave heights during storm events are modified by wave/current interactions associated with storm-generated currents.

  9. Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

    NASA Astrophysics Data System (ADS)

    Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

    2014-05-01

    While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

  10. Systematic evaluation of autoregressive error models as post-processors for a probabilistic streamflow forecast system

    NASA Astrophysics Data System (ADS)

    Morawietz, Martin; Xu, Chong-Yu; Gottschalk, Lars; Tallaksen, Lena

    2010-05-01

    A post-processor that accounts for the hydrologic uncertainty in a probabilistic streamflow forecast system is necessary to account for the uncertainty introduced by the hydrological model. In this study different variants of an autoregressive error model that can be used as a post-processor for short to medium range streamflow forecasts, are evaluated. The deterministic HBV model is used to form the basis for the streamflow forecast. The general structure of the error models then used as post-processor is a first order autoregressive model of the form dt = αdt-1 + σɛt where dt is the model error (observed minus simulated streamflow) at time t, α and σ are the parameters of the error model, and ɛt is the residual error described through a probability distribution. The following aspects are investigated: (1) Use of constant parameters α and σ versus the use of state dependent parameters. The state dependent parameters vary depending on the states of temperature, precipitation, snow water equivalent and simulated streamflow. (2) Use of a Standard Normal distribution for ɛt versus use of an empirical distribution function constituted through the normalized residuals of the error model in the calibration period. (3) Comparison of two different transformations, i.e. logarithmic versus square root, that are applied to the streamflow data before the error model is applied. The reason for applying a transformation is to make the residuals of the error model homoscedastic over the range of streamflow values of different magnitudes. Through combination of these three characteristics, eight variants of the autoregressive post-processor are generated. These are calibrated and validated in 55 catchments throughout Norway. The discrete ranked probability score with 99 flow percentiles as standardized thresholds is used for evaluation. In addition, a non-parametric bootstrap is used to construct confidence intervals and evaluate the significance of the results. The main findings of the study are: (1) Error models with state dependent parameters perform significantly better than corresponding models with constant parameters. (2) Error models using empirical distribution functions perform significantly better than corresponding models using a Standard Normal distribution. (3) For error models with constant parameters, those with logarithmic transformation perform significantly better than those with square root transformation. However, for models with state dependent parameters, this significance disappears and there is no difference in the performance of the logarithmic versus the square root transformation. The explanation is found in the flexibility that is introduced with the state dependent parameters which can account for and alleviate the more non-homoscedastic behaviour that is found for the square root transformation. The findings are derived from the application of the error models to Norwegian catchments and with the HBV model as the deterministic rainfall runoff model. However, it is anticipated that similar findings can be made in other regions and with other rainfall runoff models. Thus, the findings provide guidelines on how to construct autoregressive error models as post-processors in probabilistic streamflow forecast systems. In addition, the study gives an example on the application of bootstrap to test the significance of differences of the forecast evaluation measures for continuous probabilistic forecasts.

  11. Engine System Loads Analysis Compared to Hot-Fire Data

    NASA Technical Reports Server (NTRS)

    Frady, Gregory P.; Jennings, John M.; Mims, Katherine; Brunty, Joseph; Christensen, Eric R.; McConnaughey, Paul R. (Technical Monitor)

    2002-01-01

    Early implementation of structural dynamics finite element analyses for calculation of design loads is considered common design practice for high volume manufacturing industries such as automotive and aeronautical industries. However with the rarity of rocket engine development programs starts, these tools are relatively new to the design of rocket engines. In the NASA MC-1 engine program, the focus was to reduce the cost-to-weight ratio. The techniques for structural dynamics analysis practices, were tailored in this program to meet both production and structural design goals. Perturbation of rocket engine design parameters resulted in a number of MC-1 load cycles necessary to characterize the impact due to mass and stiffness changes. Evolution of loads and load extraction methodologies, parametric considerations and a discussion of load path sensitivities are important during the design and integration of a new engine system. During the final stages of development, it is important to verify the results of an engine system model to determine the validity of the results. During the final stages of the MC-1 program, hot-fire test results were obtained and compared to the structural design loads calculated by the engine system model. These comparisons are presented in this paper.

  12. The seamod.ro operational stochastic forecasting system of the Black Sea

    NASA Astrophysics Data System (ADS)

    Vandenbulcke, Luc; Barth, Alexander; Capet, Arthur; Gregoire, Marilaure

    2015-04-01

    Since the end of 2011, the GHER hydrodynamic model is ran daily to provide operational weekly forecasts of the Black Sea hydrodynamics, as well as the associated uncertainty. The model has ~4km horizontal resolution, 31 vertical layers, comprises 6 rivers with climatological fluxes, and is laterally forced with NCEP GFS atmospheric fields. The free model has been extensively validated in previous studies (Capet et al, 2012). Among others, it presents all the expected features in the Black Sea, and has also been shown to run 40 years without nudging or data assimilation while conserving total quantities and maintaining the mixed layer depth and the halocline. The operational model has been transformed into an ensemble, by perturbing t