Science.gov

Sample records for economic forecasting models

  1. Defense Economic Impact Modeling System (DEIMS). A New Concept in Economic Forecasting for Defense Expenditures.

    ERIC Educational Resources Information Center

    Blond, David L.

    The Defense Economic Impact Modeling System (DEIMS) analyzes the economic effect of defense expenditures on the United States economy by using a consistent, reliable framework of economic models and government policy assumptions. Planning information on defense requirements is also provided to private sector firms. The DEIMS allows the Department

  2. The economic burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model

    PubMed Central

    Grover, S A; Coupal, L; Zowall, H; Rajan, R; Trachtenberg, J; Elhilali, M; Chetner, M; Goldenberg, L

    2000-01-01

    BACKGROUND: We developed an economic model of prostate cancer management from diagnosis until death. We have used the Montreal Prostate Cancer Model to estimate the total economic burden of the disease in a cohort of Canadian men. METHODS: Using this Markov state-transition simulation model, we estimated the probability of prostate cancer, annual prostate cancer progression rates and associated direct medical costs according to patient age, tumour stage and grade, and treatment modalities in a 1997 cohort of Canadian men. The estimated lifetime costs of prostate cancer included the costs of clinical staging, initial treatments and complications, follow-up cancer therapies, routine outpatient care, and palliative care following metastatic disease. RESULTS: The clinical burden of prostate cancer forecasted using the model was similar to the projections of the National Cancer Institute. In the 1997 cohort of 5.8 million Canadian men between 40 and 80 years old, prostate cancer would be diagnosed in an estimated 701,491 men (12.1%) over their lifetime. Direct medical costs would total $9.76 billion, or $3.89 billion when discounted 5% annually. INTERPRETATION: The Montreal Prostate Cancer Model indicates that the economic burden of prostate cancer to Canada's health care system will be substantial. Further analyses are needed to identify the most efficient means of treating this disease. PMID:10763396

  3. The Economic Value of Air Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Anderson-Sumo, Tasha

    Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with bad air quality warning and forecast air quality forecasts. There is a difficulty presented with evaluating the economic utility of the forecasts. All may not comply and even with a low compliance rate of 5% and 72% as the average probability of detection of poor air quality days by the air quality models, we estimate that the forecasting program saves 412 lives or 412 million dollars per year for the region. The totals we found are great or greater than other typical yearly meteorological hazard programs such as tornado or hurricane forecasting and it is clear that the economic value of air quality forecasting in the Baltimore/Washington region is vital.

  4. An empirical investigation on the forecasting ability of mallows model averaging in a macro economic environment

    NASA Astrophysics Data System (ADS)

    Yin, Yip Chee; Hock-Eam, Lim

    2012-09-01

    This paper investigates the forecasting ability of Mallows Model Averaging (MMA) by conducting an empirical analysis of five Asia countries, Malaysia, Thailand, Philippines, Indonesia and China's GDP growth rate. Results reveal that MMA has no noticeable differences in predictive ability compared to the general autoregressive fractional integrated moving average model (ARFIMA) and its predictive ability is sensitive to the effect of financial crisis. MMA could be an alternative forecasting method for samples without recent outliers such as financial crisis.

  5. A Course in Economic Forecasting: Rationale and Content.

    ERIC Educational Resources Information Center

    Loomis, David G.; Cox, James E., Jr.

    2000-01-01

    Discusses four reasons why economic forecasting courses are important: (1) forecasting skills are in demand by businesses; (2) forecasters are in demand; (3) forecasting courses have positive externalities; (4) and forecasting provides a real-world context. Describes what should be taught in an economic forecasting course. (CMK)

  6. Evaluation and economic value of winter weather forecasts

    NASA Astrophysics Data System (ADS)

    Snyder, Derrick W.

    State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.

  7. 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.

  8. SSUSI Aurora Forecast Model

    NASA Astrophysics Data System (ADS)

    Hsieh, S. W.; Zhang, Y.; Schaefer, R. K.; Romeo, G.; Paxton, L.

    2013-12-01

    A new capability has been developed at JHU/APL for forecasting the global aurora quantities based on the DMSP SSUSI data and the TIMED/GUVI Global Aurora Model. The SSUSI Aurora Forecast Model predicts the electron energy flux, mean energy, and equatorward boundary in the auroral oval for up to 1 day or 15 DMSP orbits in advance. In our presentation, we will demonstrate this newly implemented capability and its results. The future improvement plan will be discussed too.

  9. Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

    PubMed Central

    Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

    2013-01-01

    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

  10. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

    PubMed

    Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

    2013-01-01

    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

  11. Estimating the economic value of wind forecasting to utilities

    SciTech Connect

    Milligan, M.R.; Miller, A.H.; Chapman, F.

    1995-05-01

    Utilities are sometimes reluctant to assign capacity value to wind plants because they are an intermittent resource. One of the potential difficulties is that the output of a wind plant may not be known in advance, thereby making it difficult for the utility to consider wind output as firm. In this paper, we examine the economics of an accurate wind forecast, and provide a range of estimates calculated by a production cost model and real utility data. We discuss how an accurate forecast will affect resource scheduling and the mechanism by which resource scheduling can benefit from an accurate wind forecast.

  12. Future Economics of Liver Transplantation: A 20-Year Cost Modeling Forecast and the Prospect of Bioengineering Autologous Liver Grafts.

    PubMed

    Habka, Dany; Mann, David; Landes, Ronald; Soto-Gutierrez, Alejandro

    2015-01-01

    During the past 20 years liver transplantation has become the definitive treatment for most severe types of liver failure and hepatocellular carcinoma, in both children and adults. In the U.S., roughly 16,000 individuals are on the liver transplant waiting list. Only 38% of them will receive a transplant due to the organ shortage. This paper explores another option: bioengineering an autologous liver graft. We developed a 20-year model projecting future demand for liver transplants, along with costs based on current technology. We compared these cost projections against projected costs to bioengineer autologous liver grafts. The model was divided into: 1) the epidemiology model forecasting the number of wait-listed patients, operated patients and postoperative patients; and 2) the treatment model forecasting costs (pre-transplant-related costs; transplant (admission)-related costs; and 10-year post-transplant-related costs) during the simulation period. The patient population was categorized using the Model for End-Stage Liver Disease score. The number of patients on the waiting list was projected to increase 23% over 20 years while the weighted average treatment costs in the pre-liver transplantation phase were forecast to increase 83% in Year 20. Projected demand for livers will increase 10% in 10 years and 23% in 20 years. Total costs of liver transplantation are forecast to increase 33% in 10 years and 81% in 20 years. By comparison, the projected cost to bioengineer autologous liver grafts is $9.7M based on current catalog prices for iPS-derived liver cells. The model projects a persistent increase in need and cost of donor livers over the next 20 years that's constrained by a limited supply of donor livers. The number of patients who die while on the waiting list will reflect this ever-growing disparity. Currently, bioengineering autologous liver grafts is cost prohibitive. However, costs will decline rapidly with the introduction of new manufacturing strategies and economies of scale. PMID:26177505

  13. Future Economics of Liver Transplantation: A 20-Year Cost Modeling Forecast and the Prospect of Bioengineering Autologous Liver Grafts

    PubMed Central

    Habka, Dany; Mann, David; Landes, Ronald; Soto-Gutierrez, Alejandro

    2015-01-01

    During the past 20 years liver transplantation has become the definitive treatment for most severe types of liver failure and hepatocellular carcinoma, in both children and adults. In the U.S., roughly 16,000 individuals are on the liver transplant waiting list. Only 38% of them will receive a transplant due to the organ shortage. This paper explores another option: bioengineering an autologous liver graft. We developed a 20-year model projecting future demand for liver transplants, along with costs based on current technology. We compared these cost projections against projected costs to bioengineer autologous liver grafts. The model was divided into: 1) the epidemiology model forecasting the number of wait-listed patients, operated patients and postoperative patients; and 2) the treatment model forecasting costs (pre-transplant-related costs; transplant (admission)-related costs; and 10-year post-transplant-related costs) during the simulation period. The patient population was categorized using the Model for End-Stage Liver Disease score. The number of patients on the waiting list was projected to increase 23% over 20 years while the weighted average treatment costs in the pre-liver transplantation phase were forecast to increase 83% in Year 20. Projected demand for livers will increase 10% in 10 years and 23% in 20 years. Total costs of liver transplantation are forecast to increase 33% in 10 years and 81% in 20 years. By comparison, the projected cost to bioengineer autologous liver grafts is $9.7M based on current catalog prices for iPS-derived liver cells. The model projects a persistent increase in need and cost of donor livers over the next 20 years that’s constrained by a limited supply of donor livers. The number of patients who die while on the waiting list will reflect this ever-growing disparity. Currently, bioengineering autologous liver grafts is cost prohibitive. However, costs will decline rapidly with the introduction of new manufacturing strategies and economies of scale. PMID:26177505

  14. Ups and downs of economics and econophysics — Facebook forecast

    NASA Astrophysics Data System (ADS)

    Gajic, Nenad; Budinski-Petkovic, Ljuba

    2013-01-01

    What is econophysics and its relationship with economics? What is the state of economics after the global economic crisis, and is there a future for the paradigm of market equilibrium, with imaginary perfect competition and rational agents? Can the next paradigm of economics adopt important assumptions derived from econophysics models: that markets are chaotic systems, striving to extremes as bubbles and crashes show, with psychologically motivated, statistically predictable individual behaviors? Is the future of econophysics, as predicted here, to disappear and become a part of economics? A good test of the current state of econophysics and its methods is the valuation of Facebook immediately after the initial public offering - this forecast indicates that Facebook is highly overvalued, and its IPO valuation of 104 billion dollars is mostly the new financial bubble based on the expectations of unlimited growth, although it’s easy to prove that Facebook is close to the upper limit of its users.

  15. Computerized Enrollment Driven Financial Forecasting Model.

    ERIC Educational Resources Information Center

    Sarvella, John R.

    An interactive, computerized model developed for Old Dominion University utilizes university historical data, demographic characteristics, projected selected economic variables and population figures by various age groups and planning districts to forecast enrollment, financial projections, and future fiscal conditions of the institution. The

  16. Computerized Enrollment Driven Financial Forecasting Model.

    ERIC Educational Resources Information Center

    Sarvella, John R.

    An interactive, computerized model developed for Old Dominion University utilizes university historical data, demographic characteristics, projected selected economic variables and population figures by various age groups and planning districts to forecast enrollment, financial projections, and future fiscal conditions of the institution. The…

  17. Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint

    SciTech Connect

    Orwig, K.; Hodge, B. M.; Brinkman, G.; Ela, E.; Milligan, M.; Banunarayanan, V.; Nasir, S.; Freedman, J.

    2012-09-01

    Historically, a number of wind energy integration studies have investigated the value of using day-ahead wind power forecasts for grid operational decisions. These studies have shown that there could be large cost savings gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter-term (0 to 6-hour-ahead) wind power forecasts. In 2010, the Department of Energy and National Oceanic and Atmospheric Administration partnered to fund improvements in short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined, then the economic results of a production cost model simulation are analyzed.

  18. Forecast-based Interventions Can Reduce the Health and Economic Burden of Wildfires

    EPA Science Inventory

    We simulated public health forecast-based interventions during a wildfire smoke episode in rural North Carolina to show the potential for use of modeled smoke forecasts toward reducing the health burden and showed a significant economic benefit of reducing exposures. Daily and co...

  19. Economic benefits of improved meteorological forecasts - The construction industry

    NASA Technical Reports Server (NTRS)

    Bhattacharyya, R. K.; Greenberg, J. S.

    1976-01-01

    Estimates are made of the potential economic benefits accruing to particular industries from timely utilization of satellite-derived six-hour weather forecasts, and of economic penalties resulting from failure to utilize such forecasts in day-to-day planning. The cost estimate study is centered on the U.S. construction industry, with results simplified to yes/no 6-hr forecasts on thunderstorm activity and work/no work decisions. Effects of weather elements (thunderstorms, snow and sleet) on various construction operations are indicated. Potential dollar benefits for other industries, including air transportation and other forms of transportation, are diagrammed for comparison. Geosynchronous satellites such as STORMSAT, SEOS, and SMS/GOES are considered as sources of the forecast data.

  20. A forecasting model of gaming revenues in Clark County, Nevada

    SciTech Connect

    Edwards, B.; Bando, A.; Bassett, G.; Rosen, A.; Carlson, J.; Meenan, C.

    1992-04-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain.

  1. Economic indicators selection for crime rates forecasting using cooperative feature selection

    NASA Astrophysics Data System (ADS)

    Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

    2013-04-01

    Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

  2. Environmental forecasting and turbulence modeling

    NASA Astrophysics Data System (ADS)

    Hunt, J. C. R.

    This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a limited period of time into the future and over a limited space-time target, and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space-time targets, so that they only depend on the statistical averages of the initial and edge conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the reductionist methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or blocked anticyclones) to small scale vortices. These eigen states can be predicted from the reductionist models or may be modeled specifically, for example, in terms of self-organized critical phenomena. It is noted how in certain applications of turbulent modeling its methods are beginning to resemble those of environmental simulations, because of the trend to introduce on-line controls of the turbulent flows in advanced flows in advanced engineering fluid systems. In real time simulations, for both local environmental processes and these engineering systems, maximum information is needed about the likely flow patterns in order to optimize both the assimilation of limited real-time data and the use of limited real-time computing capacity. It is concluded that philosophical studies of how scientific models develop and of the concept of determinism in science are helpful in considering these complex issues.

  3. Forecast of future aviation fuels: The model

    NASA Technical Reports Server (NTRS)

    Ayati, M. B.; Liu, C. Y.; English, J. M.

    1981-01-01

    A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

  4. 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.

  5. Mental Models of Software Forecasting

    NASA Technical Reports Server (NTRS)

    Hihn, J.; Griesel, A.; Bruno, K.; Fouser, T.; Tausworthe, R.

    1993-01-01

    The majority of software engineers resist the use of the currently available cost models. One problem is that the mathematical and statistical models that are currently available do not correspond with the mental models of the software engineers. In an earlier JPL funded study (Hihn and Habib-agahi, 1991) it was found that software engineers prefer to use analogical or analogy-like techniques to derive size and cost estimates, whereas curren CER's hide any analogy in the regression equations. In addition, the currently available models depend upon information which is not available during early planning when the most important forecasts must be made.

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

    NASA Astrophysics Data System (ADS)

    Alessandrini, S.; Dav, 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.

  7. An empirical investigation on different methods of economic growth rate forecast and its behavior from fifteen countries across five continents

    NASA Astrophysics Data System (ADS)

    Yin, Yip Chee; Hock-Eam, Lim

    2012-09-01

    Our empirical results show that we can predict GDP growth rate more accurately in continent with fewer large economies, compared to smaller economies like Malaysia. This difficulty is very likely positively correlated with subsidy or social security policies. The stage of economic development and level of competiveness also appears to have interactive effects on this forecast stability. These results are generally independent of the forecasting procedures. Countries with high stability in their economic growth, forecasting by model selection is better than model averaging. Overall forecast weight averaging (FWA) is a better forecasting procedure in most countries. FWA also outperforms simple model averaging (SMA) and has the same forecasting ability as Bayesian model averaging (BMA) in almost all countries.

  8. Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value

    NASA Astrophysics Data System (ADS)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2013-04-01

    The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more beneficial to the wind energy industry of Ireland.

  9. Transport models for numerical forecast

    NASA Technical Reports Server (NTRS)

    Burk, Stephen D.

    1987-01-01

    The explosive growth of computing power, coupled with scientific and technological emphasis on a national scale, has led to significant major advances in operational numerical weather prediction (NWP) during the last two decades. There are about half a dozen major centers around the world running global NWP models operationally. Many more countries have operational hemispheric or limited-area models which provide weather forecasts. The global models typically have several hundred kilometer resolution, while the limited-area models usually have horizontal spacing of 50 to 100 km. Given the pace of burgeoning growth in this area, it seems warranted to occasionally take an overview of aspects of the field common to all modelers. Here, a brief look is taken at the nature of subgrid scale turbulence transport parameterization, and some of the difficulties pertaining thereto, with particular emphasis on operational NWP models.

  10. 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.

  11. Essays on forecasting stationary and nonstationary economic time series

    NASA Astrophysics Data System (ADS)

    Bachmeier, Lance Joseph

    This dissertation consists of three essays. Chapter II considers the question of whether M2 growth can be used to forecast inflation at horizons of up to ten years. A vector error correction (VEC) model serves as our benchmark model. We find that M2 growth does have marginal predictive content for inflation at horizons of more than two years, but only when allowing for cointegration and when the cointegrating rank and vector are specified a priori. When estimating the cointegration vector or failing to impose cointegration, there is no longer evidence of causality running from M2 growth to inflation at any forecast horizon. Finally, we present evidence that M2 needs to be redefined, as forecasts of the VEC model using data on M2 observed after 1993 are worse than the forecasts of an autoregressive model of inflation. Chapter III reconsiders the evidence for a "rockets and feathers" effect in gasoline markets. We estimate an error correction model of gasoline prices using daily data for the period 1985--1998 and fail to find any evidence of asymmetry. We show that previous work suffered from two problems. First, nonstationarity in some of the regressors was ignored, leading to invalid inference. Second, the weekly data used in previous work leads to a temporal aggregation problem, and thus biased estimates of impulse response functions. Chapter IV tests for a forecasting relationship between the volume of litigation and macroeconomic variables. We analyze annual data for the period 1960--2000 on the number of cases filed, real GDP, real consumption expenditures, inflation, unemployment, and interest rates. Bivariate Granger causality tests show that several of the macroeconomic variables can be used to forecast the volume of litigation, but show no evidence that the volume of litigation can be used to forecast any of the macroeconomic variables. The analysis is then extended to bivariate and multivariate regression models, and we find similar evidence to that of the Granger causality tests. We conclude that agents desiring a forecast of the volume of litigation should consider the state of the economy.

  12. How to Support a One-Handed Economist: The Role of Modalisation in Economic Forecasting

    ERIC Educational Resources Information Center

    Donohue, James P.

    2006-01-01

    Economic forecasting in the world of international finance confronts economists with challenging cross-cultural writing tasks. Producing forecasts in English which convey confidence and credibility entails an understanding of linguistic conventions which typify the genre. A typical linguistic feature of commercial economic forecasts produced by…

  13. Demand forecast model based on CRM

    NASA Astrophysics Data System (ADS)

    Cai, Yuancui; Chen, Lichao

    2006-11-01

    With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

  14. AIR QUALITY MODEL EVALUATION - FORECASTING AND RETROSPECTIVES

    EPA Science Inventory

    This presentation discusses the CMAQ model evaluation framework, and presents results of evaluation of CMAQ's particulate matter estimates for PM2.5, and its components for 2005 air quality forecast predictions as well as retrospective modeling for 2001.

  15. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). [weather forecasting

    NASA Technical Reports Server (NTRS)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.

  16. Traffic flow forecasting: Comparison of modeling approaches

    SciTech Connect

    Smith, B.L.; Demetsky, M.J.

    1997-08-01

    The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will support proactive, dynamic traffic control. However, previous attempts to develop traffic volume forecasting models have met with limited success. This research effort focused on developing traffic volume forecasting models for two sites on Northern Virginia`s Capital Beltway. Four models were developed and tested for the freeway traffic flow forecasting problem, which is defined as estimating traffic flow 15 min into the future. They were the historical average, time-series, neural network, and nonparametric regression models. The nonparametric regression model significantly outperformed the other models. A Wilcoxon signed-rank test revealed that the nonparametric regression model experienced significantly lower errors than the other models. In addition, the nonparametric regression model was easy to implement, and proved to be portable, performing well at two distinct sites. Based on its success, research is ongoing to refine the nonparametric regression model and to extend it to produce multiple interval forecasts.

  17. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect

    Brainard, James Robert

    2009-10-01

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  18. Pollen Forecast and Dispersion Modelling

    NASA Astrophysics Data System (ADS)

    Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello

    2014-05-01

    The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE Bologna, Italy). With pollen and meteorological dataset was created a provisional model for Poaceae. A PLSDA (Partial Least Squares Discriminant Analysis) approach was used in order to predict Poaceae pollen critical concentration (Brighetti et al. 2013) To preserve spatial correlation between pollens and PM10, we choose a Multiavariate Linear Spatial Interpolation Method to quantify pollen concentration in function of PM10, wind, rain and temperature. A test and validation procedure have been conducted to estimate the error associated to the pollen concentration. Validation for the year 2012 shows a good agreement between measured and estimated data , in each area depending of orography and of road traffic (r >0.83, 1%< RRMSE <5% ). This study aims to be a added value to agro-meteorological data in a different branch from the classic sector of defence and of crop production, emphasizing the importance of monitoring and forecast the pollen dispersal in urban areas, evaluated its effect on health and quality of life. In the health area the combined analysis between climate, pollution and dispersal of pollen allows to realize significant operational tools and to develop a reference for subsequent implementations.

  19. Spatiotemporal drought forecasting using nonlinear models

    NASA Astrophysics Data System (ADS)

    Vasiliades, Lampros; Loukas, Athanasios

    2010-05-01

    Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with increase in lead time. Finally, the developed nonlinear models could be used in an early warning system for risk and decision analyses at the study area.

  20. Weather Forecaster Understanding of Climate Models

    NASA Astrophysics Data System (ADS)

    Bol, A.; Kiehl, J. T.; Abshire, W. E.

    2013-12-01

    Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.

  1. Frost monitoring and forecasting using MODIS Land Surface Temperature data and a Numerical Weather Prediction model forecasts for Eastern Africa

    NASA Astrophysics Data System (ADS)

    Limaye, A. S.; Kabuchanga, E. S.; Flores, A.; Mungai, J.; Sakwa, V. N.; Shaka, A.; Malaso, S.; Irwin, D.

    2014-12-01

    Frost is a major challenge across Eastern Africa, severely impacting agriculture. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  2. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    NASA Technical Reports Server (NTRS)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  3. Weather forecasts, users' economic expenses and decision strategies

    NASA Technical Reports Server (NTRS)

    Carter, G. M.

    1972-01-01

    Differing decision models and operational characteristics affecting the economic expenses (i.e., the costs of protection and losses suffered if no protective measures have been taken) associated with the use of predictive weather information have been examined.

  4. Challenging Issues on fog forecast with a three-dimensional fog forecast model

    NASA Astrophysics Data System (ADS)

    Masbou, M.

    2012-12-01

    Fog has a significant impact on economical aspect (traffic management and safety) as well as on environmental issues (fresh water source for the population and the biosphere in arid region). However, reliable fog and visibility forecasts stay challenging issue. Fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, topography, vegetation, turbulent mixing at the surface as well as its microphysical structure. In order to consider these intertwined processes, the three-dimensional fog forecast model, COSMO-FOG, with a high vertical resolution with different microphysical complexity has been developed. This model includes a microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Moreover, the turbulence scheme, based on a Mellor-Yamada 2.5 order and a closure of a 2nd order has been modified to improve the model behaviour in case of a stable atmosphere structure, occurring typically during night radiative fog episodes. The potential of COSMO-FOG will be presented in some realistic fog situations (flat, bumpy and complex terrain). The fog spatial extension will be compared with MSG satellite products for fog and low cloud. The interplays between dynamical, thermodynamical patterns and the soil-atmosphere interactions will be presented.

  5. Skill of regional and global model forecast over Indian region

    NASA Astrophysics Data System (ADS)

    Kumar, Prashant; Kishtawal, C. M.; Pal, P. K.

    2016-02-01

    The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).

  6. Linking seasonal climate forecasts with crop models in Iberian Peninsula

    NASA Astrophysics Data System (ADS)

    Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita

    2015-04-01

    Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision Support System for Agrotechnology Transfer (DSSAT).Version 4.5 [CD-ROM].University of Hawaii, Honolulu, Hawaii. Ritchie, J.T., Otter, S., 1985. Description and performanceof CERES-Wheat: a user-oriented wheat yield model. In: ARS Wheat Yield Project. ARS-38.Natl Tech Info Serv, Springfield, Missouri, pp. 159-175.

  7. Modeling, Simulation, and Forecasting of Subseasonal Variability

    NASA Technical Reports Server (NTRS)

    Waliser, Duane; Schubert, Siegfried; Kumar, Arun; Weickmann, Klaus; Dole, Randall

    2003-01-01

    A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

  8. The Hanford Site New Production Reactor (NPR) economic and demographic baseline forecasts

    SciTech Connect

    Cluett, C.; Clark, D.C. ); Pittenger, D.B. )

    1990-08-01

    The objective of this is to present baseline employment and population forecasts for Benton, Franklin, and Yakima Counties. These forecasts will be used in the socioeconomic analysis portion of the New Production Reactor Environmental Impact Statement. Aggregate population figures for the three counties in the study area were developed for high- and low-growth scenarios for the study period 1990 through 2040. Age-sex distributions for the three counties during the study period are also presented. The high and low scenarios were developed using high and low employment projections for the Hanford site. Hanford site employment figures were used as input for the HARC-REMI Economic and Demographic (HED) model to produced baseline employment forecasts for the three counties. These results, in turn, provided input to an integrated three-county demographic model. This model, a fairly standard cohort-component model, formalizes the relationship between employment and migration by using migration to equilibrate differences in labor supply and demand. In the resulting population estimates, age-sex distributions for 1981 show the relatively large work force age groups in Benton County while Yakima County reflects higher proportions of the population in the retirement ages. The 2040 forecasts for all three counties reflect the age effects of relatively constant and low fertility increased longevity, as well as the cumulative effects of the migration assumptions in the model. By 2040 the baby boom population will be 75 years and older, contributing to the higher proportion of population in the upper end age group. The low scenario age composition effects are similar. 13 refs., 5 figs., 9 tabs.

  9. Traffic Flow Forecasting using the DESFM Model

    NASA Astrophysics Data System (ADS)

    Kai, Cao; Mo, Zhao; Hamamatsu, Yoshio

    Acquiring the real-time information about traffic flow is one of the important steps toward the realization of ITS. For this purpose, we propose an approach for forecasting traffic flow. The proposed approach uses a double exponential smoothing (DES) model, first, to predict the future datum. Then, a Fourier series is employed to fit a residual series between the predicted series produced by the DES model and the actual observed series for all previous time steps, and to give a estimated value at the next step so as to correct the prediction of DES model. Furthermore, a Markov model is adopted to describe the transition behavior of the residual series and to construct a forecast trend adjustment scheme. Smoothing parameters of the DES model is determined by using Levenberg-Marquardt algorithm. The model integrated the Fourier residual correction scheme with the Markov forecast trend adjustment scheme is called DESFM model. The DESFM model is applied to predict the traffic flow of Route 23 in Nagoya-shi, Aichi-ken, and the performed results is compared with ARIMA prediction approach.

  10. Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

    NASA Astrophysics Data System (ADS)

    Shrestha, D. L.; Robertson, D. E.; Wang, Q. J.; Pagano, T. C.; Hapuarachchi, H. A. P.

    2013-05-01

    The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

  11. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). Executive summary. [weather forecasting

    NASA Technical Reports Server (NTRS)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions so as to significantly reduce the cost for frost and freeze protection and crop losses. The design and implementation of the first phase of an economic experiment which will monitor citrus growers decisions, actions, costs and losses, and meteorological forecasts and actual weather events was carried out. The economic experiment was designed to measure the change in annual protection costs and crop losses which are the direct result of improved temperature forecasts. To estimate the benefits that may result from improved temperature forecasting capability, control and test groups were established with effective separation being accomplished temporally. The control group, utilizing current forecasting capability, was observed during the 1976-77 frost season and the results are reported. A brief overview is given of the economic experiment, the results obtained to date, and the work which still remains to be done.

  12. Egg production forecasting: Determining efficient modeling approaches.

    PubMed

    Ahmad, H A

    2011-12-01

    Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures-back-propagation-3, Ward-5, and the general regression neural network-were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R(2) of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production. PMID:22661881

  13. SEASAT economic assessment. Volume 9: Ports and harbors case study and generalization. [economic benefits of SEASAT satellites to harbors and shipping industries through improved weather forecasting

    NASA Technical Reports Server (NTRS)

    1975-01-01

    This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.

  14. On the dynamics of the world demographic transition and financial-economic crises forecasts

    NASA Astrophysics Data System (ADS)

    Akaev, A.; Sadovnichy, V.; Korotayev, A.

    2012-05-01

    The article considers dynamic processes involving non-linear power-law behavior in such apparently diverse spheres, as demographic dynamics and dynamics of prices of highly liquid commodities such as oil and gold. All the respective variables exhibit features of explosive growth containing precursors indicating approaching phase transitions/catastrophes/crises. The first part of the article analyzes mathematical models of demographic dynamics that describe various scenarios of demographic development in the post-phase-transition period, including a model that takes the limitedness of the Earth carrying capacity into account. This model points to a critical point in the early 2050s, when the world population, after reaching its maximum value may decrease afterward stabilizing then at a certain stationary level. The article presents an analysis of the influence of the demographic transition (directly connected with the hyperexponential growth of the world population) on the global socioeconomic and geopolitical development. The second part deals with the phenomenon of explosive growth of prices of such highly liquid commodities as oil and gold. It is demonstrated that at present the respective processes could be regarded as precursors of waves of the global financial-economic crisis that will demand the change of the current global economic and political system. It is also shown that the moments of the start of the first and second waves of the current global crisis could have been forecasted with a model of accelerating log-periodic fluctuations superimposed over a power-law trend with a finite singularity developed by Didier Sornette and collaborators. With respect to the oil prices, it is shown that it was possible to forecast the 2008 crisis with a precision up to a month already in 2007. The gold price dynamics was used to calculate the possible time of the start of the second wave of the global crisis (July-August 2011); note that this forecast has turned out to be quite correct.

  15. A plan for the economic assessment of the benefits of improved meteorological forecasts

    NASA Technical Reports Server (NTRS)

    Bhattacharyya, R.; Greenberg, J.

    1975-01-01

    Benefit-cost relationships for the development of meteorological satellites are outlined. The weather forecast capabilities of the various weather satellites (Tiros, SEOS, Nimbus) are discussed, and the development of additional satellite systems is examined. A rational approach is development that leads to the establishment of the economic benefits which may result from the utilization of meteorological satellite data. The economic and social impacts of improved weather forecasting for industries and resources management are discussed, and significant weather sensitive industries are listed.

  16. Forecasting Austrian national elections: The Grand Coalition model

    PubMed Central

    Aichholzer, Julian; Willmann, Johanna

    2014-01-01

    Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades. PMID:26339109

  17. Total Electron Content forecast model over Australia

    NASA Astrophysics Data System (ADS)

    Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

    Ionospheric perturbations can cause serious propagation errors in modern radio systems such as Global Navigation Satellite Systems (GNSS). Forecasting ionospheric parameters is helpful to estimate potential degradation of the performance of these systems. Our purpose is to establish an Australian Regional Total Electron Content (TEC) forecast model at IPS. In this work we present an approach based on the combined use of the Principal Component Analysis (PCA) and Artificial Neural Network (ANN) to predict future TEC values. PCA is used to reduce the dimensionality of the original TEC data by mapping it into its eigen-space. In this process the top- 5 eigenvectors are chosen to reflect the directions of the maximum variability. An ANN approach was then used for the multicomponent prediction. We outline the design of the ANN model with its parameters. A number of activation functions along with different spectral ranges and different numbers of Principal Components (PCs) were tested to find the PCA-ANN models reaching the best results. Keywords: GNSS, Space Weather, Regional, Forecast, PCA, ANN.

  18. On the possibility of getting economically sound forecasts of rare space weather events

    NASA Astrophysics Data System (ADS)

    Burov, V. A.

    There is a problem of setting criteria of space weather forecast quality that allows estimation of the economic effectiveness of forecasts in comparison with other methods for real users. The overwhelming majority of such users (airlines, power lines, pipelines, space exploration, navigation, ground-induced currents, medical services, etc.), are primarily interested in large space weather disturbances that affect the operation of their systems. But powerful disturbances happen rather seldom and so the traditional criteria of quality estimation give very little useful information for an estimate of economic effectiveness of the forecast. This work proposes a specially constructed value “A” for every customer (task) and for each method (or kind) of the forecast, which allows the estimation of the comparative economic effectiveness. Special attention is paid to the statistical significance in reference to the cyclic nature of the solar activity, and there are also indicated some numeral limits, which have to be considered during such a check.

  19. 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.

  20. Modeling, Forecasting and Mitigating Extreme Earthquakes

    NASA Astrophysics Data System (ADS)

    Ismail-Zadeh, A.; Le Mouel, J.; Soloviev, A.

    2012-12-01

    Recent earthquake disasters highlighted the importance of multi- and trans-disciplinary studies of earthquake risk. A major component of earthquake disaster risk analysis is hazards research, which should cover not only a traditional assessment of ground shaking, but also studies of geodetic, paleoseismic, geomagnetic, hydrological, deep drilling and other geophysical and geological observations together with comprehensive modeling of earthquakes and forecasting extreme events. Extreme earthquakes (large magnitude and rare events) are manifestations of complex behavior of the lithosphere structured as a hierarchical system of blocks of different sizes. Understanding of physics and dynamics of the extreme events comes from observations, measurements and modeling. A quantitative approach to simulate earthquakes in models of fault dynamics will be presented. The models reproduce basic features of the observed seismicity (e.g., the frequency-magnitude relationship, clustering of earthquakes, occurrence of extreme seismic events). They provide a link between geodynamic processes and seismicity, allow studying extreme events, influence of fault network properties on seismic patterns and seismic cycles, and assist, in a broader sense, in earthquake forecast modeling. Some aspects of predictability of large earthquakes (how well can large earthquakes be predicted today?) will be also discussed along with possibilities in mitigation of earthquake disasters (e.g., on 'inverse' forensic investigations of earthquake disasters).

  1. A dynamical model for forecasting operational losses

    NASA Astrophysics Data System (ADS)

    Bardoscia, M.; Bellotti, R.

    2012-04-01

    A novel dynamical model for the study of operational risk in banks and suitable for the calculation of the Value at Risk (VaR) is proposed. The equation of motion takes into account the interactions among different bank's processes, the spontaneous generation of losses via a noise term and the efforts made by the bank to avoid their occurrence. Since the model is very general, it can be tailored on the internal organizational structure of a specific bank by estimating some of its parameters from historical operational losses. The model is exactly solved in the case in which there are no causal loops in the matrix of couplings and it is shown how the solution can be exploited to estimate also the parameters of the noise. The forecasting power of the model is investigated by using a fraction f of simulated data to estimate the parameters, showing that for f=0.75 the VaR can be forecast with an error ≃10-3.

  2. Skill Assessment of National Multi-Model Ensemble Forecasts for Seasonal Drought Prediction in East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, S.; Hoell, A.; Roberts, J. B.; Funk, C. C.; Robertson, F. R.

    2014-12-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as 2011, part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at a seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However, seasonal drought prediction in this region faces several challenges including lack of skillful seasonal rainfall forecasts. The National Multi-model Ensemble (NMME); a state-of-the-art dynamical climate forecast system is potentially a promising tool for drought prediction in this region. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ forecasts ensemble members. Recent studies have indicated that in general NMME offers improvement over forecasts from any of the individual model. However, thus far the skill of NMME for forecasting rainfall in a vulnerable region like East Africa has largely been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons of the region. (i.e. March-April-May, July-August-September, and October-November-December). Additionally we describe a hybrid approach that combines statistical method with NMME forecasts to improve rainfall forecast skill in the region when raw NMME forecasts skill is lacking. This approach uses constructed analog method to improve NMME's March-April-May rainfall forecast skill in East Africa.

  3. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    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. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  4. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2015-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  5. Modeling olive-crop forecasting in Tunisia

    NASA Astrophysics Data System (ADS)

    Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

    2016-01-01

    Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

  6. Hurricane forecasts using a suite of large-scale models

    NASA Astrophysics Data System (ADS)

    Krishnamurti, T. N.; Biswas, Mrinal K.; Mackey, Brian P.; Ellingson, Robert G.; Ruscher, Paul H.

    2011-08-01

    This paper provides an account of the performance of a multimodel ensemble for real time forecasts of Atlantic tropical cyclones during 2004, 2005 and 2006. The Florida State University (FSU) superensemble is based on a suite of model forecasts and the interpolated official forecast that were received in real time at the National Hurricane Center. The FSU superensemble is a multimodel ensemble that utilizes forecasts from the member models by removing their individual biases based on a recent past history of their performances. This superensemble carries separate statistical weights for track and intensity forecasts for every 6 h of the member model forecasts. The real time results from 2004 show an improvement up to 15% for track forecasts and up to 11% for intensity forecasts for the superensemble compared to other models and consensus aids. During 2005, the superensemble intensity performance was best for most lead times. The consistency of the superensemble forecasts of track are also illustrated for several storms of 2004 season. The superensemble methodology produced impressive intensity forecasts for Rita and Wilma during 2005. The study shows the capability of the superensemble in predicting rapidly intensifying storms when most member models failed to capture their strengthening.

  7. Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm.

    PubMed

    Li, Bai

    2014-01-01

    Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

  8. Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

    PubMed Central

    2014-01-01

    Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

  9. STATUS AND PROGRESS IN PARTICULATE MATTER FORECASTING: INITIAL APPLICATION OF THE ETA- CMAQ FORECAST MODEL

    EPA Science Inventory

    This presentation reviews the status and progress in forecasting particulate matter distributions. The shortcomings in representation of particulate matter formation in current atmospheric chemistry/transport models are presented based on analyses and detailed comparisons with me...

  10. Development of Ensemble Model Based Water Demand Forecasting Model

    NASA Astrophysics Data System (ADS)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  11. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

    PubMed Central

    Bildirici, Melike; Ersin, Özgür

    2014-01-01

    The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications. PMID:24977200

  12. Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

    PubMed

    Bildirici, Melike; Ersin, zgr

    2014-01-01

    The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications. PMID:24977200

  13. Multi-model MJO forecasting during DYNAMO/CINDY period

    NASA Astrophysics Data System (ADS)

    Fu, Xiouhua; Lee, June-Yi; Hsu, Pang-Chi; Taniguchi, Hiroshi; Wang, Bin; Wang, Wanqiu; Weaver, Scott

    2013-08-01

    The present study assesses the forecast skill of the Madden-Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air-sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air-sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high- resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO-TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks' lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting.

  14. Brief Report: Forecasting the Economic Burden of Autism in 2015 and 2025 in the United States

    ERIC Educational Resources Information Center

    Leigh, J. Paul; Du, Juan

    2015-01-01

    Few US estimates of the economic burden of autism spectrum disorders (ASD) are available and none provide estimates for 2015 and 2025. We forecast annual direct medical, direct non-medical, and productivity costs combined will be $268 billion (range $162-$367 billion; 0.884-2.009% of GDP) for 2015 and $461 billion (range $276-$1011 billion;

  15. Brief Report: Forecasting the Economic Burden of Autism in 2015 and 2025 in the United States

    ERIC Educational Resources Information Center

    Leigh, J. Paul; Du, Juan

    2015-01-01

    Few US estimates of the economic burden of autism spectrum disorders (ASD) are available and none provide estimates for 2015 and 2025. We forecast annual direct medical, direct non-medical, and productivity costs combined will be $268 billion (range $162-$367 billion; 0.884-2.009% of GDP) for 2015 and $461 billion (range $276-$1011 billion;…

  16. 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.

  17. Forecasting the Economic Impact of Future Space Station Operations

    NASA Technical Reports Server (NTRS)

    Summer, R. A.; Smolensky, S. M.; Muir, A. H.

    1967-01-01

    Recent manned and unmanned Earth-orbital operations have suggested great promise of improved knowledge and of substantial economic and associated benefits to be derived from services offered by a space station. Proposed application areas include agriculture, forestry, hydrology, public health, oceanography, natural disaster warning, and search/rescue operations. The need for reliable estimates of economic and related Earth-oriented benefits to be realized from Earth-orbital operations is discussed and recent work in this area is reviewed. Emphasis is given to those services based on remote sensing. Requirements for a uniform, comprehensive and flexible methodology are discussed. A brief review of the suggested methodology is presented. This methodology will be exercised through five case studies which were chosen from a gross inventory of almost 400 user candidates. The relationship of case study results to benefits in broader application areas is discussed, Some management implications of possible future program implementation are included.

  18. The potential economic benefits of improvements in weather forecasting

    NASA Technical Reports Server (NTRS)

    Thompson, J. C.

    1972-01-01

    The study was initiated as a consequence of the increased use of weather satellites, electronic computers and other technological developments which have become a virtual necessity for solving the complex problems of the earth's atmosphere. Neither the economic emphasis, nor the monetary results of the study, are intended to imply their sole use as criteria for making decisions concerning the intrinsic value of technological improvements in meteorology.

  19. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    NASA Technical Reports Server (NTRS)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.

  20. Evaluating the model forecasts of plume evolution in BORTAS

    NASA Astrophysics Data System (ADS)

    Matthiesen, Stephan; Palmer, Paul I.; Parrington, Mark

    2013-04-01

    We develop a novel forecast quality measure that is designed to reflect the 3-dimensional structure of biomass burning plumes and use it to evaluate the quality of the plume forecasts that were produced during the "Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites" (BORTAS) project. In recent years, several approaches have been developed to quantify numerical forecast errors, but most are designed for 2-dimensional fields and do not consider the 3-dimensional structure of plumes, in particular the altitude of individual plume features. Here, we extend a displacement-based method to include the vertical dimension. The algorithm calculates the 3-dimensional displacement that would be needed to transform the forecast field into the observed field. This displacement then forms the basis for a quantitative forecast quality measure. This method is used to evaluate model forecasts of boreal wildfire plumes within the BORTAS project. During the BORTAS intense measurement campaign (Summer 2012), forecasts of carbon monoxide concentrations from boreal biomass burning over North America and the North Atlantic were produced twice daily over several weeks using the NASA GEOS-5 model for operational purposes. We analyse the quality of the forecasts and discuss areas and meteorological situations that influence the local forecast quality.

  1. Operational forecasting based on a modified Weather Research and Forecasting model

    SciTech Connect

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  2. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    NASA Astrophysics Data System (ADS)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met ireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  3. With string model to time series forecasting

    NASA Astrophysics Data System (ADS)

    Pinčák, Richard; Bartoš, Erik

    2015-10-01

    Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.

  4. A channel dynamics model for real-time flood forecasting

    USGS Publications Warehouse

    Hoos, A.B.; Koussis, A.D.; Beale, G.O.

    1989-01-01

    A new channel dynamics scheme ASPIRE (alternative system predictor in real time), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio. -Authors

  5. Multilayer Stock Forecasting Model Using Fuzzy Time Series

    PubMed Central

    Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

    2014-01-01

    After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS. PMID:24605058

  6. Application of Conjunctive Nonlinear Model Based on Wavelet Transforms and Artificial Neural Networks to Drought Forecasting

    NASA Astrophysics Data System (ADS)

    Abrishamchi, A.; Mehdikhani, H.; Tajrishy, M.; Marino, M. A.; Abrishamchi, A.

    2007-12-01

    Drought forecasting plays an important role in mitigation of economic, environmental and social impacts of drought. Traditional statistical time series methods have a limited ability to capture non-stationarities and nonlinearities in data. Artificial Neural Network (ANN) because of highly flexible function estimator that has self- learning and self-adaptive feature has shown great ability in forecasting nonlinear and nonstationary time series in hydrology. Recently wavelet transforms have become a common tool for analyzing local variation in time series. Wavelet transforms provide a useful decomposition of a signal, or time series; therefore, hybrid models have been proposed for forecasting a time series based on a wavelet transform preprocessing. Wavelet-transformed data aids in improving the ability of forecasting models by diagnosing signal's main frequency component and abstract local information of the original time series on various resolution levels. This paper presents a conjunctive nonlinear model using Wavelet Transforms and Artificial Neural Network. Application of the model in Zayandeh-Rood River basin (Iran) shows that the conjunctive model significantly improves the ability of artificial neural networks for 1, 3, 6 and 9 months ahead forecasting of EDI (effective drought indices) time series. Improved forecasts allow water resources decision makers to develop drought preparedness plans far in advance.

  7. Evaluation of annual, global seismicity forecasts, including ensemble models

    NASA Astrophysics Data System (ADS)

    Taroni, Matteo; Zechar, Jeremy; Marzocchi, Warner

    2013-04-01

    In 2009, the Collaboratory for the Study of the Earthquake Predictability (CSEP) initiated a prototype global earthquake forecast experiment. Three models participated in this experiment for 2009, 2010 and 2011each model forecast the number of earthquakes above magnitude 6 in 1x1 degree cells that span the globe. Here we use likelihood-based metrics to evaluate the consistency of the forecasts with the observed seismicity. We compare model performance with statistical tests and a new method based on the peer-to-peer gambling score. The results of the comparisons are used to build ensemble models that are a weighted combination of the individual models. Notably, in these experiments the ensemble model always performs significantly better than the single best-performing model. Our results indicate the following: i) time-varying forecasts, if not updated after each major shock, may not provide significant advantages with respect to time-invariant models in 1-year forecast experiments; ii) the spatial distribution seems to be the most important feature to characterize the different forecasting performances of the models; iii) the interpretation of consistency tests may be misleading because some good models may be rejected while trivial models may pass consistency tests; iv) a proper ensemble modeling seems to be a valuable procedure to get the best performing model for practical purposes.

  8. Seasonal weather forecasts for crop yield modelling in Europe

    NASA Astrophysics Data System (ADS)

    Cantelaube, Pierre; Terres, Jean-Michel

    2005-05-01

    Within the European DEMETER project, ensembles of global coupled climate models have shown some skill for seasonal climate prediction. Meteorological outputs of the seasonal prediction system were used in a crop yield model to assess the performance and usefulness of such a system for crop yield forecasting.An innovative method for supplying seasonal forecast information to crop simulation models was developed. It consisted in running a crop model from each individual downscaled member output of climate models. An ensemble of crop yield was obtained and a probability distribution function (PDF) was derived. Preliminary results of wheat yield simulations in Europe using downscaled DEMETER seasonal weather forecasts suggest that reliable crop yield predictions can be obtained using an ensemble multi-model approach. When compared to the operational system, for the same level of accuracy, earlier crop forecasts are obtained with the DEMETER system. Furthermore, PDFs of wheat yield provide information on both the yield anomaly and the uncertainty of the forecast. Based on the spread of the PDF, the user can directly quantify the benefits and risk of taking weather-sensitive decisions.It is shown that the use of ensembles of seasonal weather forecast brings additional information for the crop yield forecasts and therefore has valuable benefit for decision-making in the management of European Union agricultural production.

  9. Model Combination and Weighting Methods in Operational Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Pappenberger, Florian; Cloke, Hannah L.

    2013-04-01

    In order to get maximum benefits from operational forecast systems based on different model approaches, it is necessary to find an optimal way to combine the forecasts in real-time and to derive the predictive probability distribution by assigning different weights to the different actual forecasts according to the forecast performance of the previous days. In the European Flood Alert System (EFAS) a Bayesian Forecast System has been implemented in order to derive the overall predictive probability distribution. The EFAS is driven by different numerical weather prediction systems like the deterministic forecasts from the German Weather Service and from the ECMWF, as well as Ensemble Prediction Systems from the ECMWS and COSMO-LEPS. In this study the effect of combining these different forecast systems in respect of the total predictive uncertainty are investigated by applying different weighting methods like the Non-homogenous Gaussian Regression (NGR) model, the Bayesian Model Averaging (BMA) and an empirical method. Besides that different methods of bias removal are applied, namely additive and regression based ones, and the applicability in operational forecast is tested. One of the problems identified is the difficulty in optimizing the weight parameters for each lead-time separately resulting in highly inconsistent forecasts, especially for regression based bias removal methods. Therefore in operational use methods with only sub-optimal skill score results, could be preferable showing more realistic shapes of uncertainty bands for the predicted future stream-flow values. Another possible approach could be the optimization of the weighting parameters not for each lead-time separately, but to look at different levels of aggregations over expanding windows of time ranges. First results indicate the importance of the proper choice of the model combination method in view of reliability and sharpness of the forecast system.

  10. Hybrid deterministic - stochastic model for forecasting of monthly river flows

    NASA Astrophysics Data System (ADS)

    Svetlkov, D.; Szolgay, J.; Kohnov, S.; Komornkov, M.; Szkeov, D.

    2009-04-01

    Flows of the Vh River and its tributaries in the Tatry alpine mountain region in Slovakia are predominantly fed by snowmelt during the spring period and convective precipitation in the summer. Therefore their regime properties exhibit clear seasonal patterns. Moreover left and right side tributaries of the Vh River spring in different physiographic conditions in the High and Low Tatry Mountains. This provides intuitive justification for the application of nonlinear two-regime models for modelling and forecasting of monthly time series of these rivers. In the poster the forecasting performance of several linear and nonlinear time series models is compared with respect to their capabilities of forecasting monthly flows into the Liptovsk Mara reservoir. ARMA and SETAR regime switching models were identified for each tributary respectively and forecasts of the tributary flows were composed through a simple water balance model into the forecast of the overall reservoir inflow. The combined hybrid (deterministic-stochastic) forecast, which preserves both the specific regime of the tributaries and the water balance in the catchments, was compared against different forecasts set up for the overall reservoir inflow.

  11. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  12. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling

    NASA Astrophysics Data System (ADS)

    Morcrette, J.-J.; Boucher, O.; Jones, L.; Salmond, D.; Bechtold, P.; Beljaars, A.; Benedetti, A.; Bonet, A.; Kaiser, J. W.; Razinger, M.; Schulz, M.; Serrar, S.; Simmons, A. J.; Sofiev, M.; Suttie, M.; Tompkins, A. M.; Untch, A.

    2009-03-01

    This paper presents the aerosol modeling now part of the ECMWF Integrated Forecasting System (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the forecast model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The forecast-only model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation system described in part 2 of this paper.

  13. Point-Specific Wind Forecasting using the HARMONIE Mesoscale Forecast Model with Bayes Model Averaging for Fine-Tuning

    NASA Astrophysics Data System (ADS)

    Peters, Martin; McKinstry, Alastair; O'Brien, Enda; Ralph, Adam; Sheehy, Michael

    2013-04-01

    Two distinct elements seem to be required to make accurate wind-speed forecasts for wind-farms: the first is deterministic output from a weather forecast model, and the second is some probabilistic or statistical post-processing to account for local biases, or systematic errors in the model. A variety of statistical post-processing schemes are available, and are generally worthwhile since they are cheap and at worst do no harm. More typically, they demonstrably improve the accuracy of the deterministic forecast. Gridded output from the operational HARMONIE mesoscale weather forecast model has been interpolated to forecast winds at the precise (3-dimensional) location of the met-mast of a wind farm in southwest Ireland. A sequence of 48-hour forecasts run at 6-hourly intervals for over one year have been validated against winds recorded at turbine height on the mast. All the interpolated deterministic forecasts are also post-processed using Bayesian Model Averaging (BMA) to remove systematic local bias, and to provide forecasts in a calibrated probabilistic format. Three variants of the HARMONIE model were also run during October 2010 and validated against the winds recorded at the met-mast. The HARMONIE variant with the most advanced physics and the larger domain was the most accurate in forecasting met-mast windspeed, with mean average error (MAE) of 1.5 ms-1 (i.e., about 10% of mean wind speed). The BMA analysis for this short period (using a 25-day training period) did not change the MAE for the best HARMONIE configuration, but did improve the MAE of the other two by about 15%. The most advanced HARMONIE configuration proved more accurate than an ensemble of all three. There was negligible degradation in the skill of the hourly forecasts, at least out to 24 hours (i.e., 24-hr forecasts were only marginally less accurate than 0-hr analyses or 1-hr forecasts). Results are presented from the operational 48-hr HARMONIE forecasts collected during Jan.-Mar. 2012, as compared with recorded winds at the met-mast. The added value of BMA post-processing (using a moving 25-day training period) is quantified. Forecasts from an experimental extra high-resolution HARMONIE (1km resolution, on a 1,000 x 1,000 km domain) are available for a continuous 30-day period starting 10 Nov. 2012, and the extra skill provided by this for the specific wind-farm site is also quantified.

  14. Improving statistical forecasts of seasonal streamflows using hydrological model output

    NASA Astrophysics Data System (ADS)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2013-02-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  15. Improving statistical forecasts of seasonal streamflows using hydrological model output

    NASA Astrophysics Data System (ADS)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2012-07-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  16. Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

    PubMed

    Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292

  17. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    PubMed Central

    Zhao, Xiuli; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292

  18. Arctic Economics Model

    Energy Science and Technology Software Center (ESTSC)

    1995-03-01

    AEM (Arctic Economics Model) for oil and gas was developed to provide an analytic framework for understanding the arctic area resources. It provides the capacity for integrating the resource and technology information gathered by the arctic research and development (R&D) program, measuring the benefits of alternaive R&D programs, and providing updated estimates of the future oil and gas potential from arctic areas. AEM enables the user to examine field or basin-level oil and gas recovery,more » costs, and economics. It provides a standard set of selected basin-specified input values or allows the user to input their own values. AEM consists of five integrated submodels: geologic/resource submodel, which distributes the arctic resource into 15 master regions, consisting of nine arctic offshore regions, three arctic onshore regions, and three souhtern Alaska (non-arctic) regions; technology submodel, which selects the most appropriate exploration and production structure (platform) for each arctic basin and water depth; oil and gas production submodel, which contains the relationship of per well recovery as a function of field size, production decline curves, and production decline curves by product; engineering costing and field development submodel, which develops the capital and operating costs associated with arctic oil and gas development; and the economics submodel, which captures the engineering costs and development timing and links these to oil and gas prices, corporate taxes and tax credits, depreciation, and timing of investment. AEM provides measures of producible oil and gas, costs, and ecomonic viability under alternative technology or financial conditions.« less

  19. Evaluating Rapid Models for High-Throughput Exposure Forecasting (SOT)

    EPA Science Inventory

    High throughput exposure screening models can provide quantitative predictions for thousands of chemicals; however these predictions must be systematically evaluated for predictive ability. Without the capability to make quantitative, albeit uncertain, forecasts of exposure, the ...

  20. High resolution distributed hydrological modeling for river flood forecasting

    NASA Astrophysics Data System (ADS)

    Chen, Y.

    2014-12-01

    High resolution distributed hydrological model can finely describe the river basin hydrological processes, thus having the potential to improve the flood forecasting capabilities, and is regarded as the next generation flood forecast model. But there are great challenges in deploying it in real-time river flood forecasting, such as the awesome computation resources requirement, parameter determination, high resolution precipitation assimilation and uncertainty controls. Liuxihe Model is a physically-based distributed hydrological model proposed mainly for catchment flood forecasting, which is a process-based hydrological model. In this study, based on Liuxihe Model, a parallel computation algorithm for Liuxihe model flood forecasting is proposed, and a cloudy computation system is developed on a high performance computer, this largely improves the applicability of Liuxihe Model in large river. Without the parallel computation, the Liuxihe Model is computationally incapable in application to rivers with drainage area bigger than 10,000km2 at the grid size of 100m. With the parallel computation, the Liuxihe Model is used in a river with a drainage area of 60,000km2, and could be expended indefinitely. Based on this achievement, a model parameter calibration method by using Particle Swale Optimization is proposed and tested in several rivers in southern China with drainage areas ranging from several hundreds to tens thousands km2, and with the model parameter optimization, the model performance has been approved largely. The modeling approach is also tested for coupling radar-based precipitation estimation/prediction for small catchment flash forecasting and for coupling quantitative precipitation estimation/prediction from meteorological model for large river flood forecasting.

  1. Spatio-temporal modeling for real-time ozone forecasting

    PubMed Central

    Paci, Lucia; Gelfand, Alan E.; Holland, David M.

    2013-01-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts. PMID:24010052

  2. Forecast model applications of retrieved three dimensional liquid water fields

    NASA Technical Reports Server (NTRS)

    Raymond, William H.; Olson, William S.

    1990-01-01

    Forecasts are made for tropical storm Emily using heating rates derived from the SSM/I physical retrievals described in chapters 2 and 3. Average values of the latent heating rates from the convective and stratiform cloud simulations, used in the physical retrieval, are obtained for individual 1.1 km thick vertical layers. Then, the layer-mean latent heating rates are regressed against the slant path-integrated liquid and ice precipitation water contents to determine the best fit two parameter regression coefficients for each layer. The regression formulae and retrieved precipitation water contents are utilized to infer the vertical distribution of heating rates for forecast model applications. In the forecast model, diabatic temperature contributions are calculated and used in a diabatic initialization, or in a diabatic initialization combined with a diabatic forcing procedure. Our forecasts show that the time needed to spin-up precipitation processes in tropical storm Emily is greatly accelerated through the application of the data.

  3. Three models intercomparison for Quantitative Precipitation Forecast over Calabria

    NASA Astrophysics Data System (ADS)

    Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Lavagnini, A.; Accadia, C.; Mariani, S.; Casaioli, M.

    2004-11-01

    In the framework of the National Project Sviluppo di distretti industriali per le Osservazioni della Terra (Development of Industrial Districts for Earth Observations) funded by MIUR (Ministero dell'Universit e della Ricerca Scientifica --Italian Ministry of the University and Scientific Research) two operational mesoscale models were set-up for Calabria, the southernmost tip of the Italian peninsula. Models are RAMS (Regional Atmospheric Modeling System) and MM5 (Mesoscale Modeling 5) that are run every day at Crati scrl to produce weather forecast over Calabria (http://www.crati.it). This paper reports model intercomparison for Quantitative Precipitation Forecast evaluated for a 20 month period from 1th October 2000 to 31th May 2002. In addition to RAMS and MM5 outputs, QBOLAM rainfall fields are available for the period selected and included in the comparison. This model runs operationally at Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici. Forecasts are verified comparing models outputs with raingauge data recorded by the regional meteorological network, which has 75 raingauges. Large-scale forcing is the same for all models considered and differences are due to physical/numerical parameterizations and horizontal resolutions. QPFs show differences between models. Largest differences are for BIA compared to the other considered scores. Performances decrease with increasing forecast time for RAMS and MM5, whilst QBOLAM scores better for second day forecast.

  4. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

    PubMed Central

    Ye, Hao; Beamish, Richard J.; Glaser, Sarah M.; Grant, Sue C. H.; Hsieh, Chih-hao; Richards, Laura J.; Schnute, Jon T.; Sugihara, George

    2015-01-01

    It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts. PMID:25733874

  5. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling.

    PubMed

    Ye, Hao; Beamish, Richard J; Glaser, Sarah M; Grant, Sue C H; Hsieh, Chih-Hao; Richards, Laura J; Schnute, Jon T; Sugihara, George

    2015-03-31

    It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner-recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts. PMID:25733874

  6. Ensemble forecasting of tropical cyclone motion using a baroclinic model

    NASA Astrophysics Data System (ADS)

    Zhou, Xiaqiong; Chen, Johnny C. L.

    2006-05-01

    The purpose of this study is to investigate the effectiveness of two different ensemble forecasting (EF) techniques-the lagged-averaged forecast (LAF) and the breeding of growing modes (BGM). In the BGM experiments, the vortex and the environment are perturbed separately (named BGMV and BGME). Tropical cyclone (TC) motions in two difficult situations are studied: a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM-90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also used to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the forecast tracks in some of the cases are improved over that of the control. On the other hand, the EF technique has little impact on the forecasts of Flo because the control forecast is already very close to the best track. The study provides a basis for the future development of the EF technique. The limitations of this study are also addressed. For example, the above results are based on a small sample, and the study is actually a simulation, which is different than operational forecasting. Further tests of these EF techniques are proposed.

  7. Weather Research and Forecasting Model with the Immersed Boundary Method

    Energy Science and Technology Software Center (ESTSC)

    2012-05-01

    The Weather Research and Forecasting (WRF) Model with the immersed boundary method is an extension of the open-source WRF Model available for wwww.wrf-model.org. The new code modifies the gridding procedure and boundary conditions in the WRF model to improve WRF's ability to simutate the atmosphere in environments with steep terrain and additionally at high-resolutions.

  8. A model for Long-term Industrial Energy Forecasting (LIEF)

    SciTech Connect

    Ross, M. ||; Hwang, R.

    1992-02-01

    The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

  9. Modeling and forecasting health expectancy: theoretical framework and application.

    PubMed

    Majer, Istvan M; Stevens, Ralph; Nusselder, Wilma J; Mackenbach, Johan P; van Baal, Pieter H M

    2013-04-01

    Life expectancy continues to grow in most Western countries; however, a major remaining question is whether longer life expectancy will be associated with more or fewer life years spent with poor health. Therefore, complementing forecasts of life expectancy with forecasts of health expectancies is useful. To forecast health expectancy, an extension of the stochastic extrapolative models developed for forecasting total life expectancy could be applied, but instead of projecting total mortality and using regular life tables, one could project transition probabilities between health states simultaneously and use multistate life table methods. In this article, we present a theoretical framework for a multistate life table model in which the transition probabilities depend on age and calendar time. The goal of our study is to describe a model that projects transition probabilities by the Lee-Carter method, and to illustrate how it can be used to forecast future health expectancy with prediction intervals around the estimates. We applied the method to data on the Dutch population aged 55 and older, and projected transition probabilities until 2030 to obtain forecasts of life expectancy, disability-free life expectancy, and probability of compression of disability. PMID:23104206

  10. Forecasting European Droughts using the North American Multi-Model Ensemble (NMME)

    NASA Astrophysics Data System (ADS)

    Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schfer, David; Mai, Juliane

    2015-04-01

    Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model Ensemble (NMME) provides the latest collection of a multi-institutional seasonal forecasting ensemble for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are downscaled to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the Ensemble Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new ensemble forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10W to 40E and 35N to 55N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME based ensemble forecasts have consistently higher skill than the ESP based ones (ETS of 13% as compared to 5% at a six-month lead time). Additionally, the ETS ensemble spread of NMME forecasts is considerably narrower than that of ESP; the lower boundary of the NMME ensemble spread coincides most of the time with the ensemble median of ESP. Among the NMME models, NCEP-CFSv2 outperforms the other models in terms of ETS most of the time. Removing the three worst performing models does not deteriorate the ensemble performance (neither in skill nor in spread), but would substantially reduce the computational resources required in an operational forecasting system. For major European drought events (e.g., 1990, 1992, 2003, and 2007), NMME forecasts tend to underestimate area under drought and drought magnitude during times of drought development. During drought recovery, this underestimation is weaker for area under drought or even reversed into an overestimation for drought magnitude. This indicates that the NMME models are too wet during drought development and too dry during drought recovery. In summary, soil moisture drought forecasts by NMME are more skillful than those of an ESP based approach. However, they still show systematic biases in reproducing the observed drought dynamics during drought development and recovery.

  11. A model for Long-term Industrial Energy Forecasting (LIEF)

    SciTech Connect

    Ross, M. Michigan Univ., Ann Arbor, MI . Dept. of Physics Argonne National Lab., IL . Environmental Assessment and Information Sciences Div.); Hwang, R. )

    1992-02-01

    The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

  12. Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

    PubMed Central

    Kim, Sun-Hee; Faloutsos, Christos; Yang, Hyung-Jeong

    2013-01-01

    Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with ?1 and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately. PMID:23710252

  13. Sensitivities of numerical model forecasts of extreme cyclone events

    NASA Astrophysics Data System (ADS)

    Yih, A. C.; Walsh, J. E.

    1991-03-01

    A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of January 1978, the New England blizzard of February 1978, and the Mid-Atlantic cyclone of February 1979. While medium-resolution simulations capture much of the intensification, the forecasts of the precise timing and intensity levels suffer from various degrees of error. The coastal cyclones show a 5-10 hPa dependence on the western North Atlantic sea surface temperature, which is varied within a range ( 2.5C ) compatible with interannual fluctuations. The associated vertical velocities and precipitation rates show proportionately stronger dependences on the ocean temperature perturbations. The Ohio Valley blizzard, which intensified along a track 700-800 km from the coast, shows little sensitivity to ocean temperature. The effect of a shift of 10 latitude in the position of the snow boundary is negligible in each case. The forecasts depend strongly on the model resolution, and the coarse-resolution forecasts are consistently inferior to the medium-resolution forecasts. Studies of the corresponding sensitivities of extreme cyclonic events over eastern Asia are encouraged in order to identify characteristics that are common to numerical forecasts for the two regions.

  14. Using model derived regional climate forecasts to enhance the effectiveness and skill of selected application models in reducing negative impacts

    NASA Astrophysics Data System (ADS)

    Johnston, P. A.; Hewitson, B. C.

    2001-05-01

    A new approach to regional climate forecasting in Southern Africa is involving a cross section of researchers working to integrate the key elements of the global system that determine seasonal conditions. The aim is to produce seasonal forecasts of temperature and rainfall with a 1-3 month lead-time. These forecasts, reflecting climatic variation and inter-annual change, using a combination of global and regional climate models, can be used as input for a selection of crop-yield/ hydrological/ economic models to assess the impact and usefulness in specific application areas e.g. water resources, agriculture etc. The investigation focuses on the usefulness of the information content of the forecast output. The impacts of severe droughts and flooding associated with ENSO events can be prepared for and reduced. However, until recently (Vogel, 2000; Mukara, 2000) the value of these forecasts for farming, industry and commerce in South Africa has not been assessed. An essential part of the analysis is the collaboration with others working within the forecaster-user dynamic. This ensures that forecast/model output provides the most usable content for end-users whether in small scale pastoral or commercial farming, hydrological planning, industry or fishing. Input from the users informs the modellers with respect to the format and content of forecast outputs. The parameters most useful to user applications are identified and in consultation with the modellers, specified in the model output. Different model runs are compared and various hindcasts performed. The issue is to determine the level and scope of the accuracy of the identified parameters. A model's accuracy may be temporally substantial, but spatially unreliable. When submitting the seasonal forecast data into other models within a localised region, specific accuracy for that region, during the particular season and in the individual topography, is essential. If the accuracy is lower than a critical value, then the forecast must be deemed no more useful than persistence. The usefulness of model output in existing application models must be maximised. Various models have been used to identify the impacts of climatic change on agriculture (e.g. ACRU and CERES), hydrology (e.g. ACRU) and global warming (e.g. MAGICC). Many of these models have used GCM data and it remains to be investigated how the regional model data can best be beneficial in this regard.

  15. FUSION++: A New Data Assimilative Model for Electron Density Forecasting

    NASA Astrophysics Data System (ADS)

    Bust, G. S.; Comberiate, J.; Paxton, L. J.; Kelly, M.; Datta-Barua, S.

    2014-12-01

    There is a continuing need within the operational space weather community, both civilian and military, for accurate, robust data assimilative specifications and forecasts of the global electron density field, as well as derived RF application product specifications and forecasts obtained from the electron density field. The spatial scales of interest range from a hundred to a few thousand kilometers horizontally (synoptic large scale structuring) and meters to kilometers (small scale structuring that cause scintillations). RF space weather applications affected by electron density variability on these scales include navigation, communication and geo-location of RF frequencies ranging from 100's of Hz to GHz. For many of these applications, the necessary forecast time periods range from nowcasts to 1-3 hours. For more "mission planning" applications, necessary forecast times can range from hours to days. In this paper we present a new ionosphere-thermosphere (IT) specification and forecast model being developed at JHU/APL based upon the well-known data assimilation algorithms Ionospheric Data Assimilation Four Dimensional (IDA4D) and Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE). This new forecast model, "Forward Update Simple IONosphere model Plus IDA4D Plus EMPIRE (FUSION++), ingests data from observations related to electron density, winds, electric fields and neutral composition and provides improved specification and forecast of electron density. In addition, the new model provides improved specification of winds, electric fields and composition. We will present a short overview and derivation of the methodology behind FUSION++, some preliminary results using real observational sources, example derived RF application products such as HF bi-static propagation, and initial comparisons with independent data sources for validation.

  16. Validation of Model Forecasts of the Ambient Solar Wind

    NASA Technical Reports Server (NTRS)

    Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

    2009-01-01

    Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

  17. Modelling and forecasting monthly river discharge considering autoregressive heteroscedasticity

    NASA Astrophysics Data System (ADS)

    Szolgayova, Elena

    2010-05-01

    Monitoring water scarcity conditions requires medium term streamflow forecasting. In this contribution stochastic models for the forecasting of monthly flows were compared. Data measured in monthly time step from the Hron and the Morava Rivers in Slovakia were considered. When analyzing this data in a shorter, daily time step, it was verified, that the from econometry known, so - called heteroscedasticity effect, i.e. the non-constant variance of the time series was present. Here it was investigated, whether this was the case if considering the data with a monthly time step. In addition, the time series were analyzed from two different perspectives: using a purely data driven stochastic model and a hybrid approach, combining physics based conceptual model with a data driven model for the residuals. To model the heteroscedasticity in the time series, the GARCH (generalized autoregressive conditional heteroscedasticity) family of models was fitted to the time series. So far, only a few attempts to apply GARCH class models used on discharge data were reported in the hydrological modelling literature. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological time series with the aim to test the possibility to use the GARCH family of models on time series with monthly time step and comparing forecasting performance with traditional ARMA models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. An ARMA type model combined with a GARCH model was fitted to the data (either directly on the discharge time series or on the error series resulting from a conceptual model). 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons to forecasts obtained by using only an ARMA class model on the same data. In the case of the purely data driven model it was found, that the medium time step was not fine enough to catch the heteroscedasticity effect, which is present in the data when considering a finer time step at all. Considering the hybrid framework, even though heteroscedasticity was not rejected in the error series, the GARCH family of models did not offer any forecasting improvement compared to the simpler ARMA class of models. This result shows the existence and thus the need of modelling the non-linearities in some cases in the medium step, even if different methods offering better forecasting performance need to be investigated.

  18. Forecasting coconut production in the Philippines with ARIMA model

    NASA Astrophysics Data System (ADS)

    Lim, Cristina Teresa

    2015-02-01

    The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

  19. Forecasting Diffusion of Technology by using Bass Model

    NASA Astrophysics Data System (ADS)

    Kim, Do-Hoi; Shin, Young-Geun; Park, Sang-Sung; Jang, Dong-Sik

    2009-08-01

    Generally, researching method of technology forecasting has been depended on intuition of expert until now. So there were many defects like consuming much time and money and so on. In this paper, we forecast diffusion of technology by using Bass model that is one of the quantitative analysis methods. We applied this model at technology market. And for input data of experiment, we use patent data that is representing each technology in technology market. We expect this research will be suggest new possibility that patent data can be applied in Bass model.

  20. Study of Beijiang catchment flash-flood forecasting model

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Huang, S.; Dong, Y.

    2015-05-01

    Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.

  1. Metropolitan and state economic regions (MASTER) model - overview

    SciTech Connect

    Adams, R.C.; Moe, R.J.; Scott, M.J.

    1983-05-01

    The Metropolitan and State Economic Regions (MASTER) model is a unique multi-regional economic model designed to forecast regional economic activity and assess the regional economic impacts caused by national and regional economic changes (e.g., interest rate fluctuations, energy price changes, construction and operation of a nuclear waste storage facility, shutdown of major industrial operations). MASTER can be applied to any or all of the 268 Standard Metropolitan Statistical Areas (SMSAs) and 48 non-SMSA rest-of-state-areas (ROSAs) in the continental US. The model can also be applied to any or all of the continental US counties and states. This report is divided into four sections: capabilities and applications of the MASTER model, development of the model, model simulation, and validation testing.

  2. Fuzzy temporal logic based railway passenger flow forecast model.

    PubMed

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

  3. Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

    PubMed Central

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

  4. A study for systematic errors of the GLA forecast model in tropical regions

    NASA Technical Reports Server (NTRS)

    Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

    1988-01-01

    From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.

  5. A FORECASTING MODEL OF MANPOWER REQUIREMENTS IN THE HEALTH OCCUPATIONS.

    ERIC Educational Resources Information Center

    MAKI, DENNIS R.

    REPORTED IS THE DEVELOPMENT OF A MODEL, OR CONCEPTUAL FRAMEWORK, TO BE USED IN THE ANALYSIS OF THE NATURE OF THE SUPPLY AND DEMAND FOR HEALTH MANPOWER. THE MODEL IS DESIGNED TO PREDICT, UNDER CERTAIN ASSUMPTIONS, THE DEMAND, SUPPLY, EXCESS DEMAND, AND EMPLOYMENT OF HEALTH PERSONNEL FOR SOME PERIOD IN THE FUTURE. THE MANPOWER REQUIREMENT FORECASTS

  6. Multi-model data fusion for hydrological forecasting

    NASA Astrophysics Data System (ADS)

    See, Linda; Abrahart, Robert J.

    2001-10-01

    This paper outlines some simple data fusion strategies for continuous river level forecasting where data fusion is defined as the amalgamation of information from different data sources. The objective of data fusion is to provide a better solution than could otherwise be achieved from the use of single-source data alone. In this paper, the simplest data-in/data-out fusion architecture was used to combine neural network, fuzzy logic, statistical, and persistence forecasts using four different experimental strategies to produce a single predicted output. In the first two experiments, mean and median values were calculated from the individual forecasts and used as the final forecasts. These types of approaches can be effective when the individual model residuals follow a consistent pattern of over and under prediction. In the other two experiments, amalgamation was performed with a neural network, which provided a more flexible solution based on function approximation. The four individual model outputs were input to a one hidden layer, feed-forward network that had been trained to produce a single final forecast. The second network was similar to the first, except that differenced values were used as inputs and outputs. These various data fusion strategies were implemented using hydrological data for the River Ouse gauge at Skelton, above York, in Northern England. Neither the mean nor the median produced improved results, whereas the two neural network data fusion approaches produced substantial gains with respect to their single solution components. The potential to obtain more accurate forecasts using data fusion methodologies could therefore have significant implications for the design and construction of automated flood forecasting and flood warning systems.

  7. Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Bao, Hongjun; Zhao, Linna

    2012-02-01

    A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

  8. An updated subgrid orographic parameterization for global atmospheric forecast models

    NASA Astrophysics Data System (ADS)

    Choi, Hyun-Joo; Hong, Song-You

    2015-12-01

    A subgrid orographic parameterization (SOP) is updated by including the effects of orographic anisotropy and flow-blocking drag (FBD). The impact of the updated SOP on short-range forecasts is investigated using a global atmospheric forecast model applied to a heavy snowfall event over Korea on 4 January 2010. When the SOP is updated, the orographic drag in the lower troposphere noticeably increases owing to the additional FBD over mountainous regions. The enhanced drag directly weakens the excessive wind speed in the low troposphere and indirectly improves the temperature and mass fields over East Asia. In addition, the snowfall overestimation over Korea is improved by the reduced heat fluxes from the surface. The forecast improvements are robust regardless of the horizontal resolution of the model between T126 and T510. The parameterization is statistically evaluated based on the skill of the medium-range forecasts for February 2014. For the medium-range forecasts, the skill improvements of the wind speed and temperature in the low troposphere are observed globally and for East Asia while both positive and negative effects appear indirectly in the middle-upper troposphere. The statistical skill for the precipitation is mostly improved due to the improvements in the synoptic fields. The improvements are also found for seasonal simulation throughout the troposphere and stratosphere during boreal winter.

  9. Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

    EIA Publications

    2010-01-01

    The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

  10. Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

    NASA Astrophysics Data System (ADS)

    Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

    2015-04-01

    Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

  11. Retirement Forecasting. Evaluation of Models Shows Need for Information on Forecast Accuracy. Volume I. Report to the Chairman, Subcommittee on Social Security and Income Maintenance Programs, Committee on Finance, United States Senate.

    ERIC Educational Resources Information Center

    General Accounting Office, Washington, DC.

    The Government Accounting Office (GAO) reviewed 71 actuarial, behavioral, and economic models that are used for retirement forecasting, focusing on models of federal retirement program costs, civilian retirement decisions, and retirement income. GAO wished to determine to what extent the models have been documented, to what extent the models are…

  12. Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia

    PubMed Central

    Strager, Michael P.; Strager, Jacquelyn M.; Evans, Jeffrey S.; Dunscomb, Judy K.; Kreps, Brad J.; Maxwell, Aaron E.

    2015-01-01

    Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2) gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts. PMID:26090883

  13. Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

    NASA Astrophysics Data System (ADS)

    Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

    2013-04-01

    Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in imperfect conditions. The forecasting model testing centre uses a repository to hold all the data and models and a catalogue to hold all the corresponding metadata. It allows to: Data transfer: Upload experimental data: We have developed FAST (Flexible Automated Streaming Transfer) tool to upload data from RP laboratories to the repository. FAST sets up data transfer requirements and selects automatically the transfer protocol. Metadata are automatically created and stored. Web data access: Create synthetic data: Users can choose a generator and supply parameters. Synthetic data are automatically stored with corresponding metadata. Select data and models: Search the metadata using criteria design for RP. The metadata of each data (synthetic or from laboratory) and models are well-described through their respective catalogues accessible by the web portal. Upload models: Upload and store a model with associated metadata. This provide an opportunity to share models. The web portal solicits and creates metadata describing each model. Run model and visualise results: Selected data and a model to be submitted to a High Performance Computational resource hiding technical details. Results are displayed in accelerated time and stored allowing retrieval, inspection and aggregation. The forecasting model testing centre proposed could be integrated into EPOS. Its expected benefits are: Improved the understanding of brittle failure prediction and its scalability to natural phenomena. Accelerated and extensive testing and rapid sharing of insights. Increased impact and visibility of RP and GeoScience research. Resources for education and training. A key challenge is to agree the framework for sharing RP data and models. Our work is provocative first step.

  14. Drought Patterns Forecasting using an Auto-Regressive Logistic Model

    NASA Astrophysics Data System (ADS)

    del Jesus, M.; Sheffield, J.; Mndez Incera, F. J.; Losada, I. J.; Espejo, A.

    2014-12-01

    Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Nio-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

  15. Extreme value models for wind power forecast errors

    NASA Astrophysics Data System (ADS)

    Bacher, Peder; Madsen, Henrik; Pinson, Pierre; Mortensen, Stig B.; Nielsen, Henrik Aa.

    2015-04-01

    Models for extreme negative wind power forecast errors are presented in this paper. The models are applied to forecast levels below which the wind power very rarely drops. Such levels could be call called "certain-levels" or "guaranteed levels" of wind power, well knowing that full guarantee never can be given. The levels are obtained by building models for the error from already existing wind power forecasting software. The models are based on statistical extreme value techniques, which allows extrapolation beyond the available data period. In the study data from 1.5 years is used and return levels up to a 10 years return period are estimated. The data consists of hourly wind power production in the two regions of Denmark (DK1 and DK2) and corresponding wind power forecasts, which cover horizons from 1 to 42 hours ahead in time and are updated each hour. In the paper it is outlined how a suitable model is selected using statistical measures and tests, and finally the results are presented and evaluated.

  16. Networking Sensor Observations, Forecast Models & Data Analysis Tools

    NASA Astrophysics Data System (ADS)

    Falke, S. R.; Roberts, G.; Sullivan, D.; Dibner, P. C.; Husar, R. B.

    2009-12-01

    This presentation explores the interaction between sensor webs and forecast models and data analysis processes within service oriented architectures (SOA). Earth observation data from surface monitors and satellite sensors and output from earth science models are increasingly available through open interfaces that adhere to web standards, such as the OGC Web Coverage Service (WCS), OGC Sensor Observation Service (SOS), OGC Web Processing Service (WPS), SOAP-Web Services Description Language (WSDL), or RESTful web services. We examine the implementation of these standards from the perspective of forecast models and analysis tools. Interoperable interfaces for model inputs, outputs, and settings are defined with the purpose of connecting them with data access services in service oriented frameworks. We review current best practices in modular modeling, such as OpenMI and ESMF/Mapl, and examine the applicability of those practices to service oriented sensor webs. In particular, we apply sensor-model-analysis interfaces within the context of wildfire smoke analysis and forecasting scenario used in the recent GEOSS Architecture Implementation Pilot. Fire locations derived from satellites and surface observations and reconciled through a US Forest Service SOAP web service are used to initialize a CALPUFF smoke forecast model. The results of the smoke forecast model are served through an OGC WCS interface that is accessed from an analysis tool that extract areas of high particulate matter concentrations and a data comparison tool that compares the forecasted smoke with Unattended Aerial System (UAS) collected imagery and satellite-derived aerosol indices. An OGC WPS that calculates population statistics based on polygon areas is used with the extract area of high particulate matter to derive information on the population expected to be impacted by smoke from the wildfires. We described the process for enabling the fire location, smoke forecast, smoke observation, and population statistics services to be registered with the GEOSS registry and made findable through the GEOSS Clearinghouse. The fusion of data sources and different web service interfaces illustrate the agility in using standard interfaces and help define the type of input and output interfaces needed to connect models and analysis tools within sensor webs.

  17. Time series modelling and forecasting of emergency department overcrowding.

    PubMed

    Kadri, Farid; Harrou, Fouzi; Chaabane, Sonds; Tahon, Christian

    2014-09-01

    Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand. PMID:25053208

  18. 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.

  19. Comparison of Conventional and ANN Models for River Flow Forecasting

    NASA Astrophysics Data System (ADS)

    Jain, A.; Ganti, R.

    2011-12-01

    Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

  20. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer

    Klein, Stephen

    2008-01-15

    Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

  1. Adaptation of Mesoscale Weather Models to Local Forecasting

    NASA Technical Reports Server (NTRS)

    Manobianco, John T.; Taylor, Gregory E.; Case, Jonathan L.; Dianic, Allan V.; Wheeler, Mark W.; Zack, John W.; Nutter, Paul A.

    2003-01-01

    Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.

  2. Probabilistic Forecasting of Life and Economic Losses due to Natural Disasters

    NASA Astrophysics Data System (ADS)

    Barton, C. C.; Tebbens, S. F.

    2014-12-01

    The magnitude of natural hazard events such as hurricanes, tornadoes, earthquakes, and floods are traditionally measured by wind speed, energy release, or discharge. In this study we investigate the scaling of the magnitude of individual events of the 20th and 21stcentury in terms of economic and life losses in the United States and worldwide. Economic losses are subdivided into insured and total losses. Some data sets are inflation or population adjusted. Forecasts associated with these events are of interest to insurance, reinsurance, and emergency management agencies. Plots of cumulative size-frequency distributions of economic and life loss are well-fit by power functions and thus exhibit self-similar scaling. This self-similar scaling property permits use of frequent small events to estimate the rate of occurrence of less frequent larger events. Examining the power scaling behavior of loss data for disasters permits: forecasting the probability of occurrence of a disaster over a wide range of years (1 to 10 to 1,000 years); comparing losses associated with one type of disaster to another; comparing disasters in one region to similar disasters in another region; and, measuring the effectiveness of planning and mitigation strategies. In the United States, life losses due to flood and tornado cumulative-frequency distributions have steeper slopes, indicating that frequent smaller events contribute the majority of losses. In contrast, life losses due to hurricanes and earthquakes have shallower slopes, indicating that the few larger events contribute the majority of losses. Disaster planning and mitigation strategies should incorporate these differences.

  3. 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...

  4. Performance assessment of models to forecast induced seismicity

    NASA Astrophysics Data System (ADS)

    Wiemer, Stefan; Karvounis, Dimitrios; Zechar, Jeremy; Király, Eszter; Kraft, Toni; Pio Rinaldi, Antonio; Catalli, Flaminia; Mignan, Arnaud

    2015-04-01

    Managing and mitigating induced seismicity during reservoir stimulation and operation is a critical prerequisite for many GeoEnergy applications. We are currently developing and validating so called 'Adaptive Traffic Light Systems' (ATLS), fully probabilistic forecast models that integrate all relevant data on the fly into a time-dependent hazard and risk model. The combined model intrinsically considers both aleatory and model-uncertainties, the robustness of the forecast is maximized by using a dynamically update ensemble weighting. At the heart of the ATLS approach are a variety of forecast models that range from purely statistical models, such as flow-controlled Epidemic Type Aftershock Sequence (ETAS) models, to models that consider various physical interaction mechanism (e.g., pore pressure changes, dynamic and static stress transfer, volumetric strain changes). The automated re-calibration of these models on the fly given data imperfection, degrees of freedom, and time-constraints is a sizable challenge, as is the validation of the models for applications outside of their calibrated range (different settings, larger magnitudes, changes in physical processes etc.). Here we present an overview of the status of the model development, calibration and validation. We also demonstrate how such systems can contribute to a quantitative risk assessment and mitigation of induced seismicity in a wide range of applications and time scales.

  5. Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.

    2015-03-01

    The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.

  6. Development of Parallel Code for the Alaska Tsunami Forecast Model

    NASA Astrophysics Data System (ADS)

    Bahng, B.; Knight, W. R.; Whitmore, P.

    2014-12-01

    The Alaska Tsunami Forecast Model (ATFM) is a numerical model used to forecast propagation and inundation of tsunamis generated by earthquakes and other means in both the Pacific and Atlantic Oceans. At the U.S. National Tsunami Warning Center (NTWC), the model is mainly used in a pre-computed fashion. That is, results for hundreds of hypothetical events are computed before alerts, and are accessed and calibrated with observations during tsunamis to immediately produce forecasts. ATFM uses the non-linear, depth-averaged, shallow-water equations of motion with multiply nested grids in two-way communications between domains of each parent-child pair as waves get closer to coastal waters. Even with the pre-computation the task becomes non-trivial as sub-grid resolution gets finer. Currently, the finest resolution Digital Elevation Models (DEM) used by ATFM are 1/3 arc-seconds. With a serial code, large or multiple areas of very high resolution can produce run-times that are unrealistic even in a pre-computed approach. One way to increase the model performance is code parallelization used in conjunction with a multi-processor computing environment. NTWC developers have undertaken an ATFM code-parallelization effort to streamline the creation of the pre-computed database of results with the long term aim of tsunami forecasts from source to high resolution shoreline grids in real time. Parallelization will also permit timely regeneration of the forecast model database with new DEMs; and, will make possible future inclusion of new physics such as the non-hydrostatic treatment of tsunami propagation. The purpose of our presentation is to elaborate on the parallelization approach and to show the compute speed increase on various multi-processor systems.

  7. Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting

    NASA Astrophysics Data System (ADS)

    Butts, M. B.; Yamagata, K.; Kobor, J.; Fontenot, E.

    2008-05-01

    Operational forecasting systems must provide reliable, accurate and timely flood forecasts for a range of catchments from small rapidly responding mountain catchments and urban areas to large, complex but more slowly responding fluvial systems. Flood forecasting systems have evolved from simple forecasting for flood mitigation to real-time decision support systems for real-time reservoir operations for water supply, navigation, hydropower, for managing environmental flows and habitat protection, cooling water and water quality forecasting. These different requirements lead to a number of challenges in applying distributed modelling in an operational context. These challenges include, the often short time available for forecasting that requires a trade-off between model complexity and accuracy on the one hand and on the other hand the need for efficient calculations to reduce the computation times. Limitations in the data available in real-time require modelling tools that can not only operate on a minimum of data but also take advantage of new data sources such as weather radar, satellite remote sensing, wireless sensors etc. Finally, models must not only accurately predict flood peaks but also forecast low flows and surface water-groundwater interactions, water quality, water temperature, optimal reservoir levels, and inundated areas. This paper shows how these challenges are being addressed in a number of case studies. The central strategy has been to develop a flexible modelling framework that can be adapted to different data sources, different levels of complexity and spatial distribution and different modelling objectives. The resulting framework allows amongst other things, optimal use of grid-based precipitation fields from weather radar and numerical weather models, direct integration of satellite remote sensing, a unique capability to treat a range of new forecasting problems such as flooding conditioned by surface water-groundwater interactions. Results from flood modelling on the Odra River in Poland show that this model system can perform as well as traditional models and gives good predictions in mountainous catchments. By allowing different process representations to be applied within the same framework, it is possible to develop hydrological models in a phased manner. This phased approach was used for example in the Napa Valley, California where it is important to balance water demands for urban areas, agriculture, and ecosystem preservation while maintaining flood protection and water quality. A first regional model was developed with a detailed description of the surface process and a simple linear reservoir was used to simulate the groundwater component. Then a more detailed fully-distributed finite-difference groundwater model was constructed within the same framework while maintaining the surface water components. In the DMIP case study, Blue River, Oklahoma, this flexibility has been used to evaluate the performance of different model structures, and to determine the impact of grid resolution on model accuracy. The results show clear limits to the benefit attained by increasing model complexity and resolution. In contrast, detailed flood mapping using high resolution topography carried out with this tool in South Boulder Creek, Colorado show that very detailed description of the topography and flows paths are required for accurate flood mapping and determination of the flood risk. This framework is now being used to develop a flood forecasting system for the Big Cypress Basin in Florida.

  8. Retrospective tests of hybrid operational earthquake forecasting models for Canterbury

    NASA Astrophysics Data System (ADS)

    Rhoades, D. A.; Liukis, M.; Christophersen, A.; Gerstenberger, M. C.

    2016-01-01

    The Canterbury, New Zealand, earthquake sequence, which began in September 2010, occurred in a region of low crustal deformation and previously low seismicity. Because, the ensuing seismicity in the region is likely to remain above previous levels for many years, a hybrid operational earthquake forecasting model for Canterbury was developed to inform decisions on building standards and urban planning for the rebuilding of Christchurch. The model estimates occurrence probabilities for magnitudes M ≥ 5.0 in the Canterbury region for each of the next 50 yr. It combines two short-term, two medium-term and four long-term forecasting models. The weight accorded to each individual model in the operational hybrid was determined by an expert elicitation process. A retrospective test of the operational hybrid model and of an earlier informally developed hybrid model in the whole New Zealand region has been carried out. The individual and hybrid models were installed in the New Zealand Earthquake Forecast Testing Centre and used to make retrospective annual forecasts of earthquakes with magnitude M > 4.95 from 1986 on, for time-lags up to 25 yr. All models underpredict the number of earthquakes due to an abnormally large number of earthquakes in the testing period since 2008 compared to those in the learning period. However, the operational hybrid model is more informative than any of the individual time-varying models for nearly all time-lags. Its information gain relative to a reference model of least information decreases as the time-lag increases to become zero at a time-lag of about 20 yr. An optimal hybrid model with the same mathematical form as the operational hybrid model was computed for each time-lag from the 26-yr test period. The time-varying component of the optimal hybrid is dominated by the medium-term models for time-lags up to 12 yr and has hardly any impact on the optimal hybrid model for greater time-lags. The optimal hybrid model is considerably more informative than the operational hybrid model at long time-lags, but less so when the period of the Canterbury earthquakes is excluded from the tests. The results highlight the value of including medium-term models and a range of long-term models in operational forecasting. Based on the tests carried out here, the operational hybrid model is expected to outperform most of the individual models in the next 25 yr.

  9. A New Hybrid STEP/Coulomb model for Aftershock Forecasting

    NASA Astrophysics Data System (ADS)

    Steacy, S.; Jimenez, A.; Gerstenberger, M.

    2014-12-01

    Aftershock forecasting models tend to fall into two classes - purely statistical approaches based on clustering, b-value, and the Omori-Utsu law; and Coulomb rate-state models which relate the forecast increase in rate to the magnitude of the Coulomb stress change. Recently, hybrid models combining physical and statistical forecasts have begun to be developed, for example by Bach and Hainzl (2012) and Steacy et al. (2013). The latter approach combined Coulomb stress patterns with the STEP (short-term earthquake probability) model by redistributing expected rate from areas with decreased stress to regions where the stress had increased. The chosen 'Coulomb Redistribution Parameter' (CRP) was 0.93, based on California earthquakes, which meant that 93% of the total rate was expected to occur where the stress had increased. The model was tested against the Canterbury sequence and the main result was that the new model performed at least as well as, and often better than, STEP when tested against retrospective data but that STEP was generally better in pseudo-prospective tests that involved data actually available within the first 10 days of each event of interest. The authors suggested that the major reason for this discrepancy was uncertainty in the slip models and, particularly, in the geometries of the faults involved in each complex major event. Here we develop a variant of the STEP/Coulomb model in which the CRP varies based on the percentage of aftershocks that occur in the positively stressed areas during the forecast learning period. We find that this variant significantly outperforms both STEP and the previous hybrid model in almost all cases, even when the input Coulomb model is quite poor. Our results suggest that this approach might be more useful than Coulomb rate-state when the underlying slip model is not well constrained due to the dependence of that method on the magnitude of the Coulomb stress change.

  10. Volcanic ash forecast transport and dispersion (VAFTAD) model

    SciTech Connect

    Heffter, J.L.; Stunder, B.J.B.

    1993-12-01

    The National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL) has developed a Volcanic Ash Forecast Transport And Dispersion (VAFTAD) model for emergency response use focusing on hazards to aircraft flight operations. The model is run on a workstation at ARL. Meteorological input for the model is automatically downloaded from the NOAA National Meteorological Center (NMC) twice-daily forecast model runs to ARL. Additional input for VAFTAD ragarding the volcanic eruption is supplied by the user guided by monitor prompts. The model calculates transport and dispersion of volcanic ash from an initial ash cloud that has reached its maximum height within 3 h of eruption time. The model assumes that spherical ash particles of diameters ranging from 0.3 to 30 micrometers are distributed throughout the initial cloud with a particle number distribution based on Mount St. Helens and Redoubt Volcano eruptions. Particles are advected horizontally and vertically by the winds and fall according to Stoke`s law with a slip correction. A bivariate-normal distribution is used for horizontally diffusing the cloud and determining ash concentrations. Model output gives maps with symbols representing relative concentrations in three flight layers, and throughout the entire ash cloud, for sequential 6- and 12-h time intervals. A verification program for VAFTAD has been started. Results subjectively comparing model ash cloud forecasts with satellite imagery for three separate 1992 eruptions of Mount Spurr in Alaska have been most encouraging.

  11. Forecasting Lightning Threat using Cloud-resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    McCaul, E. W., Jr.; Goodman, S. J.; LaCasse, K. M.; Cecil, D. J.

    2009-01-01

    As numerical forecasts capable of resolving individual convective clouds become more common, it is of interest to see if quantitative forecasts of lightning flash rate density are possible, based on fields computed by the numerical model. Previous observational research has shown robust relationships between observed lightning flash rates and inferred updraft and large precipitation ice fields in the mixed phase regions of storms, and that these relationships might allow simulated fields to serve as proxies for lightning flash rate density. It is shown in this paper that two simple proxy fields do indeed provide reasonable and cost-effective bases for creating time-evolving maps of predicted lightning flash rate density, judging from a series of diverse simulation case study events in North Alabama for which Lightning Mapping Array data provide ground truth. One method is based on the product of upward velocity and the mixing ratio of precipitating ice hydrometeors, modeled as graupel only, in the mixed phase region of storms at the -15\\dgc\\ level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domainwide statistics of the peak values of simulated flash rate proxy fields against domainwide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. A blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Weather Research and Forecast Model simulations of selected North Alabama cases show that this model can distinguish the general character and intensity of most convective events, and that the proposed methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because models tend to have more difficulty in correctly predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models, the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of cloud-allowing forecasts become available.

  12. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  13. Brief Report: Forecasting the Economic Burden of Autism in 2015 and 2025 in the United States.

    PubMed

    Leigh, J Paul; Du, Juan

    2015-12-01

    Few US estimates of the economic burden of autism spectrum disorders (ASD) are available and none provide estimates for 2015 and 2025. We forecast annual direct medical, direct non-medical, and productivity costs combined will be $268 billion (range $162-$367 billion; 0.884-2.009 % of GDP) for 2015 and $461 billion (range $276-$1011 billion; 0.982-3.600 % of GDP) for 2025. These 2015 figures are on a par with recent estimates for diabetes and attention deficit and hyperactivity disorder (ADHD) and exceed the costs of stroke and hypertension. If the prevalence of ASD continues to grow as it has in recent years, ASD costs will likely far exceed those of diabetes and ADHD by 2025. PMID:26183723

  14. 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.

  15. operational modelling and forecasting of the Iberian shelves ecosystem

    NASA Astrophysics Data System (ADS)

    Marta-Almeida, M.; Reboreda, R.; Rocha, C.; Dubert, J.; Nolasco, R.; Cordeiro, N.; Luna, T.; Rocha, A.; Silva, J. Lencart e.; Queiroga, H.; Peliz, A.; Ruiz-Villarreal, M.

    2012-04-01

    There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a NPZD biogeochemical module. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmolN m-3). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.

  16. Towards Operational Modeling and Forecasting of the Iberian Shelves Ecosystem

    PubMed Central

    Marta-Almeida, Martinho; Reboreda, Rosa; Rocha, Carlos; Dubert, Jesus; Nolasco, Rita; Cordeiro, Nuno; Luna, Tiago; Rocha, Alfredo; Lencart e Silva, João D.; Queiroga, Henrique; Peliz, Alvaro; Ruiz-Villarreal, Manuel

    2012-01-01

    There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD). In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m−3). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill. PMID:22666349

  17. A Dirichlet process model for classifying and forecasting epidemic curves

    PubMed Central

    2014-01-01

    Background A forecast can be defined as an endeavor to quantitatively estimate a future event or probabilities assigned to a future occurrence. Forecasting stochastic processes such as epidemics is challenging since there are several biological, behavioral, and environmental factors that influence the number of cases observed at each point during an epidemic. However, accurate forecasts of epidemics would impact timely and effective implementation of public health interventions. In this study, we introduce a Dirichlet process (DP) model for classifying and forecasting influenza epidemic curves. Methods The DP model is a nonparametric Bayesian approach that enables the matching of current influenza activity to simulated and historical patterns, identifies epidemic curves different from those observed in the past and enables prediction of the expected epidemic peak time. The method was validated using simulated influenza epidemics from an individual-based model and the accuracy was compared to that of the tree-based classification technique, Random Forest (RF), which has been shown to achieve high accuracy in the early prediction of epidemic curves using a classification approach. We also applied the method to forecasting influenza outbreaks in the United States from 1997–2013 using influenza-like illness (ILI) data from the Centers for Disease Control and Prevention (CDC). Results We made the following observations. First, the DP model performed as well as RF in identifying several of the simulated epidemics. Second, the DP model correctly forecasted the peak time several days in advance for most of the simulated epidemics. Third, the accuracy of identifying epidemics different from those already observed improved with additional data, as expected. Fourth, both methods correctly classified epidemics with higher reproduction numbers (R) with a higher accuracy compared to epidemics with lower R values. Lastly, in the classification of seasonal influenza epidemics based on ILI data from the CDC, the methods’ performance was comparable. Conclusions Although RF requires less computational time compared to the DP model, the algorithm is fully supervised implying that epidemic curves different from those previously observed will always be misclassified. In contrast, the DP model can be unsupervised, semi-supervised or fully supervised. Since both methods have their relative merits, an approach that uses both RF and the DP model could be beneficial. PMID:24405642

  18. Constraints on Rational Model Weighting, Blending and Selecting when Constructing Probability Forecasts given Multiple Models

    NASA Astrophysics Data System (ADS)

    Higgins, S. M. W.; Du, H. L.; Smith, L. A.

    2012-04-01

    Ensemble forecasting on a lead time of seconds over several years generates a large forecast-outcome archive, which can be used to evaluate and weight "models". Challenges which arise as the archive becomes smaller are investigated: in weather forecasting one typically has only thousands of forecasts however those launched 6 hours apart are not independent of each other, nor is it justified to mix seasons with different dynamics. Seasonal forecasts, as from ENSEMBLES and DEMETER, typically have less than 64 unique launch dates; decadal forecasts less than eight, and long range climate forecasts arguably none. It is argued that one does not weight "models" so much as entire ensemble prediction systems (EPSs), and that the marginal value of an EPS will depend on the other members in the mix. The impact of using different skill scores is examined in the limits of both very large forecast-outcome archives (thereby evaluating the efficiency of the skill score) and in very small forecast-outcome archives (illustrating fundamental limitations due to sampling fluctuations and memory in the physical system being forecast). It is shown that blending with climatology (J. Bröcker and L.A. Smith, Tellus A, 60(4), 663-678, (2008)) tends to increase the robustness of the results; also a new kernel dressing methodology (simply insuring that the expected probability mass tends to lie outside the range of the ensemble) is illustrated. Fair comparisons using seasonal forecasts from the ENSEMBLES project are used to illustrate the importance of these results with fairly small archives. The robustness of these results across the range of small, moderate and huge archives is demonstrated using imperfect models of perfectly known nonlinear (chaotic) dynamical systems. The implications these results hold for distinguishing the skill of a forecast from its value to a user of the forecast are discussed.

  19. New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

    ERIC Educational Resources Information Center

    Song, Qiang; Chissom, Brad S.

    Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

  20. High Resolution MOS Forecasts Based on a Low Resolution Model

    NASA Astrophysics Data System (ADS)

    Knpffer, K.

    2009-09-01

    MOS (Model Output Statistics) reduces approximately 50% of the error variance of the Direct Model Output forecasts of a numerical model - no matter whether high or low resolution - for standard weather elements like temperatures, wind and cloud cover. However, MOS forecasts are usually only provided at the very places where the observation locations are situated. In the presentation, a technology will be outlined which allows MOS forecasts for any location between the observation locations. Meteo Service has introduced a technology which relies on the interpolation of MOS coefficients (not MOS forecasts). This technology based on orographic descriptors has already been in use with the German National Weather Service (DWD) since 1996. These orographic descriptors refer to latitude, longitude, elevation and the difference between the elevation of the smoothed model orography, and the real elevation. This difference is used in order to distinguish between mountain and valley. In addition, an attribute such as land, sea, coast and mountain summits has been used as binary orographic descriptor. Nowadays high resolution grid information is available, and more complex orographic descriptors can be used. NOAA/NGDC orographic data is available in a resolution of approximately 0,5 x 0,5 km and allows for the definition of the following orographic descriptors with different spatial smoothing: - elevation - slope (= first derivative of elevation) - valley/hill (= second derivative of elevation). A difference between the weighted orographic desriptors is used to define the representativeness between interpolation points and observation locations. This representativeness is the norm of a multi-dimensional vector with the number of dimensions being equal to the number of orographic descriptors. A procedure to search for the most informative set of representative observation locations for a given interpolation point is applied. Finally interpolation weights are calculated for the selected observation locations. Results of the application of the concept of coefficient interpolation outlined above are presented using MOS forecasts for homogeneous, alpine and coastal orography. The results are convincing in particular, in areas where there is sufficient density of observation locations. Problems remain in areas with lower density of observation locations and complex orography. Interpolated Direct Model Output in the last resort is to be used in order to achieve complete fields of high resolution MOS forecasts based on a low resolution model.

  1. ENSO informed Drought Forecasting Using Nonhomogeneous Hidden Markov Chain Model

    NASA Astrophysics Data System (ADS)

    Kwon, H.; Yoo, J.; Kim, T.

    2013-12-01

    The study aims at developing a new scheme to investigate the potential use of ENSO (El Niño/Southern Oscillation) for drought forecasting. In this regard, objective of this study is to extend a previously developed nonhomogeneous hidden Markov chain model (NHMM) to identify climate states associated with drought that can be potentially used to forecast drought conditions using climate information. As a target variable for forecasting, SPI(standardized precipitation index) is mainly utilized. This study collected monthly precipitation data over 56 stations that cover more than 30 years and K-means cluster analysis using drought properties was applied to partition regions into mutually exclusive clusters. In this study, six main clusters were distinguished through the regionalization procedure. For each cluster, the NHMM was applied to estimate the transition probability of hidden states as well as drought conditions informed by large scale climate indices (e.g. SOI, Nino1.2, Nino3, Nino3.4, MJO and PDO). The NHMM coupled with large scale climate information shows promise as a technique for forecasting drought scenarios. A more detailed explanation of large scale climate patterns associated with the identified hidden states will be provided with anomaly composites of SSTs and SLPs. Acknowledgement This research was supported by a grant(11CTIPC02) from Construction Technology Innovation Program (CTIP) funded by Ministry of Land, Transport and Maritime Affairs of Korean government.

  2. Daily reservoir inflow forecasting combining QPF into ANNs model

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

    2009-01-01

    Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

  3. A flood routing Muskingum type simulation and forecasting model based on level data alone

    NASA Astrophysics Data System (ADS)

    Franchini, Marco; Lamberti, Paolo

    1994-07-01

    While the use of remote hydrometers for measuring the level in water courses is both economical and widespread, the same cannot be said for cross section and channel profile measurements and, even less, for rating curves at the measuring cross sections, all of which are more often than not incomplete, out of date, and unreliable. The mass of data involved in level measurements alone induces a degree of perplexity in those who try to use them, for example, for flood event simulations or the construction of forecasting models which are not purely statistical. This paper proposes a method which uses recorded level data alone to construct a simulation model and a forecasting model, both of them characterized by an extremely simple structure that can be used on any pocket calculator. These models, referring to a river reach bounded by two measuring sections, furnish the downstream levels, where the upstream levels are known, and the downstream level at time t + ?t*, where the upstream and downstream levels are known at time t, respectively. The numerical applications performed show that while the simulation model is somewhat penalized by the simplifications adopted, giving not consistently satisfactory results on validation, the forecasting model generated good results in all the cases examined and seems reliable.

  4. Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data.

    PubMed

    Figura, Simon; Livingstone, David M; Kipfer, Rolf

    2015-01-01

    Although temperature is an important determinant of many biogeochemical processes in groundwater, very few studies have attempted to forecast the response of groundwater temperature to future climate warming. Using a composite linear regression model based on the lagged relationship between historical groundwater and regional air temperature data, empirical forecasts were made of groundwater temperature in several aquifers in Switzerland up to the end of the current century. The model was fed with regional air temperature projections calculated for greenhouse-gas emissions scenarios A2, A1B, and RCP3PD. Model evaluation revealed that the approach taken is adequate only when the data used to calibrate the models are sufficiently long and contain sufficient variability. These conditions were satisfied for three aquifers, all fed by riverbank infiltration. The forecasts suggest that with respect to the reference period 1980 to 2009, groundwater temperature in these aquifers will most likely increase by 1.1 to 3.8 K by the end of the current century, depending on the greenhouse-gas emissions scenario employed. PMID:25412761

  5. Intercomparison of mesoscale meteorological models for precipitation forecasting

    NASA Astrophysics Data System (ADS)

    Richard, E.; Cosma, S.; Benoit, R.; Binder, P.; Buzzi, A.; Kaufmann, P.

    In the framework of the RAPHAEL EU project, a series of past heavy precipitation events has been simulated with different meteorological models. Rainfall hindcasts and forecasts have been produced by four models in use at various meteorological services or research centres of Italy, Canada, France and Switzerland. The paper is focused on the comparison of the computed precipitation fields with the available surface observations. The comparison is carried out for three meteorological situations which lead to severe flashflood over the Toce-Ticino catchment in Italy (6599 km2) or the Ammer catchment (709 km2) in Germany. The results show that all four models reproduced the occurrence of these heavy precipitation events. The accuracy of the computed precipitation appears to be more case-dependent than model-dependent. The sensitivity of the computed rainfall to the boundary conditions (hindcast v. forecast) was found to be rather weak, indicating that a flood forecasting system based upon a numerical meteo-hydrological simulation could be feasible in an operational context.

  6. Toward Improved Solar Irradiance Forecasts: a Simulation of Deep Planetary Boundary Layer with Scattered Clouds Using the Weather Research and Forecasting Model

    NASA Astrophysics Data System (ADS)

    Kim, Chang Ki; Leuthold, Michael; Holmgren, William F.; Cronin, Alexander D.; Betterton, Eric A.

    2016-02-01

    Accurate forecasts of solar irradiance are required for electric utilities to economically integrate substantial amounts of solar power into their power generation portfolios. A common failing of numerical weather models is the prediction of scattered clouds at the top of deep PBL which are generally difficult to be resolved due to complicated processes in the planetary boundary layer. We improved turbulence parameterization for better predicting solar irradiance during the scattered clouds' events using the Weather Research and Forecasting model. Sensitivity tests show that increasing the exchange coefficient leads to enhanced vertical mixing and a deeper mixed layer. At the top of mixed layer, an adiabatically ascending air parcel achieved the water vapor saturation and finally scattered cloud is generated.

  7. Forecast improvement by interactive ensemble of atmospheric models

    NASA Astrophysics Data System (ADS)

    Basnarkov, L.; Duane, G. S.; Kocarev, L.

    2013-12-01

    The advances in weather forecast traditionally have been based on two lines of improvement: 1 - deepening the understanding of physical phenomena that underlies the atmospheric dynamics; and 2 - steady increase in computer power that enables use of finer grid resolution. The meteorological centers model dynamics of the atmosphere with the same basic physical laws, but sometimes take different approaches in capturing small-scale phenomena and generally use different grid sizes. As a result there are dozens operational models around the globe with various parameterizations of the unresolved processes. Newest attempts in forecast improvements are based on using ensemble prediction. Multiple outputs are taken from runs with perturbed initial conditions, or perturbed parameter values. A novel paradigm is exploiting dynamical exchange of variables between simultaneously running models. There are already simulations of models exchanging fluxes between ocean and atmospheric models, but examples with direct coupling of different atmospheric models are rather new. Within this approach the coupling schemes can be different, but as simplest appear those that combine corresponding dynamical variables or tendency components. In this work we present results with an artificial toy model-Lorenz 96 model. To make more faithful example as reality (the atmosphere) is considered one Lorenz 96 class III system, while as its imperfect models are taken three class II systems that have different forcing terms. These resemble the models used in three different meteorological centers. The interactive ensemble has tendency that is weighted combination of the individual models' tendencies. The weights are obtained with statistical techniques based on past observations that target to minimize the mismatch between the truth's and interactive ensemble's tendencies. By means of anomaly correlation it is numerically verified that this ensemble has longer range of forecast than the individual models.

  8. Long-Term Economic and Labor Forecast Trends for Washington. 1996.

    ERIC Educational Resources Information Center

    Lefberg, Irv; And Others

    This publication provides actual historical and long-term forecast data on labor force, total wage and salary employment, industry employment, and personal income for the state of Washington. The data are based upon the Washington Office of Financial Management long-term population forecast. Chapter 1 presents long-term forecasts of Washington…

  9. Assessing Forecasting Uncertainties for Improved Snow Model Predictions

    NASA Astrophysics Data System (ADS)

    He, M.; Hogue, T.; Franz, K.; Margulis, S.

    2008-12-01

    Snowpack is a significant fraction of available water resources in many regions of the nation. Studies demonstrate that atmospheric warming over the past 50 years has led to a steady decline in snowpack depth as well as altered melt patterns across large regions of the western U.S. Changes in snowpack volume and spring melt timing obviously alter the distribution of water resources. The National Weather Service currently applies the SNOW17 model for operational forecasting of snow accumulation and melt in snow-dominated areas in the nation, relying on calibrated parameters and manual assimilation of observed model states including snow water equivalent (SWE) measurements. The current research aims to improve the predictability of the SNOW17 model by addressing forecasting uncertainties (parameters, forcing, SWE measurements, initial conditions, etc.) and using the established uncertainties to formulate an operational data assimilation framework. Initial work focuses on six SNOTEL sites in the Western U.S with a range of climatic and geographic conditions. A generalized sensitivity analysis (GSA) method is applied to identify sensitive parameters, identifiable parameter ranges, and correlations for parameters of the SNOW17 model. Impacts of uncertainties in precipitation forcing on parameter sensitivity are also investigated. Potential influences of climatic and geographic factors on parameter sensitivities are assessed. Based on the sensitivity and uncertainty analysis results, an ensemble Kalman filter framework is formulated and tested at several SNOTEL sites. Preliminary results indicate parameter sensitivity generally varies with climatic and geographic conditions, and that introducing uncertainties in precipitation forcing reduces overall sensitivity and identifiability of parameters. Improved snow state and snowmelt estimates are expected to improve short- and long-term streamflow predictions. The sensitivity and uncertainty analysis method as well as the proposed filter framework will assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

  10. Comparative verification between GEM model and official aviation terminal forecasts

    NASA Technical Reports Server (NTRS)

    Miller, Robert G.

    1988-01-01

    The Generalized Exponential Markov (GEM) model uses the local standard airways observation (SAO) to predict hour-by-hour the following elements: temperature, pressure, dew point depression, first and second cloud-layer height and amount, ceiling, total cloud amount, visibility, wind, and present weather conditions. GEM is superior to persistence at all projections for all elements in a large independent sample. A minute-by-minute GEM forecasting system utilizing the Automated Weather Observation System (AWOS) is under development.

  11. Operational hydrological ensemble forecasts in France, taking into account rainfall and hydrological model uncertainties.

    NASA Astrophysics Data System (ADS)

    Mathevet, T.; Garavaglia, F.; Gailhard, J.; Garon, R.; Dubus, L.

    2009-09-01

    In operational conditions, the actual quality of meteorological and hydrological forecasts do not allow decision-making in a certain future. In this context, meteorological and hydrological ensemble forecasts allow a better representation of forecasts uncertainties. Compared to classical deterministic forecasts, ensemble forecasts improve the human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. In this paper, we present a hydrological ensemble forecasting system under development at EDF (French Hydropower Company). Our results were updated, taking into account a longer rainfall forecasts archive. Our forecasting system both takes into account rainfall forecasts uncertainties and hydrological model forecasts uncertainties. Hydrological forecasts were generated using the MORDOR model (Andreassian et al., 2006), developed at EDF and used on a daily basis in operational conditions on a hundred of watersheds. Two sources of rainfall forecasts were used : one is based on ECMWF forecasts, another is based on an analogues approach (Obled et al., 2002). Two methods of hydrological model forecasts uncertainty estimation were used : one is based on the use of equifinal parameter sets (Beven & Binley, 1992), the other is based on the statistical modelisation of the hydrological forecast empirical uncertainty (Montanari et al., 2004 ; Schaefli et al., 2007). Daily operational hydrological 7-day ensemble forecasts during 4 years (from 2005 to 2008) in few alpine watersheds were evaluated. Finally, we present a way to combine rainfall and hydrological model forecast uncertainties to achieve a good probabilistic calibration. Our results show that the combination of ECMWF and analogues-based rainfall forecasts allow a good probabilistic calibration of rainfall forecasts. They show also that the statistical modeling of the hydrological forecast empirical uncertainty has a better probabilistic calibration, than the equifinal parameter set approach. Andreassian et al., 2006. Catalogue of the models used in MOPEX 2004/2005. Large sample basin experiments for hydrological mode parameterisation : results of the Model Parameter Experiment, IAHS Publ. 307, 41-94. Beven & Binley, 1992. The future of distributed models : model calibration and uncertainty prediction. Hydrological Processes, 6, 279-298. Obled, C., Bontron, G., Garon, R., 2002. Quantitative precipitation forecasts: a statistical adaptation of model outputs though an analogues sorting approach. Atmospheric Research, 63, 303-324. Montanari, A. and Brath, A., (2004). A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., (2007). Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.

  12. Models for forecasting the flowering of Cornicabra olive groves.

    PubMed

    Rojo, Jesús; Pérez-Badia, Rosa

    2015-11-01

    This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period. PMID:25656796

  13. Models for forecasting the flowering of Cornicabra olive groves

    NASA Astrophysics Data System (ADS)

    Rojo, Jesús; Pérez-Badia, Rosa

    2015-11-01

    This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.

  14. Comparison of modelling techniques for milk-production forecasting.

    PubMed

    Murphy, M D; O'Mahony, M J; Shalloo, L; French, P; Upton, J

    2014-01-01

    The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ? 12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%)=8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%)=12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%)=10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions. PMID:24731634

  15. Identification and Forecasting in Mortality Models

    PubMed Central

    Nielsen, Jens P.

    2014-01-01

    Mortality models often have inbuilt identification issues challenging the statistician. The statistician can choose to work with well-defined freely varying parameters, derived as maximal invariants in this paper, or with ad hoc identified parameters which at first glance seem more intuitive, but which can introduce a number of unnecessary challenges. In this paper we describe the methodological advantages from using the maximal invariant parameterisation and we go through the extra methodological challenges a statistician has to deal with when insisting on working with ad hoc identifications. These challenges are broadly similar in frequentist and in Bayesian setups. We also go through a number of examples from the literature where ad hoc identifications have been preferred in the statistical analyses. PMID:24987729

  16. A first large-scale flood inundation forecasting model

    SciTech Connect

    Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.

    2013-11-04

    At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode revealed that it is crucial to account for basin-wide hydrological response time when assessing lead time performances notwithstanding structural limitations in the hydrological model and possibly large inaccuracies in precipitation data.

  17. The Forecasting Model of Flight Delay Based On DMT-GMT Model

    NASA Astrophysics Data System (ADS)

    Ding, Jianli; Li, Huafeng

    In order to solve the problem of flight delay forecasting including the characteristics of airport flight operation, a new composite forecasting model based on the danger model theory and the grey model theory is proposed in this paper. The composite prediction method in this paper uses the pattern of weighted composition which is according to the occupancy proportion of the mean square errors forecasting .The model use the modified approach reflects the periodicity. The experimental result shows that the prediction results is qualified, the new model can meet the requirement of real-time prediction for the management of emergency departments.

  18. FORECAST MODEL FOR MODERATE EARTHQUAKES NEAR PARKFIELD, CALIFORNIA.

    USGS Publications Warehouse

    Stuart, William D.; Archuleta, Ralph J.; Lindh, Allan G.

    1985-01-01

    The paper outlines a procedure for using an earthquake instability model and repeated geodetic measurements to attempt an earthquake forecast. The procedure differs from other prediction methods, such as recognizing trends in data or assuming failure at a critical stress level, by using a self-contained instability model that simulates both preseismic and coseismic faulting in a natural way. In short, physical theory supplies a family of curves, and the field data select the member curves whose continuation into the future constitutes a prediction. Model inaccuracy and resolving power of the data determine the uncertainty of the selected curves and hence the uncertainty of the earthquake time.

  19. Economic Analysis. Computer Simulation Models.

    ERIC Educational Resources Information Center

    Sterling Inst., Washington, DC. Educational Technology Center.

    A multimedia course in economic analysis was developed and used in conjunction with the United States Naval Academy. (See ED 043 790 and ED 043 791 for final reports of the project evaluation and development model.) This volume of the text discusses the simulation of behavioral relationships among variable elements in an economy and presents

  20. Drift dynamics in a coupled model initialized for decadal forecasts

    NASA Astrophysics Data System (ADS)

    Sanchez-Gomez, Emilia; Cassou, Christophe; Ruprich-Robert, Yohan; Fernandez, Elodie; Terray, Laurent

    2016-03-01

    Drifts are always present in models when initialized from observed conditions because of intrinsic model errors; those potentially affect any type of climate predictions based on numerical experiments. Model drifts are usually removed through more or less sophisticated techniques for skill assessment, but they are rarely analysed. In this study, we provide a detailed physical and dynamical description of the drifts in the CNRM-CM5 coupled model using a set of decadal retrospective forecasts produced within CMIP5. The scope of the paper is to give some physical insights and lines of approach to, on one hand, implement more appropriate techniques of initialisation that minimize the drift in forecast mode, and on the other hand, eventually reduce the systematic biases of the models. We first document a novel protocol for ocean initialization adopted by the CNRM-CERFACS group for forecasting purpose in CMIP5. Initial states for starting dates of the predictions are obtained from a preliminary integration of the coupled model where full-field ocean surface temperature and salinity are restored everywhere to observations through flux derivative terms and full-field subsurface fields (below the prognostic ocean mixed layer) are nudged towards NEMOVAR reanalyses. Nudging is applied only outside the 15°S-15°N band allowing for dynamical balance between the depth and tilt of the tropical thermocline and the model intrinsic biased wind. A sensitivity experiment to the latitudinal extension of no-nudging zone (1°S-1°N instead of 15°, hereafter referred to as NOEQ) has been carried out. In this paper, we concentrate our analyses on two specific regions: the tropical Pacific and the North Atlantic basins. In the Pacific, we show that the first year of the forecasts is characterized by a quasi-systematic excitation of El Niño-Southern Oscillation (ENSO) warm events whatever the starting dates. This, through ocean-to-atmosphere heat transfer materialized by diabatic heating, can be viewed for the coupled model as an efficient way to rapidly adjust to its own biased climate mean state. Weak cold ENSO events tend to occur the second year of the forecast due to the so-called discharge-recharge mechanism while the spurious oscillatory behavior is progressively damped. The latter mechanism is much more pronounced in retrospective forecasts initialized from the NOEQ configuration for which the ENSO flip-flop is still detectable at leadtime 4 year. Associated atmospheric teleconnections interfere worldwide with regional drifts, especially in the North Pacific and more remotely in the North Atlantic. In the latter basin, the drift can be interpreted as the model response to intrinsic atmospheric circulation biases found in the stand-alone atmosphere component of the model, which project onto the negative phase of the North Atlantic Oscillation. A fast adjustment (up to ~5-year leadtime) occurs leading to a rapid slackening of both the vertical (Atlantic meridional overturning circulation) and horizontal circulations, especially in the subpolar gyre. Slower adjustment of the entire water masses distribution in the North Atlantic then takes over involving several mechanisms. We show that a weak feedback is locally present between the atmospheric circulation and the ocean drift that controls the timescale of the setting of the coupled model biases.

  1. Drift dynamics in a coupled model initialized for decadal forecasts

    NASA Astrophysics Data System (ADS)

    Sanchez-Gomez, Emilia; Cassou, Christophe; Ruprich-Robert, Yohan; Fernandez, Elodie; Terray, Laurent

    2015-06-01

    Drifts are always present in models when initialized from observed conditions because of intrinsic model errors; those potentially affect any type of climate predictions based on numerical experiments. Model drifts are usually removed through more or less sophisticated techniques for skill assessment, but they are rarely analysed. In this study, we provide a detailed physical and dynamical description of the drifts in the CNRM-CM5 coupled model using a set of decadal retrospective forecasts produced within CMIP5. The scope of the paper is to give some physical insights and lines of approach to, on one hand, implement more appropriate techniques of initialisation that minimize the drift in forecast mode, and on the other hand, eventually reduce the systematic biases of the models. We first document a novel protocol for ocean initialization adopted by the CNRM-CERFACS group for forecasting purpose in CMIP5. Initial states for starting dates of the predictions are obtained from a preliminary integration of the coupled model where full-field ocean surface temperature and salinity are restored everywhere to observations through flux derivative terms and full-field subsurface fields (below the prognostic ocean mixed layer) are nudged towards NEMOVAR reanalyses. Nudging is applied only outside the 15°S-15°N band allowing for dynamical balance between the depth and tilt of the tropical thermocline and the model intrinsic biased wind. A sensitivity experiment to the latitudinal extension of no-nudging zone (1°S-1°N instead of 15°, hereafter referred to as NOEQ) has been carried out. In this paper, we concentrate our analyses on two specific regions: the tropical Pacific and the North Atlantic basins. In the Pacific, we show that the first year of the forecasts is characterized by a quasi-systematic excitation of El Niño-Southern Oscillation (ENSO) warm events whatever the starting dates. This, through ocean-to-atmosphere heat transfer materialized by diabatic heating, can be viewed for the coupled model as an efficient way to rapidly adjust to its own biased climate mean state. Weak cold ENSO events tend to occur the second year of the forecast due to the so-called discharge-recharge mechanism while the spurious oscillatory behavior is progressively damped. The latter mechanism is much more pronounced in retrospective forecasts initialized from the NOEQ configuration for which the ENSO flip-flop is still detectable at leadtime 4 year. Associated atmospheric teleconnections interfere worldwide with regional drifts, especially in the North Pacific and more remotely in the North Atlantic. In the latter basin, the drift can be interpreted as the model response to intrinsic atmospheric circulation biases found in the stand-alone atmosphere component of the model, which project onto the negative phase of the North Atlantic Oscillation. A fast adjustment (up to ~5-year leadtime) occurs leading to a rapid slackening of both the vertical (Atlantic meridional overturning circulation) and horizontal circulations, especially in the subpolar gyre. Slower adjustment of the entire water masses distribution in the North Atlantic then takes over involving several mechanisms. We show that a weak feedback is locally present between the atmospheric circulation and the ocean drift that controls the timescale of the setting of the coupled model biases.

  2. A Feature Fusion Based Forecasting Model for Financial Time Series

    PubMed Central

    Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

    2014-01-01

    Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

  3. A feature fusion based forecasting model for financial time series.

    PubMed

    Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

    2014-01-01

    Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

  4. Model averaging methods to merge statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, A.; Wang, Q. J.

    2014-12-01

    The Australian Bureau of Meteorology operates a statistical seasonal streamflow forecasting service. It has also developed a dynamic seasonal streamflow forecasting approach. The two approaches produce similarly reliable forecasts in terms of ensemble spread but can differ in forecast skill depending on catchment and season. Therefore, it may be possible to augment the skill of the existing service by objectively weighting and merging the forecasts. Bayesian model averaging (BMA) is first applied to merge statistical and dynamic forecasts for 12 locations using leave-five-years-out cross-validation. It is seen that the BMA merged forecasts can sometimes be too uncertain, as shown by ensemble spreads that are unrealistically wide and even bi-modal. The BMA method applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. An alternative approach is quantile model averaging (QMA), whereby forecast variable values (quantiles) are averaged for a given cumulative probability (quantile fraction). For the 12 locations, QMA is compared to BMA. BMA and QMA perform similarly in terms of forecast accuracy skill scores and reliability in terms of ensemble spread. Both methods improve forecast skill across catchments and seasons by combining the different strengths of the statistical and dynamic approaches. A major advantage of QMA over BMA is that it always produces reasonably well defined forecast distributions, even in the special cases where BMA does not. Optimally estimated QMA weights and BMA weights are similar; however, BMA weights are more efficiently estimated.

  5. Generation of ensemble streamflow forecasts using an enhanced version of the snowmelt runoff model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1-15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt-dominated ...

  6. 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.

  7. The SOLAR2000 empirical solar irradiance model and forecast tool

    NASA Astrophysics Data System (ADS)

    Tobiska, W. K.; Woods, T.; Eparvier, F.; Viereck, R.; Floyd, L.; Bouwer, D.; Rottman, G.; White, O. R.

    2000-09-01

    SOLAR2000 is a collaborative project for accurately characterizing solar irradiance variability across the spectrum. A new image- and full-disk proxy empirical solar irradiance model, SOLAR2000, is being developed that is valid in the spectral range of 1-1,000,000 nm for historical modeling and forecasting throughout the solar system. The overarching scientific goal behind SOLAR2000 is to understand how the Sun varies spectrally and through time from X-ray through infrared wavelengths. This will contribute to answering key scientific questions and will aid national programmatic goals related to solar irradiance specification. SOLAR2000 is designed to be a fundamental energy input into planetary atmosphere models, a comparative model with numerical//first principles solar models, and a tool to model or predict the solar radiation component of the space environment. It is compliant with the developing International Standards Organization (ISO) solar irradiance standard. SOLAR2000 captures the essence of historically measured solar irradiances and this expands our knowledge about the quiet and variable Sun including its historical envelope of variability. The implementation of the SOLAR2000 is described, including the development of a new EUV proxy, E10.7, which has the same units as the commonly used F10.7. SOLAR2000 also provides an operational forecasting and global specification capability for solar irradiances and information can be accessed at the website address of http:///www.spacenvironment.net.

  8. Accuracy of short-term sea ice drift forecasts using a coupled ice-ocean model

    NASA Astrophysics Data System (ADS)

    Schweiger, Axel J.; Zhang, Jinlun

    2015-12-01

    Arctic sea ice drift forecasts of 6 h-9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h-8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high-resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24-48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast.

  9. Forecasting atmospheric angular momentum and length-of-day using operational meteorological models

    NASA Technical Reports Server (NTRS)

    Rosen, R. D.; Salstein, D. A.; Nehrkorn, T.; Dickey, J. O.; Eubanks, T. M.; Steppe, J. A.; Mccalla, M. R. P.; Miller, A. J.

    1990-01-01

    Forecasts of zonal wind fields produced by the medium-range forecast model of the U.S. National Meteorological Center are used to create predictions of the atmosphere's angular momentum at lead times of 1-10 days. The skill of these forecasts, which are of interest to those concerned with monitoring changes in the length-of-day for navigational purposes, is assessed, and the regions in the atmosphere that contribute most importantly to forecast errors are identified.

  10. Forecasting Lightning Threat using Cloud-Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    McCaul, Eugene W., Jr.; Goodman, Steven J.; LaCasse, Katherine M.; Cecil, Daniel J.

    2008-01-01

    Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models,the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of forecasts become available.

  11. Application Study of Empirical Model and Xiaohuajian Flood Forecasting Model in the Middle Yellow River

    NASA Astrophysics Data System (ADS)

    Hu, Caihong

    2013-04-01

    Xiaolandi-Huayuankou region is an important rainstorm centre in the middle Yellow river, which drainage area of 35883km2. A set of forecasting methods applied in this region was formed throughout years of practice. The Xiaohuajian flood forecasting model and empirical model were introduced in this paper. The simulated processes of the Xiaohuajian flood forecasting model include evapotranspiration, infiltration, runoff, river flow. Infiltration and surface runoff are calculated utilizing the Horton model for infiltration into multilayered soil profiles. Overland flow is routed by Nash instantaneous unit hydrograph and Section Muskingum method. The empirical model are simulated using P~Pa~R and empirical relation approach for runoff generation and concentration. The structures of these two models were analyzed and compared in detail. Yihe river basin located in Xiaolandi-Huayuankou region was selected for the purpose of the study. The results show that the accuracy of the two methods are similar, however, the accuracy of Xiaohuajian flood forecasting model for flood forecasting is relatively higher, especially the process of the flood; the accuracy of the empirical methods is much worse, but it can also be accept. The two models are both practicable, so the two models can be combined to apply. The result of the Xiaohuajian flood forecasting model can be used to guide the reservoir for flood control, and the result of empirical methods can be as a reference.

  12. 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.

  13. Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

    PubMed Central

    Dai, Yonghui; Han, Dongmei; Dai, Weihui

    2014-01-01

    The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659

  14. Modeling and computing of stock index forecasting based on neural network and Markov chain.

    PubMed

    Dai, Yonghui; Han, Dongmei; Dai, Weihui

    2014-01-01

    The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659

  15. Development of an Impact-Oriented Quantitative Coastal Inundation forecasting and early warning system with social and economic assessment

    NASA Astrophysics Data System (ADS)

    Fakhruddin, S. H. M.; Babel, Mukand S.; Kawasaki, Akiyuki

    2014-05-01

    Coastal inundations are an increasing threat to the lives and livelihoods of people living in low-lying, highly-populated coastal areas. According to a World Bank Report in 2005, at least 2.6 million people may have drowned due to coastal inundation, particularly caused by storm surges, over the last 200 years. Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. Recognizing this extreme vulnerability of coastal areas to inundation/flooding, and with a view to improve safety-related services for the community, research should strongly enhance today's forecasting, prediction and early warning capabilities in order to improve the assessment of coastal vulnerability and risks and develop adequate prevention, mitigation and preparedness measures. This paper tries to develop an impact-oriented quantitative coastal inundation forecasting and early warning system with social and economic assessment to address the challenges faced by coastal communities to enhance their safety and to support sustainable development, through the improvement of coastal inundation forecasting and warning systems.

  16. Verification of precipitation forecasts from two numerical weather prediction models in the Middle Atlantic Region of the USA: A precursory analysis to hydrologic forecasting

    NASA Astrophysics Data System (ADS)

    Siddique, Ridwan; Mejia, Alfonso; Brown, James; Reed, Seann; Ahnert, Peter

    2015-10-01

    Accurate precipitation forecasts are required for accurate flood forecasting. The structures of different precipitation forecasting systems are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding of, and accounting for, predictive uncertainty. Hence, routine verification is necessary to understand the quality of forecasts as inputs to hydrologic modeling. In this study, we verify precipitation forecasts from the National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2), as well as the 21-member Short Range Ensemble Forecast (SREF) system. Specifically, basin averaged precipitation forecasts are verified for different basin sizes (spatial scales) in the operating domain of the Middle Atlantic River Forecast Center (MARFC), using multi-sensor precipitation estimates (MPEs) as the observed data. The quality of the ensemble forecasts is evaluated conditionally upon precipitation amounts, forecast lead times, accumulation periods, and seasonality using different verification metrics. Overall, both GEFSRv2 and SREF tend to overforecast light to moderate precipitation and underforecast heavy precipitation. In addition, precipitation forecasts from both systems become increasingly reliable with increasing basin size and decreasing precipitation threshold, and the 24-hourly forecasts show slightly better skill than the 6-hourly forecasts. Both systems show a strong seasonal trend, characterized by better skill during the cool season than the warm season. Ultimately, the verification results lead to guidance on the expected quality of the precipitation forecasts, together with an assessment of their relative quality and unique information content, which is useful and necessary for their application in hydrologic forecasting.

  17. A simple satellite and model based index for forecasting large-scale flood inundation in data-poor regions

    NASA Astrophysics Data System (ADS)

    Schumann, Guy J.-P.; Andreadis, Kostas; Niebuhr, Emily; Rashid, Kashif; Njoku, Eni

    2014-05-01

    Flood inundation poses a major risk to many populated areas around the world. Despite the economic losses and the devastating societal impacts floods have, low frequency, high magnitude events are still poorly monitored, modelled and predicted in many areas across the globe, especially in data-poor regions of the developing world. In these areas, satellite observations and large scale coupled hydrologic-hydrodynamic models are currently the only option to help understand and predict high magnitude flood events. To contribute to these ongoing efforts, this paper presents a simple index for forecasting large-scale flood inundation in data poor regions. Based on a test case in the Lower Zambezi basin (Mozambique), we demonstrate how satellite data, specifically data from the upcoming SMAP mission can be used in conjunction with meteorological forecast data and outputs from a coupled hydrologic-hydrodynamic (VIC-LISFLOOD-FP) model of the region to build up meaningful correlations between rainfall, antecedent soil moisture and simulated flood inundation variables. Along with the data, these correlations can then be used to build up a long term look-up catalogue to develop a simple flood forecast index. Our project illustrates that this index can be applied to forecast flood inundation based on forecast rainfall and observed antecedent soil moisture without the need to run a model.

  18. Using constructed analogs to improve the skill of National Multi-Model Ensemble March-April-May precipitation forecasts in equatorial East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, Shraddhanand; Funk, Christopher; Hoell, Andrew

    2014-09-01

    In this study we implement and evaluate a simple hybrid forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensembles (NMME) March-April-May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2S to 8N and 36E to 46E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The hybrid approach described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30S to 30N and 30E to 270E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

  19. Using constructed analogs to improve the skill of National Multi-Model Ensemble MarchAprilMay precipitation forecasts in equatorial East Africa

    USGS Publications Warehouse

    Shukla, Shraddhanand; Funk, Christopher C.; Hoell, Andrew

    2014-01-01

    In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) MarchAprilMay (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2S to 8N and 36E to 46E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30S to 30N and 30E to 270E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

  20. River water temperature and fish growth forecasting models

    NASA Astrophysics Data System (ADS)

    Danner, E.; Pike, A.; Lindley, S.; Mendelssohn, R.; Dewitt, L.; Melton, F. S.; Nemani, R. R.; Hashimoto, H.

    2010-12-01

    Water is a valuable, limited, and highly regulated resource throughout the United States. When making decisions about water allocations, state and federal water project managers must consider the short-term and long-term needs of agriculture, urban users, hydroelectric production, flood control, and the ecosystems downstream. In the Central Valley of California, river water temperature is a critical indicator of habitat quality for endangered salmonid species and affects re-licensing of major water projects and dam operations worth billions of dollars. There is consequently strong interest in modeling water temperature dynamics and the subsequent impacts on fish growth in such regulated rivers. However, the accuracy of current stream temperature models is limited by the lack of spatially detailed meteorological forecasts. To address these issues, we developed a high-resolution deterministic 1-dimensional stream temperature model (sub-hourly time step, sub-kilometer spatial resolution) in a state-space framework, and applied this model to Upper Sacramento River. We then adapted salmon bioenergetics models to incorporate the temperature data at sub-hourly time steps to provide more realistic estimates of salmon growth. The temperature model uses physically-based heat budgets to calculate the rate of heat transfer to/from the river. We use variables provided by the TOPS-WRF (Terrestrial Observation and Prediction System - Weather Research and Forecasting) modela high-resolution assimilation of satellite-derived meteorological observations and numerical weather simulationsas inputs. The TOPS-WRF framework allows us to improve the spatial and temporal resolution of stream temperature predictions. The salmon growth models are adapted from the Wisconsin bioenergetics model. We have made the output from both models available on an interactive website so that water and fisheries managers can determine the past, current and three day forecasted water temperatures at any point along the river, and view various simulated alterations to the water discharge volume and discharge temperature. The subsequent impacts on fish growth will also be displayed so that managers can view how their operational decisions might impact salmon growth.

  1. Middle Atlantic Bight Marine Ecosystem: A Regional Forecast Model Study

    NASA Astrophysics Data System (ADS)

    Kim, H.; Coles, V. J.; Garraffo, Z. D.

    2011-12-01

    Changes in basin scale climate patterns can drive changes in mesoscale physical oceanographic processes and subsequent alterations of ecosystem states. Climatic variability can be induced in the northeastern shelfbreak large marine ecosystem by climate oscillations, such as North Atlantic Oscillation, Atlantic Multidecadal Oscillation; and long-term trends, such as a warming pattern. Short term variability can be induced by changes in the water masses in the northern and southern boundaries, by Gulf Stream path and transport variations, and by local mesoscale and submesoscale features. A coupled bio-physical model (HYbrid Coordinate Ocean Model) is being used to forecast the evolution of the frontal and current systems of the shelf and Gulf Stream, and subsequent changes in thermal conditions and ecosystem structure over the Middle Atlantic Bight (MAB). This study aims to forecast the ocean state and nutrients in the MAB, and to investigate how cross-shelf exchanges of different water masses could affect nutrient budgets, primary and secondary production, and fish populations in coastal and shelf marine ecosystems. Preliminary results are shown for a regional MAB model nested to the global 1/12o HYCOM run at NOAA/NCEP/EMC using Naval Oceanographic Office (NAVO) daily initialization. Elements of this simulation are nutrient influx condition at the northern and southern boundaries through regression to ocean thermodynamic variables, and nutrient input at the river mouths.

  2. Weather Research and Forecasting Model with Vertical Nesting Capability

    SciTech Connect

    2014-08-01

    The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improves WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.

  3. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center (ESTSC)

    2014-08-01

    The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improvesmore » WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.« less

  4. Use of observational and model-derived fields and regime model output statistics in mesoscale forecasting

    NASA Technical Reports Server (NTRS)

    Forbes, G. S.; Pielke, R. A.

    1985-01-01

    Various empirical and statistical weather-forecasting studies which utilize stratification by weather regime are described. Objective classification was used to determine weather regime in some studies. In other cases the weather pattern was determined on the basis of a parameter representing the physical and dynamical processes relevant to the anticipated mesoscale phenomena, such as low level moisture convergence and convective precipitation, or the Froude number and the occurrence of cold-air damming. For mesoscale phenomena already in existence, new forecasting techniques were developed. The use of cloud models in operational forecasting is discussed. Models to calculate the spatial scales of forcings and resultant response for mesoscale systems are presented. The use of these models to represent the climatologically most prevalent systems, and to perform case-by-case simulations is reviewed. Operational implementation of mesoscale data into weather forecasts, using both actual simulation output and method-output statistics is discussed.

  5. On the potential of numerical short range fog forecast and low clouds with three-dimensional fog forecast models

    NASA Astrophysics Data System (ADS)

    Masbou, M.; Müller, M. D.; Steeneveld, G. J.; Cermak, J.; Bott, A.

    2010-07-01

    The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. An improvement of localisation, duration and variations in visibility therefore holds an immense operational value for the field of transportation in conditions of low visibility. However, fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Therefore, a detailed description of the microphysical processes within the three-dimensional dynamical core of the forecast model is necessary. For this purpose, three-dimensional fog forecast models with a high vertical resolution with different microphysical complexity have been developed. COSMO-FOG and NMMFOG include a new microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Also, we compare WRF mesoscale model results using different boundary-layer schemes that ignore or account for specific fog microphysics. In some realistic fog situations (radiative fog) the potential of these three-dimensional fog models will be presented. The fog spatial extension will be compared with MSG satellite products for fog and low cloud. It will be shown that the initialisation and the interaction between the earth’s surface and the atmosphere is one of the most important issues for reliable fog forecasts.

  6. A Capacity Forecast Model for Volatile Data in Maintenance Logistics

    NASA Astrophysics Data System (ADS)

    Berkholz, Daniel

    2009-05-01

    Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.

  7. A high resolution WRF model for wind energy forecasting

    NASA Astrophysics Data System (ADS)

    Vincent, Claire Louise; Liu, Yubao

    2010-05-01

    The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the diffusion constant caused damping of the unrealistic fluctuations, but did not completely solve the problem. Using two-way nesting also mitigated the unrealistic fluctuations significantly. It can be concluded that for real case LES modelling of wind farm circulations, care should be taken to ensure the consistency between the mesoscale weather forcing and LES models to avoid exciting spurious noise along the forcing boundary. The development of algorithms that adequately model the sub-grid-scale mixing that cannot be resolved by LES models is an important area for further research. References Liu, Y. Y._W. Liu, W. Y.Y. Cheng, W. Wu, T. T. Warner and K. Parks, 2009: Simulating intra-farm wind variations with the WRF-RTFDDA-LES modeling system. 10th WRF Users' Workshop, Boulder, C, USA. June 23 - 26, 2009. Skamarock, W., J. Dudhia, D.O. Gill, D.M. Barker, M.G.Duda, X-Y. Huang, W. Wang and J.G. Powers, A Description of the Advanced Research WRF version 3, NCAR Technical Note TN-475+STR, NCAR, Boulder, Colorado, 2008.

  8. Initialization and Predictability of a Coupled ENSO Forecast Model

    NASA Technical Reports Server (NTRS)

    Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

    1997-01-01

    The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

  9. A multiscale statistical model for time series forecasting

    NASA Astrophysics Data System (ADS)

    Wang, W.; Pollak, I.

    2007-02-01

    We propose a stochastic grammar model for random-walk-like time series that has features at several temporal scales. We use a tree structure to model these multiscale features. The inside-outside algorithm is used to estimate the model parameters. We develop an algorithm to forecast the sign of the first difference of a time series. We illustrate the algorithm using log-price series of several stocks and compare with linear prediction and a neural network approach. We furthermore illustrate our algorithm using synthetic data and show that it significantly outperforms both the linear predictor and the neural network. The construction of our synthetic data indicates what types of signals our algorithm is well suited for.

  10. A comparative verification of forecasts from two operational solar wind models

    NASA Astrophysics Data System (ADS)

    Norquist, Donald C.; Meeks, Warner C.

    2010-12-01

    The solar wind (SW) and interplanetary magnetic field (IMF) have a significant influence on the near-Earth space environment. In this study we evaluate and compare forecasts from two models that predict SW and IMF conditions: the Hakamada-Akasofu-Fry (HAF) version 2, operational at the Air Force Weather Agency, and Wang-Sheeley-Arge (WSA) version 1.6, executed routinely at the Space Weather Prediction Center. SW speed (Vsw) and IMF polarity (Bpol) forecasts at L1 were compared with Wind and Advanced Composition Explorer satellite observations. Verification statistics were computed by study year and forecast day. Results revealed that both models' mean Vsw are slower than observed. The HAF slow bias increases with forecast duration. WSA had lower Vsw forecast-observation difference (F-O) absolute means and standard deviations than HAF. HAF and WSA Vsw forecast standard deviations were less than observed. Vsw F-O mean square skill rarely exceeds that of recurrence forecasts. Bpol is correctly predicted 65%-85% of the time in both models. Recurrence beats the models in Bpol skill in nearly every year forecast day category. Verification by "event" (flare events ?5 days before forecast start) and "nonevent" (no flares) forecasts showed that most HAF Vsw bias growth, F-O standard deviation decrease, and forecast standard deviation decrease were due to the event forecasts. Analysis of single time step Vsw increases of ?20% in the nonevent forecasts indicated that both models predicted too many occurrences and missed many observed incidences. Neither model had skill above a random guess in predicting Vsw increase arrival time at L1.

  11. Comparison of Dst Forecast Models for Intense Geomagnetic Storms

    NASA Technical Reports Server (NTRS)

    Ji, Eun-Young; Moon, Y.-J.; Gopalswamy, N.; Lee, D.-H.

    2012-01-01

    We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.

  12. Toward Submesocale Ocean Modelling and Observations for Global Ocean Forecast.

    NASA Astrophysics Data System (ADS)

    Drillet, Y.

    2014-12-01

    Mercator Ocean is the French oceanographic operational center involved in the development an operation of global high resolution ocean forecasting systems; it is part of the European Copernicus Marine service initiated during MyOcean project. Mercator Ocean currently delivers daily 1/12 global ocean forecast based on the NEMO model which allows for a good representation of mesoscale structures in main areas of the global ocean. Data assimilation of altimetry provides a precise initialization of the mesoscale structures while in situ observations, mainly based on the ARGO network, and satellite Sea Surface Temperature constrain water mass properties from the surface to intermediate depths. One of the main improvements scheduled in the coming years is the transitioning towards submesoscale permitting horizontal resolution (1/36). On the basis of numerical simulations in selected areas and standard diagnostics developed to validate operational systems, we will discuss : i) The impact of the resolution increase at the basin scale. ii) Adequacy of numerical schemes, vertical resolution and physical parameterization. iii) Adequacy of currently implemented data assimilation procedures in particular with respect to new high resolution data set such as SWOT.

  13. Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)

    NASA Astrophysics Data System (ADS)

    Leedal, D.; Weerts, A. H.; Smith, P. J.; Beven, K. J.

    2013-01-01

    The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new flood forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).

  14. Diabatic forcing and initialization with assimilation of cloud and rain water in a forecast model: Methodology

    NASA Technical Reports Server (NTRS)

    Raymond, William H.; Olson, William S.; Callan, Geary

    1990-01-01

    The focus of this part of the investigation is to find one or more general modeling techniques that will help reduce the time taken by numerical forecast models to initiate or spin-up precipitation processes and enhance storm intensity. If the conventional data base could explain the atmospheric mesoscale flow in detail, then much of our problem would be eliminated. But the data base is primarily synoptic scale, requiring that a solution must be sought either in nonconventional data, in methods to initialize mesoscale circulations, or in ways of retaining between forecasts the model generated mesoscale dynamics and precipitation fields. All three methods are investigated. The initialization and assimilation of explicit cloud and rainwater quantities computed from conservation equations in a mesoscale regional model are examined. The physical processes include condensation, evaporation, autoconversion, accretion, and the removal of rainwater by fallout. The question of how to initialize the explicit liquid water calculations in numerical models and how to retain information about precipitation processes during the 4-D assimilation cycle are important issues that are addressed. The explicit cloud calculations were purposely kept simple so that different initialization techniques can be easily and economically tested. Precipitation spin-up processes associated with three different types of weather phenomena are examined. Our findings show that diabatic initialization, or diabatic initialization in combination with a new diabatic forcing procedure, work effectively to enhance the spin-up of precipitation in a mesoscale numerical weather prediction forecast. Also, the retention of cloud and rain water during the analysis phase of the 4-D data assimilation procedure is shown to be valuable. Without detailed observations, the vertical placement of the diabatic heating remains a critical problem.

  15. Forecasting Ability of a Multi-Renewal Seismicity Model

    NASA Astrophysics Data System (ADS)

    Molchan, George; Romashkova, Leontina

    2014-09-01

    The inter-event time (IET) is sometimes used as a basis for prediction of large earthquakes. It is the case when theoretical analysis of prediction is possible. Quite recently, a specific IET model was suggested for dynamic probabilistic prediction of events in Italy (http://earthquake.bo.ingv.it). In this study we analyze some aspects of the statistical estimation of the model and its predictive ability. We find that more or less effective prediction is possible within four out of 34 seismotectonic zones where seismicity rate or clustering of events is relatively high. We show that, in the framework of the model, one can suggest a simple zone-independent strategy, which practically optimizes the relative number of non-accidental successes, or the Hanssen-Kuiper (HK) skill score. This quasi-optimal strategy declares alarm in a zone for the first 2.67 years just after the occurrence of each large event in the zone. The optimal HK skill score values are about 26 % for the three most active zones, and 2-10 % for the 26 least active zones. However, the number of false alarm time intervals per one event in each of the zones is unusually high: about 0.7 and 0.8-0.95, respectively. Both these theoretical estimations are important because any prospective testing of the model is unrealistic in most of the zones during a reasonable time. This particular analysis requires a discussion of the following issues of general interest: a specific approach to the analysis of predictions vs. the standard CSEP testing approach; prediction vs. forecasting; HK skill score vs. probability gain; the total forecast error diagram and connected false alarms.

  16. Characteristics of Operational Space Weather Forecasting: Observations and Models

    NASA Astrophysics Data System (ADS)

    Berger, Thomas; Viereck, Rodney; Singer, Howard; Onsager, Terry; Biesecker, Doug; Rutledge, Robert; Hill, Steven; Akmaev, Rashid; Milward, George; Fuller-Rowell, Tim

    2015-04-01

    In contrast to research observations, models and ground support systems, operational systems are characterized by real-time data streams and run schedules, with redundant backup systems for most elements of the system. We review the characteristics of operational space weather forecasting, concentrating on the key aspects of ground- and space-based observations that feed models of the coupled Sun-Earth system at the NOAA/Space Weather Prediction Center (SWPC). Building on the infrastructure of the National Weather Service, SWPC is working toward a fully operational system based on the GOES weather satellite system (constant real-time operation with back-up satellites), the newly launched DSCOVR satellite at L1 (constant real-time data network with AFSCN backup), and operational models of the heliosphere, magnetosphere, and ionosphere/thermosphere/mesophere systems run on the Weather and Climate Operational Super-computing System (WCOSS), one of the worlds largest and fastest operational computer systems that will be upgraded to a dual 2.5 Pflop system in 2016. We review plans for further operational space weather observing platforms being developed in the context of the Space Weather Operations Research and Mitigation (SWORM) task force in the Office of Science and Technology Policy (OSTP) at the White House. We also review the current operational model developments at SWPC, concentrating on the differences between the research codes and the modified real-time versions that must run with zero fault tolerance on the WCOSS systems. Understanding the characteristics and needs of the operational forecasting community is key to producing research into the coupled Sun-Earth system with maximal societal benefit.

  17. eWaterCycle: A global operational hydrological forecasting model

    NASA Astrophysics Data System (ADS)

    van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

    2015-04-01

    Development of an operational hyper-resolution hydrological global model is a central goal of the eWaterCycle project (www.ewatercycle.org). This operational model includes ensemble forecasts (14 days) to predict water related stress around the globe. Assimilation of near-real time satellite data is part of the intended product that will be launched at EGU 2015. The challenges come from several directions. First, there are challenges that are mainly computer science oriented but have direct practical hydrological implications. For example, we aim to make use as much as possible of existing standards and open-source software. For example, different parts of our system are coupled through the Basic Model Interface (BMI) developed in the framework of the Community Surface Dynamics Modeling System (CSDMS). The PCR-GLOBWB model, built by Utrecht University, is the basic hydrological model that is the engine of the eWaterCycle project. Re-engineering of parts of the software was needed for it to run efficiently in a High Performance Computing (HPC) environment, and to be able to interface using BMI, and run on multiple compute nodes in parallel. The final aim is to have a spatial resolution of 1km x 1km, which is currently 10 x 10km. This high resolution is computationally not too demanding but very memory intensive. The memory bottleneck becomes especially apparent for data assimilation, for which we use OpenDA. OpenDa allows for different data assimilation techniques without the need to build these from scratch. We have developed a BMI adaptor for OpenDA, allowing OpenDA to use any BMI compatible model. To circumvent memory shortages which would result from standard applications of the Ensemble Kalman Filter, we have developed a variant that does not need to keep all ensemble members in working memory. At EGU, we will present this variant and how it fits well in HPC environments. An important step in the eWaterCycle project was the coupling between the hydrological and hydrodynamic models. The hydrological model will run operationally for the whole globe. Once special situations are predicted, such as floods, navigation hindrances, or water shortages, a detailed local hydraulic model will start to predict the exact local consequences. In Vienna, we will show for the first time the operational global eWaterCycle model, including high resolution forecasts, our new data assimilation technique, and coupled hydrological/hydraulic models.

  18. Operational model for forecasting location specific quantitative precipitation and probability of precipitation over India

    NASA Astrophysics Data System (ADS)

    Maini, P.; Kumar, Ashok; Singh, S. V.; Rathore, L. S.

    2004-03-01

    The National Centre for Medium Range Weather Forecasting was established in early 1988 with the major objective to develop operational medium range weather forecasting capability and agricultural meteorological advisory services (AAS) for each of the 127 agricultural climatic zones for the farming community in India. At present, medium range weather forecast of six surface weather parameters namely, average cloud cover, 24 h accumulated precipitation, average wind speed, predominant wind direction, maximum/minimum temperature trends (up to 4 days) is provided to 83 units in different agricultural climatic zones. In addition the forecast of weekly cumulative rainfall is also provided. An objective system for obtaining location specific forecast, in the medium range, of surface weather elements is evolved at NCMRWF. The basic information used for this is the output from the general circulation model (GCM). A T80L18 model operational at the centre since 1994 has been recently upgraded to a T170L28 model. However, it is well known that in spite of higher resolution, the global models are unable to account for the small-scale effects (e.g. of topography, local environmental features) important in predicting surface weather parameters like rainfall, temperature etc. This necessitates the development of statistical-dynamical models. Hence an operational system for forecasting rainfall (quantitative, probability of precipitation (PoP)) has been developed at the centre and implemented since 1994. A Perfect Prog Method (PPM) approach is followed for statistical interpretation (SI) of Numerical Weather Prediction (NWP) products. PPM model equations are developed by using analysis data obtained from European Centre for Medium Range Weather Forecasts (ECMWF) for a period of six years (1985-1990). Rainfall forecasts are subsequently obtained from these equations by using T80 model output. A comparative study of the skill of SI forecast and the direct model output (DMO) forecast has indicated that SI forecast improves over the DMO considerably and hence can be developed as a fully automatic operational weather forecasting system.

  19. Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts

    USGS Publications Warehouse

    Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad

    2014-01-01

    Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin.

  20. Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

    NASA Astrophysics Data System (ADS)

    Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati; Pal, P. K.

    2016-02-01

    An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6-8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

  1. Forecasting rain events - Meteorological models or collective intelligence?

    NASA Astrophysics Data System (ADS)

    Arazy, Ofer; Halfon, Noam; Malkinson, Dan

    2015-04-01

    Collective intelligence is shared (or group) intelligence that emerges from the collective efforts of many individuals. Collective intelligence is the aggregate of individual contributions: from simple collective decision making to more sophisticated aggregations such as in crowdsourcing and peer-production systems. In particular, collective intelligence could be used in making predictions about future events, for example by using prediction markets to forecast election results, stock prices, or the outcomes of sport events. To date, there is little research regarding the use of collective intelligence for prediction of weather forecasting. The objective of this study is to investigate the extent to which collective intelligence could be utilized to accurately predict weather events, and in particular rainfall. Our analyses employ metrics of group intelligence, as well as compare the accuracy of groups' predictions against the predictions of the standard model used by the National Meteorological Services. We report on preliminary results from a study conducted over the 2013-2014 and 2014-2015 winters. We have built a web site that allows people to make predictions on precipitation levels on certain locations. During each competition participants were allowed to enter their precipitation forecasts (i.e. 'bets') at three locations and these locations changed between competitions. A precipitation competition was defined as a 48-96 hour period (depending on the expected weather conditions), bets were open 24-48 hours prior to the competition, and during betting period participants were allowed to change their bets with no limitation. In order to explore the effect of transparency, betting mechanisms varied across study's sites: full transparency (participants able to see each other's bets); partial transparency (participants see the group's average bet); and no transparency (no information of others' bets is made available). Several interesting findings emerged from this study. First, we found evidence for the emergence of collective intelligence, as the group's mean prediction was superior to individuals' predictions (using the metrics of Collective Intelligence Quality and Win Ratio). Second, we found that overall the group's collective intelligence was not very different from the accuracy of the meteorological model (ECMWF): in 6 out of the 12 competition the results were almost indistinguishable (error differences of less than 2 mm); in 4 cases the model clearly outperformed the group; and in 2 cases the group outperformed the model. Third, the design of the bidding mechanism - namely transparency - seems to affect collective intelligence. Fourth, an analysis of individuals' predictions suggests that local knowledge (measured by the distance between home address and the site of competition) and the level of meteorological knowledge (assessed by a short quiz) were not correlated with prediction accuracy. Although, the findings reported here present only preliminary results from a long-term project and while we acknowledge that it is not possible to draw statistically significant conclusions from a study of 12 cases, our findings do reveal some important insights. Our results inform research on collective intelligence and meteorology, as well as have implications for practice (e.g. possibly incorporating collective intelligence into weather forecasting models).

  2. Log-likelihood of earthquake models: evaluation of models and forecasts

    NASA Astrophysics Data System (ADS)

    Harte, D. S.

    2015-05-01

    There has been debate in the Collaboratory for the Study of Earthquake Predictability project about the most appropriate form of the likelihood function to use to evaluate earthquake forecasts in specified discrete space-time intervals, and also to evaluate the validity of the model itself. The debate includes whether the likelihood function should be discrete in nature, given that the forecasts are in discrete space-time bins, or continuous. If discrete, can different bins be assumed to be statistically independent, and is it satisfactory to assume that the forecasted count in each bin will have a Poisson distribution? In order to discuss these questions, we start with the most simple models (homogeneous Poisson), and progressively develop the model complexity to include self exciting point process models. For each, we compare the discrete and continuous time likelihoods. Examples are given where it is proven that the counts in discrete space-time bins are not Poisson. We argue that the form of the likelihood function is intrinsic to the given model, and the required forecast for some specified space-time region simply determines where the likelihood function should be evaluated. We show that continuous time point process models where the likelihood function is also defined in continuous space and time can easily produce forecasts over discrete space-time intervals.

  3. Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

    NASA Technical Reports Server (NTRS)

    Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

    2015-01-01

    We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

  4. Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook

    NASA Astrophysics Data System (ADS)

    Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

    2015-10-01

    We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

  5. Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

    USGS Publications Warehouse

    Clark, M.P.; Hay, L.E.

    2004-01-01

    This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow. ?? 2004 American Meteorological Society.

  6. Flood forecasting with DDD-application of a parsimonious hydrological model in operational flood forecasting in Norway

    NASA Astrophysics Data System (ADS)

    Skaugen, Thomas; Haddeland, Ingjerd

    2014-05-01

    A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal resolution. Running DDD at a 3h resolution will give a better prediction of flood peaks in small catchments, where the averaging over 24 hrs will lead to a underestimation of high events, and we can better describe the progress floods in larger catchments. Also, at a 3h temporal resolution we make better use of the meteorological forecasts that for long have been provided at a very detailed temporal resolution.

  7. Teaching Economics: A Cooperative Learning Model.

    ERIC Educational Resources Information Center

    Caropreso, Edward J.; Haggerty, Mark

    2000-01-01

    Describes an alternative approach to introductory economics based on a cooperative learning model, "Learning Together." Discussion of issues in economics education and cooperative learning in higher education leads to explanation of how to adapt the Learning Together Model to lesson planning in economics. A flow chart illustrates the process for a

  8. Forecasting the solar photospheric magnetic field using solar flux transport model and local ensemble Kalman filtering

    NASA Astrophysics Data System (ADS)

    Zhang, Ying; Du, Aimin; Feng, Xueshang

    2015-04-01

    Accurate forecasting the solar photospheric magnetic field distribution play an important role in the estimates of the inner boundary conditions of the coronal and solar wind model. Forecasting solar photospheric magnetic field using the solar flux transport (SFT) model can achieve an acceptable match to the actual field. The observations from ground-based or spacecraft instruments can be assimilated to update the modeled flux. The local ensemble Kalman filtering (LEnKF) method is utilized to improve forecasts and characterize their uncertainty by propagating the SFT model with different model parameters forward in time to control the evolution of the solar photospheric magnetic field. Optimal assimilation of measured data into the ensemble produces an improvement in the fit of the forecast to the actual field. Our approach offers a method to improve operational forecasting of the solar photospheric magnetic field. The LEnKF method also allows sensitivity analysis of the SFT model to noise and uncertainty within the physical representation.

  9. Three essays on resource economics. Demand systems for energy forecasting: Practical considerations for estimating a generalized logit model, To borrow or not to borrow: A variation on the MacDougal-Kemp theme, and, Valuing reduced risk for households with children or the retired

    NASA Astrophysics Data System (ADS)

    Weng, Weifeng

    This thesis presents papers on three areas of study within resource and environmental economics. "Demand Systems For Energy Forecasting" provides some practical considerations for estimating a Generalized Logit model. The main reason for using this demand system for energy and other factors is that the derived price elasticities are robust when expenditure shares are small. The primary objective of the paper is to determine the best form of the cross-price weights, and a simple inverse function of the expenditure share is selected. A second objective is to demonstrate that the estimated elasticities are sensitive to the units specified for the prices, and to show how price scales can be estimated as part of the model. "To Borrow or Not to Borrow: A Variation on the MacDougal-Kemp Theme" studies the impact of international capital movements on the conditional convergence of economies differing from each other only in initial wealth. We found that in assets, income, consumption and utility, convergence obtains, with and only with, the absence of international capital movement. When a rich country invests in a poor country, the balance of debt increases forever. Asset ownership is increased in all periods for the lender, and asset ownership of the borrower is deceased. Also, capital investment decreases the lender's utility for early periods, but increases it forever after a cross-over point. In contrast, the borrower's utility increases for early periods, but then decreases forever. "Valuing Reduced Risk for Households with Children or the Retired" presents a theoretical model of how families value risk and then exams family automobile purchases to impute the average Value of a Statistical Life (VSL) for each type of family. Data for fatal accidents are used to estimate survival rates for individuals in different types of accidents, and the probabilities of having accidents for different types of vehicle. These models are used to determine standardized risks for vehicles in hedonic models of the purchase price and fuel efficiency. The hedonic models determine the marginal capital and operating costs of reducing the risk of mortality. We find that households with children are valued much more highly than the average VSL of $2 million, and households with seniors are valued less than average.

  10. Methodology for national wheat yield forecast using wheat growth model, WTGROWS, and remote sensing inputs

    NASA Astrophysics Data System (ADS)

    Kalra, Naveen; Aggarwal, P. K.; Singh, A. K.; Dadhwal, V. K.; Sehgal, V. K.; Harith, R. C.; Sharma, S. K.

    2006-12-01

    Wheat is an important food crop of the country. Its productivity lies in a very wide range due to diverse bio-physical and socio-economic conditions in the growing regions. Crop cutting and sample surveys are time consuming as well tedious, and procedure of forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC Research Programme, possibility of linking the dynamic wheat growth model with the remote sensing input and other relational database layers was tried. Use of WTGROWS, a wheat growth model developed at IARI, with the remote sensing and relational databases is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs are received. National wheat yield forecast was done for three seasons on meteorological sub-division scale by using WTGROWS, relational database layers and satellite image. WTGROWS was run for historic weather dataset (last 25 years), with the relational database inputs through their associated growth rates and compared with the productivity trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained to capture the other biotic and abiotic stresses and subsequently used to bring down the yields at each sub-division to realistic scale. The satellite image was used to compute the acreage with wheat in each sub-division. Meteorological data for each-subdivision was obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was prepared. This was updated on weekly basis, and the methodology could forecast the wheat yield well in advance with a great accuracy. This procedure shows the pathway for Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning and agri-production estimates, which although looks difficult for diverse agro-ecologies and wide range of bio-physical and socio-economic characters contributing to differential productivity trends.

  11. THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION

    EPA Science Inventory

    In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

  12. 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-Frre 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.

  13. Evaluation of spatially distributed snow models for streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Rittger, K. E.; Dozier, J.; Kahl, A.

    2012-12-01

    Water supply forecasts in the Sierra Nevada using ground-based measurements of snow water equivalent are uncertain because neither point measurements nor transects adequately explain spatial or temporal variability in mountainous terrain. The statistical relationships between the snow observations and streamflow do not perform well in extreme years or in basins with ephemeral snow and may prove less reliable in the future with a changing climate. Since 1990, forecast errors in the Sacramento, San Joaquin, Tulare and Lahontan drainages have had median errors of 10% to 30% and an error in every 1 out of 5 years of 25% to 70%. To address this problem, we combine satellite-based retrievals of fractional snow cover for a 12-year period starting in 2000 with spatially distributed energy balance calculations to reconstruct the snow water equivalent (SWE) values throughout each melt season. The 12-year period of study captures an average of 70% of the streamflow range of the last 80 years in the 18 basins with such estimates available. Reconstructed SWE is validated with: (i) snow pillows (ii) snow courses that show the model can accurately predict maximum SWE at the regularly sampled locations for a range of wet, mean and dry years. Validation from snow surveys in 2010 on slopes of up to 21 at the highest elevations in the American River basin show the model also performs well in a variety of topography. The relationship of SWE with elevation is significantly different for wet, mean and dry years as well as between drainages. Certain latitudes receive proportionally less water in dry years and more water in wet years than other latitudes. At the scale at which water is managed the relationship between SWE and SCA becomes increasingly correlated from March 1st to July 1st, such that real-time SCA observations may be sufficient for SWE prediction. We compare spatially integrated volumes of snow water equivalent from the retrospective model and 2 near real time models with full natural flow estimates in all 18 Sierra Nevada basins. The near real time models consist of an interpolation constrained by remotely sensed maps of snow-covered area and the Snow Data and Assimilation System (SNODAS). April 1 SWE is compared with unimpaired streamflow using the absolute magnitudes, the Spearman rank correlation coefficient, and linear regressions. The results show that the reconstruction performs the best at estimating the unimpaired streamflow, followed by the interpolation and then SNODAS. The implication is that the real-time models can be evaluated with the retrospective one. Moreover, the reconstruction provides a historical perspective to put the real-time estimates into context.

  14. Cone Model for Halo CMEs: Application to Space Weather Forecasting

    NASA Technical Reports Server (NTRS)

    Xie, Hong; Ofman, Leon; Lawrence, Gareth

    2004-01-01

    In this study, we present an innovative analytical method to determine the angular width and propagation orientation of halo Coronal Mass Ejections (CMEs). The relation of CME actual speed with apparent speed and its components measured at different position angle has been investigated. The present work is based on the cone model proposed by Zhao et al. We have improved this model by: (1) eliminating the ambiguity via a new analytical approach, (2) using direct measurements of projection onto the plane of the sky (POS), and (3) determining the actual radial speeds from projection speeds at different position angles to clarify the uncertainty of projection speeds in previous empirical models. Our analytical approach allows us to use coronagraph data to determine accurately the geometrical features of POS projections, such as major axis, minor axis, and the displacement of the center of its projection, and to determine the angular width and orientation of a given halo CME. Our approach allows for the first time the determination of the actual CME speed, width, and source location by using coronagraph data quantitatively and consistently. The method greatly enhances the accuracy of the derived geometrical and kinematical properties of halo CMEs, and can be used to optimize Space Weather forecasts. The applied model predications are in good agreement with observations.

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

    SciTech Connect

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

    2013-07-25

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

  16. The impact of vertical resolution in mesoscale model AROME forecasting of radiation fog

    NASA Astrophysics Data System (ADS)

    Philip, Alexandre; Bergot, Thierry; Bouteloup, Yves; Bouyssel, François

    2015-04-01

    Airports short-term forecasting of fog has a security and economic impact. Numerical simulations have been performed with the mesoscale model AROME (Application of Research to Operations at Mesoscale) (Seity et al. 2011). Three vertical resolutions (60, 90 and 156 levels) are used to show the impact of radiation fog on numerical forecasting. Observations at Roissy Charles De Gaulle airport are compared to simulations. Significant differences in the onset, evolution and dissipation of fog were found. The high resolution simulation is in better agreement with observations than a coarser one. The surface boundary layer and incoming long-wave radiations are better represented. A more realistic behaviour of liquid water content evolution allows a better anticipation of low visibility procedures (ceiling < 60m and/or visibility < 600m). The case study of radiation fog shows that it is necessary to have a well defined vertical grid to better represent local phenomena. A statistical study over 6 months (October 2011 - March 2012 ) using different configurations was carried out. Statistically, results were the same as in the case study of radiation fog. Seity Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, V. Masson, 2011: The AROME-France convective scale operational model. Mon.Wea.Rev., 139, 976-991.

  17. A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model

    NASA Astrophysics Data System (ADS)

    Morse, Andrew P.; Doblas-Reyes, Francisco J.; Hoshen, Moshe B.; Hagedorn, Renate; Palmer, Tim N.

    2005-05-01

    We discuss a novel three-tier hierarchical approach to the validation of an end-to-end seasonal climate forecast system. We present a malaria transmission simulation model (MTSM) driven with output from the DEMETER multi-model seasonal climate predictions, to produce probabilistic hindcasts of malaria prevalence. These prevalence hindcasts are second-tier validated against estimates from the MTSM driven with ERA-40 gridded analyses. The DEMETER MTSM prevalence hindcasts are shown to be (tier-2) skilful for the one-month lead seasonal predictions as well as for the period covering the seasonal malaria peak with a 4 6 month forecast window for the event prevalence above the median. Interestingly, the tier-2 Brier skill score for the forecast window of the hindcasts starting in February, for the event prevalence above the median, is higher than for either the tier-1 precipitation or temperature forecasts, which were the MTSM driving variables.

  18. A Kp forecast model based on neural network

    NASA Astrophysics Data System (ADS)

    Gong, J.; Liu, Y.; Luo, B.; Liu, S.

    2013-12-01

    As an important global geomagnetic disturbance index, Kp is difficult to predict, especially when Kp reaches 5 which means that the disturbance has reached the scales of geomagnetic storm and can cause spacecraft and power system anomaly. Statistical results showed that there exists high correlation between solar wind-magnetosphere coupling function and Kp index, and a linear combination of two solar wind-magnetosphere coupling terms, merging term and viscous term, proved to be good in predicting the Kp index. In this study, using the upstream solar wind parameters by the ACE satellite since 1998 and the two derived coupling terms mentioned above, a Kp forecast model based on artificial neural network is developed. For the operational need of predicting the geomagnetic disturbance as soon as possible, we construct the solar wind data and develop the model in an innovative way. For each Kp value at time t (the universal times of 8 Kp values in each day are noted as t=3, 6, 9, ..., 18, 21, 24), the model gives 6 predicted values every half an hour at t-3.5, t-3.0, t-2.5, t-2.0, t-1.5, t-1.0, based on the half-hour averaged model inputs (solar wind parameters and derived solar wind-magnetosphere coupling terms). The last predicted value at t-1.0 provides the final prediction. Evaluated with the test set data including years 1998, 2002 and 2006, the model yields the linear correlation coefficient (LC) of 0.88 and the root mean square error (RMSE) of 0.65 between the modeled and observed Kp values. Furthermore, if the nowcast Kp is available and included in the model input, the model can be improved and gives an LC of 0.90 and an RMSE of 0.62.

  19. A Model For Rapid Estimation of Economic Loss

    NASA Astrophysics Data System (ADS)

    Holliday, J. R.; Rundle, J. B.

    2012-12-01

    One of the loftier goals in seismic hazard analysis is the creation of an end-to-end earthquake prediction system: a "rupture to rafters" work flow that takes a prediction of fault rupture, propagates it with a ground shaking model, and outputs a damage or loss profile at a given location. So far, the initial prediction of an earthquake rupture (either as a point source or a fault system) has proven to be the most difficult and least solved step in this chain. However, this may soon change. The Collaboratory for the Study of Earthquake Predictability (CSEP) has amassed a suite of earthquake source models for assorted testing regions worldwide. These models are capable of providing rate-based forecasts for earthquake (point) sources over a range of time horizons. Furthermore, these rate forecasts can be easily refined into probabilistic source forecasts. While it's still difficult to fully assess the "goodness" of each of these models, progress is being made: new evaluation procedures are being devised and earthquake statistics continue to accumulate. The scientific community appears to be heading towards a better understanding of rupture predictability. Ground shaking mechanics are better understood, and many different sophisticated models exists. While these models tend to be computationally expensive and often regionally specific, they do a good job at matching empirical data. It is perhaps time to start addressing the third step in the seismic hazard prediction system. We present a model for rapid economic loss estimation using ground motion (PGA or PGV) and socioeconomic measures as its input. We show that the model can be calibrated on a global scale and applied worldwide. We also suggest how the model can be improved and generalized to non-seismic natural disasters such as hurricane and severe wind storms.

  20. Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

    EPA Science Inventory

    High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

  1. An application of ensemble/multi model approach for wind power production forecast.

    NASA Astrophysics Data System (ADS)

    Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

    2010-09-01

    The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

  2. Modelling dinoflagellates as an approach to the seasonal forecasting of bioluminescence in the North Atlantic

    NASA Astrophysics Data System (ADS)

    Marcinko, Charlotte L. J.; Martin, Adrian P.; Allen, John T.

    2014-11-01

    Bioluminescence within ocean surface waters is of significant interest because it can enhance the study of subsurface movement and organisms. Little is known about how bioluminescence potential (BPOT) varies spatially and temporally in the open ocean. However, light emitted from dinoflagellates often dominates the stimulated bioluminescence field. As a first step towards forecasting surface ocean bioluminescence in the open ocean, a simple ecological model is developed which simulates seasonal changes in dinoflagellate abundance. How forecasting seasonal changes in BPOT may be achieved through combining such a model with relationships derived from observations is discussed and an example is given. The study illustrates a potential new approach to forecasting BPOT through explicitly modelling the population dynamics of a prolific bioluminescent phylum. The model developed here offers a promising platform for the future operational forecasting of the broad temporal changes in bioluminescence within the North Atlantic. Such forecasting of seasonal patterns could provide valuable information for the targeting of scientific field campaigns.

  3. A model to forecast data centre infrastructure costs.

    NASA Astrophysics Data System (ADS)

    Vernet, R.

    2015-12-01

    The computing needs in the HEP community are increasing steadily, but the current funding situation in many countries is tight. As a consequence experiments, data centres, and funding agencies have to rationalize resource usage and expenditures. CC-IN2P3 (Lyon, France) provides computing resources to many experiments including LHC, and is a major partner for astroparticle projects like LSST, CTA or Euclid. The financial cost to accommodate all these experiments is substantial and has to be planned well in advance for funding and strategic reasons. In that perspective, leveraging infrastructure expenses, electric power cost and hardware performance observed in our site over the last years, we have built a model that integrates these data and provides estimates of the investments that would be required to cater to the experiments for the mid-term future. We present how our model is built and the expenditure forecast it produces, taking into account the experiment roadmaps. We also examine the resource growth predicted by our model over the next years assuming a flat-budget scenario.

  4. Low-Level Wind Forecast over the La Plata River Region with a Mesoscale Boundary-Layer Model Forced by Regional Operational Forecasts

    NASA Astrophysics Data System (ADS)

    Sraibman, L.; Berri, G. J.

    2009-03-01

    A mesoscale boundary-layer model (BLM) is used for running 12-h low-level wind forecasts for the La Plata River region. Several experiments are performed with different boundary conditions that include operational forecasts of the Eta/CPTEC model, local observations, as well as a combination of both. The BLM wind forecasts are compared to the surface wind observations of five weather stations during the period November 2003-April 2004. Two accuracy measures are used: the hit rate or percentage of cases with agreement in the wind direction sector, and the root-mean-squared error (RMSE) of the horizontal wind components. The BLM surface wind forecasts are always more accurate, since its averaged hit rate is three times greater and its averaged RMSE is one half smaller than the Eta forecasts. Despite the large errors in the surface winds displayed by the Eta forecasts, its 850 hPa winds and surface temperature forecasts are able to drive the BLM model to obtain surface winds forecasts with smaller errors than the Eta model. An additional experiment demonstrates that the advantage of using the BLM model for forecasting low-level winds over the La Plata River region is the result of a more appropriate definition of the land-river surface temperature contrast. The particular formulation that the BLM model has for the geometry of the river coasts is fundamental for resolving the smaller scale details of the low-level local circulation. The main conclusion of the study is that operational low-level wind forecasts for the La Plata River region can be improved by running the BLM model forced by the Eta operational forecasts.

  5. Uses of Satellite Data for Hurricane Modeling and Coastal Precipitation Forecasts: Part I: Observations and Tropical Cyclone Track Forecasts

    NASA Astrophysics Data System (ADS)

    Weng, F.; Zou, X.; Shi, Q.; Zhang, B.

    2012-12-01

    In hurricane and severe storm conditions, data obtained from satellite microwave temperature and water vapor sounders provide three dimensional warm core features that could be indirectly and directly used for vortex initialization and hurricane data assimilation. The Advanced Technology of Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS) on board the recently launched Suomi National Polar-Orbiting Partnership (NPP) satellite launched on October 28, 2011, as well as Advanced Microwave Scanning Radiometer (AMSR-2) on board the Global Change Observation Mission 1st - Water (GCOM-W1) satellite launched on May 18, 2012, are providing data for atmospheric temperature profiles, moisture profiles, sea surface temperature, and sea surface wind within and around tropical cyclones. Over 90% of the satellite data ingested by NWP models is only a small fraction of available satellite data. Many satellite data are excluded by either a data thinning process for timely processing and for avoiding horizontal correlation of observation errors, or a quality control process to eliminate cloud- or rain-affected radiances and surface-sensitive channels that would otherwise render assimilation results more prone to error. However, we will show that high-resolution observations, cloud-sensitive and surface-sensitive channels contain useful and rich information about hurricane structures, and satellite data assimilation is extremely sensitive to model top height and vertical and horizontal resolutions. As an example, sensitivity of hurricane track forecasts to both model top heights and vertical resolutions for 2012 hurricane season will be presented. The control experiment (EXP1_L42T50) uses a model configuration same as the 2011 NCEP trunk version, with 42 vertical levels from surface to 50hPa. The second experiment (EXP2_L42T1) is the same as EXP1_L42T50 but with model top raised to 1hPa, and the third experiment (EXP3_L64T1) is the same as EXP1_L42T50 except for having 64 vertical levels from surface to 1hPa. So far numerical experiments for a total of 10 tropical storms, hurricanes, and typhoons over Atlantic and Pacific are carried out. On average, a 10% improvement is obtained for hurricane track forecasts by EXP2_L42T1 compared with EXP1_L42T50 but only during the first 48 hours model forecast period. The track forecast skills of EXP3_L64T1 are consistently improved for all forecast leading hours compared with EXP1_L42T50. It is worth mentioning that our control experiment EXP2_L42T1 outperforms HWRF operational (HOPS) forecasts for both track and intensity forecasts. The forecast error statistics for different ocean basins and more cases will be presented at the conference when more 2012 tropical storms, hurricanes, and typhoons become available.

  6. A Hierarchical Bayesian Model to Quantify Uncertainty of Stream Water Temperature Forecasts

    PubMed Central

    Bal, Guillaume; Rivot, Etienne; Baglinire, Jean-Luc; White, Jonathan; Prvost, Etienne

    2014-01-01

    Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i) an emotive simulated example, ii) application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife. PMID:25541732

  7. The Generalized FLaIR Model (GFM) for landslide forecasting

    NASA Astrophysics Data System (ADS)

    De Luca, Davide Luciano; Versace, Pasquale

    2015-04-01

    A new version of the hydrological model named FLaIR (Forecasting of Landslides Induced by Rainfall, Capparelli and Versace 2011) is proposed, named as GFM (Generalized FLaIR Model). Non stationary rainfall thresholds, depending on antecedent precipitation, are introduced in this new release, which allow for a better prediction of landslide occurrences. It is possible to demonstrate that GFM reproduces all the Antecedent Precipitation models (AP) proposed in technical literature as particular cases, besides Intensity-Duration schemes (ID) and more conceptual approaches, whose reconstruction with the first release of FlaIR model, which adopts only stationary thresholds, was already discussed in Capparelli and Versace (2011). GFM is extremely flexible, and the main advantage of the model is represented by the possibility of using well-established procedures for the choice of the most appropriate configuration for the selected case study, and of facilitating the comparison between several options, through the use of a mobility function. Gimigliano municipality, located in Calabria region (southern Italy) was chosen as case study, where a consistent number of landslides occurred in the past years; in particular, during the period 2008-2010 this area (like the whole Calabria region) was affected by persistent rainfall events, which induced several damages related to infrastructures and buildings. For the selected case study GFM allows to obtain significant improvements in landslide prediction; in details a substantial reduction of False Alarms is obtained with respect to application of classical ID and AP schemes. REFERENCES Capparelli G, Versace P (2011). FLaIR and SUSHI: Two mathematical models for Early Warning Systems for rainfall induced landslides. Landslides 8:67-79. doi: 10.1007/s10346-010-0228-6

  8. Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models

    NASA Astrophysics Data System (ADS)

    Tiwari, Mukesh K.; Adamowski, Jan

    2013-10-01

    A new hybrid wavelet-bootstrap-neural network (WBNN) model is proposed in this study for short term (1, 3, and 5 day; 1 and 2 week; and 1 and 2 month) urban water demand forecasting. The new method was tested using data from the city of Montreal in Canada. The performance of the WBNN method was compared with the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average model with exogenous input variables (ARIMAX), traditional NNs, wavelet analysis-based NNs (WNN), bootstrap-based NNs (BNN), and a simple nave persistence index model. The WBNN model was developed as an ensemble of several NNs built using bootstrap resamples of wavelet subtime series instead of raw data sets. The results demonstrated that the hybrid WBNN and WNN models produced significantly more accurate forecasting results than the traditional NN, BNN, ARIMA, and ARIMAX models. It was also found that the WBNN model reduces the uncertainty associated with the forecasts, and the performance of WBNN forecasted confidence bands was found to be more accurate and reliable than BNN forecasted confidence bands. It was found in this study that maximum temperature and total precipitation improved the accuracy of water demand forecasts using wavelet analysis. The performance of WBNN models was also compared for different numbers of bootstrap resamples (i.e., 25, 50, 100, 200, and 500) and it was found that WBNN models produced optimum results with different numbers of bootstrap resamples for different lead time forecasts with considerable variability.

  9. Estimating Demand for Industrial and Commercial Land Use Given Economic Forecasts

    PubMed Central

    Batista e Silva, Filipe; Koomen, Eric; Diogo, Vasco; Lavalle, Carlo

    2014-01-01

    Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on ‘land use intensity’ measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models’ results were compared with estimations generated using the ‘null model’ (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the ‘null model’, but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models’ performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions. PMID:24647587

  10. 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.

  11. A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

    NASA Technical Reports Server (NTRS)

    Chin, Mian; Ginoux, Paul; Lucchesi, Robert; Huebert, Barry; Weber, Rodney; Anderson, Tad; Masonis, Sarah; Blomquist, Byron; Bandy, Alan; Thornton, Donald

    2003-01-01

    We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.

  12. Accuracy analysis by using WARIMA model to forecast TEC in China

    NASA Astrophysics Data System (ADS)

    Liu, Lilong; Chen, Jun; Wu, Pituan; Cai, Chenghui; Huang, Liangke

    2015-12-01

    Aiming at the characteristic of nonlinear and non-stationary in ionospheric total electron content(TEC), this article bring Wavelet Analysis into the autoregressive integrated moving average model to forecast the next four days' TEC values by using six days' ionospheric grid observation data of Chinese area in 2010 provided by IGS station. Taking IGS station's observation data as true value, compare the forecast value with it then count the forecast accuracies which are to prove that it has a quite good result by using WARIMA model to forecast Chinese area's Ionospheric grid data. But near the geomagnetic latitude of about +/-20grid, the model's forecast results are a little worse than others' because Geomagnetic activity is irregular which lead to the TEC values there change greatly.

  13. Forecasting exposure to volcanic ash based on ash dispersion modeling

    NASA Astrophysics Data System (ADS)

    Peterson, Rorik A.; Dean, Ken G.

    2008-03-01

    A technique has been developed that uses Puff, a volcanic ash transport and dispersion (VATD) model, to forecast the relative exposure of aircraft and ground facilities to ash from a volcanic eruption. VATD models couple numerical weather prediction (NWP) data with physical descriptions of the initial eruptive plume, atmospheric dispersion, and settling of ash particles. Three distinct examples of variations on the technique are given using ERA-40 archived reanalysis NWP data. The Feb. 2000 NASA DC-8 event involving an eruption of Hekla volcano, Iceland is first used for analyzing a single flight. Results corroborate previous analyses that conclude the aircraft did encounter a diffuse cloud of volcanic origin, and indicate exposure within a factor of 10 compared to measurements made on the flight. The sensitivity of the technique to dispersion physics is demonstrated. The Feb. 2001 eruption of Mt. Cleveland, Alaska is used as a second example to demonstrate how this technique can be utilized to quickly assess the potential exposure of a multitude of aircraft during and soon after an event. Using flight tracking data from over 40,000 routes over three days, several flights that may have encountered low concentrations of ash were identified, and the exposure calculated. Relative changes in the quantity of exposure when the eruption duration is varied are discussed, and no clear trend is evident as the exposure increased for some flights and decreased for others. A third application of this technique is demonstrated by forecasting the near-surface airborne concentrations of ash that the cities of Yakima Washington, Boise Idaho, and Kelowna British Columbia might have experienced from an eruption of Mt. St. Helens anytime during the year 2000. Results indicate that proximity to the source does not accurately determine the potential hazard. Although an eruption did not occur during this time, the results serve as a demonstration of how existing cities or potential locations of research facilities or military bases can be assessed for susceptibility to hazardous and unhealthy concentrations of ash and other volcanic gases.

  14. Forecasting Dust Storms Using the CARMA-Dust Model and MM5 Weather Data

    NASA Astrophysics Data System (ADS)

    Barnum, B. H.; Winstead, N. S.; Wesely, J.; Hakola, A.; Colarco, P.; Toon, O. B.; Ginoux, P.; Brooks, G.; Hasselbarth, L. M.; Toth, B.; Sterner, R.

    2002-12-01

    An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5), and a modified version of the Colorado Aerosol and Radiation Model for Atmospheres (CARMA). AFWA conducted a 60 day evaluation of the dust model to look at the model's ability to forecast dust storms for short, medium and long range (72 hour) forecast periods. The study used satellite and ground observations of dust storms to verify the model's effectiveness. Each of the main mesoscale forecast theaters was broken down into smaller sub-regions for detailed analysis. The study found the forecast model was able to forecast dust storms in Saharan Africa and the Sahel region with an average Probability of Detection (POD)exceeding 68%, with a 16% False Alarm Rate (FAR). The Southwest Asian theater had average POD's of 61% with FAR's averaging 10%.

  15. Modeling the wind-fields of accidental releases by mesoscale forecasting

    SciTech Connect

    Albritton, J.R.; Lee, R.L.; Mobley, R.L.; Pace, J.C.; Hodur, R.A.; Lion, C.S.

    1997-07-01

    Modeling atmospheric releases even during fair weather can present a sever challenge to diagnostic, observed-data-driven, models. Such schemes are often handicapped by sparse input data from meteorological surface stations and soundings. Forecasting by persistence is only acceptable for a few hours and cannot predict important changes in the diurnal cycle or from synoptic evolution. Many accident scenarios are data-sparse in space and/or time. Here we describe the potential value of limited-area, mesoscale, forecast models for real-time emergency response. Simulated wind-fields will be passed to ARAC`s operational models to produce improved forecasts of dispersion following accidents.

  16. A Nested Probabilistic and Deterministic Flood Forecasting Model: Toward an Early Warning System

    NASA Astrophysics Data System (ADS)

    AghaKouchak, A.

    2011-12-01

    Floods are the most life threatening hydrologic extremes and are one of the most widespread natural disasters. A 20-year (1985-2004) record shows that on average 150 flood events occurred each year across the globe resulting in 7500 deaths and approximately $15 billion in property and economic damage. We present a nested hydrologic model for quasi-global flood forecasting using satellite data. The model operates in two steps. Step 1: A 0.25 degree gridded probabilistic quasi-global hydrologic model is used to identify locations with high probability of flooding. The model provides an ensemble of runoff simulations using a set of resampled model parameters. Step 2: A deterministic semi-distributed model, nested within the probabilistic model, is employed for detailed flood analysis in watershed scale. The semi-distributed mode (Step 2) is activated only for areas identified as high risk in the first step. Satellite-based precipitation, snow covered areas, evapotranspiration and soil moisture data, derived from multiple sensors, are used as input data. Other forcings are obtained from NASA Land Information System (LIS). The results indicate that the model can provide reasonable probability of flooding. Efforts are underway to use this model for an early flood warning system that can provide probability of flood events and their uncertainties.

  17. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

    NASA Astrophysics Data System (ADS)

    Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

    2015-08-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

  18. Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

    NASA Astrophysics Data System (ADS)

    Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

    2014-10-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.

  19. Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

    NASA Astrophysics Data System (ADS)

    Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

    2013-04-01

    The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

  20. Gary Becker: Model Economic Scientist

    PubMed Central

    2015-01-01

    This paper presents Gary Becker’s approach to conducting creative, empirically fruitful economic research. It describes the traits and methodology that made him such a productive and influential scholar. PMID:26705367

  1. Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the Lez river, Southern France)

    NASA Astrophysics Data System (ADS)

    Darras, T.; Raynaud, F.; Borrell Estupina, V.; Kong-A-Siou, L.; Van-Exter, S.; Vayssade, B.; Johannet, A.; Pistre, S.

    2015-06-01

    Flash floods forecasting in the Mediterranean area is a major economic and societal issue. Specifically, considering karst basins, heterogeneous structure and nonlinear behaviour make the flash flood forecasting very difficult. In this context, this work proposes a methodology to estimate the contribution from karst and non-karst components using toolbox including neural networks and various hydrological methods. The chosen case study is the flash flooding of the Lez river, known for his complex behaviour and huge stakes, at the gauge station of Lavallette, upstream of Montpellier (400 000 inhabitants). After application of the proposed methodology, discharge at the station of Lavallette is spited between hydrographs of karst flood and surface runoff, for the two events of 2014. Generalizing the method to future events will allow designing forecasting models specifically for karst and surface flood increasing by this way the reliability of the forecasts.

  2. Increasing Foresight and Forecast Quality with Skillful Low-Cost Empirical Models

    NASA Astrophysics Data System (ADS)

    Du, H.; Smith, L. A.; Suckling, E.; Thompson, E. L.

    2014-12-01

    Simulation models are widely employed to make probability forecasts on seasonal to annual time-scales and increasingly on decadal scales. While simulation models based on physical principles are often expected, in principle, to outperform purely empirical models, that claim must be established empirically for any given generation of models; direct comparison of the forecast skill of simulation models and empirical models provides information on progress toward that goal which is not available in model-model intercomparisons. More importantly, the blending of forecasts from both sources can lead to better operational forecasts. Direct comparison can also reveal the space and time scales on which simulation models exploit their physical basis effectively, perhaps indicating the origins of their weaknesses. The skill of seasonal and decadal probabilistic hindcasts for global and regional mean temperatures from the ENSEMBLES project and CMIP5 are interpreted in this context. Physically inspired empirical models are shown to display probabilistic skill comparable to that of today's state-of-the-art simulation models as well as to that of the multi-model ensemble. The inclusion of empirical models (blending) with simulation models is shown to significantly improve forecasts. Inasmuch as the cost of building or running empirical models is negligible comparing to large simulation models, it is suggested that the direct comparison of simulation models with empirical models become a regular component of large model forecast evaluations, that rank order evaluations include empirical models whenever the timescales allow, and that blending simulation models with empirical models becomes a regular component of seasonal and decadal forecasting.

  3. Forecasting gaming revenues in Clark County, Nevada: Issues and methods

    SciTech Connect

    Edwards, B.K.; Bando, A.

    1992-07-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.

  4. Forecasting gaming revenues in Clark County, Nevada: Issues and methods

    SciTech Connect

    Edwards, B.K.; Bando, A.

    1992-01-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.

  5. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

    NASA Technical Reports Server (NTRS)

    Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

    2012-01-01

    Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

  6. A gene-wavelet model for long lead time drought forecasting

    NASA Astrophysics Data System (ADS)

    Danandeh Mehr, Ali; Kahya, Ercan; zger, Mehmet

    2014-09-01

    Drought forecasting is an essential ingredient for drought risk and sustainable water resources management. Due to increasing water demand and looming climate change, precise drought forecasting models have recently been receiving much attention. Beginning with a brief discussion of different drought forecasting models, this study presents a new hybrid gene-wavelet model, namely wavelet-linear genetic programing (WLGP), for long lead-time drought forecasting. The idea of WLGP is to detect and optimize the number of significant spectral bands of predictors in order to forecast the original predictand (drought index) directly. Using the observed El Nio-Southern Oscillation indicator (NINO 3.4 index) and Palmer's modified drought index (PMDI) as predictors and future PMDI as predictand, we proposed the WLGP model to forecast drought conditions in the State of Texas with 3, 6, and 12-month lead times. We compared the efficiency of the model with those of a classic linear genetic programing model developed in this study, a neuro-wavelet (WANN), and a fuzzy-wavelet (WFL) drought forecasting models formerly presented in the relevant literature. Our results demonstrated that the classic linear genetic programing model is unable to learn the non-linearity of drought phenomenon in the lead times longer than 3 months; however, the WLGP can be effectively used to forecast drought conditions having 3, 6, and 12-month lead times. Genetic-based sensitivity analysis among the input spectral bands showed that NINO 3.4 index has strong potential effect in drought forecasting of the study area with 6-12-month lead times.

  7. Forecasting wind power production from a wind farm using the RAMS model

    NASA Astrophysics Data System (ADS)

    Tiriolo, L.; Torcasio, R. C.; Montesanti, S.; Sempreviva, A. M.; Calidonna, C. R.; Transerici, C.; Federico, S.

    2015-04-01

    The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution of the RAMS model, which is 3 km (R3) and 12 km (R12), respectively. Both forecasts use the 12 UTC analysis/forecast cycle issued by the European Centre for Medium range Weather Forecast (ECMWF) as initial and boundary conditions. As an additional comparison, the results of R3 and R12 are compared with those of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. v Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available at 00:00, 06:00, 12:00 and 18:00 UTC for the years 2010 and 2011. Also, for R3 and R12, the RAMS model was used to refine the horizontal resolution of the ECMWF analyses by a two-years hindcast at 3 and 12 km horizontal resolution, respectively. The R3 reduces the RMSE of the predicted wind power of the whole 2011 by 5% compared to R12, showing an impact of the meteorological model horizontal resolution in forecasting the wind power for the specific site.

  8. Tools and Products of Real-Time Modeling: Opportunities for Space Weather Forecasting

    NASA Technical Reports Server (NTRS)

    Hesse, Michael

    2009-01-01

    The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the last element. Specifically, we will discuss present capabilities, and the potential to derive further tools. These capabilities will be interpreted in the context of a broad-based, bootstrapping activity for modern Space Weather forecasting.

  9. Modelling and forecasting monthly and daily river discharge data using hybrid models and considering autoregressive heteroscedasticity

    NASA Astrophysics Data System (ADS)

    Szolgayova, Elena

    2010-05-01

    Hybrid modelling, used for simulation and forecasting of hydrological time series, involving both process-based and data-driven types of models combines the available domain knowledge and process physics with the recent advances in data driven tools. In this way, complex hydrological processes can be modelled and forecasted by decomposing the problem into several smaller sub - problems and using process physics based models where these are most appropriate, and data dictated tools (such as ANN, time series models or traditional statistics) for the residual data, when necessary. The fitting and forecasting performance of such models have to be explored case based. So far, only a few attempts to apply various nonlinear time series models within such a framework were reported in the hydrological modelling literature. This contribution presents results concerning the possibility to use GARCH type of models for such purposes. More specifically, error time series from two hydrological conceptual models were analyzed (applied on time series measured from the Hron and Morava Rivers in Slovakia), concentrating on the improvement of the modelling and forecasting performance of these models. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological model error time series with the aim to test and develop appropriate methods for various time steps from the GARCH family of models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. A model from the GARCH family was fitted on the data, comparing the fit with a fit of an ARMA model. 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons. The investigation of model properties and performances was thoroughly tested under various conditions of their future practical applications. In general, heteroscedasticity was present in the majority of the error time series of the hydrological models. However, the GARCH family of models proved to be suited in removing it only in daily time step. The basic GARCH model was not applicable on any of the time series. In all other investigated cases, the EGARCH(1,1) model had to be used. Unlike in econometric time series, where the so called leverage effect (i.e. the series reacts more strongly to negative changes) is present and pointed out by this model, here the data tends to react more strongly on positive changes. In this particular case it was found, that the general property of hydrological processes, that the rise of discharge is rainfall driven (a highly nonlinear chaotic intermittent process) and the decrease of discharge is ruled by the damping effects of the water storage in the driven system (catchment or river reach), is present also in the hydrological model error series. This shows, that the modelling and forecasting of floods (pulse like rising discharge) is a more demanding task than that of droughts (slowly decreasing flows). Even though the GARCH models did show partial improvements in the modelling and forecasting of flows, they still have several serious disadvantages (such as high sensitivity to the chosen fitting period) and possible further use should be further investigated. These results are of importance with respect to future attempts of modelling of error time series of hydrological models in such hybrid frameworks. They underpin the need of a non-mechanistic approach in the case based analysis of such data and the physical interpretation of statistical modelling results.

  10. Integrated water and sediment flow simulation and forecasting models for river reaches

    NASA Astrophysics Data System (ADS)

    Choudhury, Parthasarathi; Sil, Briti Sundar

    2010-05-01

    SummaryIn the present study integrated water and sediment flow simulation and forecasting models for a river reach have been developed. The new models combine Muskingum model and the sediment rating model leading to integrated water discharge-sediment concentration model ( WSCM) and water discharge-sediment discharge model ( WSDM) for a reach. The models depict coherence in water discharge and sediment load variations at a site; incorporate two hydrologic variables, water discharge and sediment load for the gauge sites and represent revised forms of the basic Muskingum model. The models can be recast into forecasting form useful for obtaining downstream water and sediment flow forecasts ?t'=2kx time unit ahead. During calibration the models can select a commensurate inflow-outflow set depending on upstream and the downstream relative sediment discharge characteristics for a reach. The models can be used for developing Muskingum model for river reaches having no water discharge records. With forecasting capabilities the present models are useful in the real time management of sediment related pollution hazards in water courses. The study indicates that a single model could be used to describe both water and sediment flow in river reaches. The proposed model formulations are demonstrated for simulating and forecasting sediment concentration, sediment discharge and water discharge in the Mississippi River Basin, USA. Model parameters are estimated using non-dominated sorting Genetic Algorithm II (NSGA-II). Comparison of models performances with reported works show better performances by the present models.

  11. Training the next generation of scientists in Weather Forecasting: new approaches with real models

    NASA Astrophysics Data System (ADS)

    Carver, Glenn; Váňa, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah

    2014-05-01

    The European Centre for Medium Range Weather Forecasts operationally produce medium range forecasts using what is internationally acknowledged as the world leading global weather forecast model. Future development of this scientifically advanced model relies on a continued availability of experts in the field of meteorological science and with high-level software skills. ECMWF therefore has a vested interest in young scientists and University graduates developing the necessary skills in numerical weather prediction including both scientific and technical aspects. The OpenIFS project at ECMWF maintains a portable version of the ECMWF forecast model (known as IFS) for use in education and research at Universities, National Meteorological Services and other research and education organisations. OpenIFS models can be run on desktop or high performance computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. ECMWF also provide the Metview desktop application, a modern, graphical, and easy to use tool for analysing and visualising forecasts that is routinely used by scientists and forecasters at ECMWF and other institutions. The combination of Metview with the OpenIFS models has the potential to deliver classroom-friendly tools allowing students to apply their theoretical knowledge to real-world examples using a world-leading weather forecasting model. In this paper we will describe how the OpenIFS model has been used for teaching. We describe the use of Linux based 'virtual machines' pre-packaged on USB sticks that support a technically easy and safe way of providing 'classroom-on-a-stick' learning environments for advanced training in numerical weather prediction. We welcome discussions with interested parties.

  12. Probabilistic streamflow predictions combining ensemble meteorological forecasts and a multi-model approach

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Brissette, Franois; Arsenault, Richard; Gatien, Philippe; Roy, Pierre-Olivier; Li, Zhi; Turcotte, Richard

    2013-04-01

    Probabilistic streamflow prediction based on past climate records or meteorological forecasts have drawn much attention in recent years. It is usually incorporated into operational forecasting systems by government agencies and industries to deal with water resources management and regulation problems. This work presents an operational prototype for short to medium term ensemble streamflow predictions over Quebec, Canada. The system uses ensemble meteorological forecasts for short term (up to 7 days) forecasting, transitioning to a stochastic weather generator conditioned on historical data for the period exceeding 7 days. The precipitation and temperature series are then fed into a combination of 32 hydrology models to account for both the meteorological and hydrology modelling uncertainties. A novel post-processing approach was implemented to correct the biases and the under-dispersion of ensemble meteorological forecasts. This post-processing approach links the mean of the ensemble meteorological forecast to parameters of a stochastic weather generator (absolute probability of precipitation and observed precipitation mean in the case of precipitation). The stochastic weather generator is then used to generated unbiased times series with accurate spread. Results show that the post-processed meteorological forecasts displayed skill for a period up to 7 days for both precipitation and temperature. The ensemble streamflow prediction displayed more skill than when using the deterministic forecast or the stochastic weather generator not conditioned on the ensemble meteorological forecasts. To tackle the uncertainty linked to the hydrology model, 4 different models calibrated with up to 9 different efficiency metrics (for a combination of 32 models/calibrations). Nine different averaging schemes were compared to attribute weights to the 32 combinations. The best averaging method (Granger-Ramanathan) produced estimates with a much better efficiency than the best performing model, while removing all biases linked to the hydrology modelling.

  13. Snowmelt runoff modeling in simulation and forecasting modes with the Martinec-Mango model

    NASA Technical Reports Server (NTRS)

    Shafer, B.; Jones, E. B.; Frick, D. M. (Principal Investigator)

    1982-01-01

    The Martinec-Rango snowmelt runoff model was applied to two watersheds in the Rio Grande basin, Colorado-the South Fork Rio Grande, a drainage encompassing 216 sq mi without reservoirs or diversions and the Rio Grande above Del Norte, a drainage encompassing 1,320 sq mi without major reservoirs. The model was successfully applied to both watersheds when run in a simulation mode for the period 1973-79. This period included both high and low runoff seasons. Central to the adaptation of the model to run in a forecast mode was the need to develop a technique to forecast the shape of the snow cover depletion curves between satellite data points. Four separate approaches were investigated-simple linear estimation, multiple regression, parabolic exponential, and type curve. Only the parabolic exponential and type curve methods were run on the South Fork and Rio Grande watersheds for the 1980 runoff season using satellite snow cover updates when available. Although reasonable forecasts were obtained in certain situations, neither method seemed ready for truly operational forecasts, possibly due to a large amount of estimated climatic data for one or two primary base stations during the 1980 season.

  14. The Study of Pavement Performance Index Forecasting Via Improving Grey Model

    NASA Astrophysics Data System (ADS)

    Chiang, Ziping; Jan, Dar-Ying; Chang, Hsueh-Sheng

    This paper proposed a time series forecasting approach based on improving grey model (IGM). This method is based on fitting difference equation and yields better predictive result than the traditional one, and is demonstrated by forecasting pavement performance index as international roughness index. The results show that this approach can minimizes the error based on the traditional grey model, adaptive ?, and grey rolling model with 19.4%, 17.7%, and 9.5%.

  15. Earthquake Forecasting in Northeast India using Energy Blocked Model

    NASA Astrophysics Data System (ADS)

    Mohapatra, A. K.; Mohanty, D. K.

    2009-12-01

    In the present study, the cumulative seismic energy released by earthquakes (M ≥ 5) for a period 1897 to 2007 is analyzed for Northeast (NE) India. It is one of the most seismically active regions of the world. The occurrence of three great earthquakes like 1897 Shillong plateau earthquake (Mw= 8.7), 1934 Bihar Nepal earthquake with (Mw= 8.3) and 1950 Upper Assam earthquake (Mw= 8.7) signify the possibility of great earthquakes in future from this region. The regional seismicity map for the study region is prepared by plotting the earthquake data for the period 1897 to 2007 from the source like USGS,ISC catalogs, GCMT database, Indian Meteorological department (IMD). Based on the geology, tectonic and seismicity the study region is classified into three source zones such as Zone 1: Arakan-Yoma zone (AYZ), Zone 2: Himalayan Zone (HZ) and Zone 3: Shillong Plateau zone (SPZ). The Arakan-Yoma Range is characterized by the subduction zone, developed by the junction of the Indian Plate and the Eurasian Plate. It shows a dense clustering of earthquake events and the 1908 eastern boundary earthquake. The Himalayan tectonic zone depicts the subduction zone, and the Assam syntaxis. This zone suffered by the great earthquakes like the 1950 Assam, 1934 Bihar and the 1951 Upper Himalayan earthquakes with Mw > 8. The Shillong Plateau zone was affected by major faults like the Dauki fault and exhibits its own style of the prominent tectonic features. The seismicity and hazard potential of Shillong Plateau is distinct from the Himalayan thrust. Using energy blocked model by Tsuboi, the forecasting of major earthquakes for each source zone is estimated. As per the energy blocked model, the supply of energy for potential earthquakes in an area is remarkably uniform with respect to time and the difference between the supply energy and cumulative energy released for a span of time, is a good indicator of energy blocked and can be utilized for the forecasting of major earthquakes. The proposed process provides a more consistent model of gradual accumulation of strain and non-uniform release through large earthquakes and can be applied in the evaluation of seismic risk. The cumulative seismic energy released by major earthquakes throughout the period from 1897 to 2007 of last 110 years in the all the zones are calculated and plotted. The plot gives characteristics curve for each zone. Each curve is irregular, reflecting occasional high activity. The maximum earthquake energy available at a particular time in a given area is given by S. The difference between the theoretical upper limit given by S and the cumulative energy released up to that time is calculated to find out the maximum magnitude of an earthquake which can occur in future. Energy blocked of the three source regions are 1.35*1017 Joules, 4.25*1017 Joules and 0.12*1017 in Joules respectively for source zone 1, 2 and 3, as a supply for potential earthquakes in due course of time. The predicted maximum magnitude (mmax) obtained for each source zone AYZ, HZ, and SPZ are 8.2, 8.6, and 8.4 respectively by this model. This study is also consistent with the previous predicted results by other workers.

  16. Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis. Revision 1.12

    NASA Technical Reports Server (NTRS)

    Hoffman, Ross N.; Nehrkorn, Thomas; Grassotti, Christopher

    1997-01-01

    We proposed a novel characterization of errors for numerical weather predictions. In its simplest form we decompose the error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and is required to vary slowly and smoothly with position. A general distortion representation allows for the displacement and amplification or bias correction of forecast anomalies. Characterizing and decomposing forecast error in this way has two important applications, which we term the assessment application and the objective analysis application. For the assessment application, our approach results in new objective measures of forecast skill which are more in line with subjective measures of forecast skill and which are useful in validating models and diagnosing their shortcomings. With regard to the objective analysis application, meteorological analysis schemes balance forecast error and observational error to obtain an optimal analysis. Presently, representations of the error covariance matrix used to measure the forecast error are severely limited. For the objective analysis application our approach will improve analyses by providing a more realistic measure of the forecast error. We expect, a priori, that our approach should greatly improve the utility of remotely sensed data which have relatively high horizontal resolution, but which are indirectly related to the conventional atmospheric variables. In this project, we are initially focusing on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically, we study the forecast errors of the sea level pressure (SLP) and 500 hPa geopotential height fields for forecasts of the short and medium range. Since the forecasts are generated by the GEOS (Goddard Earth Observing System) data assimilation system with and without ERS 1 scatterometer data, these preliminary studies serve several purposes. They (1) provide a testbed for the use of the distortion representation of forecast errors, (2) act as one means of validating the GEOS data assimilation system and (3) help to describe the impact of the ERS 1 scatterometer data.

  17. Short-term Forecasting of the Prevalence of Trachoma: Expert Opinion, Statistical Regression, versus Transmission Models

    PubMed Central

    Liu, Fengchen; Porco, Travis C.; Amza, Abdou; Kadri, Boubacar; Nassirou, Baido; West, Sheila K.; Bailey, Robin L.; Keenan, Jeremy D.; Solomon, Anthony W.; Emerson, Paul M.; Gambhir, Manoj; Lietman, Thomas M.

    2015-01-01

    Background Trachoma programs rely on guidelines made in large part using expert opinion of what will happen with and without intervention. Large community-randomized trials offer an opportunity to actually compare forecasting methods in a masked fashion. Methods The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Given antibiotic coverage and biannual assessments from baseline through 30 months, forecasts of the prevalence of infection in each of the 24 communities at 36 months were made by three methods: the sum of 15 experts’ opinion, statistical regression of the square-root-transformed prevalence, and a stochastic hidden Markov model of infection transmission (Susceptible-Infectious-Susceptible, or SIS model). All forecasters were masked to the 36-month results and to the other forecasts. Forecasts of the 24 communities were scored by the likelihood of the observed results and compared using Wilcoxon’s signed-rank statistic. Findings Regression and SIS hidden Markov models had significantly better likelihood than community expert opinion (p = 0.004 and p = 0.01, respectively). All forecasts scored better when perturbed to decrease Fisher’s information. Each individual expert’s forecast was poorer than the sum of experts. Interpretation Regression and SIS models performed significantly better than expert opinion, although all forecasts were overly confident. Further model refinements may score better, although would need to be tested and compared in new masked studies. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models. PMID:26302380

  18. Using Science Data and Models for Space Weather Forecasting - Challenges and Opportunities

    NASA Technical Reports Server (NTRS)

    Hesse, Michael; Pulkkinen, Antti; Zheng, Yihua; Maddox, Marlo; Berrios, David; Taktakishvili, Sandro; Kuznetsova, Masha; Chulaki, Anna; Lee, Hyesook; Mullinix, Rick; Rastaetter, Lutz

    2012-01-01

    Space research, and, consequently, space weather forecasting are immature disciplines. Scientific knowledge is accumulated frequently, which changes our understanding or how solar eruptions occur, and of how they impact targets near or on the Earth, or targets throughout the heliosphere. Along with continuous progress in understanding, space research and forecasting models are advancing rapidly in capability, often providing substantially increases in space weather value over time scales of less than a year. Furthermore, the majority of space environment information available today is, particularly in the solar and heliospheric domains, derived from research missions. An optimal forecasting environment needs to be flexible enough to benefit from this rapid development, and flexible enough to adapt to evolving data sources, many of which may also stem from non-US entities. This presentation will analyze the experiences obtained by developing and operating both a forecasting service for NASA, and an experimental forecasting system for Geomagnetically Induced Currents.

  19. Forecasting Daytime Seeing Conditions Using a Mesoscale Numerical Weather Predcition Model

    NASA Astrophysics Data System (ADS)

    Mozer, J. B.; van Wey, N. J.; Gordon, S. C.; Jumper, G. Y.; Seeley, G. P.

    2002-12-01

    "Seeing" is an astronomical term to describe the quality of observing conditions due to optical turbulence in the Earth's atmosphere which can blur images of astronomical objects. The ability to diagnose and forecast daytime seeing conditions at a specific location would be of use to solar observatories in scheduling observations and interpreting data, as well as a source of supporting information for site surveys of future telescopes. In the present work, we explore the feasibility of using the Air Force Weather Agency's MM5 forecasts over the continental United States (CONUS) as input to an AFRL optical turbulence modeling tool, to forecast seeing out to 48 hours in the future at several sites proposed for the Advanced Technology Solar Telescope (ATST). These forecasts are compared with optical turbulence measurements made at these sites using the Solar Differential Image Motion Monitor (S-DIMM) and SHAdow BAnd Ranger(SHABAR) instruments. These comparisons show a positive correlation between forecasted and measured daytime seeing.

  20. Multi-model ensemble forecasting and glider path planning in the Mid-Atlantic Bight

    NASA Astrophysics Data System (ADS)

    Wang, Xiaochun; Chao, Yi; Thompson, David R.; Chien, Steve A.; Farrara, John; Li, Peggy; Vu, Quoc; Zhang, Hongchun; Levin, Julia C.; Gangopadhyay, Avijit

    2013-07-01

    During the first two weeks of November 2009, a field experiment was conducted in the Mid-Atlantic Bight region to demonstrate a coastal ocean observatory that can collect observations from heterogeneous platforms and forecast fields from four different ocean models, provide multi-model ensemble forecasts based on either an equal weighting (EQ) or objective weighting (OBJ) method, and use model forecasts in a path planning system to relocate autonomous gliders. This experiment is a prototype for the command and control component of cyberinfrastructure of the Ocean Observatories Initiative funded by the National Science Foundation. The four individual models use different forcing fields, boundary conditions and data assimilation techniques, and have resolutions varying from 2km to 15km. Our results indicate that for sea surface temperature and surface currents, the OBJ ensemble outperforms the four individual models, while the EQ ensemble can also provide an effective way to improve individual model forecasts. In terms of glider path planning, the OBJ ensemble has a performance similar to the best individual model, which has the finest horizontal resolution. This field experiment demonstrates the first-ever use of ensemble current forecasts to guide glider path planning in the context of real-time data collection and ocean model forecasting.

  1. Multi-model ensemble forecasting and glider path planning in the Mid-Atlantic Bight

    NASA Astrophysics Data System (ADS)

    Wang, Xiaochun; Chao, Yi; Thompson, David R.; Chien, Steve A.; Farrara, John; Li, Peggy; Vu, Quoc; Zhang, Hongchun; Levin, Julia C.; Gangopadhyay, Avijit

    2013-07-01

    During the first two weeks of November 2009, a field experiment was conducted in the Mid-Atlantic Bight region to demonstrate a coastal ocean observatory that can collect observations from heterogeneous platforms and forecast fields from four different ocean models, provide multi-model ensemble forecasts based on either an equal weighting (EQ) or objective weighting (OBJ) method, and use model forecasts in a path planning system to relocate autonomous gliders. This experiment is a prototype for the command and control component of cyberinfrastructure of the Ocean Observatories Initiative funded by the National Science Foundation. The four individual models use different forcing fields, boundary conditions and data assimilation techniques, and have resolutions varying from 2 km to 15 km. Our results indicate that for sea surface temperature and surface currents, the OBJ ensemble outperforms the four individual models, while the EQ ensemble can also provide an effective way to improve individual model forecasts. In terms of glider path planning, the OBJ ensemble has a performance similar to the best individual model, which has the finest horizontal resolution. This field experiment demonstrates the first-ever use of ensemble current forecasts to guide glider path planning in the context of real-time data collection and ocean model forecasting.

  2. Fishery landing forecasting using EMD-based least square support vector machine models

    NASA Astrophysics Data System (ADS)

    Shabri, Ani

    2015-05-01

    In this paper, the novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EMD) and least square support machine (LSSVM) is proposed to improve the accuracy of fishery landing forecasting. This hybrid is formulated specifically to address in modeling fishery landing, which has high nonlinear, non-stationary and seasonality time series which can hardly be properly modelled and accurately forecasted by traditional statistical models. In the hybrid model, EMD is used to decompose original data into a finite and often small number of sub-series. The each sub-series is modeled and forecasted by a LSSVM model. Finally the forecast of fishery landing is obtained by aggregating all forecasting results of sub-series. To assess the effectiveness and predictability of EMD-LSSVM, monthly fishery landing record data from East Johor of Peninsular Malaysia, have been used as a case study. The result shows that proposed model yield better forecasts than Autoregressive Integrated Moving Average (ARIMA), LSSVM and EMD-ARIMA models on several criteria..

  3. A 30-day-ahead forecast model for grass pollen in north London, United Kingdom

    NASA Astrophysics Data System (ADS)

    Smith, Matt; Emberlin, Jean

    2006-03-01

    A 30-day-ahead forecast method has been developed for grass pollen in north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21 May to 8 August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961 to 1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of 1 to 4; the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of 1 to 4, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002, respectively, when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

  4. Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model.

    PubMed

    An, Yan; Zou, Zhihong; Zhao, Yanfei

    2015-03-01

    An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. PMID:25766025

  5. Applying Forecast Models from the Center for Integrated Space Weather Modeling

    NASA Astrophysics Data System (ADS)

    Gehmeyr, M.; Baker, D. N.; Millward, G.; Odstrcil, D.

    2007-12-01

    The Center for Integrated Space Weather Modeling (CISM) has developed three forecast models (FMs) for the Sun-Earth chain. They have been matured by various degrees toward the operational stage. The Sun-Earth FM suite comprises empirical and physical models: the Planetary Equivalent Amplitude (AP-FM), the Solar Wind (SW- FM), and the Geospace (GS-FM) models. We give a brief overview of these forecast models and touch briefly on the associated validation studies. We demonstrate the utility of the models: AP-FM supporting the operations of the AIM (Aeronomy of Ice in the Mesosphere) mission soon after launch; SW-FM providing assistance with the interpretation of the STEREO beacon data; and GS-FM combining model and observed data to characterize the aurora borealis. We will then discuss space weather tools in a more general sense, point out where the current capabilities and shortcomings are, and conclude with a look forward to what areas need improvement to facilitate better real-time forecasts.

  6. Development of metrics and diagnostics for climate model short-range forecast experiments during YOTC

    NASA Astrophysics Data System (ADS)

    Ma, H.; Xie, S.; Boyle, J. S.; Klein, S. A.; Zhang, Y.

    2011-12-01

    We developed several metrics and diagnostic packages, and apply them to systematically examine the connection between biases of short-term forecasts, and biases of long-term climate simulations in tropical convection from a climate model during the Year of Tropical Convection (YOTC). The purpose is to systematically assess model performance skill in simulating climate-relevant quantities: precipitation, clouds and radiation, in short-range forecasts and identify how model errors evolve with time. The forecast approach allows specific parameterization deficiencies to be identified before various feedbacks occur in a climate system. Specifically, the climate model is the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). A series of CAM4 6-days forecast experiments initialized at 00Z every day with the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses were performed under the US Department of Energy's Cloud-Associated Parameterizations Testbed (CAPT). For comparison, we also performed a three-year long CAM4 Atmospheric Model Intercomparison Project (AMIP)-type climate simulation prescribed with observed weekly SST from 2008 to 2010. The metrics calculated from simulations include six statistics quantities, and they are calculated over the global tropics and selected regions. We also examine how model errors identified by the metrics evolve with forecast lead time to establish connection between errors in weather forecasts and errors in climate simulations with proper diagnostic tools. Our results reveal that Indian and western Pacific oceans show similar biases in the mean tropical precipitation between forecasts and AMIP run, except early days of forecasts show smaller biases. Our results also show that precipitation biases over the Africa and South America continents as well as over Atlantic ITCZ are larger in the early days of forecasts (day 2 and 3) rather than in the later days of forecasts (day 5 and 6) or AMIP runs. The underlying mechanisms are discussed. The usefulness and benefit of the metrics and diagnostics for the forecast experiments are also demonstrated in this study.

  7. A crop loss-related forecasting model for sclerotinia stem rot in winter oilseed rape.

    PubMed

    Koch, S; Dunker, S; Kleinhenz, B; Röhrig, M; Tiedemann, A von

    2007-09-01

    Sclerotinia stem rot (SSR) is an increasing threat to winter oilseed rape (OSR) in Germany and other European countries due to the growing area of OSR cultivation. A forecasting model was developed to provide decision support for the fungicide spray against SSR at flowering. Four weather variables-air temperature, relative humidity, rainfall, and sunshine duration-were used to calculate the microclimate in the plant canopy. From data reinvestigated in a climate chamber study, 7 to 11 degrees C and 80 to 86% relative humidity (RH) were established as minimum conditions for stem infection with ascospores and expressed as an index to discriminate infection hours (Inh). Disease incidence (DI) significantly correlated with Inh occurring post-growth stage (GS) 58 (late bud stage) (r(2) = 0.42, P model calculates the developmental stages of OSR based on temperature in the canopy and starts the model calculation at GS 58. The novel forecasting system, SkleroPro, consists of a two-tiered approach, the first providing a regional assessment of the disease risk, which is assumed when 23 Inh have accumulated after the crop has passed GS 58. The second tier provides a field-site-specific, economy-based recommendation. Based on costs of spray, expected yield, and price of rapeseed, the number of Inh corresponding to DI at the economic damage threshold (Inh(i)) is calculated. A decision to spray is proposed when Inh >/= Inh(i). Historical field data (1994 to 2004) were used to assess the impact of agronomic factors on SSR incidence. A 2-year crop rotation enhanced disease risk and, therefore, lowered the infection threshold in the model by a factor of 0.8, whereas in 4-year rotations, the threshold was elevated by a factor 1.3. Number of plants per square meter, nitrogen fertilization, and soil management did not have significant effects on DI. In an evaluation of SkleroPro with 76 historical (1994 to 2004) and 32 actual field experiments conducted in 2005, the percentage of economically correct decisions was 70 and 81%, respectively. Compared with the common practice of routine sprays, this corresponded to savings in fungicides of 39 and 81% and to increases in net return for the grower of 23 and 45 euro/ha, respectively. This study demonstrates that, particularly in areas with abundant inoculum, the level of SSR in OSR can be predicted from conditions of stem infection during late bud or flowering with sufficient accuracy, and does not require simulation of apothecial development and ascospore dispersal. SkleroPro is the first crop-loss-related forecasting model for a Sclerotinia disease, with the potential of being widely used in agricultural practice, accessible through the Internet. Its concept, components, and implementation may be useful in developing forecasting systems for Sclerotinia diseases in other crops or climates. PMID:18944183

  8. Land-use forecasting and hydrologic model integration for improved land-use decision support.

    PubMed

    McColl, Chris; Aggett, Graeme

    2007-09-01

    This paper develops a methodology for integrating a land-use forecasting model with an event scale, rainfall-runoff model in support of improving land-use policy formulation at the watershed scale. The models selected for integration are loosely coupled, structured upon a common GIS platform that facilitates data exchange. The hydrologic model HEC-HMS is calibrated for a specific storm event that occurred within central Washington State. The land-use forecasting model, What If? is implemented to forecast future spatial distributions of low-density residential land-uses under low and high population growth estimates. Forecasted land-use distribution patterns for the years 2015, 2025, and 2050 are then used as land-use data input for the calibrated hydrologic model, keeping all other parameters constant. Impacts to the stream discharge hydrograph are predicted as the study area becomes increasingly developed as forecasted by What If?. The initial results of this integration process demonstrate the synergy that can be generated through the linkage of the selected models. The ability to quantifiably forecast the potential hydrologic implications of proposed land-use policies before their implementation offers land-use decision-makers a valuable tool for discerning which proposed land-use alternatives will be effective at minimizing storm water runoff. PMID:16965852

  9. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    NASA Astrophysics Data System (ADS)

    Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

    2015-10-01

    Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

  10. The Forecasting of Economics Final Results--A Case of Post Hoc Ergo Propter Hoc?

    ERIC Educational Resources Information Center

    Wilson, P. R. D.

    1986-01-01

    A university economics department tested the commonly held opinion that college teachers can predict their students' eventual level of educational attainment from their personal observations of the student. A larger-than-anticipated margin of prediction error was revealed. (MSE)

  11. METRo: A New Model for Road-Condition Forecasting in Canada.

    NASA Astrophysics Data System (ADS)

    Crevier, Louis-Philippe; Delage, Yves

    2001-11-01

    A numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), has been developed to run at Canadian weather centers. METRo uses roadside observations from road weather information systems stations as input, together with meteorological forecasts from the operational Global Environmental Multiscale (GEM) model of the Canadian Meteorological Centre; the meteorologist can modify this forecast using the `SCRIBE' interface. METRo solves the energy balance at the road surface and the heat conduction in the road material to calculate the temperature evolution; it also accounts for water accumulation on the road in liquid and solid form. Radiative fluxes reaching the surface are taken from the GEM model in automatic mode or are parameterized as a function of cloud cover and temperature when run in manual mode. The road-condition forecast is done in three stages: initialization of the road temperature profile using past observations, coupling of the forecast with observations during the overlap period when the meteorological forecast and the roadside observations are both available, and the forecast itself. The coupling stage allows for adjusting the radiative fluxes to local conditions. Results for road temperature are presented for three stations in Ontario for a period of 3 months. The 24-h forecasts are issued 2 times per day at 0300 and 1500 LT. Overall, about one-half of the time the error in surface road temperature (verified every 20 min) is within 2 K, and the nighttime rms error is about 2 K. The impact of the coupling stage is large and allows METRo to produce automatic forecasts almost as good as the manual ones, especially for the first few hours. When METRo is run in manual mode, several nearby stations can use the same meteorological input, saving preparation time for the meteorologist. METRo also contains a mechanism for correcting systematic errors at each station, and it is hoped that this capability will permit its application to many new sites without major adjustments.

  12. Hydroclimate Forecasts in Ethiopia: Benefits, Impediments, and Ways Forward

    NASA Astrophysics Data System (ADS)

    Block, P. J.

    2014-12-01

    Numerous hydroclimate forecast models, tools, and guidance exist for application across Ethiopia and East Africa in the agricultural, water, energy, disasters, and economic sectors. This has resulted from concerted local and international interdisciplinary efforts, yet little evidence exists of rapid forecast uptake and use. We will review projected benefits and gains of seasonal forecast application, impediments, and options for the way forward. Specific case studies regarding floods, agricultural-economic links, and hydropower will be reviewed.

  13. Forecasting turbulent modes with nonparametric diffusion models: Learning from noisy data

    NASA Astrophysics Data System (ADS)

    Berry, Tyrus; Harlim, John

    2016-04-01

    In this paper, we apply a recently developed nonparametric modeling approach, the "diffusion forecast", to predict the time-evolution of Fourier modes of turbulent dynamical systems. While the diffusion forecasting method assumes the availability of a noise-free training data set observing the full state space of the dynamics, in real applications we often have only partial observations which are corrupted by noise. To alleviate these practical issues, following the theory of embedology, the diffusion model is built using the delay-embedding coordinates of the data. We show that this delay embedding biases the geometry of the data in a way which extracts the most stable component of the dynamics and reduces the influence of independent additive observation noise. The resulting diffusion forecast model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and when the observation noise vanishes. As in any standard forecasting problem, the forecasting skill depends crucially on the accuracy of the initial conditions. We introduce a novel Bayesian method for filtering the discrete-time noisy observations which works with the diffusion forecast to determine the forecast initial densities. Numerically, we compare this nonparametric approach with standard stochastic parametric models on a wide-range of well-studied turbulent modes, including the Lorenz-96 model in weakly chaotic to fully turbulent regimes and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. We show that when the only available data is the low-dimensional set of noisy modes that are being modeled, the diffusion forecast is indeed competitive to the perfect model.

  14. System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts.

    PubMed

    Qi, Cheng; Chang, Ni-Bin

    2011-06-01

    Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments. PMID:21324581

  15. Technical and economic models of a DBS system for Canada

    NASA Astrophysics Data System (ADS)

    Roscoe, O. S.

    A comprehensive, multidisciplinary study program to develop information regarding the possible implementation of a direct broadcasting satellite system for Canada was completed in 1983. The program included market studies and technical and economic modeling of alternative DBS systems. Both 50 dBW and 54 dBW edge-of-coverage EIRP systems were modeled, with both 4 and 6 beam coverage. It is estimated that an eight to ten channel system for Canada would cost between $400 million and $650 million (1982 Canadian dollars). The main requirement for DBS television service is in rural Canada. Market forecasts are that up to 2-1/2 million households would purchase DBS home receivers. Allowing for a real rate of return of 6 percent, the monthly cost per household for delivery of all channels would range from $5 to $7.

  16. HTGR Application Economic Model Users' Manual

    SciTech Connect

    A.M. Gandrik

    2012-01-01

    The High Temperature Gas-Cooled Reactor (HTGR) Application Economic Model was developed at the Idaho National Laboratory for the Next Generation Nuclear Plant Project. The HTGR Application Economic Model calculates either the required selling price of power and/or heat for a given internal rate of return (IRR) or the IRR for power and/or heat being sold at the market price. The user can generate these economic results for a range of reactor outlet temperatures; with and without power cycles, including either a Brayton or Rankine cycle; for the demonstration plant, first of a kind, or nth of a kind project phases; for up to 16 reactor modules; and for module ratings of 200, 350, or 600 MWt. This users manual contains the mathematical models and operating instructions for the HTGR Application Economic Model. Instructions, screenshots, and examples are provided to guide the user through the HTGR Application Economic Model. This model was designed for users who are familiar with the HTGR design and Excel and engineering economics. Modification of the HTGR Application Economic Model should only be performed by users familiar with the HTGR and its applications, Excel, and Visual Basic.

  17. Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

    NASA Astrophysics Data System (ADS)

    MacLeod, Dave A.; Jones, Anne; Di Giuseppe, Francesca; Caminade, Cyril; Morse, Andrew P.

    2015-04-01

    The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982-2006 the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January-May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change.

  18. Alaska North Slope regional gas hydrate production modeling forecasts

    USGS Publications Warehouse

    Wilson, S.J.; Hunter, R.B.; Collett, T.S.; Hancock, S.; Boswell, R.; Anderson, B.J.

    2011-01-01

    A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside" case captures the full potential of unconstrained development with widely spaced wells. The results of this study indicate that recoverable gas hydrate resources may exist in the Eileen accumulation and that it represents a good opportunity for continued research. ?? 2010 Elsevier Ltd.

  19. The Influence of Seasonal Forecast Accuracy on Farmer Behavior: An Agent-Based Modeling Approach

    NASA Astrophysics Data System (ADS)

    Jacobi, J. H.; Nay, J.; Gilligan, J. M.

    2013-12-01

    Seasonal climates dictate the livelihoods of farmers in developing countries. While farmers in developed countries often have seasonal forecasts on which to base their cropping decisions, developing world farmers usually make plans for the season without such information. Climate change increases the seasonal uncertainty, making things more difficult for farmers. Providing seasonal forecasts to these farmers is seen as a way to help buffer these typically marginal groups from the effects of climate change, though how to do so and the efficacy of such an effort is still uncertain. In Sri Lanka, an effort is underway to provide such forecasts to farmers. The accuracy of these forecasts is likely to have large impacts on how farmers accept and respond to the information they receive. We present an agent-based model to explore how the accuracy of seasonal rainfall forecasts affects the growing decisions and behavior of farmers in Sri Lanka. Using a decision function based on prospect theory, this model simulates farmers' behavior in the face of a wet, dry, or normal forecast. Farmers can either choose to grow paddy rice or plant a cash crop. Prospect theory is used to evaluate outcomes of the growing season; the farmer's memory of the level of success under a certain set of conditions affects next season's decision. Results from this study have implications for policy makers and seasonal forecasters.

  20. A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

    PubMed

    Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

    2015-02-01

    Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. PMID:25461118

  1. Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

    NASA Astrophysics Data System (ADS)

    Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

    2015-12-01

    The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

  2. Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

    NASA Astrophysics Data System (ADS)

    Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

    2011-12-01

    The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

  3. Local flood forecasting using guided model construction, data assimilation and web interfaces

    NASA Astrophysics Data System (ADS)

    Smith, Paul; Beven, Keith

    2013-04-01

    An important aspect of improving resilience to flooding is the provision of timely warnings to flood sensitive locations thus allowing mitigating measures to be implemented. For specific locations such small communities (often in head water catchments) or river side factories the ability of traditional centralised forecasting systems to provide timely & accurate forecasts may be challenged. This is due in part to the finite resources of monitoring agencies which results in courser spatial scales of model and data collection then may be required for the generation of accurate forecasts. One strategy to improve flood resilience at such locations is to install adequate telemetered monitoring equipment; generally a water level sensor and a rain gauge; which allows the construction of a local flood forecast. In this presentation we outline a methodology for providing detailed and location specific forecasts which can be computed either 'on-' or `off-site'. The basis of this is a guided model building process which incorporates both data assimilation and representation of the forecast uncertainty. The process requires the modeller to make only a few choices thus allowing rapid model deployment and revision. To be of use such forecasts require must be made available in real time and updated frequently; maybe every five minutes. Traditional practices in issuing warnings dependent on expert interpretation must therefore be altered so that those at the site of interest become their own `experts'. To aid in this a web interface, showing both the predictions and past performance of the model, designed to encourage realistic interpretation of the forecasts and their uncertainties is presented. This tool and the guided model build are outlined using case studies based in the North West of the UK.

  4. A spatial-temporal projection model for 10-30 day rainfall forecast in South China

    NASA Astrophysics Data System (ADS)

    Hsu, Pang-Chi; Li, Tim; You, Lijun; Gao, Jianyun; Ren, Hong-Li

    2015-03-01

    Extended-range (10-30 days) forecast, lying between well-developed short-range weather and long-range (monthly and seasonal) climate predictions, is one of the most challenging forecast currently faced by operational meteorological centers around the world. In this study, a set of spatial-temporal projection (STP) models was developed to predict low-frequency rainfall events at lead times of 5-30 days. We focused on early monsoon rainy season (mid April-mid July) in South China. To ensure that the model developed can be used for real-time forecast, a non-filtering method was developed to extract the low-frequency atmospheric signals of 10-60 days without using a band-pass filter. The empirical models were built based on 12-year (1996-2007) data, and independent forecast was then conducted for a 5 year (2008-2012) period. The assessment of the 5-year forecast of rainfall over South China indicates that the ensemble prediction of the STP models achieved a useful skill (with a temporal correlation coefficient exceeding 95 % confidence level) at a lead time of 20 days. The amplitude error was generally less than one standard deviation at all lead times of 5-30 days. Furthermore, the STP models provided useful probabilistic forecasts with the ranked probability skill score between 0.3-0.5 up to 30-day forecast in advance. The evaluation demonstrated that the STP models exhibited useful 10-30 days forecast skills for real-time extended-range rainfall prediction in South China.

  5. On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models

    NASA Astrophysics Data System (ADS)

    Noh, S.; Rakovec, O.; Weerts, A.; Tachikawa, Y.

    2013-12-01

    While important advances have been achieved in flood forecasting, due to various uncertainties that originate from simulation models, observations, and forcing data, they are still insufficient to obtain accurate prediction results with the required lead times. To increase the certainty of the hydrological forecast, data assimilation (DA) may be utilized to consider or propagate all of these sources of uncertainty through the hydrological modelling chain embedded in a flood forecasting system. Although numerous sophisticated DA algorithms have been proposed to mitigate uncertainty, DA methods dealing with the correction of model inputs, states, and initial conditions are conducted in a rather empirical and subjective way, which may reduce credibility and transparency to operational forecasts. In this study, we investigate the effect of noise specification on the quality of hydrological forecasts via an advanced DA procedure using a distributed hydrological model driven by numerical weather predictions. The sequential DA procedure is based on (1) a multivariate rainfall ensemble generator, which provides spatial and temporal correlation error structures of input forcing and (2) lagged particle filtering to update past and current state variables simultaneously in a lag-time window to consider the response times of internal hydrologic processes. The strength of the proposed procedure is that it requires less subjectivity to implement DA compared to conventional methods using consistent and objectively-induced error models. The procedure is evaluated for streamflow forecasting of three flood events in two Japanese medium-sized catchments. The rainfall ensembles are derived from ground based rain gauge observations for the analysis step and numerical weather predictions for the forecast step. Sensitivity analysis is performed to assess the impacts of uncertainties coming from DA such as random walk state noise and different DA methods with/without objectively-induced rainfall uncertainty conditions. The results show that multivariate rainfall ensembles provide sound input perturbations and model states updated by lagged particle filtering produce improved streamflow forecasts in conjunction with fine-resolution numerical weather predictions.

  6. Forecasting sales of new vehicle with limited data using Bass diffusion model and Grey theory

    NASA Astrophysics Data System (ADS)

    Abu, Noratikah; Ismail, Zuhaimy

    2015-02-01

    New product forecasting is a process that determines a reasonable estimate of sales attainable under a given set of conditions. There are several new products forecasting method in practices and Bass Diffusion Model (BDM) is one of the most common new product diffusion model used in many industries to forecast new product and technology. Hence, this paper proposed a combining BDM with Grey theory to forecast sales of new vehicle in Malaysia that certainly have limited data to build a model on. The aims of this paper is to examine the accuracy of different new product forecasting models and thus identify which is the best among the basic BDM and combining BDM with Grey theory. The results show that combining BDM with Grey theory performs better than the basic BDM based on in-sample and out-sample mean absolute percentage error (MAPE). Results also reveals combining model forecast more effectively and accurately even with insufficient previous data on the new vehicle in Malaysia.

  7. Variations in the Predictability of Extremes in Subseasonal Multi-Model Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Collins, D. C.

    2014-12-01

    Recently there is much interest in bridging the gap between daily weather forecasts out to two weeks lead-time and seasonal climate forecasts and prediction of extremes or rare events such as for heat and precipitation at longer lead times. While predictability, defined as the magnitude of the climate signal relative to the magnitude of noise is small for subseasonal forecasts, climate extremes result from both an increase in the magnitude of the forced climate signal and chance occurrence from random noise. Although the skill of subseasonal forecasts is limited, the greater magnitude of the signal of climate extremes may result in increased predictability, if uncertainty due to noise increases more slowly than the signal magnitude. Using subseasonal ensemble model forecasts (CFSv2 and ECMWF for weeks 2 through 4, or NMME models for months 1 and 2), this study will examine the strength of the signal in subseasonal MME forecasts for extreme events relative to the strength of the signal for non-extreme events. Variations in ensemble spread or model-predicted uncertainty, as well as correlation and mean square error between extreme event and non-extreme event forecasts will be examined. MME forecasts will be partitioned by both observed and predicted extremes. While the skill of weather forecasts rely to a great extent on the initial state of the atmosphere, seasonal forecasts derive signals from the evolution of slowly varying boundary conditions, such as sea surface temperatures, and processes of climate variability. Climate change on multi-decadal timescales provides an additional source of predictability, as prediction of subseasonal climate variability will depend on signals due to any climate process with a subseasonal timescale or longer. Subseasonal predictability has been shown to arise from MJO and ENSO (Johnson et al., 2014). Differences in MME spread, correlation, and mean square error will be used to examine changes in predictability related to ENSO and climate change, while assessing the ability of models to reproduce observed signals related to climate processes. Johnson, N. C., Collins, D. C., Feldstein, S. B., L'Heureux, M. L., & Riddle, E. E. (2014). Skillful Wintertime North American Temperature Forecasts out to 4 Weeks Based on the State of ENSO and the MJO. Weather and Forecasting, 29(1), 23-38.

  8. 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.

  9. Forecasting the weather at the TAL sites during STS-40 using the grid point forecast output from the NMC MRF model

    NASA Technical Reports Server (NTRS)

    Hafele, Gene M.

    1992-01-01

    The NOAA's Spaceflight Meteorology Group has used the point forecast output from the Global Profile Archive and Global Profile Archive since 1990, and found this product to allow forecasters to examine the MRF model in a vertical profile, and thereby determine how different model parameters behave over time. Attention is presently given to the use of these resources in the illustrative case of the STS-40 mission, over northwestern Spain.

  10. ECONOMIC MODELING OF ELECTRIC POWER SECTOR

    EPA Science Inventory

    CAMD performs a variety of economic modeling analyses to evaluate the impact of air emissions control policies on the electric power sector. A range of tools are used for this purpose including linear programming models, general equilibrium models, and spreadsheet models. Examp...

  11. Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaoli; Peng, Yong; Zhang, Chi; Wang, Bende

    2015-11-01

    A number of hydrological studies have proven the superior prediction performance of hybrid models coupled with data preprocessing techniques. However, many studies first decompose the entire data series into components and later divide each component into calibration and validation datasets to establish models, which sends some amount of future information into the decomposition and reconstruction processes. As a consequence, the resulting components used to forecast the value of a particular moment are computed using information from future values, which are not available at that particular moment in a forecasting exercise. Since most papers don't present their model framework in detail, it is difficult to identify whether they are performing a real forecast or not. Even though several other papers have explicitly stated which experiment they are performing, a comparison between results in the hindcast and forecast experiments is still missing. Therefore, it is necessary to investigate and compare the performance of these hybrid models in the two experiments in order to estimate whether they are suitable for real forecasting. With the combination of three preprocessing techniques, such as wavelet analysis (WA), empirical mode decomposition (EMD) and singular spectrum analysis (SSA), and two modeling methods (i.e. ANN model and ARMA model), six hybrid models are developed in this study, including WA-ANN, WA-ARMA, EMD-ANN, EMD-ARMA, SSA-ANN and SSA-ARMA. Preprocessing techniques are used to decompose the data series into sub-series, and then these sub-series are modeled using ANN and ARMA models. These models are examined in hindcasting and forecasting of the monthly streamflow of two sites in the Yangtze River of China. The results of this study indicate that the six hybrid models perform better in the hindcast experiment compared with the original ANN and ARMA models, while the hybrid models in the forecast experiment perform worse than the original models and the performances of WA-based and EMD-based models vary largely across different extension methods. It can be concluded that the hybrid models are not suitable for monthly streamflow forecasting in this study. New extension methods and modified preprocessing techniques can improve the prediction performance of these hybrid models in forecast experiments.

  12. Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

    PubMed

    Zhu, Qing; He, Kaijian; Zou, Yingchao; Lai, Kin Keung

    2014-01-01

    As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations. PMID:25061614

  13. Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

    PubMed Central

    Zhu, Qing; Zou, Yingchao; Lai, Kin Keung

    2014-01-01

    As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations. PMID:25061614

  14. Models and applications for space weather forecasting and analysis at the Community Coordinated Modeling Center.

    NASA Astrophysics Data System (ADS)

    Kuznetsova, Maria

    The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) was established at the dawn of the new millennium as a long-term flexible solution to the problem of transition of progress in space environment modeling to operational space weather forecasting. CCMC hosts an expanding collection of state-of-the-art space weather models developed by the international space science community. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment and developing and maintaining custom displays and powerful web-based systems and tools ready to be used by researchers, space weather service providers and decision makers. In support of space weather needs of NASA users CCMC is developing highly-tailored applications and services that target specific orbits or locations in space and partnering with NASA mission specialists on linking CCMC space environment modeling with impacts on biological and technological systems in space. Confidence assessment of model predictions is an essential element of space environment modeling. CCMC facilitates interaction between model owners and users in defining physical parameters and metrics formats relevant to specific applications and leads community efforts to quantify models ability to simulate and predict space environment events. Interactive on-line model validation systems developed at CCMC make validation a seamless part of model development circle. The talk will showcase innovative solutions for space weather research, validation, anomaly analysis and forecasting and review on-going community-wide model validation initiatives enabled by CCMC applications.

  15. Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

    NASA Astrophysics Data System (ADS)

    Ma, Feng; Wei, Yu; Huang, Dengshi; Chen, Yixiang

    2014-07-01

    In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

  16. Overview of the Diagnostic Cloud Forecast Model at the Air Force Weather Agency

    NASA Astrophysics Data System (ADS)

    Hildebrand, E. P.

    2014-12-01

    The Air Force Weather Agency (AFWA) is responsible for running and maintaining the Diagnostic Cloud Forecast (DCF) model to support DoD missions and those of their external partners. The DCF model generates three-dimensional cloud forecasts for global and regional domains at various resolutions. Regional domains are chosen based on Air Force mission needs. DCF is purely a statistical model that can be appended to any numerical weather prediction (NWP) model. Operationally, AFWA runs the DCF model deterministically using GFS data from NCEP and WRF data that are created in-house. In addition, AFWA also runs an ensemble version of the DCF model using the Mesoscale Ensemble Prediction System (MEPS). The deterministic DCF uses predictor variables from the WRF or GFS models, depending on whether the domain is regional or global, and statistically relates them to observed cloud cover from the World-Wide Merged Cloud Analysis (WWMCA). The forecast process of the model uses an ordinal logistic regression to predict membership in one of 101 groups (every 1% from 0-100%). The predicted group membership then is translated into a cloud amount. This is performed on 21 pressure levels ranging from 1000 hPa to 100 hPa. Cloud amount forecasts on these 21 levels are used along with the NWP geopotential height forecasts to estimate the base and top heights of cloud layers in the vertical. DCF also includes routines to estimate the amount and type of cloud within each layer. Forecasts of total cloud amount are verified using the WWMCA, as well as independent sources of cloud data. This presentation will include an overview of the DCF model and its use at AFWA. Results will be presented to show that DCF adds value over the raw cloud forecasts from NWP models. Ideas for future work also will be addressed.

  17. Streamflow forecast in the Alto do Rio Doce watershed in Brazil, using hydrological and atmospheric model

    NASA Astrophysics Data System (ADS)

    Silva, J. M.; Saad, S. I.; Palma, G.; Rocha, H.; Palmeira, R. M.; Silva, B. L.; Pessoa, A. A.; Ramos, C. G.; Cecchini, M. A.

    2013-05-01

    Electrical energy in Brazil depends essentially on the streamflow, as hydropowers accounts for up to 79% of the total electrical energy installed capacity. Therefore, streamflow forecasts are very important tools to assist in the planning and operation of Brazilian hydroelectric reservoirs. This study evaluated the performance of a distributed hydrological model, Soil and Water Assessment Tool (SWAT) daily streamflow forecasts into four Reservoirs sited in the Alto do Rio Doce Watershed, in Southeast of Brazil. SWAT model was used with precipitation forecast from the regional meteorological model MM5. The calibration and validation processes of SWAT were accomplished using data from four monitoring stations. The model has been run for the 2010-2012 period, and while the apr/2010-set/2011 period has been used for calibration conducted manually, the validation reached the rest of the period. The manual calibration was conducted by the means of sensibility tests of parameters that control surface runoff and groundwater flow, specially the surlag and alpha_bf, respectively the surface runoff lag coefficient and the baseflow recession constant. The daily and monthly Nash-Sutcliffe, R2 and the mean relative error performance indicators were used to assess the relative performance of the model. Results showed that streamflow forecast was very similar toobservations, except in reservoirs with lower drainage areas, where the model did not simulated the beginning of the flood (Dec-Feb). The streamflow forecasts was strongly dependent on the quality of precipitation forecasts used. Given that no correction in the simulated rainfall by the MM5 model in the Alto do Rio Doce watershed has been conducted and no automated calibration method was applied to the parameters of the hydrologic model, we can conclude that the application of the SWAT hydrologic model employing the output data from the MM5 atmospheric model for the streamflow forecast was shown to be a tool of great potential for real-time operation of reservoirs.

  18. Evaluation of the Mesoscale Meteorological Model (MM5)-Community Multi-Scale Air Quality Model (CMAQ) performance in hindcast and forecast of ground-level ozone.

    PubMed

    Nghiem, Le Hoang; Kim Oanh, Nguyen Thi

    2008-10-01

    This paper presents the first attempt to apply the Mesoscale Meteorological Model (MM5)-Community Multi-Scale Air Quality Model (CMAQ) model system to simulate ground-level ozone (O3) over the continental Southeast Asia (CSEA) region for both hindcast and forecast purposes. Hindcast simulation was done over the CSEA domain for two historical O3 episodes, January 26-29, 2004 (January episode, northeast monsoon) and March 24-26, 2004 (March episode, southwest monsoon). Experimental forecast was done for next-day hourly O3 during January 2006 over the central part of Thailand (CENTHAI). Available data from 20 ambient monitoring stations in Thailand and 3 stations in Ho Chi Minh City, Vietnam, were used for the episode analysis and for the model performance evaluation. The year 2000 anthropogenic emission inventory prepared by the Center for Global and Regional Environmental Research at the University of Iowa was projected to the simulation year on the basis of the regional average economic growth rate. Hourly emission in urban areas was prepared using ambient carbon monoxide concentration as a surrogate for the emission intensity. Biogenic emissions were estimated based on data from the Global Emissions Inventory Activity. Hindcast simulations (CSEA) were performed with 0.5 degree x 0.5 degree resolution, whereas forecast simulations (CENTHAI) were done with 0.1 degree x 0.1 degree hourly emission input data. MM5-CMAQ model system performance during the selected episodes satisfactorily met U.S. Environmental Protection Agency criteria for O3 for most simulated days. The experiment forecast for next-day hourly O3 in January 2006 yielded promising results. Modeled plumes of ozone in both hindcast and forecast cases agreed with the main wind fields and extended over considerable downwind distances from large urban areas. PMID:18939781

  19. New statistical models for long-range forecasting of southwest monsoon rainfall over India

    NASA Astrophysics Data System (ADS)

    Rajeevan, M.; Pai, D. S.; Anil Kumar, R.; Lal, B.

    2007-06-01

    The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981 2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.

  20. 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).

  1. Evaluation of glass model precipitation forecasts for North America during SOP-1

    NASA Technical Reports Server (NTRS)

    Atlas, R.; Firestone, J.

    1984-01-01

    A study has been made of the accuracy of the GLAS fourth order forecast model's precipitation forecasts over North America during the first Special Observing Period (SOP-1) of FGGE and of the impact of the FGGE special observing systems on these forecasts. Fourteen 120 h predictions were generated using the coarse 4 deg latitude by 5 deg longitude version of the GLAS model from both the FGGE and NOSAT assimilation cycles. These forecasts were then verified against a detailed set of precipitation observations. Separate verifications were performed for precipitation accumulations or = .01 in., or = .1 in., and or = 1 in. occuring in 6, 12 and 24 time periods. In addition, three different methods of verification were applied. In the first method, model precipitation forecasts are verified against the greatest precipitation amount observed within a 4 deg latitude by 5 deg longitude gridbox centered on each gridpoint. For the prediction at a gridpoint to be verified only one observation within the gridbox is required. In the second method, a weighted mean of the closest observations to the gridpoint is used, provided that at least two observations on opposite sides of a gridpoint are available. For the third method, the model precipitation forecasts were interpolated to the observation locations.

  2. 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.

  3. Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Xu, H.

    2016-01-01

    Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

  4. Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

    NASA Technical Reports Server (NTRS)

    MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan

    2011-01-01

    The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

  5. Climate information based streamflow and rainfall forecasts for Huai River Basin using Hierarchical Bayesian Modeling

    NASA Astrophysics Data System (ADS)

    Chen, X.; Hao, Z.; Devineni, N.; Lall, U.

    2013-09-01

    A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.

  6. Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling

    NASA Astrophysics Data System (ADS)

    Chen, X.; Hao, Z.; Devineni, N.; Lall, U.

    2014-04-01

    A Hierarchal Bayesian model is presented for one season-ahead forecasts of summer rainfall and streamflow using exogenous climate variables for east central China. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multi-level structure with regression coefficients modeled from a common multi-variate normal distribution resulting in partial pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include receiver operating characteristic, reduction of error, coefficient of efficiency, rank probability skill scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast season-ahead regional summer rainfall and streamflow offers potential for developing adaptive water risk management strategies.

  7. Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis

    NASA Astrophysics Data System (ADS)

    Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae

    The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.

  8. An empirical model to forecast solar wind velocity through statistical modeling

    NASA Astrophysics Data System (ADS)

    Gao, Y.; Ridley, A. J.

    2013-12-01

    The accurate prediction of the solar wind velocity has been a major challenge in the space weather community. Previous studies proposed many empirical and semi-empirical models to forecast the solar wind velocity based on either the historical observations, e.g. the persistence model, or the instantaneous observations of the sun, e.g. the Wang-Sheeley-Arge model. In this study, we use the one-minute WIND data from January 1995 to August 2012 to investigate and compare the performances of 4 models often used in literature, here referred to as the null model, the persistence model, the one-solar-rotation-ago model, and the Wang-Sheeley-Arge model. It is found that, measured by root mean square error, the persistence model gives the most accurate predictions within two days. Beyond two days, the Wang-Sheeley-Arge model serves as the best model, though it only slightly outperforms the null model and the one-solar-rotation-ago model. Finally, we apply the least-square regression to linearly combine the null model, the persistence model, and the one-solar-rotation-ago model to propose a 'general persistence model'. By comparing its performance against the 4 aforementioned models, it is found that the accuracy of the general persistence model outperforms the other 4 models within five days. Due to its great simplicity and superb performance, we believe that the general persistence model can serve as a benchmark in the forecast of solar wind velocity and has the potential to be modified to arrive at better models.

  9. Reserve growth in oil pools of Alberta: Model and forecast

    USGS Publications Warehouse

    Verma, M.; Cook, T.

    2010-01-01

    Reserve growth is recognized as a major component of additions to reserves in most oil provinces around the world, particularly in mature provinces. It takes place as a result of the discovery of new pools/reservoirs and extensions of known pools within existing fields, improved knowledge of reservoirs over time leading to a change in estimates of original oil-in-place, and improvement in recovery factor through the application of new technology, such as enhanced oil recovery methods, horizontal/multilateral drilling, and 4D seismic. A reserve growth study was conducted on oil pools in Alberta, Canada, with the following objectives: 1) evaluate historical oil reserve data in order to assess the potential for future reserve growth; 2) develop reserve growth models/ functions to help forecast hydrocarbon volumes; 3) study reserve growth sensitivity to various parameters (for example, pool size, porosity, and oil gravity); and 4) compare reserve growth in oil pools and fields in Alberta with those from other large petroleum provinces around the world. The reported known recoverable oil exclusive of Athabasca oil sands in Alberta increased from 4.5 billion barrels of oil (BBO) in 1960 to 17 BBO in 2005. Some of the pools that were included in the existing database were excluded from the present study for lack of adequate data. Therefore, the known recoverable oil increased from 4.2 to 13.9 BBO over the period from 1960 through 2005, with new discoveries contributing 3.7 BBO and reserve growth adding 6 BBO. This reserve growth took place mostly in pools with more than 125,000 barrels of known recoverable oil. Pools with light oil accounted for most of the total known oil volume, therefore reflecting the overall pool growth. Smaller pools, in contrast, shrank in their total recoverable volumes over the years. Pools with heavy oil (gravity less than 20o API) make up only a small share (3.8 percent) of the total recoverable oil; they showed a 23-fold growth compared to about 3.5-fold growth in pools with medium oil and 2.2-fold growth in pools with light oil over a fifty-year period. The analysis indicates that pools with high porosity reservoirs (greater than 30 percent porosity) grew more than pools with lower porosity reservoirs which could possibly be attributed to permeability differences between the two types. Reserve growth models for Alberta, Canada, show the growth at field level is almost twice as much as at pool level, possibly because the analysis has evaluated fields with two or more pools with different discovery years. Based on the models, the growth in oil volumes in Alberta pools over the next five-year period (2006-2010) is expected to be about 454 million barrels of oil. Over a twenty-five year period, the cumulative reserve growth in Alberta oil pools has been only 2-fold compared to a 4- to- 5-fold increase in other petroleum producing areas such as Saskatchewan, Volga-Ural, U.S. onshore fields, and U.S. Gulf of Mexico. However, the growth at the field level compares well with that of U.S. onshore fields. In other petroleum provinces, the reserves are reported at field levels rather than at pool levels, the latter basically being the equivalent of individual reservoirs. ?? 2010 by the Canadian Society of Petroleum Geologists.

  10. Bayesian modeling of rainfall-runoff uncertainty to improve probabilistic forecasts

    NASA Astrophysics Data System (ADS)

    Courbariaux, Marie; Parent, Éric; Favre, Anne-Catherine; Perreault, Luc; Gailhard, Joël; Barbillon, Pierre

    2015-04-01

    Probabilistic forecasts aim at accounting for uncertainty by producing a predictive distribution of the quantity of interest instead of a single best guess estimate. With regard to river flow forecasts, uncertainty is mainly due (a) to the unknown future rainfalls and temperatures, (b) to the possible inadequacy of the deterministic model mimicking the rainfall-runoff transformation. The first source of uncertainty can nowadays be taken into account using ensemble forecasts as inputs to the rainfall-runoff model (RRM). However, the second source of uncertainty due to the possible RRM misrepresentation remains. A simple way to integrate it consists in adjusting the forecast's density as much as necessary to get a prediction consistent with the observations. This step is called "post-processing". Our work focuses on series of river flow forecasts routinely issued at EDF (Electricity of France) and at Hydro-Québec. We aim at reducing the sharpness loss in the post-processing step while guaranteeing point-wise and temporal consistency. To do so, we write a joint model on the RRM errors along the whole trajectory to be predicted. Point-wise and temporal consistency are then obtained relying on a Bayesian approach. As in Krzysztofowicz's works, we first consider the prior behavior of the natural river flow and then update it by taking into account the likelihood of the information conveyed through RRM's outputs. In the spirit of Markov switching models, we establish a classification of time periods remaining on RRM's state variables through a Probit model. Conditioning on such a classification yields a mixture model of RRM errors. We finally compare the results to EDF's present operational forecasting system. Key words : probabilistic forecasts, sharpness, rainfall-runoff, post-processing, river flow, model error.

  11. Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Sankarasubramanian, A.; Ranjithan, R. S.; Brill, E. D.

    2014-08-01

    Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.

  12. A Stochastic Deterministic Air Quality Forecasting System : Combining Time Series Models with Data-Assimilation

    NASA Astrophysics Data System (ADS)

    Kumar, U.; De Ridder, K.; Lefebvre, W.; Janssen, S.

    2012-04-01

    A new air quality forecast system has been developed in which all the corrections for the air quality model output by assimilating observations have been carried out in post-processing mode. In order to make more accurate forecasts of the air pollutants, time series models have been used in combination with data-assimilation. The approach has been validated for one day ahead forecasts of daily mean PM10 and daily mean NO2. First, the air quality model AURORA has been applied over the domain Belgium including part of its neighbouring areas with grid resolution of 33 km2 for a total of 12171 grids. The observations data from AIRBASE archive has been used for the assimilation purpose. Only the background stations (urban or rural) data has been used. For data-assimilation, optimal interpolation in conjunction with Hollingsworth-Lnnberg method has been applied. The time series of the residuals, i.e., observations minus model output (for the daily mean PM10 and NO2) has been collected for the grids where monitoring stations were available. These time series were tested for their suitability for time series modelling applications. We applied the ARIMA(p,d,q) (Autoregressive Integrated Moving Average) as time series modelling technique to forecast the residuals in the future (one day ahead). In the next step, these forecasted residuals were assimilated with forecasted AURORA model output in order to get improved forecasted fields. The validation was carried out by leaving three stations out in one run of data-assimilation/time series forecasting. Thus, the validation results for one day ahead forecasts at the 15 stations for the duration 1-Mar-07 to 31-Dec-07 reveal that there has been substantial improvement in root mean square error (RMSE), a reduction ranging from 2% to 30%, has been observed. Similarly, correlation has also increased upto 30%. The results show that the approach presented here has tremendous potential to be applied in air quality forecasts.

  13. Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Bauman, William H., III

    2008-01-01

    NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

  14. Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?

    NASA Astrophysics Data System (ADS)

    Wei, Yu

    2012-11-01

    In most previous works on forecasting oil market volatility, squared daily returns were taken as the proxy of unobserved actual volatility. However, as demonstrated by Andersen and Bollerslev (1998) [22], this proxy with too high measurement noise could be perfectly outperformed by a so-called realized volatility (RV) measure calculated by the cumulative sum of squared intraday returns. With this motivation, we further extend earlier works by employing intraday high-frequency data to compare the performance of three typical volatility models in the daily out-of-sample volatility forecasting of fuel oil futures on the Shanghai Futures Exchange (SHFE): the GARCH-type, stochastic volatility (SV) and realized volatility models. By taking RV as the proxy of actual daily volatility and then computing forecasting errors, we find that the realized volatility model based on intraday high-frequency data produces significantly more accurate volatility forecasts than the GARCH-type and SV models based on daily returns. Furthermore, the SV model outperforms many linear and nonlinear GARCH-type models that capture long-memory volatility and/or the asymmetric leverage effect in volatility. These results also prove that abundant volatility information is available in intraday high-frequency data, and can be used to construct more accurate oil volatility forecasting models.

  15. The use of numerical weather forecast model predictions as a source of data for irrigation modelling

    NASA Astrophysics Data System (ADS)

    Venlinen, A.; Salo, T.; Fortelius, C.

    2005-12-01

    The use of numerical weather forecast model data as a source of data for soil moisture modelling was tested. Results show that the potential evaporation calculated using the Penman-Monteith equation can be estimated accurately using data obtained from the output of a high resolution numerical atmospheric model (HIRLAM, High Resolution Limited Area Model). The mean bias error was 0.26 mm for a 36-hour sum and the root mean square error was 2.14 mm. The evaporation obtained directly from HIRLAM was systematically smaller because this direct model output represents the real evaporation rather than the potential evaporation. The precipitation forecasts were less accurate. When the accuracy of parameters required for the calculation of potential evaporation were studied for one station, no serious bias was found. When two different irrigation models (AMBAV and SWAP) were run over one summer using either measured or HIRLAM data as the input, the results given by the models were quite similar regardless of input data source. The largest differences between the model outputs were caused by the formulation of crop and soil characteristics in the irrigation models.

  16. A four-stage hybrid model for hydrological time series forecasting.

    PubMed

    Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

    2014-01-01

    Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

  17. A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

    PubMed Central

    Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

    2014-01-01

    Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of denoising, decomposition and ensemble. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

  18. Multi-Model Long-Range Ensemble Forecast for Decision Support in Hydroelectric Operations

    NASA Astrophysics Data System (ADS)

    Kunkel, M. L.; Parkinson, S.; Blestrud, D.; Holbrook, V. P.

    2014-12-01

    Idaho Power Company (IPC) is a hydroelectric based utility serving over a million customers in southern Idaho and eastern Oregon. Hydropower makes up ~50% of our power generation and accurate predictions of streamflow and precipitation drive our long-term planning and decision support for operations. We investigate the use of a multi-model ensemble approach for mid and long-range streamflow and precipitation forecasts throughout the Snake River Basin. Forecast are prepared using an Idaho Power developed ensemble forecasting technique for 89 locations throughout the Snake River Basin for periods of 3 to 18 months in advance. A series of multivariable linear regression, multivariable non-linear regression and multivariable Kalman filter techniques are combined in an ensemble forecast based upon two data types, historical data (streamflow, precipitation, climate indices [i.e. PDO, ENSO, AO, etc…]) and single value decomposition derived values based upon atmospheric heights and sea surface temperatures.

  19. Estimating market penetration of new district heating and cooling systems using a combination of economic cost and diffusion models

    SciTech Connect

    Teotia, A.P.S.; Karvelas, D.E.

    1991-05-10

    The economic-cost model and the diffusion model are among the many market-penetration forecasting approaches that are available. These approaches have been used separately in many applications. In this paper, the authors briefly review these two approaches and then describe a methodology for forecasting market penetration using both approaches sequentially. This methodology is illustrated with the example of market-penetration forecasting of new district heating and cooling (DHC) systems in the Argonne DHC Market Penetration Model, which was developed and used over the period 1979--1983. This paper discusses how this combination approach, which incorporates the strengths of the economic-cost and diffusion models, has been superior to any one approach for market forecasts of DHC systems. Also discussed are the required modifications for revising and updating the model in order to generate new market-penetration forecasts for DHC systems. These modifications are required as a result of changes in DHC engineering, economic, and market data from 1983 to 1990. 13 refs., 5 figs., 2 tabs.

  20. A multi-model approach to tephra dispersal forecast: The Mt. Etna’s case

    NASA Astrophysics Data System (ADS)

    Neri, A.; Barsotti, S.; Coltelli, M.; Costa, A.; Folch, A.; Macedonio, G.; Nannipieri, L.; Prestifilippo, M.; Scollo, S.; Spata, G.

    2009-12-01

    Since 1979, Mt. Etna has produced several explosive events that are of concern to civil aviation, especially since it is located close to the Catania International Airport. During the 2006 crisis, there was persistent explosive activity for several months. This disrupted airport operations several times, causing discomfort to the population and resulting in severe economic losses. These and many other examples worldwide highlight the importance to know in advance the volcanic cloud movements and its dispersion in the atmosphere. However, atmospheric transport dynamics are complex as they depend on: the nature of air-borne particles; the type of explosive activity, and the transient, 3D structure of the atmosphere. Numerical modelling is a powerful tool to quantitatively describe such phenomena and today several numerical codes exist to simulate an explosive eruption and its associated tephra dispersal. The fundamental aim of this work is to analyze, and possibly improve, the tephra dispersal forecasts by using a multi-model approach. In fact the use of different codes, based on different physical and mathematical formulations, allows to gain crucial insight on the strengths and weaknesses of different models as well as produce quantitative comparisons on key model outputs. In detail, each day an automatic web-based procedure produces ash concentration maps of FALL3D, PUFF, and VOL-CALPUFF models and ground deposition maps of TEPHRA, PUFF, FALL3D, VOL-CALPUFF, and HAZMAP models for two eruptive scenarios. These maps are then synthesised to establish the spatial regions that have air and mass loadings that are higher than fixed thresholds. Results of different models are compared allowing to produce a first estimate of the model-dependent uncertainty also as a function of eruptive and atmospheric conditions.

  1. Probabilistic Quantitative Precipitation Forecasting over East China using Bayesian Model Averaging

    NASA Astrophysics Data System (ADS)

    Yang, Ai; Yuan, Huiling

    2014-05-01

    The Bayesian model averaging (BMA) is a post-processing method that weights the predictive probability density functions (PDFs) of individual ensemble members. This study investigates the BMA method for calibrating quantitative precipitation forecasts (QPFs) from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database. The QPFs over East Asia during summer (June-August) 2008-2011 are generated from six operational ensemble prediction systems (EPSs), including ECMWF, UKMO, NCEP, CMC, JMA, CMA, and multi-center ensembles of their combinations. The satellite-based precipitation estimate product TRMM 3B42 V7 is used as the verification dataset. In the BMA post-processing for precipitation forecasts, the PDF matching method is first applied to bias-correct systematic errors in each forecast member, by adjusting PDFs of forecasts to match PDFs of observations. Next, a logistic regression and two-parameter gamma distribution are used to fit the probability of rainfall occurrence and precipitation distribution. Through these two steps, the BMA post-processing bias-corrects ensemble forecasts systematically. The 60-70% cumulative density function (CDF) predictions well estimate moderate precipitation compared to raw ensemble mean, while the 90% upper boundary of BMA CDF predictions can be set as a threshold of extreme precipitation alarm. In general, the BMA method is more capable of multi-center ensemble post-processing, which improves probabilistic QPFs (PQPFs) with better ensemble spread and reliability. KEYWORDS: Bayesian model averaging (BMA); post-processing; ensemble forecast; TIGGE

  2. Evaluation of a conceptual rainfall forecasting model from observed and simulated rain events

    NASA Astrophysics Data System (ADS)

    Dolcin, L.; Andrieu, H.; French, M. N.

    Very short-term rainfall forecasting models designed for runoff analysis of catchments, particularly those subject to flash-floods, typically include one or more variables deduced from weather radars. Useful variables for defining the state and evolution of a rain system include rainfall rate, vertically integrated rainwater content and advection velocity. The forecast model proposed in this work complements recent dynamical formulations by focusing on a formulation incorporating these variables using volumetric radar data to define the model state variables, determining the rainfall source term directly from multi-scan radar data, explicitly accounting for orographic enhancement, and explicitly incorporating the dynamical model components in an advection-diffusion scheme. An evaluation of this model is presented for four rain events collected in the South of France and in the North-East of Italy. Model forecasts are compared with two simple methods: persistence and extrapolation. An additional analysis is performed using an existing mono-dimensional microphysical meteorological model to produce simulated rain events and provide initialization data. Forecasted rainfall produced by the proposed model and the extrapolation method are compared to the simulated events. The results show that the forecast model performance is influenced by rainfall temporal variability and performance is better for less variable rain events. The comparison with the extrapolation method shows that the proposed model performs better than extrapolation in the initial period of the forecast lead-time. It is shown that the performance of the proposed model over the extrapolation method depends essentially on the additional vertical information available from voluminal radar.

  3. Numerical modelling for real-time forecasting of marine oil pollution and hazard assessment

    NASA Astrophysics Data System (ADS)

    De Dominicis, Michela; Pinardi, Nadia; Bruciaferri, Diego; Liubartseva, Svitlana

    2015-04-01

    Many factors affect the motion and transformation of oil at sea. The most relevant of these are the meteorological and marine conditions at the air-sea interface; the chemical characteristics of the oil; its initial volume and release rates; and, finally, the marine currents at different space scales and timescales. All these factors are interrelated and must be considered together to arrive at an accurate numerical representation of oil evolution and movement in seawater. The oil spill model code MEDSLIK-II is a freely available community model. By using a Lagrangian approach, MEDSLIK-II predicts the transport and diffusion of a surface oil slick governed by water currents, winds and waves, which are provided by operational oceanographic and meteorological models. In addition, the model simulates the oil transformations at sea: evaporation, spreading, dispersion, adhesion to coast and emulsification. The model results have been validated using surface drifters and oil slicks observed by satellite in different regions of the Mediterranean Sea. It is found that the forecast skill largely depends on the accuracy of the Eulerian ocean currents: the operational models give useful estimates of currents, but high-frequency (hourly) and high spatial resolution is required, and the Stokes drift velocity has to be often added, especially in coastal areas. MEDSLIK-II is today available at the Mediterranean scale allowing a possible support to oil spill emergencies. The model has been used during the Costa Concordia disaster, the partial sinking of the Italian cruise ship Costa Concordia when it ran aground at Isola del Giglio, Italy. MEDSLIK-II system was run to produce forecast scenarios of the possible oil spill from the Costa Concordia, to be delivered to the competent authorities, by using the currents provided every day by the operational ocean models available in the area. Moreover, MEDSLIK-II is part of the Mediterranean Decision Support System for Marine Safety (MEDESS4MS) system, which is an integrated operational multi-model oil spill prediction service, that can be used by different users to run simulations of oil spills at sea, even in real time, through a web portal. The MEDESS4MS system gathers different oil spill modelling systems and data from meteorological and ocean forecasting systems, as well as operational information on response equipment, together with environmental and socio-economic sensitivity maps. MEDSLIK-II has been also used to provide an assessment of hazard stemming from operational oil ship discharges in the Southern Adriatic and Northern Ionian (SANI) Seas. Operational pollution resulting from ships consists of a movable hazard with a magnitude that changes dynamically as a result of a number of external parameters varying in space and time (temperature, wind, sea currents). Simulations of oil releases have been performed with realistic oceanographic currents and the results show that the oil pollution hazard distribution has an inherent spatial and temporal variability related to the specific flow field variability.

  4. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models

    NASA Astrophysics Data System (ADS)

    Demirel, M. C.; Booij, M. J.; Hoekstra, A. Y.

    2015-01-01

    This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.

  5. Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets

    SciTech Connect

    Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

    2005-06-30

    The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

  6. Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Xu, H.

    2015-10-01

    Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

  7. Toward Improved Solar Irradiance Forecasts: Introduction of Post-Processing to Correct the Direct Normal Irradiance from the Weather Research and Forecasting Model

    NASA Astrophysics Data System (ADS)

    Kim, Chang Ki; Clarkson, Matthew

    2015-11-01

    Solar electricity production is highly dependent on atmospheric conditions. This study focuses on comparing model forecasts with observations for the period of May-December, 2011. The Weather Research and Forecasting model was run for two nested domains centered on Arizona in order to better capture the complex terrain driven dynamics of the region. The modeling performance from the simulation with the Global Forecast System model output as initial and boundary condition was better, with respect to both direct normal irradiance and global horizontal irradiance, than that with the North American Mesoscale model output. The observed aerosol optical depth is correlated with the water vapor, soil moisture and wind-blown dust and therefore, the aerosol optical depth is parameterized by the modeling outputs for these variables. The aerosol correction factor reduces the relative root mean square error from 12 to 6 %. In cases where dust was transported at high altitude, our algorithm did not correct the bias of direct normal irradiance.

  8. 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.

  9. Modeling and Forecasting Livestock Feed Resources in India Using Climate Variables

    PubMed Central

    Suresh, K. P.; Kiran, G. Ravi; Giridhar, K.; Sampath, K. T.

    2012-01-01

    The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030. PMID:25049586

  10. FOGCAST: Probabilistic fog forecasting based on operational (high-resolution) NWP models

    NASA Astrophysics Data System (ADS)

    Masbou, M.; Hacker, M.; Bentzien, S.

    2013-12-01

    The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. The improvement of localization, duration and variations in visibility therefore holds an immense operational value. Fog is generally a small scale phenomenon and mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Sophisticated three-dimensional fog models, based on advanced microphysical parameterization schemes and high vertical resolution, have been already developed and give promising results. Nevertheless, the computational time is beyond the range of an operational setup. Therefore, mesoscale numerical weather prediction models are generally used for forecasting all kinds of weather situations. In spite of numerous improvements, a large uncertainty of small scale weather events inherent in deterministic prediction cannot be evaluated adequately. Probabilistic guidance is necessary to assess these uncertainties and give reliable forecasts. In this study, fog forecasts are obtained by a diagnosis scheme similar to Fog Stability Index (FSI) based on COSMO-DE model outputs. COSMO-DE I the German-focused high-resolution operational weather prediction model of the German Meteorological Service. The FSI and the respective fog occurrence probability is optimized and calibrated with statistical postprocessing in terms of logistic regression. In a second step, the predictor number of the FOGCAST model has been optimized by use of the LASSO-method (Least Absolute Shrinkage and Selection Operator). The results will present objective out-of-sample verification based on the Brier score and is performed for station data over Germany. Furthermore, the probabilistic fog forecast approach, FOGCAST, serves as a benchmark for the evaluation of more sophisticated 3D fog models. Several versions have been set up based on different numerical weather prediction systems: 1- COSMO-DE operational forecasts (50 vertical layers, dz_min=20m), 2- COSMO-DE forecasts with different vertical grid setups, 3- COSMO-DE forecasts with fog microphysics of the one dimensional fog forecast model, PAFOG 4- COSMO-FOG forecasts with a very high vertical resolution (60 layers, dz_min=4m) and an one-moment fog microphysics based on the PAFOG model. The results will quantify the impact of vertical grid resolution, and the importance of detailed cloud microphysics, considering explicitly cloud droplet distribution and sedimentation processes.

  11. Combining forecast weights: Why and how?

    NASA Astrophysics Data System (ADS)

    Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

    2012-09-01

    This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

  12. Economic preon models with supersymmetry

    SciTech Connect

    Gupta, V.; Joshipura, A.S.; Mani, H.S.; Ramachandran, R.

    1984-11-01

    Supersymmetric models with preons in a pseudoreal representation of the hypercolor group G/sub HC/ are considered. Anomaly-matching equations for the unbroken flavor group G/sub f/ yield a unique solution for a given G/sub HC/ and G/sub f/. With a suitable set of spectators, these solutions may be interpreted as grand unified theories having a specific number of generations. When the G/sub f/ and G/sub HC/ representations of preons are chosen to have equal dimensions, a unique choice (G/sub f/,G/sub HC/) = (SU(6) x U(1),Sp(6)) and three generations of fermions emerge.

  13. Airborne castanea pollen forecasting model for ecological and allergological implementation.

    PubMed

    Astray, G; Fernández-González, M; Rodríguez-Rajo, F J; López, D; Mejuto, J C

    2016-04-01

    Castanea sativa Miller belongs to the natural vegetation of many European deciduous forests prompting impacts in the forestry, ecology, allergological and chestnut food industry fields. The study of the Castanea flowering represents an important tool for evaluating the ecological conservation of North-Western Spain woodland and the possible changes in the chestnut distribution due to recent climatic change. The Castanea pollen production and dispersal capacity may cause hypersensitivity reactions in the sensitive human population due to the relationship between patients with chestnut pollen allergy and a potential cross reactivity risk with other pollens or plant foods. In addition to Castanea pollen's importance as a pollinosis agent, its study is also essential in North-Western Spain due to the economic impact of the industry around the chestnut tree cultivation and its beekeeping interest. The aim of this research is to develop an Artificial Neural Networks for predict the Castanea pollen concentration in the atmosphere of the North-West Spain area by means a 20years data set. It was detected an increasing trend of the total annual Castanea pollen concentrations in the atmosphere during the study period. The Artificial Neural Networks (ANNs) implemented in this study show a great ability to predict Castanea pollen concentration one, two and three days ahead. The model to predict the Castanea pollen concentration one day ahead shows a high linear correlation coefficient of 0.784 (individual ANN) and 0.738 (multiple ANN). The results obtained improved those obtained by the classical methodology used to predict the airborne pollen concentrations such as time series analysis or other models based on the correlation of pollen levels with meteorological variables. PMID:26802339

  14. Determining economic benefits of satellite data in short-range forecasting

    NASA Technical Reports Server (NTRS)

    Suchman, D.; Auvine, B.; Hinton, B.

    1981-01-01

    The chances of enhanced short term weather predictions and economic benefits from the use of GOES satellite data were examined. Results for a meteorological consulting firm before and after the introduction of GOES data were chosen as the method, and monetary benefits were selected as the measure. Services were provided for use by road and street departments, commodities dealers, and marine clients of the consulting firm. The Man-computer Interactive Data Access Program (McIDAS) was employed to furnish 1/2 hour visual or IR imagery for remote access. The commodities clients reconnected the GOES real-time imagery once the study was completed, while the consulting firm, which was personnel and not equipment intensive, did not. Further development of the flexibility of access to the GOES data and improvements in the projected grids are indicated.

  15. Data driven models applied in building load forecasting for residential and commercial buildings

    NASA Astrophysics Data System (ADS)

    Rahman, SM Mahbobur

    A significant portion of the operating costs of utilities comes from energy production. Machine learning methods are widely used for short-term load forecasts for commercial buildings and also the utility grid. These forecasts are used to minimize unit power production costs for the energy managers for better planning of power units and load management. In this work, three different state-of-art machine learning methods i.e. Artificial Neural Network, Support Vector Regression and Gaussian Process Regression are applied in hour ahead and 24 --hour ahead building energy forecasting. The work uses four residential buildings and one commercial building located in Downtown, San Antonio as test-bed using energy consumption data from those buildings monitored in real-time. Uncertainty quantification analysis is conducted to understand the confidence in each forecast using Bayesian Network. Using a combination of weather variables and historical load, forecasting is done in a supervised way based on a moving window training algorithm. A range of comparisons between different forecasting models in terms of relative accuracy are then presented.

  16. Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California

    PubMed Central

    Lee, Ya-Ting; Turcotte, Donald L.; Holliday, James R.; Sachs, Michael K.; Rundle, John B.; Chen, Chien-Chih; Tiampo, Kristy F.

    2011-01-01

    The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M?4.95 earthquakes in 0.1נ0.1 cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M?4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M=7.2, April 4, 2010, El MayorCucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most successful in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts. PMID:21949355

  17. Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California.

    PubMed

    Lee, Ya-Ting; Turcotte, Donald L; Holliday, James R; Sachs, Michael K; Rundle, John B; Chen, Chien-Chih; Tiampo, Kristy F

    2011-10-01

    The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ? 4.95 earthquakes in 0.1 0.1 cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ? 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor-Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts. PMID:21949355

  18. Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis

    NASA Technical Reports Server (NTRS)

    Hoffman, Ross N.; Nehrkorn, Thomas; Grassotti, Christopher

    1998-01-01

    We proposed a novel characterization of errors for numerical weather predictions. A general distortion representation allows for the displacement and amplification or bias correction of forecast anomalies. Characterizing and decomposing forecast error in this way has several important applications, including the model assessment application and the objective analysis application. In this project, we have focused on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically, we study the forecast errors of the sea level pressure (SLP), the 500 hPa geopotential height, and the 315 K potential vorticity fields for forecasts of the short and medium range. The forecasts are generated by the Goddard Earth Observing System (GEOS) data assimilation system with and without ERS-1 scatterometer data. A great deal of novel work has been accomplished under the current contract. In broad terms, we have developed and tested an efficient algorithm for determining distortions. The algorithm and constraints are now ready for application to larger data sets to be used to determine the statistics of the distortion as outlined above, and to be applied in data analysis by using GEOS water vapor imagery to correct short-term forecast errors.

  19. Post-processing of multi-model ensemble river discharge forecasts using censored EMOS

    NASA Astrophysics Data System (ADS)

    Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

    2014-05-01

    When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a daily basis over a period of three years. For the two catchments considered, this resulted in well calibrated and sharp forecast distributions over all lead-times from 1 to 114 h. Training observations tended to be better indicators for the dependence structure than the raw ensemble.

  20. Modeling demographic relationships: an analysis of forecast functions for Australian births.

    PubMed

    Macdonald, J

    1981-12-01

    "This paper discusses the problem of modeling demographic variables for the purpose of forecasting." Two empirical model selection procedures, a time series approach and a sequential testing procedure, are applied to suggest final-form forecasting equations for an Australian births series, namely, first nuptial confinements. The models are compared with the method used to construct the Australian government's IMPACT demographic module. Comments by Joseph B. Kadane, Ronald Lee, Roderick J. A. Little, John F. Long, and Kenneth F. Wallis are included, together with a rejoinder by the author. PMID:12312151

  1. Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

    NASA Technical Reports Server (NTRS)

    Goodman, H. Michael; Conover, Helen; Zavodsky, Bradley; Maskey, Manil; Jedlovec, Gary; Regner, Kathryn; Li, Xiang; Lu, Jessica; Botts, Mike; Berthiau, Gregoire

    2008-01-01

    The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

  2. Wheat forecast economics effect study. [value of improved information on crop inventories, production, imports and exports

    NASA Technical Reports Server (NTRS)

    Mehra, R. K.; Rouhani, R.; Jones, S.; Schick, I.

    1980-01-01

    A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function.

  3. Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

    2007-01-01

    Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model- the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.

  4. Fog forecasting: ``old fashioned'' semi-empirical methods from radio sounding observations versus ``modern'' numerical models

    NASA Astrophysics Data System (ADS)

    Holtslag, M. C.; Steeneveld, G. J.; Holtslag, A. A. M.

    2010-07-01

    Fog forecasting is a very challenging task due to the local and small-scale nature of the relevant physical processes and land surface heterogeneities. Despite the many research efforts, numerical models remain to have difficulties with fog forecasting, and forecast skill from direct model output is relatively poor. In order to put the progress of fog forecasting in the last decades into a historical perspective, we compare the fog forecasting skill of a semi-empirical method based on radio sounding observations (developed in the 60s and 70s) with the forecasting skill of a state-of-the-art numerical weather prediction model (MM5) for The Netherlands. The semi-empirical method under investigation, the Fog Stability Index, depends solely on the temperature difference between the surface and 850 hPa, the surface dew point depression and the wind speed at 850 hPa, and a threshold value to indicate the probability of fog in the coming hours. Using the critical success index (CSI) as a criterion for forecast quality, we find that the Fog Stability Index is a rather successful predictor for fog, with similar performance as MM5. The FSI could even been optimized for different observational stations in the Netherlands. Also, it appears that adding the 10 m wind as a predictor did not increase the CSI score for all stations. The results of the current study clearly indicate that the current state of knowledge requires improvement of the physical insight in different physical processes in order to beat simple semi-empirical methods.

  5. Atmospheric and seeing forecast: WRF model validation with in situ measurements at ORM

    NASA Astrophysics Data System (ADS)

    Giordano, C.; Vernin, J.; Vázquez Ramió, H.; Muñoz-Tuñón, C.; Varela, A. M.; Trinquet, H.

    2013-04-01

    We present a comparison between in situ measurements and forecasted data at the Observatorio del Roque de Los Muchachos. Forecasting is obtained with the Weather Research and Forecasting (WRF) model associated with a turbulence parametrization which follows Trinquet-Vernin model. The purpose of this study is to validate the capability of the WRF model to forecast the atmospheric and optical conditions (seeing and related adaptive optics parameters). The final aim is to provide a tool to optimize the observing time in the observatories, the so-called flexible scheduling. More than 4500 h of simulations above Observatorio del Roque de Los Muchachos (ORM) site with WRF in 2009 were calculated, and compared with data acquired during 2009 with Automatic Weather Station, Differential Image Motion Monitor and Multiple Aperture Scintillation Sensor. Each simulation corresponds to a 24h in advance forecasting with one predicted value each hour. Comparison shows that WRF forecasting agrees well with the effective meteorological parameters at ground level, such as pressure (within a scatter σP = 1.1 hPa), temperature (σT = 2 K), wind speed (σ|V| = 3.9 m s-1) and relative humidity (σ _{R_h}=18.9 per cent). Median precipitable water vapour content above the ORM predicted by WRF in 2009 is 3 mm, close to 3.8 mm reported in the literature over the period 2001-2008. For what concern optical parameters (seeing, coherence time, isoplanatic angle), WRF forecasting are in good agreement on nightly or monthly basis, better than random or carbon-copy tries. We hope to improve these results with a better vertical and horizontal grid resolution. Our method is robust enough to be applied to potential astronomical sites, where no instruments are available.

  6. Relating seasonal streamflow forecast skill to uncertainties in initial conditions, future forcings, and hydrologic modeling

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Hopson, T. M.; Newman, A. J.; Sampson, K. M.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Clark, M. P.

    2013-12-01

    Skill in model-based hydrologic forecasting depends on the ability to estimate a watershed's initial moisture and energy conditions, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We investigate these influences in a series of predictability experiments using calibrated hydrologic simulation models for a 630-watershed dataset that spans the continental US (CONUS), and using the current major simulation models of National Weather Service streamflow forecasting operations. Earlier work in this area (Wood and Lettenmaier, GRL 2008) outlined an ensemble-based strategy for attributing streamflow forecast uncertainty between two endpoints representing zero information about future forcings (ie, the NWS ensemble streamflow prediction, or ESP approach) versus zero information about initial conditions (termed ';reverse-ESP'). This study adopts a more comprehensive approach to characterize the effects of varying levels of uncertainty, from zero knowledge to perfect information in the model world, on streamflow prediction uncertainty. Ensemble hindcasts reflecting varying levels of uncertainty are initialized on a monthly basis for the basins' periods of record, creating background sensitivity information that helps to decompose total hydrologic prediction error into the three components identified above. Observed streamflow prediction errors are then coupled with estimates of realistic uncertainties in future forcing and with model simulation error to infer initial condition errors. This presentation reports findings from the predictability experiments, summarizing the relative importance of uncertainties in basin initial conditions and weather and climate forecasts, and their dependence on forecast lead time, initiation date and regional hydroclimate characteristics.

  7. Assessment of ocean forecast models for search area prediction in the eastern Indian Ocean

    NASA Astrophysics Data System (ADS)

    Brushett, Ben A.; King, Brian A.; Lemckert, Charles J.

    2016-01-01

    The following study describes a technique to improve maritime search area prediction by using consensus forecasting to quantify areas of higher probability within a model defined search area. The study included forecasting search areas for 45 five-day drifter tracks, each simulated independently using different ocean models (BLUElink, FOAM, HYCOM and NCOM) throughout 2012 in the eastern Indian Ocean, off the coast of Western Australia. It was found that zones where all four model search areas overlapped (defined here as a consensus search area) were significantly smaller than those areas generated by any single model forecast. The average consensus search area was quantified to be up to 56.9% smaller at 24h and 72.5% smaller at 120h than the average single model search areas at corresponding times. However the average hit rate (the frequency that the drifter was contained within the forecast search area) for the consensus search area was reduced by up to 26.2% at 24h and 52.8% at 120h, when compared to average hit rates from single model search areas. This indicated that the four model consensus search area had a higher hit rate per unit of search area than any individual model search area. Hence if search resources were a limiting factor for a particular search effort, then search resources may be most effectively deployed by prioritising the effort initially to the smaller, four model consensus search area.

  8. The effect of horizontal resolution on systematic errors of the GLA forecast model

    NASA Technical Reports Server (NTRS)

    Chen, Tsing-Chang; Chen, Jau-Ming; Pfaendtner, James

    1990-01-01

    Systematic prediction errors of the Goddard Laboratory for Atmospheres (GLA) forecast system are reduced when the higher-resolution (2 x 2.5 deg) model version is used. Based on a budget analysis of the 200-mb eddy streamfunction, the improvement of stationary eddy forecasting is seen to be caused by the following mechanism: by increasing the horizontal spatial resolution of the forecast model, atmospheric diabatic heating over the three tropical continents is changed in a way that intensifies the planetary-scale divergent circulations associated with the three pairs of divergent-convergent centers over these continents. The intensified divergent circulation results in an enhancement of vorticity sources in the Northern Hemisphere. The additional vorticity is advected eastward by a stationary wave train along 30 deg N, thereby reducing systematic errors in the lower-resolution (4 x 5 deg) GLA model.

  9. Sensitivity of NCEP GFS Forecast of Hurricane Sandy to Model Biases

    NASA Astrophysics Data System (ADS)

    Yang, F.

    2014-12-01

    Hurricane Sandy was the most destructive hurricane of the 2012 Atlantic hurricane season. It developed from a tropical depression on October 22 and became a Category three storm at its peak intensity on October 25. Early on October 29 Sandy became a post-tropical cyclone with hurricane-force winds and made landfall along the New Jersey seashores. While all NWP models correctly predicted that the storm will strike the New Jersey Seashore within 72 hours of its landfall, most models struggled to predict its path at longer forecast lead times. The United States GFS (Global Forecast Systems) predicted a northeast instead of northwest path from the forecast cycles before October 25 and a path biased toward the north from the cycles before October 27. This study investigates the impact of GFS biases in environmental flow and surface forcing on the predicted Sandy storm path and intensity. A set of sensitivity experiments were carried out to explore the cause of forecast biases. In particular, the sensitivity of forecasts to model resolution and different physics parameterization options were examined.

  10. Current challenges using models to forecast seawater intrusion: lessons from the Eastern Shore of Virginia, USA

    USGS Publications Warehouse

    Sanford, Ward E.; Pope, Jason P.

    2010-01-01

    A three-dimensional model of the aquifer system of the Eastern Shore of Virginia, USA was calibrated to reproduce historical water levels and forecast the potential for saltwater intrusion. Future scenarios were simulated with two pumping schemes to predict potential areas of saltwater intrusion. Simulations suggest that only a few wells would be threatened with detectable salinity increases before 2050. The objective was to examine whether salinity increases can be accurately forecast for individual wells with such a model, and to address what the challenges are in making such model forecasts given current (2009) simulation capabilities. The analysis suggests that even with current computer capabilities, accurate simulations of concentrations within a regional-scale (many km) transition zone are computationally prohibitive. The relative paucity of data that is typical for such regions relative to what is needed for accurate transport simulations suggests that even with an infinitely powerful computer, accurate forecasting for a single well would still be elusive. Useful approaches may include local-grid refinement near wells and geophysical surveys, but it is important to keep expectations for simulated forecasts at wells in line with chloride concentration and other data that can be obtained at that local scale.

  11. AtmoSwing, an Analogue Technique model for statistical weather forecasting

    NASA Astrophysics Data System (ADS)

    Horton, Pascal; Obled, Charles; Jaboyedoff, Michel; Garca Hernndez, Javier

    2013-04-01

    The analogue method has been implemented for real-time precipitation forecasting in the framework of flood management in the upper Rhne catchment in Switzerland. It identifies analogue days, in terms of atmospheric circulation and humidity variables, in a long archive of past situations and then uses the corresponding measured precipitation to establish an empirical conditional distribution considered as the probabilistic forecast for the target day. This method is used in different institutions for hydro-meteorological forecasting in the framework of real-time flood management or electricity production. The developed software is called AtmoSwing for "Analog Technique MOdel for Statistical Weather forecastING" and is constituted of two tools: the Forecaster that automatically processes the forecast and the Viewer that displays the resulting files in a GIS environment. AtmoSwing is written in C++ and uses open source libraries. It is fully cross-platform and has the native look of the corresponding operating system thanks to wxWidgets. The model is standalone and automatically handles the download of the GFS global numerical weather prediction forecasts on which the analogy is processed. The development aimed at creating a very modular object oriented tool that can be used to parameterize any known version of the analogue method. There is no limitation on the number of analogy steps, neither on the amount of atmospheric variables used as input. The Viewer has a GIS engine that allows changing the map layers in order to be adapted to any new region. It offers different levels of detail, from an overview of all lead times and all parameterizations, which provides a quick identification of potential critical situations, to local time series and details of analogues distributions. AtmoSwing is running operationally since October 2011 in the Swiss Alps. The implemented parameterizations are the most common reference methods developed by Bontron (2004). These presented globally good results on the whole period, and forecasted in a very satisfying way some significant events. Last improvements to the parameterization by means of a global optimization technique should now even improve the reliability of the forecast.

  12. Initial assessment of a multi-model approach to spring flood forecasting in Sweden

    NASA Astrophysics Data System (ADS)

    Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.

    2015-06-01

    Hydropower is a major energy source in Sweden and proper reservoir management prior to the spring flood onset is crucial for optimal production. This requires useful forecasts of the accumulated discharge in the spring flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialised set-up of the HBV model. In this study, a number of new approaches to spring flood forecasting, that reflect the latest developments with respect to analysis and modelling on seasonal time scales, are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for three main Swedish rivers over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for specific locations and lead times improvements of 20-30 % are found. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 10 % was indicated. This demonstrates the potential of the approach and further development and optimisation into an operational system is ongoing.

  13. Modeling of the 2011 Japan Tsunami: Lessons for Near-Field Forecast

    NASA Astrophysics Data System (ADS)

    Wei, Yong; Chamberlin, Christopher; Titov, Vasily V.; Tang, Liujuan; Bernard, Eddie N.

    2013-06-01

    During the devastating 11 March 2011 Japanese tsunami, data from two tsunami detectors were used to determine the tsunami source within 1.5 h of earthquake origin time. For the first time, multiple near-field tsunami measurements of the 2011 Japanese tsunami were used to demonstrate the accuracy of the National Oceanic and Atmospheric Administration (NOAA) real-time flooding forecast system in the far field. To test the accuracy of the same forecast system in the near field, a total of 11 numerical models with grids telescoped to 2 arcsec (~60 m) were developed to hindcast the propagation and coastal inundation of the 2011 Japanese tsunami along the entire east coastline of Japan. Using the NOAA tsunami source computed in near real-time, the model results of tsunami propagation are validated with tsunami time series measured at different water depths offshore and near shore along Japan's coastline. The computed tsunami runup height and spatial distribution are highly consistent with post-tsunami survey data collected along the Japanese coastline. The computed inundation penetration also agrees well with survey data, giving a modeling accuracy of 85.5 % for the inundation areas along 800 km of coastline between Ibaraki Prefecture (north of Kashima) and Aomori Prefecture (south of Rokkasho). The inundation model results highlighted the variability of tsunami impact in response to different offshore bathymetry and flooded terrain. Comparison of tsunami sources inferred from different indirect methods shows the crucial importance of deep-ocean tsunami measurements for real-time tsunami forecasts. The agreement between model results and observations along Japan's coastline demonstrate the ability and potential of NOAA's methodology for real-time near-field tsunami flooding forecasts. An accurate tsunami flooding forecast within 30 min may now be possible using the NOAA forecast methodology with carefully placed tsunameters and large-scale high-resolution inundation models with powerful computing capabilities.

  14. Tsunami Simulation Using Sources Inferred from Various Measurement Data: Implications for the Model Forecast

    NASA Astrophysics Data System (ADS)

    Gica, Edison; Titov, Vasily V.; Moore, Christopher; Wei, Yong

    2015-03-01

    Model forecast applications use various models of tsunami sources inferred from different measurement data. Even the same type of observation data can produce substantially different tsunami source models during a real-time forecast when more data are obtained during the real-time analysis. Improved tsunami observations enable investigation of the influence of such model source variability on the final forecast using different source data sets of several events. The 2010 Maule, Chile and 2011 Tohoku, Japan tsunamis were two recent events that provide ample observations throughout the Pacific and were, thus, used here to study the sensitivity of different model inputs for forecasting. The sources for these events were derived using the following three different methods: (1) real time or post event inversion of tsunameter water level data; (2) prediction of sea floor deformations via analysis of seismic wave forms and application of a finite fault model; and (3) prediction of sea floor deformation using real-time GPS data. For the March 11, 2011 Tohoku tsunami, two examples of each method are used, while for the February 27, 2010 Maule event, only one tsunameter inversion and one finite fault model method were used due to a much more limited data set. Observed data from the Deep-ocean Assessment and Reporting for Tsunamis (DART) network, Japan GPS buoys, and select tide gauges across the Pacific were compared with forecasts to assess the sensitivity of these three methods using root-mean-square error analysis. We divided the analysis by the type of data and the distance from the source. This sensitivity analysis showed that increasing the resolution of a tsunami source model does not necessarily improve tsunami forecast quality, even in the near-field. Instead, the findings suggest that when forecasting coastal impact, defining the overall energy characteristic of a tsunami source may be more important than refining small source details. Source models based on direct tsunami observations are better at reproducing a tsunami signal: this finding is not very surprising but has implications for tsunami forecasting and warning operations.

  15. Ionospheric data availability for space weather modelling and forecasting

    NASA Astrophysics Data System (ADS)

    Terkildsen, Michael

    2012-07-01

    This paper reviews and contrasts current, and future, ionospheric observations available for space weather forecasting, with its requirement for continuity and low latency; and for space weather and solar-terrestrial research, generally requiring higher precision and often lengthy data pre-processing. Historically, instrumentation projects or missions have been designed with the primary aim of addressing scientific research goals. The space weather service industry has often relied on this data to augment their own limited real time instrumentation networks, and adapted as necessary. Increasingly, however, missions are being established, or tailored, with space weather service provision as a key goal, improving the latency and minimising the data pre-processing requirements. This has opened up the range of ionospheric monitoring data now available for space weather products and services. This paper reviews some of these ionospheric data sources, and how they impact space weather service provision.

  16. Real-time deployment of artificial neural network forecasting models: Understanding the range of applicability

    NASA Astrophysics Data System (ADS)

    Bowden, Gavin J.; Maier, Holger R.; Dandy, Graeme C.

    2012-10-01

    When an operational artificial neural network (ANN) model is deployed, new input patterns are collected in order to make real-time forecasts. However, ANNs (like other empirical and statistical methods) are unable to reliably extrapolate beyond the calibration range. Consequently, when deployed in real-time operation there is a need to determine if new input patterns are representative of the data used in calibrating the model. To address this problem, a novel detection system for identifying uncharacteristic data patterns is presented. This approach combines a self-organizing map (SOM), to partition the data set, with nonparametric kernel density estimators to calculate local density estimates (LDE). The SOM-LDE method determines the degree to which a new input pattern can be considered to be contained within the domain of the calibration set. If a new pattern is found to be uncharacteristic, a warning can be issued with the forecast, and the ANN model retrained to include the new pattern. This approach of selectively retraining the model is compared to no retraining and the more computationally onerous case of retraining the model after each new sample. These three approaches are applied to forecast flow in the Kentucky River, USA, using multilayer perceptron (MLP) models. The results demonstrate that there is a significant advantage in retraining an ANN that has been deployed as a real-time, operational model, and that the SOM-LDE classifier is an effective approach for identifying the model's range of applicability and assessing the usefulness of the forecast.

  17. Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

    2007-01-01

    Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

  18. Development of fuzzy system and nonlinear regression models for ozone and PM2.5 air quality forecasts

    NASA Astrophysics Data System (ADS)

    Lin, Yiqiu

    2007-12-01

    Ozone forecast models using nonlinear regression (NLR) have been successfully applied to daily ozone forecast for seven metro areas in Kentucky, including Ashland, Bowling Green, Covington, Lexington, Louisville, Owensboro, and Paducah. In this study, the updated 2005 NLR ozone forecast models for these metro areas were evaluated on both the calibration data sets and independent data sets. These NLR ozone forecast models explained at least 72% of the variance of the daily peak ozone. Using the models to predict the ozone concentrations during the 2005 ozone season, the metro area mean absolute errors (MAEs) of the model hindcasts ranged from 5.90 ppb to 7.20 ppb. For the model raw forecasts, the metro area MAEs ranged from 7.90 ppb to 9.80 ppb. Based on previously developed NLR ozone forecast models for those areas, Takagi-Sugeno fuzzy system models were developed for the seven metro areas. The fuzzy "c-means" clustering technique coupled with an optimal output predefuzzification approach (least square method) was used to train the Takagi-Sugeno fuzzy system. Two types of fuzzy models, basic fuzzy and NLR-fuzzy system models, were developed. The basic fuzzy and NLR-fuzzy models exhibited essentially equivalent performance to the existing NLR models on 2004 ozone season hindcasts and forecasts. Both types of fuzzy models had, on average, slightly lower metro area averaged MAEs than the NLR models. Among the seven Kentucky metro areas Ashland, Covington, and Louisville are currently designated nonattainment areas for both ground level O 3 and PM2.5. In this study, summer PM2.5 forecast models were developed for providing daily average PM2.5 forecasts for the seven metro areas. The performance of the PM2.5 forecast models was generally not as good as that of the ozone forecast models. For the summer 2004 model hindcasts, the metro-area average MAE was 5.33 mug/m 3. Exploratory research was conducted to find the relationship between the winter PM2.5 concentrations and the meteorological parameters and other derived prediction parameters. Winter PM2.5 forecast models were developed for seven selected metro areas in Kentucky. For the model fits, the MAE for the seven forecast models ranged from 3.23 mug/m3 to 4.61 mug/m3 (26--28% NMAE). The fuzzy technique was also applied on PM2.5 forecast models to seek more accurate PM2.5 prediction. The NLR-fuzzy PM2.5 had slightly better performance than the NLR models.

  19. Understanding and modeling the economics of ECM

    NASA Astrophysics Data System (ADS)

    Wells, Wayne E.; Edinbarough, Immanuel A.

    2004-12-01

    Traditional economic analysis methods for manufacturing decisions include only the clearly identified immediate cost and revenue streams. Environmental issues have generally been seen as costs, in the form of waste material losses, conformance tests and pre-discharge treatments. The components of the waste stream often purchased as raw materials, become liabilities at the "end of the pipe" and their intrinsic material value is seldom recognized. A new mathematical treatment of manufacturing economics is proposed in which the costs of separation are compared with the intrinsic value of the waste materials to show how their recovery can provide an economic advantage to the manufacturer. The model is based on a unique combination of thermodynamic analysis, economic modeling and linear optimization. This paper describes the proposed model, and examines case studies in which the changed decision rules have yielded significant savings while protecting the environment. The premise proposed is that by including the value of the waste materials in the profit objective of the firm and applying the appropriate technological solution, manufacturing processes can become closed systems in which losses approach zero and environmental problems are converted into economic savings.

  20. Forecasting Taiwan's GDP by the novel nash nonlinear grey Bernoulli model with trembling-hand perfect equilibrium

    NASA Astrophysics Data System (ADS)

    Hsin, Pei-Han

    2013-09-01

    The traditional grey forecasting model, GM (1, 1), is criticized for its unsatisfied forecasting accuracy. The Nash Nonlinear Grey Bernoulli Model (NNGBM (1, 1)) is proposed and proven with high prediction accuracy. Because of the multiple Nash solutions, this study uses trembling-hand perfect equilibrium to refine the NNGBM and then obtain higher forecasting accuracy. This study mathematically proves that the proposed model is feasible and efficient. Finally, NNGBM with trembling-hand perfect equilibrium is used to forecast Taiwan's GDP. The results show Taiwan's GDP is keeping on growing.

  1. Sensitivity Analysis of a Spatio-Temporal Avalanche Forecasting Model Based on Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Matasci, G.; Pozdnoukhov, A.; Kanevski, M.

    2009-04-01

    The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the sensitivity analysis is to shed light on the particular abilities of the model in assessing the likelihood of avalanche releases under evolving meteorological/snowpack conditions. Both spatial resolution (the abilities to produce reliable forecasts for individual avalanche paths) and temporal behaviour of the model are explored in details. Based on the sensitivity analysis, the uncertainty estimation for the provided forecasts is discussed. Particularly, the ensembles of prediction models are run and analysed in order to estimate the variability of the provided forecast and assess the uncertainty coming from the variety of sources: imprecise input data, uncertainty in weather forecast, sub-optimal parameters of the prediction model and variability in the choice of the training dataset.

  2. Flood forecasting for the Ukrainian part of the Tisza Basin: linking with the numerical weather forecasts, comparative testing of distributed and lumped models

    NASA Astrophysics Data System (ADS)

    Belov, S.; Donchytz, G.; Kivva, S.; Kuschan, A.; Zheleznyak, M.

    2003-04-01

    The implementation of new flood forecasting systems for the Ukrainian part of the Tisza basin has started last years by the customisation of Mike-11 model for the Uzh River and Latoritsa River (part of the Bodrog Catchment) in the frame of the joint project with the 'DHI Water&Environment'. The calibration and testing of the lumped parameter model NAM was provided in collaboration with the Ukrainian Hydrometcenter and the Uzhgorod Hydrometcenter for the period 1998-2000, which includes two hazardous floods of years 1998 and 2000. The tuning of hydrodynamical module of Mike-11 is provided in collaboration with the Transcarpathian Branch of State Committee of Water Management (SCWM), Uzhgorod. The information about existing and designed hydraulic structures in the river channels, -bridges, polders, dikes, pump stations is used for the model tuning. The flood forecasting system for Uzh River and Latoritsa River based on Mike -11 is in pre-operational use in Uzhgorod Hydromet and SCUWM offices. The advance time of the flood forecasts can be increased by the real-time assimilation of the precipitation forecasts of a Numerical Weather Predictions (NWP) model. The Penn State University /UCAR NWP model MM5 was customized for the Ukrainian territory in resolution 30*30 km on the basis of the rare gridded forecasting data from the German meteorological center Offenbach, assimilating the data from the Ukrainian meteorological stations, processed by the Ukrainian Hydrometcenter. The region of the Uzh and Latoritsa watersheds was simulated by MM5 in the resolution 10*10 km for the linking with the Mike -11 (NAM). The preliminary results of flood forecasting on the basis of the meteorological forecasts are analyzed. For further improvement of the flood forecasting systems the implementations of GIS based distributed models are planned. Two types of distributed models based upon physically meaningful parameters are comparatively studied- 2-D finite- difference model RUNTOX (Kivva, Zheleznyak, 2001) based on Saint Venant equations and TOPographic Kinematic Approximation and Integration - TOPKAPI model (Todini, 1995,2000). The new computer code was developed, based on the TOPKAPI equations. Both models was initially tested for the small watersheds ( from 0.085 km2 to 0.40 km2 ) of the Boguslav Field Experimental Laboratory of the Ukrainian Hydrometeorological Institute. The comparison with the experimental data shows that TOPKAPI produces the reasonable results for the different floods without special tuning of the model parameters. The study of the applicability of TOPKAPI for the sub-watersheds of Uzh and Latoritsa rivers is going on.

  3. Using ensemble rainfall predictions in a countrywide flood forecasting model in Scotland

    NASA Astrophysics Data System (ADS)

    Cranston, M. D.; Maxey, R.; Tavendale, A. C. W.; Buchanan, P.

    2012-04-01

    Improving flood predictions for all sources of flooding is at the centre of flood risk management policy in Scotland. With the introduction of the Flood Risk Management (Scotland) Act providing a new statutory basis for SEPA's flood warning responsibilities, the pressures on delivering hydrological science developments in support of this legislation has increased. Specifically, flood forecasting capabilities need to develop in support of the need to reduce the impact of flooding through the provision of actively disseminated, reliable and timely flood warnings. Flood forecasting in Scotland has developed significantly in recent years (Cranston and Tavendale, 2012). The development of hydrological models to predict flooding at a catchment scale has relied upon the application of rainfall runoff models utilising raingauge, radar and quantitative precipitation forecasts in the short lead time (less than 6 hours). Single or deterministic forecasts based on highly uncertain rainfall predictions have led to the greatest operational difficulties when communicating flood risk with emergency responders, therefore the emergence of probability-based estimates offers the greatest opportunity for managing uncertain predictions. This paper presents operational application of a physical-conceptual distributed hydrological model on a countrywide basis across Scotland. Developed by CEH Wallingford for SEPA in 2011, Grid-to-Grid (G2G) principally runs in deterministic mode and employs radar and raingauge estimates of rainfall together with weather model predictions to produce forecast river flows, as gridded time-series at a resolution of 1km and for up to 5 days ahead (Cranston, et al., 2012). However the G2G model is now being run operationally using ensemble predictions of rainfall from the MOGREPS-R system to provide probabilistic flood forecasts. By presenting a range of flood predictions on a national scale through this approach, hydrologists are now able to consider an objective measure of the likelihood of flooding impacts to help with risk based emergency communication.

  4. Development and Evaluation of Storm Surge Ensemble Forecasting for the Philippines Using JMA Storm Surge Model

    NASA Astrophysics Data System (ADS)

    Lapidez, J. P. B.; Tablazon, J. P.; Lagmay, A. M. F. A.; Suarez, J. K. B.; Santiago, J. T.

    2014-12-01

    The Philippines is one of the countries most vulnerable to storm surge. It is located in the North-western Pacific basin which is the most active basin in the planet. An average of 20 tropical cyclones enters the Philippine area of responsibility (PAR) every year. The archipelagic nature of the country with regions having gently sloping coasts and shallow bays also contribute to the formation of extreme surges. Last November 2013, storm surge brought by super typhoon Haiyan severely damaged several coastal regions in the Visayan Islands. Haiyan left more than 6 300 casualties and damages amounting to more than $ 2 billion. Extreme storm surge events such as this highlight the need to establish a storm surge early warning system for the country. This study explores the development and evaluation of storm surge ensemble forecasting for the Philippines using the Japan Meteorological Agency (JMA) storm surge model. 36-hour, 24-hour, and 12-hour tropical cyclone forecasts are used to generate an ensemble storm surge forecast to give the most probable storm surge height at a specific point brought by an incoming tropical cyclone. The result of the storm surge forecast is compared to tide gauge record to evaluate the accuracy. The total time of computation and dissemination of forecast result is also examined to assess the feasibility of using the JMA storm surge model for operational purposes.

  5. Characteristics of surface cyclone forecasts in the Aviation Run of the Global Spectral Model

    SciTech Connect

    Grumm, R.H. )

    1993-03-01

    Results are presented of an evaluation of the performance of the Aviation Run (AVN) of the NMC Global Spectral Model (GSM) in predicting surface cyclones, which was conducted during the autumn of 1990 through the winter of 1992. The results indicated that the finer-resolution T126 GSM produces stronger and deeper cyclones than the old T80 GSM. The errors in AVN position forecasts of surface cyclones were smaller than those found in the NMC Nested Grid Model (NGM). The geographical distribution of the pressure errors were similar to those found in the NGM over eastern North America and the adjacent western Atlantic Ocean. The AVN tended to underpredict the 1000-500-mb thickness over surface cyclones, especially during the first 36 h of the forecast cycle. The T126 AVN forecasts are accurate enough to provide guidance for basic weather forecasts to three days, as has been done for the two-day forecasts for the past 25-30 yr. 19 refs.

  6. SEP modeling and forecasts based on the ENLIL global heliospheric model

    NASA Astrophysics Data System (ADS)

    Mays, M. Leila; Luhmann, Janet; Odstrcil, Dusan; Bain, Hazel; Li, Yan; Kuznetsova, Maria

    2015-04-01

    Understanding gradual SEP events (often driven by CMEs) well enough to forecast their properties at a given location requires a realistic picture of the global background solar wind through which the shocks and SEPs propagate. The global 3D MHD WSA-ENLIL model (Odstrcil et al., 2004) provides a time-dependent background heliospheric description, into which a cone-shaped CME can be inserted. It is clear from our preliminary runs that the CMEs sometimes generate multiple shocks, some of which fade while others merge and/or strengthen as they propagate. In order to completely characterize the SEP profiles observed at various locations with the aid of these simulations it is essential to include all of the relevant CMEs and allow enough time for the events to propagate and interact. From ENLIL v2.8 simulations one can extract the magnetic topologies of observer-connected magnetic field lines and all plasma and shock properties along those field lines. ENLIL "likelihood/all-clear" forecasting maps provide expected intensity, timing/duration of events at locations throughout the heliosphere with "possible SEP affected areas" color-coded based on shock strength. Accurate descriptions of the heliosphere, and hence modeled SEPs, are achieved by ENLIL only when the background solar wind is well-reproduced and CME parameters are accurate. ENLIL derived information is also useful to drive SEP models such as the Solar Energetic Particle Model (SEPMOD) which calculates the time series of ~10-100 MeV protons at a specific observer location using a passive test particle population (Luhmann et al. 2007, 2010). In this presentation we demonstrate SEP event modeling which utilizes routine ENLIL runs important for space weather forecasting and research. Making SEP models available for research and operational users is one of Community Coordinated Modeling Center's (CCMC) top priorities. Heliospheric model outputs are a necessary ingredient for SEP simulations. The CCMC is making steps towards offering a system to run SEP models driven by a variety of heliospheric models available at CCMC such as the ones described in this presentation.

  7. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

    NASA Astrophysics Data System (ADS)

    El-Shafie, A.; Noureldin, A.; Taha, M. R.; Hussain, A.

    2011-07-01

    Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network namely; Multi-Layer Peceptron Neural network (MLP-NN), Radial Basis Function Neural Network (RBFNN) and Input Delay Neural Network (IDNN), respectively, have been examined in this study. Those models had been developed for two time horizon in monthly and weekly rainfall basis forecasting at Klang River, Malaysia. Data collected over 12 yr (1997-2008) on weekly basis and 22 yr (1987-2008) for monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural network. Results showed that MLP-NN neural network model able to follow the similar trend of the actual rainfall, yet it still relatively poor. RBFNN model achieved better accuracy over the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model outperformed during training and testing stage which prove a consistent level of accuracy with seen and unseen data. Furthermore, the IDNN significantly enhance the forecasting accuracy if compared with the other static neural network model as they could memorize the sequential or time varying patterns.

  8. Forecasting the High Energy Electron Radiation Belts Using Physics Based Models

    NASA Astrophysics Data System (ADS)

    Horne, R. B.

    2012-12-01

    Wave-particle interactions waves play an important role in the loss and acceleration of electrons in the radiation belts. Here we present results from the SPACECAST project to forecast the high energy electron radiation belts using physics based models in the UK and France. The forecasting models include wave-particle interactions, radial diffusion, and losses by Coulomb collisions, and highlight the importance of various types of wave-particle interactions. The system is driven by a time series of the Kp index derived from solar wind data and ground based magnetometers and provides a forecast of the radiation belts up to 3 hours ahead, updated every hour. We show that during the storm of 8-9 March, 2012 the forecasts were able to reproduce the electron flux at geostationary orbit measured by GOES 13 to within a factor of two initially, and to within a factor of 10 later on during the event. By including wave-particle interactions between L* = 6.5 and 8 the forecast of the electron flux at geostationary orbit was significantly improved for the month of March 2012. We show examples of particle injection into the slot region, and relativistic flux drop-outs and suggest that flux drop outs are more likely to be associated with magnetopause motion than losses due to wave-particle interactions. To improve the forecasts we have developed a new database of whistler mode chorus waves from 5 different satellite missions. We present data on the power spectra of the waves as a function of magnetic local time, latitude and radial distance, and present pitch angle and energy diffusion coefficients for use in global models. We show that waves at different latitudes result in structure in the diffusion rates and we illustrate the effects on the trapped electron flux. We present forecasting skill scores which show quantitatively that including wave-particle interactions improves our ability to forecast the high energy electron radiation belt. Finally we suggest several areas where more data and more research are needed from missions such as RBSP to reduce uncertainty and improve forecasting skill.

  9. Modeling Regional Economic Impacts of Natural Disasters

    NASA Astrophysics Data System (ADS)

    Boissonnade, A.; Hallegate, S.; Muir-Wood, R.; Schlumberger, M.; Onur, T.

    2007-05-01

    Common features of natural disasters are intense regional impacts and the need for assessing their economic impacts on the construction sectors. The years 2004 and 2005 were record-setting time for natural disasters with major disasters or catastrophic (Cat) events all around the world with dramatic consequences in human lives and economic losses around the world, affecting developed and developing countries. Although there is a large body of literature on assessing the impact of cat events, there is little available research on the quantification and modeling of the regional economic impact of such events on the cost and length of reconstruction. Current available econometric models have serious limitations because they need detailed information for modeling the complex interactions between the different stakeholders of the economy at a regional level that is generally not available. Also, very little research was performed for quantifying the demand surge, defined as the sudden increase in the cost of repairs due to amplified payments, following a hurricane or a series of hurricane events or other natural disasters. Demand surge is an important component of the overall economic impact of cat events and there is a need to better quantify it. This paper presents results of a research program that started after the 2004 and 2005 U.S. hurricane seasons. A large data set of economic and observed losses resulting from the hurricanes that affected Florida and the Gulf states in the US was collected at county level. This provided us with the basis for assessing the change in repair costs before and after these historical events, to quantify the demand surge (after removing the underlying baseline trends) at several dozens of locations across the areas affected, and to provide information on how the changes in demand surge vary spatially and temporally in affected areas for which the amount of structure losses were reported. A parallel research effort was undertaken for identifying and quantifying the main drivers behind the demand surge by conducting survey among the different stakeholders involved in the reconstruction. Results of this work were used for developing a relatively simple economic model that is dependent on information available at the county level that includes econometric metrics prior to the event and the losses following catastrophic events. These losses are either observed or modeled with physical models. The economic model was validated and tested with data collected from the 2004 and 2005 hurricanes. Historical reconstructions of economic losses from 1992 Andrew and other historical events were performed for different regions of the US. The goal is to develop an economic model that can include regional economic conditions at the time of the events for a better modeling of economic losses resulting from cat events that can be used for a better assessment of the risk.

  10. Delft FEWS: an open interface that connects models and data streams for operational forecasting systems

    NASA Astrophysics Data System (ADS)

    de Rooij, Erik; Werner, Micha

    2010-05-01

    Many of the operational forecasting systems that are in use today are centred around a single modelling suite. Over the years these systems and the required data streams have been tailored to provide a closed-knit interaction with their underlying modelling components. However, as time progresses it becomes a challenge to integrate new technologies into these model centric operational systems. Often the software used to develop these systems is out of date, or the original designers of these systems are no longer available. Additionally, the changing of the underlying models may requiring the complete system to be changed. This then becomes an extensive effort, not only from a software engineering point of view, but also from a training point of view. Due to significant time and resources being committed to re-training the forecasting teams that interact with the system on a daily basis. One approach to reducing the effort required in integrating new models and data is through an open interface architecture, and through the use of defined interfaces and standards in data exchange. This approach is taken by the Delft-FEWS operational forecasting shell, which has now been applied in some 40 operational forecasting centres across the world. The Delft-FEWS framework provides several interfaces that allow models and data in differing formats to be flexibly integrated with the system. The most common approach to the integration of modes is through the Delft-FEWS Published Interface. This is an XML based data exchange format that supports the exchange of time series data, as well as vector and gridded data formats. The Published Interface supports standardised data formats such as GRIB and the NetCDF-CF standard. A wide range of models has been integrated with the system through this approach, and these are used operationally across the forecasting centres using Delft FEWS. Models can communicate directly with the interface of Delft-FEWS, or through a SOAP service. This giving the flexibility required for a state-of-the-art operational forecasting service. While Delft-FEWS comes with a user-friendly GIS based interface, a time series viewer and editor, and a wide range of tools for visualization, analysis, validation and data conversion, the available graphical display can be extended. New graphical components can be seamlessly integrated with the system through the SOAP service. Thanks to this open infrastructure, new models can easily be incorporated into an operational system without having to change the operational process. This allows the forecaster to focus on the science instead of having to worry about model details and data formats. Furthermore all model formats introduced to the Delft-FEWS framework will in principle become available to the Delft-FEWS community (in some cases subject to the licence conditions of the model supplier). Currently a wide range of models has been integrated and is being used operationally; Mike 11, HEC-RAS & HEC-RESSIM, HBV, MODFLOW, SOBEK and more. In this way Delft-FEWS not only provides a modelling interface but also a platform for model inter-comparison or multi-model ensembles, as well as a knowledge interface that allows forecasters throughout the world to exchange their views and ideas on operational forecasting. Keywords: FEWS; forecasting; modelling; timeseries; data; XML; NetCDF; interface; SOAP

  11. Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon 2011

    NASA Astrophysics Data System (ADS)

    Kaur, Inderpreet; Kumar, Prashant; Deb, S. K.; Kishtawal, C. M.; Pal, P. K.; Kumar, Raj

    2015-05-01

    The atmospheric motion vectors (AMVs) retrieved from multi-spectral geostationary satellites form a very crucial input to improve the initial conditions of numerical weather prediction (NWP) models at all operational agencies throughout the globe. With the recent update of operational AMV retrieval algorithm using infrared, water vapor, and visible channels of Indian geostationary meteorological satellite Kalpana-1, an attempt has been made to assess the impact of AMVs in the NWP models. In this study, the impact of Kalpana-1 AMVs is assessed by assimilating them in the Weather Research and Forecasting (WRF) model using three-dimensional variational data assimilation method during the entire month of July 2011 over the Indian Ocean region. Apart from Kalpana-1 AMVs, the other AMVs available from Global Telecommunications System (GTS) are also assimilated to generate the WRF model analyses. After the initial verification of WRF model analyses, the 12-h wind forecasts from the WRF model are compared with National Centers for Environmental Prediction Global Data Assimilation System final analyses. The assimilation of Kalpana-1 AMVs shows positive impact in 12-h wind forecast over the tropical region in the upper troposphere. Similar results are obtained when other AMVs available through GTS are used for assimilation, though the magnitude of positive impact of Kalpana-1 AMVs is slightly higher over tropical region. The 24-h rainfall forecasts are also improved over the Western India and the Bay of Bengal region, when Kalpana-1 AMVs are used for assimilation against control experiments.

  12. Typhoon event-based evolutionary fuzzy inference model for flood stage forecasting

    NASA Astrophysics Data System (ADS)

    Chen, Chang-Shian; Jhong, You-Da; Wu, Ting-Ying; Chen, Shien-Tsung

    2013-05-01

    This study proposes an evolutionary fuzzy inference model that combines a fuzzy inference model, genetic programming (GP), and a genetic algorithm (GA) to forecast flood stages during typhoons. The number of fuzzy inference rules in the proposed approach is based on the number of typhoon flood events. The consequent part of the rule was formed by constructing GP models that depict the rainfall-stage relationship of a specific flood event, whereas the GA was used to search the parameters of the fuzzy membership functions in the premise part of the rule. This study uses the proposed event-based evolutionary fuzzy inference model to forecast the typhoon flood stages of Wu River in Taiwan. Forecasting results based on stage hydrographs and performance indices verify the forecasting ability of the proposed model. This study also identifies the weights of triggered fuzzy rules during the fuzzy inference process, showing that a fuzzy rule is triggered according to the characteristics of the flood event that forms the rule. Moreover, physical explanation of the proposed evolutionary fuzzy inference model was discussed.

  13. Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market

    NASA Astrophysics Data System (ADS)

    Gong, Pu; Weng, Yingliang

    2016-01-01

    This paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns. We support the view that stock returns are affected not only by the absolute values of factors such as firm size, book-to-market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period. This article studies a new method for constructing stocks' reference groups; the method is called quartile method. Applying the method empirically to the Shanghai Stock Exchange 50 Index, we compare the daily volatility forecasting performance and the out-of-sample forecasting performance of Value-at-Risk (VaR) estimated by different models. The empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks, and it produces more accurate VaR forecasts than the other three models introduced in the previous literature. Moreover, the findings indicate that both allowing for serial correlation in the disturbances and using time-varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model.

  14. Model-based approach to seasonal ensemble forecast of snowmelt water inflow into a reservoir

    NASA Astrophysics Data System (ADS)

    Gelfan, Alexander; Motovilov, Yuri; Moreido, Vsevolod

    2014-05-01

    An approach to seasonal ensemble forecast of snowmelt runoff has been developed and applied for forecasting lateral water inflow into the Cheboksary Reservoir (the watershed area is 374,000 km2) located in the middle Volga River basin. The approach combines a physically-based semi-distributed hydrological ECOMAG model with ensembles of future weather scenarios for a specified lead-time of the forecast, which are then used as inputs for a hydrological model. The ECOMAG model describes processes of snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface and channel flow. The hydrological model is forced using daily meteorological variables (precipitation, air temperature, and air humidity) taken from the available observation data prior to the forecast date. Using these datasets, the initial watershed state (primarily, areal distribution of snow water equivalent, soil moisture content and soil freezing depth) as well as the initial river channel state are simulated by the model. Results from these spin-up simulations are routinely controlled by comparing them with observations from snow and agricultural surveys and streamflow observations. To assign ensemble of weather scenarios for the specified lead-time of the forecast (3 months ahead in this study), two approaches are applied: (1) the historical, observed daily weather patterns are utilized which assumed to be representative of possible future weather conditions; and (2) the artificial daily weather patterns Monte-Carlo are simulated by a stochastic weather generator. Being forced by the assigned ensembles of weather patterns for the forecast lead time, the ECOMAG model produces ensembles of hydrographs of inflow into the Cheboksary Reservoir. Using the developed approach, hindcasts have been produced for 30 spring seasons beginning from the filling of the reservoir in 1982 and the statistical properties of the obtained ensembles of runoff characteristics (volume and peak discharge) have been evaluated. The median forecast traces have been analyzed using the traditional Nash-and-Sutcliffe criterion as well as the distribution-oriented verification measures have been utilized to assess the probabilistic information contained in both forecast ensembles.

  15. Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models

    NASA Astrophysics Data System (ADS)

    Kogan, Felix; Kussul, Nataliia; Adamenko, Tatiana; Skakun, Sergii; Kravchenko, Oleksii; Kryvobok, Oleksii; Shelestov, Andrii; Kolotii, Andrii; Kussul, Olga; Lavrenyuk, Alla

    2013-08-01

    Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April-May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2-3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April-May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha-1 in June and 0.4 t ha-1 in April, while performance of three approaches for 2011 was almost the same (0.5-0.6 t ha-1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2-3 months prior to harvest, while providing minimum requirements to input datasets.

  16. Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method

    NASA Astrophysics Data System (ADS)

    He, Chengfei; Zhi, Xiefei; You, Qinglong; Song, Bin; Fraedrich, Klaus

    2015-08-01

    This study conducted 24- to 72-h multi-model ensemble forecasts to explore the tracks and intensities (central mean sea level pressure) of tropical cyclones (TCs). Forecast data for the northwestern Pacific basin in 2010 and 2011 were selected from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency, and National Centers for Environmental Prediction datasets of the Observing System Research and Predictability Experiment Interactive Grand Global Ensemble project. The Kalman Filter was employed to conduct the TC forecasts, along with the ensemble mean and super-ensemble for comparison. The following results were obtained: (1) The statistical-dynamic Kalman Filter, in which recent observations are given more importance and model weighting coefficients are adjusted over time, produced quite different results from that of the super-ensemble. (2) The Kalman Filter reduced the TC mean absolute track forecast error by approximately 50, 80 and 100 km in the 24-, 48- and 72-h forecasts, respectively, compared with the best individual model (ECMWF). Also, the intensity forecasts were improved by the Kalman Filter to some extent in terms of average intensity deviation (AID) and correlation coefficients with reanalysis intensity data. Overall, the Kalman Filter technique performed better compared to multi-models, the ensemble mean, and the super-ensemble in 3-day forecasts. The implication of this study is that this technique appears to be a very promising statistical-dynamic method for multi-model ensemble forecasts of TCs.

  17. Improved gridded wind forecasts with statistical post-processing of numerical models with functional and/or block regressions

    NASA Astrophysics Data System (ADS)

    Zamo, Michael; Bel, Liliane; Mestre, Olivier

    2015-04-01

    Numerical weather forecasts' errors are routinely improved through statistical post-processing by several national weather services. These statistical post-processing methods build a regression function called model output statistics (MOS) between observations and forecasts based on an archive of past forecasts and corresponding observations. Since observations are usually available only for meteorological stations, the improved forecasts are generally available only at the locatiosn of those meterological stations. This may prove insufficient for forecasters or forecast users, who increasingly ask gridded improved forecasts. We present our work in building improved forecasts on the grid of a model for wind in the boundary layer. First we introduce our method to build a new analysis of wind measurements which is used as gridded pseudo-observations. We show how this new analysis performs better than existing ones. Then we build and compare several regression methods based on scalar or functional statistics. In order to reduce the computational burden and improve the quality of the regression each regression function is built by pooling together data from small geographical domains. We study the impact of the domain size on the quality of the final forecast. The performance of the best improved forecast is studied.

  18. Manpower Forecasting Model for Educational Planning. Technical Report No. 13.

    ERIC Educational Resources Information Center

    Boettcher, J.; Brekka, Lawrence T.

    This report describes how Educational Radio and Television of Iran (ERTI) can use economic and manpower projection data in its planning. A summary of the conclusions reached in regard to Iran's most important education and training needs is followed by discussion of the particular types of information needed by ERTI and its availability. The

  19. Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

    NASA Astrophysics Data System (ADS)

    Dominguez, F.; Castro, C. L.

    2009-12-01

    The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the North American Regional Reanalysis. With these conditions, downscaled CFS-WRF reforecast simulations can produce realistic continental-scale patterns of warm season precipitation. This includes a reasonable representation of the North American monsoon in the southwest U.S. and northwest Mexico, which is notoriously difficult to represent in a global atmospheric model. We anticipate that this research will help lead the way toward substantially improved real time operational forecasts of North American summer climate with a RCM.

  20. Recent results from the GISS model of the global atmosphere. [circulation simulation for weather forecasting

    NASA Technical Reports Server (NTRS)

    Somerville, R. C. J.

    1975-01-01

    Large numerical atmospheric circulation models are in increasingly widespread use both for operational weather forecasting and for meteorological research. The results presented here are from a model developed at the Goddard Institute for Space Studies (GISS) and described in detail by Somerville et al. (1974). This model is representative of a class of models, recently surveyed by the Global Atmospheric Research Program (1974), designed to simulate the time-dependent, three-dimensional, large-scale dynamics of the earth's atmosphere.

  1. Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling.

    PubMed

    Dyson, Brian; Chang, Ni-Bin

    2005-01-01

    Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach--system dynamics modeling--for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool--Stella. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues. PMID:16009300

  2. Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time

    NASA Astrophysics Data System (ADS)

    Prasad, Kanchan; Gorai, Amit Kumar; Goyal, Pramila

    2016-03-01

    This study aims to develop adaptive neuro-fuzzy inference system (ANFIS) for forecasting of daily air pollution concentrations of five air pollutants [sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particular matters (PM10)] in the atmosphere of a Megacity (Howrah). Air pollution in the city (Howrah) is rising in parallel with the economics and thus observing, forecasting and controlling the air pollution becomes increasingly important due to the health impact. ANFIS serve as a basis for constructing a set of fuzzy IF-THEN rules, with appropriate membership functions to generate the stipulated input-output pairs. The ANFIS model predictor considers the value of meteorological factors (pressure, temperature, relative humidity, dew point, visibility, wind speed, and precipitation) and previous day's pollutant concentration in different combinations as the inputs to predict the 1-day advance and same day air pollution concentration. The concentration value of five air pollutants and seven meteorological parameters of the Howrah city during the period 2009 to 2011 were used for development of the ANFIS model. Collinearity tests were conducted to eliminate the redundant input variables. A forward selection (FS) method is used for selecting the different subsets of input variables. Application of collinearity tests and FS techniques reduces the numbers of input variables and subsets which helps in reducing the computational cost and time. The performances of the models were evaluated on the basis of four statistical indices (coefficient of determination, normalized mean square error, index of agreement, and fractional bias).

  3. 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

  4. 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 atmosphereocean 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 atmosphereocean climate system in operational forecast mode, and the latest seasonal forecasting systemSystem 4has 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 19812010 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 MaddenJulian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Nio 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 PacificNorth America region. PMID:24842026

  5. 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 Nio 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

  6. Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels

    EIA Publications

    2003-01-01

    This paper presents a short-term monthly forecasting model of West Texas Intermediate crude oil spot price using Organization for Economic Cooperation and Development (OECD) petroleum inventory levels.

  7. The Verhulst Model with Remedy and Its Application in Forecasting Quantity of Student Taking Entrance Examination to College

    ERIC Educational Resources Information Center

    Liu, Bin; Bi, Qing-sheng

    2010-01-01

    The Verhulst model can be used to forecast the sequence, which is characterized as non-monotone and fluctuant sequence or saturated S-form sequence. According to the situation of national enrollment scale of college, this paper forecasts the quantity of students taking entrance examination to college with a Verhulst model with remedy based on data…

  8. Stock price forecasting using secondary self-regression model and wavelet neural networks

    NASA Astrophysics Data System (ADS)

    Yang, Chi-I.; Wang, Kai-Cheng; Chang, Kuei-Fang

    2015-07-01

    We have established a DWT-based secondary self-regression model (AR(2)) to forecast stock value. This method requires the user to decide upon the trend of the stock prices. We later used WNN to forecast stock prices which does not require the user to decide upon the trend. When comparing these two methods, we could see that AR(2) does not perform as well if there are no trends for the stock prices. On the other hand, WNN would not be influenced by the presence of trends.

  9. Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics

    PubMed Central

    Yang, Wan; Karspeck, Alicia; Shaman, Jeffrey

    2014-01-01

    A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filtersa basic particle filter (PF) with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)and three ensemble filtersthe ensemble Kalman filter (EnKF), the ensemble adjustment Kalman filter (EAKF), and the rank histogram filter (RHF)were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS) model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 20032012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 15 weeks in the future; the ensemble filters are more accurate predicting peaks in the past. PMID:24762780

  10. Likelihood- and residual-based evaluation of medium-term earthquake forecast models for California

    NASA Astrophysics Data System (ADS)

    Schneider, Max; Clements, Robert; Rhoades, David; Schorlemmer, Danijel

    2014-09-01

    Seven competing models for forecasting medium-term earthquake rates in California are quantitatively evaluated using the framework of the Collaboratory for the Study of Earthquake Predictability (CSEP). The model class consists of contrasting versions of the Every Earthquake a Precursor According to Size (EEPAS) and Proximity to Past Earthquakes (PPE) modelling approaches. Models are ranked by their performance on likelihood-based tests, which measure the consistency between a model forecast and observed earthquakes. To directly compare one model against another, we run a classical paired t-test and its non-parametric alternative on an information gain score based on the forecasts. These test scores are complemented by several residual-based methods, which offer detailed spatial information. The experiment period covers 2009 June-2012 September, when California experienced 23 earthquakes above the magnitude threshold. Though all models fail to capture seismicity during an earthquake sequence, spatio-temporal differences between models also emerge. The overall best-performing model has strong time- and magnitude-dependence, weights all earthquakes equally as medium-term precursors of larger events and has a full set of fitted parameters. Models with this time- and magnitude-dependence offer a statistically significant advantage over simpler baseline models. In addition, models that down-weight aftershocks when forecasting larger events have a desirable feature in that they do not overpredict following an observed earthquake sequence. This tendency towards overprediction differs between the simpler model, which is based on fewer parameters, and more complex models that include more parameters.

  11. Legitimising neural network river forecasting models: a new data-driven mechanistic modelling framework

    NASA Astrophysics Data System (ADS)

    Mount, N. J.; Dawson, C. W.; Abrahart, R. J.

    2013-01-01

    In this paper we address the difficult problem of gaining an internal, mechanistic understanding of a neural network river forecasting (NNRF) model. Neural network models in hydrology have long been criticised for their black-box character, which prohibits adequate understanding of their modelling mechanisms and has limited their broad acceptance by hydrologists. In response, we here present a new, data-driven mechanistic modelling (DDMM) framework that incorporates an evaluation of the legitimacy of a neural network's internal modelling mechanism as a core element in the model development process. The framework is exemplified for two NNRF modelling scenarios, and uses a novel adaptation of first order, partial derivate, relative sensitivity analysis methods as the means by which each model's mechanistic legitimacy is explored. The results demonstrate the limitations of standard, goodness-of-fit validation procedures applied by NNRF modellers, by highlighting how the internal mechanisms of complex models that produce the best fit scores can have much lower legitimacy than simpler counterparts whose scores are only slightly inferior. The study emphasises the urgent need for better mechanistic understanding of neural network-based hydrological models and the further development of methods for elucidating their mechanisms.

  12. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian

    2015-04-01

    Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called Ensemble Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.

  13. Diabatic forcing and intialization with assimilation of cloud water and rainwater in a forecast model

    NASA Technical Reports Server (NTRS)

    Raymond, William H.; Olson, William S.; Callan, Geary

    1995-01-01

    In this study, diabatic forcing, and liquid water assimilation techniques are tested in a semi-implicit hydrostatic regional forecast model containing explicit representations of grid-scale cloud water and rainwater. Diabatic forcing, in conjunction with diabatic contributions in the initialization, is found to help the forecast retain the diabatic signal found in the liquid water or heating rate data, consequently reducing the spinup time associated with grid-scale precipitation processes. Both observational Special Sensor Microwave/Imager (SSM/I) and model-generated data are used. A physical retrieval method incorporating SSM/I radiance data is utilized to estimate the 3D distribution of precipitating storms. In the retrieval method the relationship between precipitation distributions and upwelling microwave radiances is parameterized, based upon cloud ensemble-radiative model simulations. Regression formulae relating vertically integrated liquid and ice-phase precipitation amounts to latent heating rates are also derived from the cloud ensemble simulations. Thus, retrieved SSM/I precipitation structures can be used in conjunction with the regression-formulas to infer the 3D distribution of latent heating rates. These heating rates are used directly in the forecast model to help initiate Tropical Storm Emily (21 September 1987). The 14-h forecast of Emily's development yields atmospheric precipitation water contents that compare favorably with coincident SSM/I estimates.

  14. Year of Coordinated Observations, Modeling and Forecasting: Addressing the Challenge of Organized Tropical Convection

    NASA Technical Reports Server (NTRS)

    Waliser, Duane E.

    2006-01-01

    The multi-scale organization of tropical convection and scale interaction are grand challenges in the prediction of weather and climate. As part of a international effort UN Year of Planet Earth, this proposed effort to observe, model and forecast the effects of organized tropical convection is reviewed. This viewgraph presentation reviews the proposal.

  15. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect

    Brown, C. W.; Hood, Raleigh R.; Long, Wen; Jacobs, John M.; Ramers, D. L.; Wazniak, C.; Wiggert, J. D.; Wood, R.; Xu, J.

    2013-09-01

    The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

  16. Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin

    NASA Astrophysics Data System (ADS)

    Tucci, Carlos Eduardo Morelli; Clarke, Robin Thomas; Collischonn, Walter; da Silva Dias, Pedro Leite; de Oliveira, Gilvan Sampaio

    2003-07-01

    This paper describes a procedure for predicting seasonal flow in the Rio Uruguay drainage basin (area 75,000 km2, lying in Brazilian territory), using sequences of future daily rainfall given by the global climate model (GCM) of the Brazilian agency for climate prediction (Centro de Previso de Tempo e Clima, or CPTEC). Sequences of future daily rainfall given by this model were used as input to a rainfall-runoff model appropriate for large drainage basins. Forecasts of flow in the Rio Uruguay were made for the period 1995-2001 of the full record, which began in 1940. Analysis showed that GCM forecasts underestimated rainfall over almost all the basin, particularly in winter, although interannual variability in regional rainfall was reproduced relatively well. A statistical procedure was used to correct for the underestimation of rainfall. When the corrected rainfall sequences were transformed to flow by the hydrologic model, forecasts of flow in the Rio Uruguay basin were better than forecasts based on historic mean or median flows by 37% for monthly flows and by 54% for 3-monthly flows.

  17. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    NASA Astrophysics Data System (ADS)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

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

    NASA Astrophysics Data System (ADS)

    Arumugam, S.; Oh, J.

    2010-12-01

    Successful water quality trading programs in the country rely on expected point and nonpoint nutrient loadings from multiple sources. Pollutant sources, through nutrient transactions, are in pursuit of minimum allocation strategies that can keep both the loadings and the associated concentrations under the target limit. It is well established in the hydroclimatic literature that interannual variability in seasonal streamflow could be explained partially using SST conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. We intend to bridge these two findings to develop probabilistic nutrient loading model for supporting water quality trading in the Tar River basin, NC. Utilizing the precipitation forecasts derived from ECHAM4.5 General Circulation Model, we develop season-ahead forecasts of total nitr