The dynamics of economics functions: modelling and forecasting the yield curve
Clive G. Bowsher; Roland Meeks
2008-01-01
The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or \\
The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve
Clive G. Bowsher; Roland Meeks
2008-01-01
The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or `signal') is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or 'y-values') at the knots of the spline. The
Towards a Better Forecasting Model for Economic Jing Tao YAO
Yao, JingTao
financial time series. Neural network based financial forecasting has been explored for about a decade. Many a study of neural network forecasting construction system. Forecasting, especially financial forecasting to the outputs. Data are partitioned into several sets to find out the particular knowledge of this time series
J. Boehnert; O. Wilhelmi; K. M. Sampson
2009-01-01
The integration of weather forecast models and socio-economic data is key to better understanding of the weather forecast and its impact upon society. Whether the forecast is looking at a hurricane approaching land or a snow storm over an urban corridor; the public is most interested in how this weather will affect day-to-day activities, and in extreme events how it
Norman R. Swanson; Halbert White
1997-01-01
Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks)
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.
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.
Energy demand modeling and forecasting
W. M. McHugh
1977-01-01
Results from an all energy econometric demand model were described and preserved. The model forecasts the demand for electricity, oil, natural gas, and coal for the Pacific Northwest as a whole, for the states of Idaho, Washington, and Oregon separately, and individually for seven distinct economic subregions therein. Individual forecasts were prepared for the residential, commercial, and industrial sectors and
Modeling for Tsunami Forecast Vasily Titov NOAA Center for Tsunami Research Pacific Marine Environmental Laboratory Seattle, WA #12;Outline Tsunami Modeling Development Toward Real- time Tsunami Forecast Challenges Modeling development in 1990 -2000 Short-term Inundation Forecast for Tsunamis Forecast system
The economic burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model
Steven A. Grover; Louis Coupal; Hanna Zowall; Raghu Rajan; John Trachtenberg; Mostafa Elhilali; Michael Chetner; Larry Goldenberg
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 as- sociated direct
Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts
Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts April 14, 2009 MassoudImplication of these updates Load growth is slower than draft forecast Medium forecast before conservation Energy growing at 1,000 MW #12;6 Demand Forecasts Price Effect (prior to conservation) - 5,000 10,000 15,000 20,000 25,000 30
Economic Value of Weather and Climate Forecasts
NASA Astrophysics Data System (ADS)
Katz, Richard W.; Murphy, Allan H.
1997-06-01
Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects of weather prediction. Chapters by area specialists provide a comprehensive view of this timely topic. They encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behavior. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential text for economists, statisticians, and meteorologists.
The Economic Value Of Ensemble-Based Weather Forecasts
Yuejian Zhu; Zoltan Toth; Richard Wobus; David Richardson; Kenneth Mylne
2002-01-01
ABSTRACT The potential economic,benefitassociated with the use of an ensemble,of forecasts vs. an,equivalent or higher resolution control forecast is discussed. Neither forecast systems are postprocessed, except a simple calibration that is applied to make them reliable. A simple decision making,model,is used where,all potential users of weather forecastsare characterized by the ratiobetween,the cost of their action to prevent weather related damages,
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.
Aggregate vehicle travel forecasting model
Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)
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.
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
CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014
de Lijser, Peter
CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director, Center for Economic Analysis and Forecasting -- Dean, Mihaylo College of Business and Economics Mira Farka, Ph.D. -- Co-Director, Center for Economic Analysis and Forecasting -- Associate Professor
L. J. Williams; J. W. Boyd; R. T. Crow
1978-01-01
Forecasts of end-use consumption of electricity, petroleum, natural gas, and coal for the years 1980 to 2000 are presented. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end-use sector. The forecasts are based on a forecast of longrun economic growth coupled with three scenarios concerning energy prices and
A model for disruptive technology forecasting in strategic regional economic development
Sul Kassicieh; Nabeel Rahal
2007-01-01
As regions look to increase their economic development activities, technology-based developments and the penchant for long-term developments in disruptive technologies like nanotechnology become an important part of the options available to these regions. There are typically many technologies and therefore product areas that the region, however, can further develop by investing resources in these areas. At the same time, other
Content Horizons for Forecasts of Economic Time Series
John Galbraith
1999-01-01
We consider the problem of determining the horizon beyond which forecasts from time series models of stationary processes add nothing to the forecast implicit in the conditional mean. We refer to this as the content horizon for forecasts, and define a forecast content function at horizons s = 1, ... S as the proportionate reduction in mean squared forecast error
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
Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint
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.
Regional Energy Demand Modeling and Forecasting
Yu Hang; Xiao Deyun; Liu Zhentao
2009-01-01
Because of the essential role played by energy in economic development, particularly in view of the two major global energy crises and recent high oil prices, whether or not a region or the whole world can successfully satisfy its energy demand has been an issue of great importance. This study uses stochastic models to forecast regional energy demand in the
Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast
................................................................................................ 28 Economic Drivers for Industrial Sector Demand...................................................................................... 36 Forecast for Retail Electricity Prices by SectorSixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast Role
Potential Economic Value of Seasonal Hurricane Forecasts
Emanuel, Kerry Andrew
This paper explores the potential utility of seasonal Atlantic hurricane forecasts to a hypothetical property insurance firm whose insured properties are broadly distributed along the U.S. Gulf and East Coasts. Using a ...
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.
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.
Interpretation of Global Forecast Model 'Flipflops'
NSDL National Science Digital Library
2014-09-14
All forecasters are familiar with occasional run-to-run changes in forecast direction that occur with medium-range (and sometimes even short-range) forecasts in the Global Forecast Model (aka AVN/MRF). This case describes two recent model "flipflops" in a pair of time-adjacent operational MRF runs, and shows how MRF ensemble forecasts shed light on what is actually going on in the operational MRF seasons.
Evolutionary computation and economic time series forecasting
Dipti Srinivasan; Vishal Sharma
2007-01-01
This paper summarizes the collective work done in the application of evolutionary computation for financial time series forecasting. These are mainly stock market indices and foreign exchange rate prediction. The time series corresponding to these indices is a non-linear dynamic stochastic system different from other static patterns which are independent of time. Evolutionary techniques have capabilities of efficient search space
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.
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.
Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis
Hamidreza Zareipour; Claudio A. Canizares; Kankar Bhattacharya
2010-01-01
Several techniques have been proposed in the literature to forecast electricity market prices and improve forecast accuracy. However, no studies have been reported examining the economic impact of price forecast inaccuracies on forecast users. Therefore, in this paper, the application of electricity market price forecasts to short-term operation scheduling of two typical and inherently different industrial loads is examined and
Forecasting Inflation Using Dynamic Model Averaging
Gary Koop; Dimitris Korobilis
2010-01-01
We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more
Economic Perspectives of Technological Progress: New Dimensions for Forecasting Technology
ERIC Educational Resources Information Center
Twiss, Brian
1976-01-01
Discusses the causal relationship between the allocation of financial resources and technological growth. Argues that economic constraints are becoming an important determinant of technological progress that must be incorporated into technology forecasting techniques. (Available from IPC (America) Inc., 205 East 42 Street, New York, NY 10017;…
L. J. Williams; J. W. Boyd; R. T. Crow
1978-01-01
This report presents forecasts of end-use consumption of electricity, petroleum, natural gas, and coal for the years 1980 to 2000. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end-use sector. The forecasts are based on a forecast of long-run economic growth coupled with three scenarios concerning energy prices
A New Forecasting Model for Agricultural Commodities
He Yong
1995-01-01
This paper presents a mathematical model for forecasting the production of some agricultural commodities. This method takes into account the general trend of the time series and random fluctuations about this trend. It has the merits of both simplicity of application and high forecasting precision. In particular, the forecast values of the model are more precise than those of other
Comparing Information in Forecasts from Econometric Models
Ray C Fair; Robert J Shiller
1990-01-01
The information contained in one model's forecast compared to that in another can be assessed from a regression of actual values on predicted values from the two models. The authors do this for forecasts of real GNP growth rates for different pairs of models. The models include a structural model (the Fair model), various versions of the vector autoregressive model,
Economic impact of wind power forecast
I. Marti; M. J. San Isidro; M. Gastón; Y. Loureiro; J. Sanz; I. Pérez
1. Abstract Prediction tools can make wind energy be competitive in a liberalized energy market, where deviations in production have a penalty which is usually an obstacle for developers to access to the energy market. This paper analyses these deviations and the economic impact over any developer according to the actual pricing policy. This is made through simulations based on
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.
CSUF Economic Outlook and Forecasts MidYear Update -April 2013
de Lijser, Peter
CSUF Economic Outlook and Forecasts MidYear Update - April 2013 Anil Puri & Mira Farka Mihaylo College of Business and Economics California State University, Fullerton U.S. Economic Outlook to the forecast and a are-up in the region can easily derail the global economic recovery. Nonetheless
Joseph, Andreas; Stanley, Eugene; Chen, Guanrong
2014-01-01
The combination of the network theoretic approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modelling and forecasting key economic indicators. The main idea is to introduce a topological component into the analysis, taking into account consistently all higher-order interactions. We present three basic methodologies to demonstrate different approaches to harness the resulting network gain. First, a multiple linear regression optimisation algorithm is used to generate a relational network between individual components of national balance of payment accounts. This model describes annual statistics with a high accuracy and delivers good forecasts for the majority of indicators. Second, an early-warning mechanism for global financial crises is presented, which combines network measures with standard economic indicators. From the analysis of the cross-border portfolio investment network of long-term debt securities, the proliferation of a w...
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.
Earthquake Forecasting Using Hidden Markov Models
Daniel W. Chambers; Jenny A. Baglivo; John E. Ebel; Alan L. Kafka
2011-01-01
This paper develops a novel method, based on hidden Markov models, to forecast earthquakes and applies the method to mainshock seismic activity in southern California and western Nevada. The forecasts are of the probability of a mainshock within 1, 5, and 10 days in the entire study region or in specific subregions and are based on the observations available at
AIR QUALITY MODEL EVALUATION - FORECASTING AND RETROSPECTIVES
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....
Traffic flow forecasting: Comparison of modeling approaches
Smith, B.L. [Virginia Transportation Research Council, Charlottesville, VA (United States); Demetsky, M.J. [Univ. of Virginia, Charlottesville, VA (United States). Dept. of Civil Engineering
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.
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.
Earthquake Forecasting Using Hidden Markov Models
Daniel W. Chambers; Jenny A. Baglivo; John E. Ebel; Alan L. Kafka
This paper develops a novel method, based on hidden Markov models, to forecast earthquakes and applies the method to mainshock\\u000a seismic activity in southern California and western Nevada. The forecasts are of the probability of a mainshock within 1,\\u000a 5, and 10 days in the entire study region or in specific subregions and are based on the observations available at the
Risk forecasting models and optimal portfolio selection
David Moreno; Paulina Marco; Ignacio Olmeda
2005-01-01
This study analyses, from an investor's perspective, the performance of several risk forecasting models in obtaining optimal portfolios. The plausibility of the homoscedastic hypothesis implied in the classical Markowitz model is dicussed and more general models which take into account assymetry and time varying risk are analysed. Specifically, it studies whether ARCH-type based models obtain portfolios whose risk-adjusted returns exceed
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.
Skill of regional and global model forecast over Indian region
NASA Astrophysics Data System (ADS)
Kumar, Prashant; Kishtawal, C. M.; Pal, P. K.
2015-01-01
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).
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.
Forecasting Turbulent Modes with Nonparametric Diffusion Models
Tyrus Berry; John Harlim
2015-01-27
This paper presents a nonparametric diffusion modeling approach for forecasting partially observed noisy turbulent modes. The proposed forecast model uses a basis of smooth functions (constructed with the diffusion maps algorithm) to represent probability densities, so that the forecast model becomes a linear map in this basis. We estimate this linear map by exploiting a previously established rigorous connection between the discrete time shift map and the semi-group solution associated to the backward Kolmogorov equation. In order to smooth the noisy data, we apply diffusion maps to a delay embedding of the noisy data, which also helps to account for the interactions between the observed and unobserved modes. We show that this delay embedding biases the geometry of the data in a way which extracts the most predictable component of the dynamics. The resulting model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and in the observation noise limit. We will show numerical examples on a wide-range of well-studied turbulent modes, including the Fourier modes of the energy conserving Truncated Burgers-Hopf (TBH) model, the Lorenz-96 model in weakly chaotic to fully turbulent regimes, and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. In these examples, forecasting skills of the nonparametric diffusion model are compared to a wide-range of stochastic parametric modeling approaches, which account for the nonlinear interactions between the observed and unobserved modes with white and colored noises.
Multiscale forecasting in the western North Atlantic: Sensitivity of model forecast skill to glider Received in revised form 15 September 2012 Accepted 26 September 2012 Keywords: Glider data assimilation mode with other forecasts to guide glider control. A reanalysis was then carried out by sequentially
Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model
Walter Collischonn; Reinaldo Haas; Ivanilto Andreolli; Carlos Eduardo Morelli Tucci
2005-01-01
The use of quantitative rainfall forecasts as input to a rainfall-runoff model, thereby extending the lead-time of flow forecasts, is relatively new. This paper presents results from a study in which real-time river flow forecasts were calculated for the River Uruguay basin lying within southern Brazil, using a method based on observed rainfall, quantitative forecasts of rainfall given by a
Modeling and Forecasting Electric Daily Peak Loads
Abdel-Aal, Radwan E.
such as neural networks. With such techniques, the user does not need to explicitly specify the modelModeling and Forecasting Electric Daily Peak Loads Using Abductive Networks R. E. Abdel techniques including neural networks have been used for this purpose. This paper proposes the alternative
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.
The Hanford Site New Production Reactor (NPR) economic and demographic baseline forecasts
Cluett, C.; Clark, D.C. (Battelle Human Affairs Research Center, Seattle, WA (USA)); Pittenger, D.B. (Demographics Lab., Olympia, WA (USA))
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.
THE ECONOMIC VALUE OF USING REALIZED VOLATILITY IN FORECASTING FUTURE IMPLIED VOLATILITY
Wing Hong Chan; Ranjini Jha; Madhu Kalimipalli
2009-01-01
We examine the economic benefits of using realized volatility to forecast future implied volatility for pricing, trading, and hedging in the S&P 500 index options market. We propose an encompassing regression approach to forecast future implied volatility, and hence future option prices, by combining historical realized volatility and current implied volatility. Although the use of realized volatility results in superior
U.S. Economic Outlook and Forecasts Surviving the Recovery: Shaken, and Stirred...
de Lijser, Peter
5 U.S. Economic Outlook and Forecasts Surviving the Recovery: Shaken, and Stirred... "It ain't over litany of bleak macro data and gloomy economic projec- tions. Indeed, for most sectors and most folks economic activity fell by an astounding -5.1% during the recession -- a much deeper collapse than
Technological Forecasting---Model Selection, Model Stability, and Combining Models
Nigel Meade; Towhidul Islam
1998-01-01
The paper identifies 29 models that the literature suggests are appropriate for technological forecasting. These models are divided into three classes according to the timing of the point of inflexion in the innovation or substitution process. Faced with a given data set and such a choice, the issue of model selection needs to be addressed. Evidence used to aid model
Near real time wind energy forecasting incorporating wind tunnel modeling
William David Lubitz
2005-01-01
A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power
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.
Increasing NOAA's computational capacity to improve global forecast modeling
Hamill, Tom
. Introduction Global atmospheric forecast models are the backbone of NOAA's weather predictions. While 1 Increasing NOAA's computational capacity to improve global forecast modeling A NOAA, Physical Sciences Division Michael Fiorino and Steven E. Koch NOAA Earth System Research Lab, Global
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.
Forecasting women's apparel sales using mathematical modeling
Celia Frank; Ashish Garg; Les M. Sztandera; Amar Raheja
2003-01-01
Traditionally, statistical time series methods like moving average (MA), auto-regression (AR), or combinations of them are used for forecasting sales. Since these models predict future sales only on the basis of previous sales, they fail in an environment where the sales are more influenced by exogenous variables such as size, price, color, climatic data, effect of media, price changes or
Evolving Time Series Forecasting ARMA Models
Paulo Cortez; Miguel Rocha; José Neves
2004-01-01
Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level
Verification of an Operational Gulf Stream Forecasting Model
Scott M. Glenn; Allan R. Robinson
A verification study for an operational ocean forecasting system that uses the quasi- geostrophic version of the Harvard Open Ocean Model as its dynamical model com- ponent is presented. The study is designed to test the ability of both the model and the system to perform 1-week duration forecasts in the Gulf Stream Meander and Ring region. The forecast system
Climate Model Forecast Experiments for TOGA COARE
J. Boyle; S. Klein; G. Zhang; S. Xie; X. Wei
2008-01-01
Short-term (1-10 day) forecasts are made with climate models to assess the parameterizations of the physical processes. The time period for the integrations is that of the intensive observing period (IOP) of the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). The models used are the National Center for Atmospheric Research (NCAR) Community Climate Model, version 3.1 (CAM3.1);
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.
Using multi-stage data mining technique to build forecast model for Taiwan stocks
Chien-Jen Huang; Peng-Wen Chen; Wen-Tsao Pan
Taiwan stock market trend is fast changing. It is affected by not only the individual investors and the three major institutional\\u000a investors, but also impacted by domestic political and economic situations. Therefore, to precisely grasp the stock market\\u000a movement, one must build a perfect stock forecast model. In this article, we used a multi-stage optimized stock forecast model\\u000a to grasp
Efficient testing of earthquake forecasting models
David A. Rhoades; Danijel Schorlemmer; Matthew C. Gerstenberger; Annemarie Christophersen; J. Douglas Zechar; Masajiro Imoto
2011-01-01
Computationally efficient alternatives are proposed to the likelihood-based tests employed by the Collaboratory for the Study\\u000a of Earthquake Predictability for assessing the performance of earthquake likelihood models in the earthquake forecast testing\\u000a centers. For the conditional L-test, which tests the consistency of the earthquake catalogue with a model, an exact test using convolutions of distributions\\u000a is available when the number
Pattern Modelling in Time-series Forecasting
Sameer Singh
2000-01-01
Pattern modelling in time-series prediction refers to the process of identifying pastrelationships and trends in historical data for predicting future values. This paper describesthe development of a new pattern matching technique for univariate time-series forecasting.The pattern modelling technique out-performs frequently used statistical methods such asExponential Smoothing on different error measures and predicting the direction of change intime-series. The paper discusses
Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics
Washington at Seattle, University of
Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics using Heteroskedastic, University of Washington December 2008 Abstract As wind energy penetration continues to grow relevant uses for forecasts of wind speed, ranging from aviation to ship routing and recreational boating
Identifying and Forecasting Economic Regimes in TAC SCM
Wolfgang Ketter; John Collins; Maria Gini; Alok Gupta; Paul Schrater
\\u000a We present methods for an autonomous agent to identify dominant market conditions, such as over-supply or scarcity, and to\\u000a forecast market changes. We show that market conditions can be characterized by distinguishable statistical patterns that\\u000a can be learned from historic data and used, together with real-time observable information, to identify the current market\\u000a regime and to forecast market changes. We
Hydro-economic assessment of hydrological forecasting systems
NASA Astrophysics Data System (ADS)
Boucher, M.-A.; Tremblay, D.; Delorme, L.; Perreault, L.; Anctil, F.
2012-01-01
SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.
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.
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.
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.
Garment E-Commerce Forecast Based on Grey Model
Hongqi Hui; Yidan Zu
2009-01-01
Garment e-commerce sales forecast is important for the e-business development strategy planning and the integration of garment supply chain upstream and downstream enterprises. GDP, per capita consumption expenditure of urban residents, the total retail sales of consumer goods, the number of internet users are selected as economic forecast indexes. On the basis of grey incidence degree, close correlation indexes are
Flood forecasting for River Mekong with data-based models
NASA Astrophysics Data System (ADS)
Shahzad, Khurram M.; Plate, Erich J.
2014-09-01
In many regions of the world, the task of flood forecasting is made difficult because only a limited database is available for generating a suitable forecast model. This paper demonstrates that in such cases parsimonious data-based hydrological models for flood forecasting can be developed if the special conditions of climate and topography are used to advantage. As an example, the middle reach of River Mekong in South East Asia is considered, where a database of discharges from seven gaging stations on the river and 31 rainfall stations on the subcatchments between gaging stations is available for model calibration. Special conditions existing for River Mekong are identified and used in developing first a network connecting all discharge gages and then models for forecasting discharge increments between gaging stations. Our final forecast model (Model 3) is a linear combination of two structurally different basic models: a model (Model 1) using linear regressions for forecasting discharge increments, and a model (Model 2) using rainfall-runoff models. Although the model based on linear regressions works reasonably well for short times, better results are obtained with rainfall-runoff modeling. However, forecast accuracy of Model 2 is limited by the quality of rainfall forecasts. For best results, both models are combined by taking weighted averages to form Model 3. Model quality is assessed by means of both persistence index PI and standard deviation of forecast error.
Using GARCH-GRNN Model to Forecast Financial Time Series
Weimin Li; Jianwei Liu; Jiajin Le
2005-01-01
\\u000a Recent researches in forecasting with generalized regression neural network (GRNN) suggest that GRNN can be a promising alternative\\u000a to the linear and nonlinear time series models. It has shown great abilities in modeling and forecasting nonlinear time series.\\u000a Generalized autoregressive conditional heteroscedastic (GARCH) model is a popular time series model in forecasting volatility\\u000a of financial returns. In this paper, a
J Syst Sci Complex (2012) 25: 641674 MODELING AND FORECASTING OF STOCK
Benmei, Chen
2012-01-01
forecasting, system economics. 1 Introduction A financial market is a complex system involving various] . Traditional models in time series analysis are expanded to investigate how the stock market correlates model in fore- casting marketing time series with explanatory variables[3] by expanding the famous ARMA
Electricity generation modeling and photovoltaic forecasts in China
NASA Astrophysics Data System (ADS)
Li, Shengnan
With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979--2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.
A Fuzzy Forecasting Model for Women's Casual Sales
Celia Frank; Les Sztandera; Balaji Vemulapali; Asish Garg; Amar Raheja
2004-01-01
In this research, forecasting models were built based on both univariate and multivariate analysis. Models built on multivariate fuzzy logic analysis were better in comparison to those built on other models. The performance of the models was tested by comparing one of the goodness-of-fit statistics, R2, and also by comparing actual sales with the forecasted sales of different types of
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.
Draft Report A Forecast Model of Long-Term PCB
Draft Report A Forecast Model of Long-Term PCB Fate in San Francisco Bay John J. Oram and Jay A................................................................................................ 8 Estimation of Future PCB Loads
Representing Hurricanes with a Nested Global Forecast Model
M. J. Otte; R. L. Walko; R. Avissar
2007-01-01
A global forecast model is essential for predicting hurricane tracks beyond a period of ~2 days since global processes that may influence the longer-term storm tracks can be represented explicitly and there are no errors from the lateral boundary conditions that can propagate into the model domain and diminish the accuracy of the track forecasts. However, global models usually do
ORNL rural electric-energy-demand forecasting model
R. J. Maddigan; W. S. Chern; C. A. Gallagher; B. D. Holcomb; J. C. Cobbs
1981-01-01
The development of a forecasting model of annual electrical-energy sales for the Rural Electrification Administration (REA) borrowers is discussed. The Oak Ridge National Laboratory, Rural Electric Energy Demand (ORNL-REED) model highlights the unique features of rural electricity demand by empirically examining the customers of the electric cooperatives. The model is used to forecast annual electricity sales by state and sector
Forecast model bias correction in ocean data assimilation
Carton, James
Forecast model bias correction in ocean data assimilation Gennady A. Chepurin, James A. Carton/Goddard Space Flight Center, Greenbelt, MD #12;Abstract Numerical models of ocean circulation are subject their importance, most assimilation algorithms incorrectly assume that the forecast model is unbiased
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)
J.-J. Morcrette; O. Boucher; L. Jones; D. Salmond; P. Bechtold; A. Benedetti; A. Bonet; J. W. Kaiser; M. Razinger; M. Schulz; S. Serrar; A. J. Simmons; M. Sofiev; M. Suttie; A. M. Tompkins; A. Untch
2009-01-01
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
Real-time landslide forecasting with the incorporation of landslide modeling and typhoon forecast
NASA Astrophysics Data System (ADS)
Chiang, Shou-hao; Chang, Kang-Tsung; Chen, Yi-Chin; Chen, Chi-Farn
2014-05-01
Heavy rainfall brought by typhoons has been recognized as a major trigger of landslides in Taiwan. On average, three to four typhoons strike the island every year, and cause large amounts of landslides and damages in mountainous areas. Because landslide occurrence strongly corresponds to the storm dynamics, a reliable typhoon forecast is therefore essential to landslide hazard management in Taiwan. The study proposes a real-time forecasting system which integrates a landslide model and a precipitation forecast data to assess landslide hazard affected by typhoon. The system uses an event-based landslide model, ILIR-W (Integrated Landslide Initiation prediction and landslide Runout simulation at Watershed level) for landslide hazard prediction, and uses precipitation forecast data with 18 ensemble members from the Taiwan Cooperative Precipitation Ensemble Forecast Experiment (TAPEX). The study applied the system to provide landslide hazard forecast of 6 h, 12 h, 24 h, 48 h and 72 h before the arrival of three past typhoons. The system performs reasonably well in the prediction of landslide area and timing. The landslide forecasting system is useful for landslide hazard reduction.
Forecasting regional energy demand with linked macro\\/micro models. [Monograph
S. Caldwell; W. Greene; T. Mount; S. Saltzman; R. Broyd
1978-01-01
Regional demand for energy is usually closely related to the socio-economic characteristics of the spatial area under study and the prices of alternative energy sources relative to each other and to the prices of other goods and services. This paper describes a system of integrated and linked models designed to forecast the long-run energy demand in a region (state) within
A hybrid multi-model approach to river level forecasting
LINDA SEE; STAN OPENSHAW
2001-01-01
This paper presents four different approaches for integrating conventional and AI-based forecasting models to provide a hybridized solution to the continuous river level and flood prediction problem. Individual forecasting models were developed on a stand alone basis using historical time series data from the River Ouse in northern England. These include a hybrid neural network, a simple rule-based fuzzy logic
Time Series Forecasting Using Hybrid Neuro-Fuzzy Regression Model
Arindam Chaudhuri; Kajal De
2009-01-01
During the past few decades various time-series forecasting methods have been developed for financial market forecasting leading\\u000a to improved decisions and investments. But accuracy remains a matter of concern in these forecasts. The quest is thus on improving\\u000a the effectiveness of time-series models. Artificial neural networks (ANN) are flexible computing paradigms and universal approximations\\u000a that have been applied to a
From Social Data Mining to Forecasting SocioEconomic Crisis
Dirk Helbing; Stefano Balietti
2010-01-01
Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It
CSUF Economic Outlook and Forecasts Midyear Update, April 2011
de Lijser, Peter
, and exports rose boosted by strong global growth. All of these factors point to a more mature, broader-based market, (2) drastic budget cuts from state and local governments, (3) concerns about the fiscal deficit risks for the global economy Real GDP and economic activity. After growing at a stall speed of 1.7% in Q
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.
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 Éireann 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.
Operational forecasting based on a modified Weather Research and Forecasting model
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.
Forecasting wave height probabilities with numerical weather prediction models
Stevenson, Paul
to reflect uncertainty in current global weather conditions. In this paper a method for post of 51 global 10-day forecasts, which are produced daily using a version of the ECMWF numerical weatherForecasting wave height probabilities with numerical weather prediction models Mark S. Roulstona
On Modeling and Forecasting Time Series of Smooth Curves
Shen, Haipeng
and dynamically updating the forecasts. The re- search problem is motivated by efficient operations management. For effective management of such centers, managers rely on accurate forecasts of the underlying arrival rates curves. To achieve dimension reduction, we introduce a low-dimensional factor model that constraints
Technology diffusion: forecasting with bibliometric analysis and Bass model
Tugrul Daim; Pattharaporn Suntharasaj
2009-01-01
Purpose – The purpose of this paper is to use bibliometric analysis to forecast RFID technology and uses the adoption of barcode scanner to model the RFID scanner adoption in the US retail market. Design\\/methodology\\/approach – Forecasting emerging technologies and identifying the rate of diffusion of products based on these technologies is difficult because of lack of data. This paper
A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE
Boyer, Edmond
A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE Wensheng Zhang1,* , Hongfu Chen1 and excessive fluctuation of agricultural and livestock products price is not only harmful to residents' living social stability. Therefore it is important to forecast the price of agriculture and livestock products
A channel dynamics model for real-time flood forecasting
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
Forecasting with a Repeat Purchase Diffusion Model
Ambar G. Rao; Masataka Yamada
1988-01-01
A methodology for forecasting the sales of an ethical drug as a function of marketing effort before any sales data are available and for updating the forecast with a few periods of sales data is presented. Physicians' perceptions of the drug on a number of attributes, e.g. effectiveness, range of ailments for which appropriate, frequency of prescriptions, are used to
[Application of SIR model in forecasting and analyzing for SARS].
Chen, Qizhi
2003-05-31
An SIR epidemic model is applied on the research of SARS. Parameters in the model are estimated and forecasted by two methods. Time-varying estimated parameters are also compared for Beijing and Hong Kong SARS epidemic situations. PMID:12914225
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Carlos Gomes Da Silva
2008-01-01
Forecasting the behavior of variables (e.g., economic, financial, physical) is of strategic value for organizations, which helps to sustain practical interest in the development of alternative models and resolution procedures. This paper presents a non-linear model that combines radial basis functions and the ARMA(p,q) structure. The optimal set of parameters for such a model is difficult to find. In this
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 2011—each 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.
Monthly mean forecast experiments with the GISS model
NASA Technical Reports Server (NTRS)
Spar, J.; Atlas, R. M.; Kuo, E.
1976-01-01
The GISS general circulation model was used to compute global monthly mean forecasts for January 1973, 1974, and 1975 from initial conditions on the first day of each month and constant sea surface temperatures. Forecasts were evaluated in terms of global and hemispheric energetics, zonally averaged meridional and vertical profiles, forecast error statistics, and monthly mean synoptic fields. Although it generated a realistic mean meridional structure, the model did not adequately reproduce the observed interannual variations in the large scale monthly mean energetics and zonally averaged circulation. The monthly mean sea level pressure field was not predicted satisfactorily, but annual changes in the Icelandic low were simulated. The impact of temporal sea surface temperature variations on the forecasts was investigated by comparing two parallel forecasts for January 1974, one using climatological ocean temperatures and the other observed daily ocean temperatures. The use of daily updated sea surface temperatures produced no discernible beneficial effect.
Brajendra C. Sutradhar
2008-01-01
Forecasting for a time series of low counts, such as forecasting the number of patents to be awarded to an industry, is an important research topic in socio-economic sectors. Recently (2004), Freeland and McCabe introduced a Gaussian type stationary correlation model-based forecasting which appears to work well for the stationary time series of low counts. In practice, however, it may
Sea Fog Forecasting with Lagrangian Models
NASA Astrophysics Data System (ADS)
Lewis, J. M.
2014-12-01
In 1913, G. I. Taylor introduced us to a Lagrangian view of sea fog formation. He conducted his study off the coast of Newfoundland in the aftermath of the Titanic disaster. We briefly review Taylor's classic work and then apply these same principles to a case of sea fog formation and dissipation off the coast of California. The resources used in this study consist of: 1) land-based surface and upper-air observations, 2) NDBC (National Data Buoy Center) observations from moored buoys equipped to measure dew point temperature as well as the standard surface observations at sea (wind, sea surface temperature, pressure, and air temperature), 3) satellite observations of cloud, and 4) a one-dimensional (vertically directed) boundary layer model that tracks with the surface air motion and makes use of sophisticated turbulence-radiation parameterizations. Results of the investigation indicate that delicate interplay and interaction between the radiation and turbulence processes makes accurate forecasts of sea fog onset unlikely in the near future. This pessimistic attitude stems from inadequacy of the existing network of observations and uncertainties in modeling dynamical processes within the boundary layer.
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
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.
Evaluating Rapid Models for High-Throughput Exposure Forecasting (SOT)
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 ...
Radiation fog forecasting using a 1-dimensional model
Peyraud, Lionel
2001-01-01
with regards to the onset and dissipation of the phenomenon. Fortunately, now with computers becoming ever-increasingly powerful, numerical models have been utilized to attempt to more accurately deal with the fog forecasting problem. This study uses a 1...
A model for short term electric load forecasting
Tigue, John Robert
1975-01-01
A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE, III Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE May 1975 Major... Subject: Electrical Engineering A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE& III Approved as to style and content by: (Chairman of Committee) (Head Depart t) (Member) ;(Me r (Member) (Member) May 1975 ABSTRACT...
Forecasting European Droughts using the North American Multi-Model Ensemble (NMME)
NASA Astrophysics Data System (ADS)
Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, 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 (10°W to 40°E and 35°N to 55°N). 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.
Spatio-temporal modeling for real-time ozone forecasting
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
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.
Metropolitan and state economic regions (MASTER) model - overview
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.
NASA Astrophysics Data System (ADS)
Kucera, P. A.; Brown, B.; Williams, C.; Nance, L. B.
2014-12-01
The Tropical Cyclone Modeling Team (TCMT) in NCAR's Joint Numerical Testbed (JNT) Program focuses on the verification of experimental forecasts of tropical cyclones (TCs). Activities of the team include the development of new verification methods and tools for TC forecasts and the design and implementation of diagnostic verification experiments to evaluate the performance of tropical cyclone forecast models. For the NOAA Hurricane Forecast Improvement Project (HFIP), the TCMT has designed and conducted verification studies involving various deterministic, ensemble, and statistical regional and global forecast models that participate in the annual HFIP real-time forecast Demonstration experiment. The HFIP Demonstration experiment is conducted during the months of August through October each year. The TCMT has applied new and established statistical approaches to provide statistically meaningful diagnostic evaluations of TC forecasts for storms observed during the Demonstration period. For this study, the TCMT has conducted evaluation of operational and experimental forecast performance for the 2012-2014 hurricane seasons in the North Atlantic and Eastern Pacific Oceans. This presentation will provide an overview of the Demonstration experiment along with a summary of results from the experimental model forecasts for the Atlantic and Eastern Pacific Ocean basins with the goal of documenting potential improvements to hurricane forecasts in comparison to operational baselines.
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.
NASA Astrophysics Data System (ADS)
De Felice, M.; Alessandri, A.; Ruti, P. M.
2012-04-01
Energy demand forecasting is a critical task and it allows to anticipate any problems that might affect power systems operators, especially during periods with high demand peaks. The difficulties of this task are due to the complexity of the systems involved: energy usage patterns are particularly variable and influenced by many factors, such as weather conditions, social, economic and political aspects (i.e. national regulations, international relations). The strong influence of weather on electricity demand in Italy is due to the wide use of residential air-conditioning devices and, more in general, refrigeration and ventilation equipments. For this reasons, accurate climate information may help in obtaining precise energy demand forecasts, usually performed with statistical methods which show their effectiveness particularly where large amount of data is available. We present a study with the aim of assess the effects of the quality of weather data on statistical modelling performance on energy demand forecasting, using data provided by national transmission grid operator.
A Fuzzy Logic Fog Forecasting Model for Perth Airport
NASA Astrophysics Data System (ADS)
Miao, Y.; Potts, R.; Huang, X.; Elliott, G.; Rivett, R.
2012-05-01
Perth Airport is a major airport along the southwest coast of Australia. Even though, on average, fog only occurs about twelve times a year, the lack of suitable alternate aerodromes nearby for diversion makes fog forecasts for Perth Airport very important to long-haul international flights. Fog is most likely to form in the cool season between April and October. This study developed an objective fuzzy logic fog forecasting model for Perth Airport for the cool season. The fuzzy logic fog model was based on outputs from a high-resolution operational NWP model called LAPS125 that ran twice daily at 00 and 12 UTC, but fuzzy logic was employed to deal with the inaccuracy of NWP prediction and uncertainties associated with relationships between fog predictors and fog occurrence. The outcome of the fuzzy logic fog model is in one of the four categories from low to high fog risk as FM0, FM5, FM15 or FM30, intended to map to approximate fog probability of 0, 5, 15 and 30%, respectively. The model was found useful in its 5 year performance in the cool seasons between 2004 and 2008 and required little recalibration if mist was treated as if it were also a fog event in the skill evaluation. To generate an operational fog forecast for Perth Airport, the outcome of the fuzzy logic fog model was averaged with the outcomes of two other fog forecasting methods using a simple consensus approach. Fog forecast so generated is known as the operational consensus forecast. Skill assessment using frequency distribution diagram, Hansen and Kuiper skill score, and Relative Operating Characteristic curve showed that the operational consensus forecast outperformed all three individual methods. Out of the three methods, the fuzzy logic fog model ranked second. It performed better than the other objective method called GASM but worse than the subjective method which relied on forecaster's subjective assessment. The skills of the fuzzy logic fog model can be further improved with the tuning of fuzzy functions. In addition, similar models can be customised for other airports. The study also suggested the use of the simple consensus approach to enhance forecasting skills for other stations or weather phenomena if there were two or more independent forecasting methods available.
From oceanographic to acoustic forecasting: acoustic model calibration
Jesus, Sérgio M.
From oceanographic to acoustic forecasting: acoustic model calibration using in situ acoustic on acoustic propagation models and envi- ronmental representations of the oceanic area in which the sonar of a propagation model. Though well developed nowadays, acoustic propagation modeling is limited in practice
CONSTRAINED FORECASTS IN ARMA MODELS: A BAYESIAN APPROACH
West, Mike
CONSTRAINED FORECASTS IN ARMA MODELS: A BAYESIAN APPROACH Enrique de Alba I.T.A.M. Rio Hondo No. 1 casts in autoregressivemoving average time series models. Both are calculated by formulating the ARMA applied to ARMA time series models is rather limited. It refers mostly for AR models. Early references
NSDL National Science Digital Library
This site is a joint effort of NOAA Research and the College of Education at the University of South Alabama. The goal of the site is to provide middle school science students and teachers with research and investigation experiences using on-line resources. In this unit students look at the science of weather forecasting as a science by exploring cloud, temperatures, and air pressure data and information. Students apply this information to interpret and relate meteorological maps to each other. Parts of the unit include gathering information from other websites, applying the data gathered, and performing enrichment exercises. This site contains a downloadable teachers guide, student guide, and all activity sheets to make the unit complete.
Fuzzy logic-based forecasting model
Tapio Frantti; Petri Mähönen
2001-01-01
In this paper a fuzzy logic-based software tool, fuzzy logic advisory tool (FLAT), for demand forecasting of signal transmission products is presented. The FLAT was developed for the prediction of demand of about 1000 different products in order to aid materials purchasing process of about 14,000 different components in the electronics manufacturing processes of Nokia Network Systems's Haukipudas factory. The
Real-time Modeling Forecast of the 29 September 2009 Samoa Tsunami
Y. Wei; V. V. Titov; L. Tang; C. Chamberlin; B. U. Uslu; D. Arcas; D. W. Denbo; C. W. Moore; M. C. Eble
2009-01-01
The 29 September 2009 Samoa tsunami provided an unexpected exercise for the NOAA's tsunami forecast system, undergoing operational testing at U.S. Tsunami Warning Centers (TWCs). Both TWCs and staff of the Pacific Marine Environmental Lab exercise the forecast system to provide tsunami prediction for the Pacific U.S. coastal communities where forecast models have already been developed. The forecast model from
can the forecasts completely cover the evolution of earthquake-generated tsunami waves: generation-time forecasting L. Tang,1,2 V. V. Titov,2 and C. D. Chamberlin1,2 Received 28 April 2009; revised 12 August 2009 and applications of site-specific tsunami inundation models (forecast models) for use in NOAA's tsunami forecast
Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG
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
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.
A Statistical Forecast Model of Tropical Intraseasonal Convective Anomalies.
NASA Astrophysics Data System (ADS)
Jones, Charles; Carvalho, Leila M. V.; Higgins, R. Wayne; Waliser, Duane E.; Schemm, J.-K. E.
2004-06-01
Tropical intraseasonal convective anomalies (TICAs) play a significant role in the coupled ocean atmosphere system and the Madden Julian oscillation (MJO) is the primary mode of this variability. This study describes statistical forecast models of intraseasonal variations. Twenty-four years of outgoing longwave radiation (OLR) and zonal components of the wind at 200 (U200) and 850 hPa (U850) are used. The models use the principal components (PCs) of combined EOF analysis of 20 90-day anomalies of OLR, U200, and U850 data. Forecast models are developed for each lead time from 1 to 10 pentads and for winter and summer seasons separately. The forecast models use a combination of the five most recent pentad values of the first five PCs of the combined EOF of (OLR, U200, U850) to predict the future values of a given PCK (k = 1, 5). The spatial structures are obtained by reconstructing the fields of OLR, U200, and U850 using the forecasts of PCK (k = 1, 5) and the associated EOFs. Verification with independent winter and summer data indicates useful forecasts of the first five PCs extending up to five pentads of lead time. The verification against 20 90-day anomalies indicates useful forecasts of the reconstructed fields of OLR, U200, and U850 extending up to four pentads of lead time over most of the Tropics. Furthermore, the statistical models provide useful forecasts of U200 and U850 intraseasonal anomalies up to two to three pentads of lead times in portions of the North Pacific region.
Traffic Accident Macro Forecast Based on ARIMAX Model
Chunyan Li; Jun Chen
2009-01-01
To overcome the deficiency of traditional traffic accident estimation models, this paper introduced a new way. It gave two categories on traffic accident affected factors and selected the main ones, using stepwise regression model. ARIMAX model, a dynamic regression one, was used to forecast traffic accident volumes. The former job ensures the precision of estimation, while the latter one owns
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.
Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia
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
Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia.
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
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.
is demonstrated for forecasting of timeseries and compared to approximate methods. 1. INTRODUCTION The problem mapping in direct forecasting increases with the forecast horizon and for a fixed length timeseries of nonlinear forecasting is relevant to numerous ap plication domains e.g. in financial modelling and control
A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting
Sameer Singh
1999-01-01
In this paper, the concept of a long memory system for forecasting is developed. Pattern modelling and recognition systems are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, this system has been successfully used for forecasting the Santa Fe
Kloepfer, J.
1994-08-01
Volume I provides a description of forecasts of economic growth for California and its large metropolitan areas. The California economy will continue to lag the national economy until 1996. In the longer term, California will surpass the nation in growth near the end of the decade, as the State`s comparative advantages in industry structure, natural and human resources, and geography will enable it to prosper. Real output will grow faster than employment as continued competitive pressures from across the nation and around the world will drive productivity gains in California`s manufacturing industries. Inland areas in the state are expected to experience stronger growth than the coastal areas at the same competitive pressures.
River flood forecasting with a neural network model
Marina Campolo; Paolo Andreussi; Alfredo Soldati
1999-01-01
A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing
Application of nonlinear forecasting techniques for meteorological modeling
Boyer, Edmond
Application of nonlinear forecasting techniques for meteorological modeling V. PeÂ rez-MunÄ uzuri-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature meteorology; general) ± General (new ®elds) 1 Introduction The accuracy of Earth radiation budget estimates
Forecasting time series with missing data using Holt's model
José D. Bermúdez; Ana Corberán-Vallet; Enriqueta Vercher
2009-01-01
This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood
A Friction Model for Describing and Forecasting Price Changes
Wayne S. DeSarbo; Vithala R. Rao; Joel H. Steckel; Jerry Wind; Richard Colombo
1987-01-01
This paper presents a new friction model for describing the price changes of a product or brand over time and for forecasting both the timing and magnitude of such changes from one period to the next. After a review of the related pricing literature, we present our model and a modified controlled random search procedure for estimating its parameters. The
PROBABILISTIC FLOOD FORECASTING USING A DISTRIBUTED RAINFALL-RUNOFF MODEL
Fernandez, Thomas
PROBABILISTIC FLOOD FORECASTING USING A DISTRIBUTED RAINFALL-RUNOFF MODEL PAUL JAMES SMITH 2005 #12 nowcasting of rainfall patterns provided by Professor Eiichi Nakakita. Thanks is extended to Mr. Katsuyoshi., for their assistance regarding rainfall- runoff modeling, and to Yoshiyuki Zushi of the Foundation of River and Basin
A combination of traditional time series forecasting models with fuzzy learning neural networks
Chang-Yang Wen; Min Yao
2002-01-01
Discusses a combined model of three traditional time series forecasting (TSF) models, which involves a regression model, an exponential smoothing model and a gray forecasting model, using neural networks (NNs) to assemble them based on a fuzzy learning algorithm. Finally we represent an example of a TSF financial application in telecom enterprises to show its improvement in forecasting accuracy.
Peter A. Bogenschutz
2004-01-01
Under the auspices of a nationwide effort led by NOAA, known as the Coastal Storms Initiative (CSI), the new Weather Research and Forecast (WRF) mesoscale model has been installed at the Jacksonville, FL (JAX) National Weather Service (NWS) Weather Forecast Office (WFO). The purpose of the CSI project is to lessen the impacts of storms on coastal communities. This research
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.
Selecting single model in combination forecasting based on cointegration test and encompassing test.
Jiang, Chuanjin; Zhang, Jing; Song, Fugen
2014-01-01
Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability. PMID:24892061
Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
Jiang, Chuanjin; Zhang, Jing; Song, Fugen
2014-01-01
Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability. PMID:24892061
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.
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.
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.
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.
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.
CCPP-ARM Parameterization Testbed Model Forecast Data
Klein, Stephen
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).
A. Borges; J. W. Boyd; R. T. Crow; L. J. Williams
1979-01-01
The report documents the major assumptions concerning economic growth and energy prices used for DEMAND 77 forecasts of national energy consumption to the year 2000. DEMAND 77 used six energy-consumption scenarios based on energy prices, technological change, and public policy. The three basic scenarios were titled baseline, high electricity consumption, and energy conservation. Each of these was explored with and
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.
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.
Forecasting for the freeway traffic accidents base on Markov model
Yan Lin; Yan Chen; Guoshun Lin; Jun Zhai
2011-01-01
Frequent fatal accidents occur on Chinese freeways, and the damage increases in recent years. A challenge to freeway administration is how to accurately forecast the damage caused by accidents in coming period. Factors that will cause fatal accidents are complex, and some of them can not be made clear. In this paper, we use the Markov Chain model, by which
Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging
Washington at Seattle, University of
Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging J. Mc source of energy, in addition to a wide range of other uses, from avia- tion to recreational boating fore- casts of maximum wind speed over the North American Pacific Northwest in 2003 using
Computational Intelligence in Financial Forecasting and Agent-Based Modeling
Fernandez, Thomas
Computational Intelligence in Financial Forecasting and Agent-Based Modeling: Applications: A Proposed Test", International Review of Financial Analysis, special issue on Complexity and Non thank you to my family for their support, emotional and financial. Lastly, a special thank you to my
ARM Processes and Their Modeling and Forecasting Methodology Benjamin Melamed
Chapter 73 ARM Processes and Their Modeling and Forecasting Methodology Benjamin Melamed Abstract The class of ARM (Autoregressive Modular) processes is a class of stochastic processes, defined by a non- linear autoregressive scheme with modulo-1 reduction and additional transformations. ARM processes
Spectral Dissipation Term for Wave Forecast Models, Experimental Study
Alexander Babanin; Ian Young; Richard Manasseh; Eric Schultz
A field experimental study of wave energy dissipation is presented. The experiment was conducted at Lake George, Australia and allowed simultaneous measurements of the source functions in a broad range of conditions, including extreme wind-wave circumstances. Results revealed new physical mechanisms in the processes of spectral dissipation of wave energy, which are presently not accounted for in wave forecast models.
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.
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…
William Scott Lincoln
2009-01-01
National Weather Service (NWS) forecasters currently have access to a limited set of models that may not be suitable for all Iowa basins or forecasting situations, such as small, fast responding streams. Flexible modeling systems that allow model configurations to change according to the watershed characteristics may provide useful predictive information to supplement existing forecast products. The United States Army
METRo: A New Model for Road-Condition Forecasting in Canada
Louis-Philippe Crevier; Yves Delage
2001-01-01
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
Alternative methods for forecasting GDP Dominique Gugan
Paris-Sud XI, Université de
confidence intervals for point forecast in time series. Keywords: Forecast - Economic indicators - GDP - Euro Modeling of Economic and Financial Time-Series, R. Barnett, F. Jawady (Ed.) (2010) Chapiter 5 (29 p.)" #12 normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing
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.
Distributed Hydrologic Models for Flow Forecasts - Part 1
NSDL National Science Digital Library
2014-09-14
Distributed Hydrologic Models for Flow Forecasts – Part 1 provides a basic description of distributed hydrologic models and how they work. This module is the first in a two-part series focused on the science of distributed models and their applicability in different situations. Presented by Dr. Dennis Johnson, the module begins with a review of hydrologic models, and then examines the differences between lumped and distributed models. It explains how lumped models may be distributed by subdividing the basin and suggests when distributed hydrologic models are most appropriate. Other topics covered include the advantages of physically-based versus conceptual approaches and some strengths and challenges associated with distributed modeling.
Volcanic ash forecast transport and dispersion (VAFTAD) model
Heffter, J.L.; Stunder, B.J.B. [NOAA Air Resources Laboratory, Silver Spring, MD (United States)] [NOAA Air Resources Laboratory, Silver Spring, MD (United States)
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.
Review of Wind Energy Forecasting Methods for Modeling Ramping Events
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.
Towards Operational Modeling and Forecasting of the Iberian Shelves Ecosystem
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
Forecasting automobile demand using disaggregate choice models
James Berkovec
1985-01-01
This paper presents a simulation model of the American automobile market. The simulation model combines a disaggregate model of household automobile number and type choice with an econometric model of used vehicle scrappage and simple models of new car supply. For fixed vehicle designs, consumer and producer interactions determine new car sales, used car scrappage and consumer vehicle holdings. The
EC-EARTH: an Earth System Model based on the ECWMF Integrated Forecasting System
F. Selten; R. Bintanja; S. Yang; C. Severijns; T. Semmler; K. Wyser; X. Wang; W. Hazeleger
2009-01-01
EC-EARTH is the name of an Earth system model that is being developed by a number of institutes in Europe. It is based on the Integrated Forecast System of the European Centre for Medium Range Weather Forecasts (ECWMF). The ECMWF model delivers the best weather forecasts in the world by an objective measure. However, when applied to climate time scales,
Assessment of point process models for earthquake forecasting Andrew Bray1
Schoenberg, Frederic Paik (Rick)
Assessment of point process models for earthquake forecasting Andrew Bray1 and Frederic Paik Models for forecasting earthquakes are currently tested prospectively in well- organized testing centers some of these tests and residual methods for determining the goodness-of-fit of earthquake forecasting
Financial time series forecasting with a bio-inspired fuzzy model Jos Luis Aznarte a,
Granada, Universidad de
Financial time series forecasting with a bio-inspired fuzzy model José Luis Aznarte a, , Jesús n f o Keywords: Time series forecasting Fuzzy rule-based systems Regime switching models Financial time series a b s t r a c t In general, times series forecasting is considered as a highly complex
Sorin Vlad; Paul Pascu; Nicolae Morariu
2010-01-20
The paper discusses the main ideas of the chaos theory and presents mainly the importance of the nonlinearities in the mathematical models. Chaos and order are apparently two opposite terms. The fact that in chaos can be found a certain precise symmetry (Feigenbaum numbers) is even more surprising. As an illustration of the ubiquity of chaos, three models among many other existing models that have chaotic features are presented here: the nonlinear feedback profit model, one model for the simulation of the exchange rate and one application of the chaos theory in the capital markets.
ESP forecasts for water resources: Model Uncertainty, Climate Uncertainty or Both?
NASA Astrophysics Data System (ADS)
Park, G.; Imam, B.; Ferrer-Capdevila, M.; Sorooshian, S.
2007-12-01
Operational water resources management relies on the streamflow forecasts of reservoir inflow. In the Western U.S, where seasonal snowmelt represents the larger portion of water supplies, these forecasts are traditionally obtained through statistical regression-based estimates of April-July and water year runoff, which are then disaggregated to monthly volumes using historical relationships and forecaster judgment. An alternative approach is to use hydrologic forecasting systems, such as the West-Wide Seasonal Hydrologic Forecast System, developed by the University of Washington, to provide probabilistic forecasts in the form of ensemble streamflow predictions (ESP). Whether or not ESP forecasts are conditioned by seasonal climate forecasts, the approach places the natural variability of hydrometeorologic forcing (e.g. precipitation, temperature, snow extent¡¦) as the primary source of forecast uncertainty. This presentation will attempt to evaluate the effect of model uncertainty on the uncertainty statements issued by probabilistic forecasts generated from the California Hydrologic Forecast System (CaliForecast). CaliForecast, which is a regional implementation by the University of California, Irvine, of the west-wide forecasting system, will be used to issue ESP forecasts that account for uncertainty in model parameters. Within the system, parameter uncertainties will be assessed using the Bayesian-based Particle Filtering technique in order to obtain posteriori distributions of key model parameters for the Variable Infiltration Capacity Model (VIC-3L). The posterior distributions of model parameters, in conjunction with traditional ESP will allow the propagation of parameter uncertainty into the probabilistic streamflow forecasts. Comparison between probabilistic forecasts issued with and without parameter uncertainties will be conducted to assess the impact of parameter uncertainty for a sub-basin within the Feather River in Northern California.
Models for forecasting energy use in the US farm sector
NASA Astrophysics Data System (ADS)
Christensen, L. R.
1981-07-01
Econometric models were developed and estimated for the purpose of forecasting electricity and petroleum demand in US agriculture. A structural approach is pursued which takes account of the fact that the quantity demanded of any one input is a decision made in conjunction with other input decisions. Three different functional forms of varying degrees of complexity are specified for the structural cost function, which describes the cost of production as a function of the level of output and factor prices. Demand for materials (all purchased inputs) is derived from these models. A separate model which break this demand up into demand for the four components of materials is used to produce forecasts of electricity and petroleum is a stepwise manner.
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 Ensemble’s (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). 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 30°S to 30°N and 30°E to 270°E) 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.
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.
An Analytic Network Process model for financial-crisis forecasting
Michael P. Niemira; Thomas L. Saaty
2004-01-01
We discuss and develop an imbalance-crisis turning point model to forecast the likelihood of a financial crisis based on an Analytic Network Process framework. The Analytic Network Process (ANP) is a general theory of relative measurement used to derive composite-priority-ratio scales from individual-ratio scales that represent relative influence of factors that interact with respect to control criteria. Through its supermatrix,
[Forecasting model of transfer of 137Cs to the plants].
Spirin, E V; Anisimov, V S; Dikarev, D B; Kochetkov, I V; Krylenkin, D V
2013-01-01
The forecasting model of the concentration ratio (CR) of 137Cs in the plants taking into consideration organic carbon, pH, mobile and total content of potassium in soil has been developed on the basis of the radioecological investigations in the valleys of the Resseta and Vytebet rivers. The type of functional dependence of CR from soil characteristics can be used for an estimation of the content of radionuclides in various species and productive parts of plants. PMID:23786034
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.
Ohio Economics: K-12 Model Course of Study in Economics.
ERIC Educational Resources Information Center
Ohio Council on Economic Education.
The K-12 Model Course of Study in Economics (MCSE) provides Ohio school district personnel with assistance in the development and implementation of economics courses of study for kindergarten through twelfth grade. The guide also offers information to help readers integrate economics into their curricula across disciplines. In addition to an…
Models for forecasting the flowering of Cornicabra olive groves
NASA Astrophysics Data System (ADS)
Rojo, Jesús; Pérez-Badia, Rosa
2015-02-01
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.
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 ...
Intraseasonal Forecasting of the Asian Summer Monsoon in Four Operational and Research Models*
Fu, Joshua Xiouhua
Intraseasonal Forecasting of the Asian Summer Monsoon in Four Operational and Research Models FREDERIC VITART European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom (Manuscript for intraseasonal forecasting of the Asian summer monsoon. The present study provides a preliminary, yet up
Kevin Judd
2008-01-01
This paper is part of a larger study investigating the meaning of, and appropriate procedures for, forecasting with imperfect models. (In the author’s opinion there is currently no satisfactory general theory and practice for doing so with complex nonlinear systems.) The focus of this paper is on initialisation of the forecast. At the heart of every forecasting scheme there is
A new hybrid artificial neural networks and fuzzy regression model for time series forecasting
Mehdi Khashei; Seyed Reza Hejazi; Mehdi Bijari
2008-01-01
Quantitative methods have nowadays become very important tools for forecasting purposes in financial markets as for improved decisions and investments. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method; hence, never has research directed at improving upon the effectiveness of time series models stopped. Artificial neural networks (ANNs) are flexible computing frameworks and universal
Pareto Multi-Objective Non-Linear Regression Modelling to Aid CAPM Analogous Forecasting.
Coello, Carlos A. Coello
, in the area of financial time series forecasting, correctly predicting the directional movement of a time form of most economet- ric time series forecasting methods (e.g. Auto-Regressive (AR) models, the underlying approach to time series forecasting itself has remained relatively un- changed during its
A first large-scale flood inundation forecasting model
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.
Modeling and computing of stock index forecasting based on neural network and Markov chain.
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
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…
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.
FORECAST MODEL FOR MODERATE EARTHQUAKES NEAR PARKFIELD, CALIFORNIA.
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.
USING BOX-JENKINS MODELS TO FORECAST FISHERY DYNAMICS: IDENTIFICATION, ESTIMATION, AND CHECKING
USING BOX-JENKINS MODELS TO FORECAST FISHERY DYNAMICS: IDENTIFICATION, ESTIMATION, AND CHECKING Roy MENDELSSOHN! ABSTRACT Box·Jenkins models are suggested as appropriate models for forecasting fishery dynamics. Unlike standard production models, these models are empirical, dynamic, stochastic models. Box
A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty
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.
Time Dependent Directional Profit Model for Financial Time Series Forecasting
Jingtao Yao; Chew Lim Tan
2000-01-01
Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for
Norman R. Swanson; Halbert White
1997-01-01
We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolling window prediction methods
A Feature Fusion Based Forecasting Model for Financial Time Series
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
Forecasting volatility with neural regression: a contribution to model adequacy.
Refenes, A N; Holt, W T
2001-01-01
Neural nets' usefulness for forecasting is limited by problems of overfitting and the lack of rigorous procedures for model identification, selection and adequacy testing. This paper describes a methodology for neural model misspecification testing. We introduce a generalization of the Durbin-Watson statistic for neural regression and discuss the general issues of misspecification testing using residual analysis. We derive a generalized influence matrix for neural estimators which enables us to evaluate the distribution of the statistic. We deploy Monte Carlo simulation to compare the power of the test for neural and linear regressors. While residual testing is not a sufficient condition for model adequacy, it is nevertheless a necessary condition to demonstrate that the model is a good approximation to the data generating process, particularly as neural-network estimation procedures are susceptible to partial convergence. The work is also an important step toward developing rigorous procedures for neural model identification, selection and adequacy testing which have started to appear in the literature. We demonstrate its applicability in the nontrivial problem of forecasting implied volatility innovations using high-frequency stock index options. Each step of the model building process is validated using statistical tests to verify variable significance and model adequacy with the results confirming the presence of nonlinear relationships in implied volatility innovations. PMID:18249917
A feature fusion based forecasting model for financial time series.
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
Identification and Forecasting in Mortality Models
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
Emmanuel Marin
2003-01-01
This article is based on the main results of a pre-doctoral dissertation (DEA) on transport done at the ENPC. The research is about an economic evaluation of an automated highway network in the Paris urban agglomeration in the Ile de France Region.To this end, an automatic highway network incorporated into the road network in Ile de France has been modelled
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.
Christian Lutz; Bernd Meyer; Marc Ingo Wolter
2010-01-01
The paper presents the multisector\\/multicountry energy?economic?environment model GINFORS, which has already been used as the simulation engine in the project MOSUS of the 5th EU framework programme. The detailed description of the model features the application ability for energy forecasts and simulations. An overview of the system is given, and the structures of the multisector international trade model are depicted
M. R. Taghizadeh; H. G. Shakouri; M. B. Menhaj; M. R. Mehregan; A. Kazemi
2009-01-01
Linear regression has been used for many years for forecasting in marketing, management, sales and energy. In this paper, a fuzzy-based approach is applied for the transport energy demand forecasting using socio-economic and transport related indicators. This forecasting is analyzed based on gross domestic product (GDP), population and the number of vehicles together with historical energy data from 1993 to
NASA Astrophysics Data System (ADS)
Cole, S. J.; Robson, A. J.; Bell, V. A.; Moore, R. J.
2009-04-01
The hydrological forecasting component of the Natural Environment Research Council's FREE (Flood Risk from Extreme Events) project "Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting" addresses the initialisation, data assimilation and uncertainty of hydrological flood models utilising advances in rainfall estimation and forecasting. Progress will be reported on the development and assessment of simple model-initialisation and state-correction methods for a distributed grid-based hydrological model, the G2G Model. The potential of the G2G Model for area-wide flood forecasting is demonstrated through a nationwide application across England and Wales. Probabilistic flood forecasting in spatial form is illustrated through the use of high-resolution NWP rainfalls, and pseudo-ensemble forms of these, as input to the G2G Model. The G2G Model is configured over a large area of South West England and the Boscastle storm of 16 August 2004 is used as a convective case study. Visualisation of probabilistic flood forecasts is achieved through risk maps of flood threshold exceedence that indicate the space-time evolution of flood risk during the event.
James V. Hansen; Ray D. Nelson
1997-01-01
Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments.
Lang, K.
1982-01-01
.), are displayed using a list of the judgment criteria. This method was used to forecast the use of electricity conservation technologies in industries located in the Pacific Northwest for the Bonneville Power Administration....
MJO empirical modeling and improved prediction by "Past Noise Forecasting"
NASA Astrophysics Data System (ADS)
Kondrashov, D. A.; Chekroun, M.; Robertson, A. W.; Ghil, M.
2011-12-01
The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropics and plays an important role in global climate. Here we presents modeling and prediction study of MJO by using Empirical Model Reduction (EMR). EMR is a methodology for constructing stochastic models based on the observed evolution of selected climate fields; these models represent unresolved processes as multivariate, spatially correlated stochastic forcing. In EMR, multiple polynomial regression is used to estimate the nonlinear, deterministic propagator of the dynamics, as well as multi-level additive stochastic forcing -"noise", directly from the observational dataset. The EMR approach has been successfully applied on the seasonal-to-interannual time scale for real-time ENSO prediction (Kondrashov et al. 2005), as well as atmospheric midlatitude intraseasonal variability (Kondrashov et al. 2006,2010). In this study nonlinear (quadratic) with annual cycle, three-level EMR model was developed to model and predict leading pair of real-time multivariate Madden-Julian oscillation (RMM1,2) daily indices (June 1974- January 2009, http://cawcr.gov.au/staff/mwheeler/maproom/RMM/). The EMR model captures essential MJO statistical features, such as seasonal dependence, RMM1,2 autocorrelations and spectra. By using the "Past Noise Forecasting" (PNF) approach developed and successfully applied to improve long-term ENSO prediction in Chekroun et al. (2011), we are able to notably improve the cross-validated prediction skill of RMM indices- especially at lead times of 15-to-30 days. The EMR/PNF method has two steps: (i) select noise samples - or "snippets" - from the past noise, which have forced the EMR model to yield the MJO phase resembling the one at the the currently observed state; and (ii) use these "noise" snippets to create ensemble forecast of EMR model. The MJO phase identification is based on Singular Spectrum Analysis reconstruction of 30-60 day MJO cycle.
Short-Range Ensemble Forecasting of AN Explosive Cyclogenesis with a Limited-Area Model
NASA Astrophysics Data System (ADS)
Du, Jun
Since the atmosphere is a chaotic system, small errors in the initial condition of any numerical weather prediction (NWP) model amplify as the forecast evolves. To estimate and possibly reduce the uncertainty of NWP associated with initial-condition uncertainty (ICU), ensemble forecasting has been proposed which is a method of, differently from the traditional deterministic forecasting, running several model forecasts starting from slightly different; initial states. In this dissertation, the impact of ICU and short -range ensemble forecasting (SREF) on quantitative precipitation forecasts (QPFs), as well as on sea-level cyclone position and central pressure, is examined for a case of explosive cyclogenesis that occurred over the contiguous United States. A limited-area model (the PSU/NCAR NM4) is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for the MM4 model are provided by ensemble forecasts from a global spectral model (the NCAR CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition which closely match estimates of global analysis error, but they were not dynamically-conditioned. Results for 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different initial conditions. Acute sensitivity to ICU marks ensemble QPF and the forecasts of cyclone position and central pressure. Ensemble averaging always reduces the rms error for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8-10. However, ensemble averaging can adversely affect the forecasts related to precipitation areal coverage because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of ~10 can account for 90% of improvement in probability density function. Our results indicate that SREF techniques can now provide useful QPF guidance and increase the accuracy of precipitation, cyclone position, and cyclone's central pressure forecasts. With current analysis/forecast systems, the benefit from simple ensemble averaging is comparable to or exceed that obtainable from improvement in the analysis/forecast system.
Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models
West, Mike
missing data are present in some of the series at some time points, and in making forecasts for future of conditionally Gaussian dynamic models for nonnormal, multivariate time series. In such models, dataBayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models
Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts
Torben G. Andersen; Tim Bollerslev
1998-01-01
A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models. While most of these studies have documented highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence, traditional ex post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts. Contrary to this contention, the authors show that
Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models
C Hannay; D L Williamson; J J Hack; J T Kiehl; J G Olson; S A Klein; C S Bretherton; M Khler
2008-01-01
We examine forecasts of Southeast Pacific stratocumulus at 20S and 85W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized
The financial time series forecasting based on proposed ARMA-GRNN model
Wei-Min Li; Jian-Wei Liu; Jia-Jin Le; Xiang-Rong Wang
2005-01-01
Autoregressive moving average (ARMA) was one of the popular linear models in financial time series forecasting in the past. Generalized regression neural network (GRNN) is a branch of RBF neural network. Recent research activities in forecasting with GRNN suggest that GRNN can be a promising alternative to the traditional time series model. It has shown great ability in modeling and
Formulation of gaussian probability forecasts based on model extended-range integrations
NASA Astrophysics Data System (ADS)
Déqué, M.; Royer, J. F.; Stroe, R.; France, Meteo-
1994-01-01
A sample of 40, 44-day winter forecasts is used to investigate the predictability of 850 hPa temperature over Europe. These forecasts exhibit a significant skill when averages of day 5 to day 14 and day 15 to day 44 are considered. This skill is, however, very close to that of the trivial climatology forecast. A probability forecast is performed, using a gaussian density with the deterministic forecast for the mean, and the climatological standard deviation (SD). The rank probability score (RPS) of such a forecast is better than, but again very close to, that of the probabilistic climatology forecast. The categorical forecast is also studied as a limit case when the SD is zero. The RPS is minimal when using the conditional probabilities of the verification analyses, but the results are not widely improved when robust estimates are used. The results could be widely improved if we used a suitable SD in our forecasts. However, the attempts to predict a priori the optimal SD lead to non-significant results. The best available probability forecast, in our local gaussian approach, uses, for the mean, the regression of the verification analyses by the model forecasts, and, for the SD, a scaled climatological SD.
A stochastic HMM-based forecasting model for fuzzy time series.
Li, Sheng-Tun; Cheng, Yi-Chung
2010-10-01
Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast. PMID:20028637
A Wind Forecasting System for Energy Application
NASA Astrophysics Data System (ADS)
Courtney, Jennifer; Lynch, Peter; Sweeney, Conor
2010-05-01
Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.
Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls.
NASA Astrophysics Data System (ADS)
El-Fandy, M. G.; Taiel, S. M. M.; Ashour, Z. H.
1994-01-01
Long-term rainfall forecasting is used in making economic and agricultural decisions in many countries. It may also be a tool in minimizing the devastation resulting from recurrent droughts. To be able to forecast the total annual rainfall or the levels of seasonal floods, a class of models has first been chosen. The model parameters have then been estimated with an appropriate parameter estimation algorithm. Finally, diagnostic tests have been performed to verify the adequacy of the model. These are the general principles of system identification, which is the most crucial part of the forecasting procedure. In this paper several sets of data have been studied using different statistical procedures. The examined data include a historical 835-year record representing the levels of the seasonal Nile floods in Cairo, Egypt, during the period A.D. 622-1457. These readings were originally carried out by the Arabsto a great degree of accuracy in order to be used in estimating yearly taxes or Zacat (islamic duties). The observations also comprise recent total annual rainfall data over Addis Ababa (Ethiopia) (1907-1984), the total annual discharges of Ethiopian rivers (including the river Sobat discharges at Hillet Doleib, Blue Nile discharge at Roseris, river Dinder, river Rahar, and river Atbara), equatorial lake plateau supply as contributed at Aswan during the period 1912-1982, and the total annual discharges at Aswan during the period 1871-1982. Periodograms have been used to uncover possible peridodicities. Trends of rainfall and discharges of some rivers of east and central Africa have been also estimated.Using the first half of the available record, two autoregressive integrated moving average (ARIMA) time series models have been identified, one for the levels of the seasonal Nile floods in Cairo, the second to model the annual rainfall over Ethiopia. The time series models have been applied in 1-year-ahead forecasting to the other hall of the available record and give fairly promising results, thus indicating the adequacy of the fitted models.
Improving the Model for Energy Consumption Load Demand Forecasting
NASA Astrophysics Data System (ADS)
Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak
This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.
A 30-day forecast experiment with the GISS model and updated sea surface temperatures
NASA Technical Reports Server (NTRS)
Spar, J.; Atlas, R.; Kuo, E.
1975-01-01
The GISS model was used to compute two parallel global 30-day forecasts for the month January 1974. In one forecast, climatological January sea surface temperatures were used, while in the other observed sea temperatures were inserted and updated daily. A comparison of the two forecasts indicated no clear-cut beneficial effect of daily updating of sea surface temperatures. Despite the rapid decay of daily predictability, the model produced a 30-day mean forecast for January 1974 that was generally superior to persistence and climatology when evaluated over either the globe or the Northern Hemisphere, but not over smaller regions.
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.
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.
A stochastic ground-motion forecast model with geophysical considerations
Suzuki, S.
1989-01-01
A method for site-hazard estimates from subduction earthquakes is developed by combining a time-dependent earthquake recurrence model and a theoretical ground-motion model into a unified hazard model. The time-dependent model is used to estimate probabilities of earthquake occurrences in regions characterized by large infrequent earthquake events. The model considers random stress accumulation and release rates and variations in earthquake recurrence patterns. In order to estimate ground motion at a site, a theoretical model is used based on the normal-mode method for the vibrations of the spherical earth. A stochastic rupture model, which represents an incoherent slip over a fault plane, is used to simulate ground motions in the higher-frequency range. The proposed seismic hazard model forecasts probabilities of exceedence of specified ground motion levels and provides risk-consistent response spectra based on geophysical information in a region. Results from application of the model indicate that a site ground motion should be consistent with regional earthquake occurrence patterns, source mechanisms, and wave-propagation path effects.
THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION
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...
From Alarm-Based to Rate-Based Earthquake Forecast Models by Peter Shebalin,*
Narteau, Clément
From Alarm-Based to Rate-Based Earthquake Forecast Models by Peter Shebalin,* Clément Narteau forecast models. A differential probability gain gref alarm is the absolute value of the local slope of earthquake magnitude in both retrospective and prospective tests. This conversion method offers
ARTIFICIAL NEURAL NETWORK MODELS INVESTIGATION FOR EUPHRATES RIVER FORECASTING &BACK CASTING
Cheleng A. Arslan
2013-01-01
The development of stream flow forecasting model is one of the most important aspects in water resources planning and management , since it can help in providing early warning of river flooding as well as in short term operation of water supply system. In this research the best ANN artificial neural networks model for simulation and forecasting of Euphrates river
Progress in Tourism Management Tourism demand modelling and forecasting—A review of recent research
Haiyan Song; Gang Lib
This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time-series and econometric models, a number of new
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.
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.
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.
Did the ECMWF seasonal forecast model outperform a statistical model over the last 15 years?
G. J. van Oldenborgh; Magdalena A. Balmaseda; Laura Ferranti; Timothy N. Stockdale; David L. T. Anderson
2003-01-01
Seasonal predictability is to a large extend due to ENSO and persistence. In the regions and seasons where these factors perturb the average weather, the ECMWF seasonal forecast models have been fairly skillful. The question arises, however, whether they also performed better than a simple statistical model based on observed ENSO teleconnections and persistence. A set of statistical models has
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.
A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty
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.
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).
Estimating demand for industrial and commercial land use given economic forecasts.
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
Improving solar radiation forecasts from Eta/CPTEC model using statistical post-processing
NASA Astrophysics Data System (ADS)
Guarnieri, R. A.; Pereira, E. B.; Chou, S. C.
Solar radiation forecasts are mainly demanded by the energy sector besides other applications Accurate short-term forecasts of solar energy resources are required for management of co-generation systems and energy dispatch in transmission lines Mesoscale weather forecast models usually have radiation parameterization codes since solar radiation is the main energy source for atmospheric processes The Eta model running operationally in the Brazilian Center of Weather Forecast and Climate Studies CPTEC INPE is a mesoscale model with 40 km horizontal resolution This model has outputs for many meteorological variables including solar radiation incidence on ground These radiation forecasts are nevertheless greatly overestimated As an attempt to improve the forecasts of solar energy resources using Eta model statistical post-processing models or refining models were used Multiple linear regression MLR models were adjusted and artificial neural networks ANN were trained using a statistically selected group of 7 variables predicted by the Eta model not including the Eta solar radiation forecast itself This group of variables expresses the future weather and surface conditions Theoretical solar radiation amount on the top of atmosphere TOA was calculated and used as another input Solar radiation measurements from piranometers Kipp Zonen CM-21 installed on two ground-stations of the SONDA Project were used as the targets to be simulated throughout the adjustment training of the models These measurements were also used
A Hierarchical Bayesian Model to Quantify Uncertainty of Stream Water Temperature Forecasts
Bal, Guillaume; Rivot, Etienne; Baglinière, Jean-Luc; White, Jonathan; Prévost, 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
A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.
Bal, Guillaume; Rivot, Etienne; Baglinière, Jean-Luc; White, Jonathan; Prévost, 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
Determining Plausible Forecast Outcomes
NSDL National Science Digital Library
2014-09-14
The content of this lesson will assist the forecaster with the third step of the forecast process, namely, determining plausible forecast outcomes forward in time. The lesson will highlight the role of probabilistic forecast tools to assess the degree of uncertainty in a forecast, as well as suggest an approach for evaluating past and present model performance.
Modelling and Forecasting Energy Demand: Principles and Difficulties
Martin Fischer
We give a brief description of how energy demand can be modelled as a function of calendar data, meteorological data and economic\\u000a variables. The principles of energy demand models are presented and a brief overview of commonly used mathematical methods\\u000a is given. For each method advantages and disadvantages are described. Some examples illustrate difficulties that may be encountered\\u000a when using
Integrated Forecasting Model, synthetic-fuels study. Volume 2. Model structure and input data
Marshalla
1982-01-01
The future of a synthetic fuels industry in the United States, with particular emphasis on the consequences for the electric power industry, is assessed in this study. The assessment is based on use of the Integrated Forecasting Model (IFM), a technology based integrated system model of the national energy economy. The study was performed under the general direction and advice
An econometric modeling approach to short-term crude oil price forecasting
Weiqi Li; Linwei Ma; Yaping Dai; Pei Liu
2011-01-01
In the competitive petroleum markets, oil price forecasting is becoming increasingly relevant to producers and consumers. This paper develops a structural econometric model of the Brent crude spot price using the explanatory variable of defined relative inventory and OPEC production to analyze and forecast short-run oil price. A Hodrick-Prescott filter method presented obtains the relative inventory variables caused by the
Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow
Martyn P. Clark; Lauren E. Hay
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%
T GREAT ALASKA WEATHER MODELING SYMPOSIUM WHAT: About 50 scientists, forecasters, decision
Moelders, Nicole
T GREAT ALASKA WEATHER MODELING SYMPOSIUM WHAT: About 50 scientists, forecasters, decision makers, and others interested in Alaska weather prediction met to present recent research, introduce new application tools, and identify difficulties in Alaska and polar weather forecasting that need to be addressed
Northwest Energy Policy Project: energy demand modeling and forecasting final report
McHugh
1977-01-01
The Northwest Energy Policy Project was undertaken to develop the necessary tools for energy policy development in the Pacific Northwest states individually and as a region. Mathematical Sciences Northwest, Inc. (MSNW) prepared the demand forecasting model for this project. This volume is the final report and incorporates a discussion of alternative methods of demand forecasting, the detailed formulation of MSNW's
Comparison of Short-Term and Time-Independent Earthquake Forecast Models for Southern California
Agnes Helmstetter; Yan Y. Kagan; David D. Jackson
2006-01-01
We have initially developed a time-independent forecast for southern California by smoothing the locations of magnitude 2 and larger earthquakes. We show that using small m 2 earthquakes gives a reasonably good prediction of m 5 earthquakes. Our forecast outperforms other time-independent models (Kagan and Jackson, 1994; Frankel et al., 1997), mostly because it has higher spatial resolution. We have
Technology Forecasting of RFID by Using Bibliometric Analysis and Bass Diffusion Model
J. Bae; K. Seetharaman; P. Suntharasaj; Y. Ding
2007-01-01
Forecasting emerging technologies and identifying the rate of diffusion of products based on these technologies is difficult because of lack of data. Even here techniques such as bibliometric analysis and bass model based on analogous products provides an opportunity to identify the technology growth and possible diffusion of the product. This paper uses bibliometric analysis to forecast RFID technology and
Author's personal copy A neural network based dynamic forecasting model for Trend
Abu-Mostafa, Yaser S.
large number of scenarios (e.g. one million). Technological Forecasting & Social Change 76 (2009) 952Author's personal copy A neural network based dynamic forecasting model for Trend Impact Analysis, Faculty of Engineering, Cairo University, Giza, Egypt c Advanced Technology and Center for Advanced
A Behavioral Finance Model of the Exchange Rate with Many Forecasting Rules
Paul De Grauwe; Pablo Rovira Kaltwasser
2006-01-01
This paper presents a behavioral finance model of the exchange rate. Agents forecast the exchange rate by means of very simple rules. They can choose between three groups of forecasting rules: fundamentalist, extrapolative and momentum rules. Agents using a fundamentalist rule are not able to observe the true value of the fundamental exchange and therefore have to rely on an
Modeling of Spatial Dependence in Wind Power Forecast Uncertainty
George Papaefthymiou; Pierre Pinson
2008-01-01
It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by
NASA Astrophysics Data System (ADS)
Deki?, L.; Mihalovi?, A.; Jovi?i?, I.; Vladikovi?, D.; Jerini?, J.; Ivkovi?, M.
2012-04-01
This paper examines two episodes of heavy rainfall and significantly increased water levels. The first case relates to the period including the beginning and the end of the third decade of June 2010 at the Kolubara river basin, where extreme rainfall led to two big flood waves on the Kolubara river, whereat water levels exceeded both regular and extraordinary flood defence and approached their historical maximum. The second case relates to the period including the end of November and the beginning of December 2010 at the Jadar river basin, where heavier precipitation caused the water levels of the basin to reach and surpass the occurrence limit (warning level). The HBV (Hydrological Bureau Waterbalance-section) rainfall/snowmelt - runoff model installed at the RHMSS uses gridded quantitative precipitation and air temperature forecast for 72 hours in advance based on meteorological weather forecast WRF-NMM mesoscale model. Nonhydrostatic Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system is flexible state-of-the-art numerical weather prediction model capable to describe and estimate powerful nonhydrostatic mechanism in convective clouds that cause heavy rain. The HBV model is a semi-distributed conceptual catchment model in which the spatial structure of a catchment area is not explicitly modelled. Instead, the sub-basin represents a primary modelling unit while the basin is characterised by area-elevation distribution and classification of vegetation cover and land use distributed by height zone. WRF-NMM forecast shows very good agreement with observations in terms of timing, location and amount of precipitation. They are used as input for HBV model, forecasted discharges at the output profile of the selected river basin represent model output for consideration. 1 Republic Hydrometeorological Service of Serbia
NASA Astrophysics Data System (ADS)
Soltanzadeh, I.; Azadi, M.; Vakili, G. A.
2011-07-01
Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.
A multiple model assessment of seasonal climate forecast skill for applications
NASA Astrophysics Data System (ADS)
Lavers, David; Luo, Lifeng; Wood, Eric F.
2009-12-01
Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.
Fuzzy short-term electric load forecasting
A. M. Al-Kandari; S. A. Soliman; M. E. El-Hawary
2004-01-01
Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling, and for system security such as peak load shaving by power interchange with interconnected utilities. In this paper a fuzzy linear regression model for summer and winter seasons is developed. The estimation fuzzy problem for the model is turned out to linear
Forecast of ionospheric disturbances using a high-resolution atmosphere-ionosphere coupled model
NASA Astrophysics Data System (ADS)
Shinagawa, Hiroyuki; Miyoshi, Yasunobu; Fujiwara, Hitoshi; Yokoyama, Tatsuhiro; Jin, Hidekatsu
Space weather forecasts are about to enter a stage incorporating numerical forecasts based on realistic numerical simulation, in addition to conventional methods used by forecasters to make predictions based on observational data and experience. At the National Institute of Information and Communications Technology (NICT) of Japan, we have developed an atmosphere-ionosphere coupled model, which includes the whole neutral atmosphere and the ionosphere. The model is called GAIA (Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy). The present version has spatial resolution of about 1 degree in horizontal direction. In addition, we are also developing a high-resolution regional ionospheric model, which has a horizontal resolution of about 10 km.We plan to combine GAIA and the regional model to reproduce mesoscale ionospheric phenomena, such as plasma bubbles and SED (storm enhanced density). The model will be a useful tool for space weather forecast. We will report previous results, and a plan for the new model.
2010-01-01
In this paper, four simple dynamic prediction methods and two supervised learning techniques including a linear regression model, a quadratic regression model, an original grey prediction model, a modified grey prediction model, a back?propagation neural network model, and an epsilon?SVM regression model were investigated for the forecasting of flood stage one hour ahead for early warning of flooding hazards. Quantitative
Game Theory and Economic Modelling
David M. Kreps
1990-01-01
Over the past two decades, academic economics has undergone a mild revolution in methodology. The language, concepts and techniques of noncooperative game theory have become central to the discipline. This book provides the reader with some basic concepts from noncooperative theory, and then goes on to explore the strengths, weaknesses, and future of the theory as a tool of economic
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.
NASA Astrophysics Data System (ADS)
Chen, Jie; Brissette, François; 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.
Training the next generation of scientists in Weather Forecasting: new approaches with real models
NASA Astrophysics Data System (ADS)
Carver, Glenn; Vá?a, 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.
Shyh-Jier Huang; Kuang-Rong Shih
2003-01-01
In this paper, the short-term load forecast by use of autoregressive moving average (ARMA) model including non-Gaussian process considerations is proposed. In the proposed method, the concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process. With embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately. Therefore,
Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL, and ECMWF Models
Cécile Hannay; David L. Williamson; James J. Hack; Jeffrey T. Kiehl; Jerry G. Olson; Stephen A. Klein; Christopher S. Bretherton; Martin Köhler
2009-01-01
We examine forecasts of Southeast Pacific stratocumulus at 20ºS and 85ºW during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized
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.
An Overview of Computational Modeling in Agricultural and Resource Economics
Tesfatsion, Leigh
An Overview of Computational Modeling in Agricultural and Resource Economics James Nolan,1 Dawn and resource economics (referred to hereafter as agricultural economics) is its tradition. However, when modeling human-environment interactions, economics in general has had difficulty linking
Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy
J. Isaac Miller; Shawn Ni
2010-01-01
We examine how future real GDP growth relates to changes in the forecasted long-term average of discounted real oil prices and to changes in unanticipated fluctuations of real oil prices around the forecasts. Forecasts are conducted using a state-space oil market model, in which global real economic activity and real oil prices share a common stochastic trend. Changes in unanticipated
The SEEC UK Energy Demand Forecast (1993-2000)
Roger Fouquet; David Hawdon; Peter J G Pearson; Colin Robinson; Paul Stevens
1993-01-01
The aims of SEEC’s energy demand forecast’s (1993-2000) are to present the underlying determinants of fuel consumption, such as economic activity and prices; develop a series of simple yet reliable sectoral models of energy demand, which incorporate recent modelling developments; provide forecasts of energy demand and its environmental consequences; examine the effects of the V.A.T. on domestic fuel and increased
A crop loss-related forecasting model for sclerotinia stem rot in winter oilseed rape.
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
NASA Astrophysics Data System (ADS)
Madadgar, S.; Moradkhani, H.
2014-12-01
Bayesian Model Averaging (BMA) develops the probability density function of the forecast variable given the predictions of different models. It applies a linear weighted average to the posterior distributions of individual models to characterize the uncertainty induced by model structures in hydro-climatologic predictions. In the original form of BMA, the posterior distribution of forecast given each model prediction is assumed to be a particular probability distribution (e.g. normal, gamma, etc.). The weight and variance of each conditional PDF is approximated with an iterative Expectation-Maximization algorithm (EM) over a calibration period. In this presentation, we demonstrate the integration of a group of multivariate functions, the so-called copula functions, to approximate the posterior distribution of forecast given individual model predictions. Here we introduce a copula-embedded BMA (Cop-BMA) method that skips the iterative procedure in the EM algorithm and also relaxes any assumptions about the shape of conditional PDFs. Both BMA and Cop-BMA are applied to hydrologic forecasts from different rainfall-runoff and land-surface models. We consider the streamflow observation and simulations of ten river basins provided by the Model Parameter Estimation Experiment (MOPEX) project. Results demonstrate that the predictive distributions are generally overconfident and have insufficient spread after BMA application. In contrast, the post-processed forecasts by Cop-BMA are more accurate and reliable. In addition, Cop-BMA outperforms BMA in the river basins with poor initial forecasts (e.g., dry regions).
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..
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.
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 investigators)
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.
Probabilistic Performance Forecasting for Unconventional Reservoirs With Stretched-Exponential Model
Can, Bunyamin
2011-08-08
a reserves-evaluation workflow that couples the traditional decline-curve analysis with a probabilistic forecasting frame. The stretched-exponential production decline model (SEPD) underpins the production behavior. Our recovery appraisal workflow...
A forecasting model of tourist arrivals from major markets to Thailand
Hao, Ching
1998-01-01
important to forecast tourism demand in the region and understand the factors affecting demand. Considering the national importance of tourism, Thailand was chosen as the destination country with nine major markets as the countries of origin. A model...
THE GLOBAL IMPACT OF SATELLITE-DERIVED POLAR WINDS ON MODEL FORECASTS
Wisconsin at Madison, University of
THE GLOBAL IMPACT OF SATELLITE-DERIVED POLAR WINDS ON MODEL FORECASTS by David A. Santek........................................................................................................... 1 2. Satellite-derived winds algorithm........................................................................... 6 2.1 Geostationary satellite winds algorithm
Forecasting Financial Time-Series using Artificial Market Models
Gupta, N; Johnson, N F; Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.
2005-01-01
We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to sayi...
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.
Flood forecasting with the A&M watershed model: a hydrometeorological study
Robinson, Cedric Glynn
1990-01-01
Integrator and Processor (VIP) levels and the shape of the storm on the precipitation grid. VIP levels represent precipitation intensity ranges. The VIP levels and the default rainfall intensity values in the ARM Watershed Model are as follows: VIP LEVEL... of Precipitation Single Parameter Measurement Technique Dual Parameter Measurement Technique III FLOOD FORECASTING DATA PROCESSING 4 5 7 15 24 IV FLOOD FORECASTING WITH THE A&M WATERSHED MODEL 28 Preparation of the Watershed, Radar and Precipitation...
Mountain range specific analog weather forecast model for northwest Himalaya in India
D. Singh; A. Ganju
2008-01-01
Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations\\u000a in each mountain range in northwest Himalaya (NW-Himalaya). The model searches past similar cases from historical dataset\\u000a of reference observatory in each mountain range based on current situation. The searched past similar cases of each mountain\\u000a range are used to draw weather forecast for
E. G. Kolomyts; L. S. Sharaya; N. A. Surova
2010-01-01
Experience in landscape-ecological modeling of the biotic regulation of carbon cycle by forest ecosystems is outlined. Regional forecasting mapping of the carbon balance is based on landscape-ecological survey data using the method of hierarchical extrapolation. Empirical-statistical models are used to make forecasting estimates of the changes of the carbon balance components and of the respective influence of forest ecosystems on
An investigation of model selection criteria for neural network time series forecasting
Min Qi; Guoqiang Peter Zhang
2001-01-01
Artificial neural networks (ANNs) have received more and more attention in time series forecasting in recent years. One major disadvantage of neural networks is that there is no formal systematic model building approach. In this paper, we expose problems of the commonly used information-based in-sample model selection criteria in selecting neural networks for financial time series forecasting. Specifically, Akaike’s information
Vassiliki Kotroni; Evangelos Floros; Konstantinos Lagouvardos; Goran Pejanovic; Luka Ilic; Momcilo Zivkovic
2010-01-01
Weather forecasting is based on the use of numerical weather prediction (NWP) models that are able to perform the necessary\\u000a calculations that describe\\/predict the major atmospheric processes. One common problem in weather forecasting derives from\\u000a the uncertainty related to the chaotic behaviour of the atmosphere. A solution to that problem is to perform in addition to\\u000a “deterministic” forecasts, “stochastic” forecasts
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.
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.
Gaussian Mixture Models for forecasting and filling of climatological time series
NASA Astrophysics Data System (ADS)
Calandria-Hernández, D.; Hidalgo-Muñoz, J. M.; Argüeso, D.; Gámiz-Fortis, S. R.; Esteban-Parra, M. J.; Castro-Díez, Y.
2010-09-01
In Statistics, a Gaussian Mixture Model (GMM) is a probabilistic density estimation method that consists of a linear combination of normal distributions. Model parameters, i.e. means and variances of each normal distribution, as well as the linear combination coefficients, can be estimated easily using the Expectation Maximization (EM) algorithm. Since the model expression is non-linear in its parameters (particularly regarding to the means and variances), GMMs are considered non-linear. In this work, a methodology to forecast time series based on a GMM is presented. Such model provides several advantages over other classical forecasting models, i.e. Autoregressive with exogenous variables (ARX) model. To forecast a multimodal variable (for example, temperature), GMM offers a more intuitive representation because each normal distribution can be adjusted over each mode. Besides, GMMs can be viewed as an ensemble of ARX models. Because of each ARX model can have different variance, the ensemble provides a similar behavior to an Autoregressive Conditional Heteroskedasticity (ARCH) model, allowing a better forecasting of the error band. The model has been applied for forecasting monthly time series of river flow in the Iberian Peninsula. The streamflow from a particular station is forecasted using the station itself (autoregression) and also the information provided by those stations with better correlation coefficients (exogenous variables). Data used in the model embraced the period between 1960-2005, using the interval 1960-1980 for calibration and 1961-2005 for validation. Results obtained highlight the usefulness of this methodology regarding to other classical forecasting models. This technique results particularly successful for filling missing data in the Iberian streamflow series. Acknowledgements: The Spanish Ministry of Science and Innovation, with additional support from the European Community Funds (FEDER), project CGL2007-61151/CLI, and the Regional Government of Andalusia project P06-RNM-01622, have financed this study.
Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model
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
Day-ahead crude oil price forecasting using a novel morphological component analysis based model.
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
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.
Li, Su-Wen; Liu, Wen-Qing; Xie, Pin-Hua; Wang, Feng-Sui; Yang, Yi-Jun
2009-11-01
For real-time and on-line monitoring DOAS (differential optical absorption spectroscopy) system, a model based on an improved Elman network for monitoring pollutant concentrations was proposed. In order to reduce the systematical complexity, the forecasting factors have been obtained based on the step-wise regression method. The forecasting factors were current concentrations, temperature and relative humidity, and wind speed and wind direction. The dynamic back propagation (BP) algorithm was used for creating training set. The experiment results show that the predicted value follows the real well. So the modified Elman network can meet the demand of DOAS system's real time forecasting. PMID:20101985
Microcomputers and Dynamic Economic Models.
ERIC Educational Resources Information Center
Taylor, Peter
1985-01-01
Two computer programs that simulate the multiplier and accelerator processes are described. The simulations can be used in college-level economics classes or by pupils using their own personal computers at home. Very little knowledge of programing is required to implement the simulations. (Author/RM)
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.
Evaluation of North American Multi-Model Ensemble (NMME) Climate Forecasts in China
NASA Astrophysics Data System (ADS)
Duan, Q.; Ma, F.; Ye, A.; Gong, W.
2014-12-01
The North American Multi-Model Ensemble (NMME) system is a newly developed, multi-institutional, multi-model ensemble system for intra-seasonal to interannual global climate prediction. NMME includes in its system ensemble forecasts produced by nine climate models from six research centers. It not only produces near real-time multi-model climate forecasts, but also has a 30-year hindcast database from all models. This talk presents evaluation results of the performance skill of NMME precipitation and temperature forecasts over 17 climatic regions in China. We find that the predictive skill varies seasonally and regionally in China, exhibiting higher values in autumn and spring and lower values in summer. Significant predictive skill is observed in most regions except the Huai River basin and the source areas of the Yangtze and Yellow Rivers. We also found that the Bayesian multi-model averaging results outperform individual model and simple model averaging results.
Heterogeneous Agent Models in Economics and Finance
Cars H. Hommes
2005-01-01
This paper surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect,
Low-order stochastic model and "past-noise forecasting" of the Madden-Julian Oscillation
NASA Astrophysics Data System (ADS)
Kondrashov, D.; Chekroun, M. D.; Robertson, A. W.; Ghil, M.
2013-10-01
This paper presents a predictability study of the Madden-Julian Oscillation (MJO) that relies on combining empirical model reduction (EMR) with the "past-noise forecasting" (PNF) method. EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity, seasonality and serial correlation in the estimated noise, while PNF constructs an ensemble of forecasts that accounts for interactions between (i) high-frequency variability (noise), estimated here by EMR, and (ii) the low-frequency mode of MJO, as captured by singular spectrum analysis (SSA). A key result is that—compared to an EMR ensemble driven by generic white noise—PNF is able to considerably improve prediction of MJO phase. When forecasts are initiated from weak MJO conditions, the useful skill is of up to 30 days. PNF also significantly improves MJO prediction skill for forecasts that start over the Indian Ocean.
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.
Evaluation of WRF model seasonal forecasts for tropical region of Singapore
NASA Astrophysics Data System (ADS)
Singh, J.; Yeo, K.; Liu, X.; Hosseini, R.; Kalagnanam, J. R.
2015-04-01
The Weather and Research Forecast (WRF) model is evaluated for the monsoon and inter-monsoon seasons over the tropical region of Singapore. The model configuration, physical parameterizations and performance results are described in this paper. In addition to the ready-to-use data available with the WRF model, the model configuration includes high resolution MODIS land use (500 m horizontal resolution) and JPL-NASA sea surface temperature (1 km horizontal resolution) data. The model evaluation is performed against near surface observations for temperature, relative humidity, wind speed and direction, available from a dense network of weather monitoring stations across Singapore. It is found that the high resolution data sets bring significant improvement in the model forecasts. The results also indicate that the model forecasts are more accurate in the monsoon seasons compared to the inter-monsoon seasons.
Comparison of short-term rainfall prediction models for real-time flood forecasting
E. Toth; A. Brath; A. Montanari
2000-01-01
This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive moving-average (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the considered methods are then routed through a lumped, conceptual, rainfall–runoff
Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States
Haiganoush K. Preisler; Anthony L. Westerling
2007-01-01
The ability to forecast the number and location of large wildfire events (with specified confidence bounds) is important to fire managers attempting to allocate and distribute suppression efforts during severe fire seasons. This paper describes the development of a statistical model for assessing the forecasting skills of fire-danger predictors and producing 1-month-ahead wildfire-danger probabilities in the western United States. The
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.
Joel E. Cohen
1986-01-01
This paper compares several methods of generating confidence intervals for forecasts of population size. Two rest on a demographic\\u000a model for age-structured populations with stochastic fluctuations in vital rates. Two rest on empirical analyses of past forecasts\\u000a of population sizes of Sweden at five-year intervals from 1780 to 1980 inclusive. Confidence intervals produced by the different\\u000a methods vary substantially. The
Near-field Tsunami Inundation Forecast Modeling of the 2009 Samoa Tsunami
NASA Astrophysics Data System (ADS)
Chamberlin, C.; Wei, Y.; Titov, V. V.; Uslu, B. U.; Tang, L.; Arcas, D.; Moore, C. W.
2009-12-01
The 29 September 2009 Samoa tsunami was the first tsunami event in which detailed, high-resolution tsunami inundation model results were available for the impacted near-field areas before any other quantitative information had been obtained. This first forecast of the near-field tsunami impact was used by disaster recovery and scientific survey teams in advance of their arrival in the field, and for testing the real-time capability of the new tsunami forecast system. While there were no tsunami forecast models completed for American Samoa before the event, we assembled a preliminary model hours after the event, followed by a high-resolution model covering all of the island of Tutuila. The inundation forecast was prepared from inversion of deep-ocean bottom pressure recorder timeseries, and was later validated with tide gauge records. We describe the modeling products produced immediately after the event, and assess the quality of the initial modeling results by comparison with field survey results and with modeling performed after the surveys were completed. This forecast comparison provides lessons for the use of inundation modeling in surveying and recovering from future tsunamis.
Energy Demand and CO2 Emission Forecasting Using State Space and Dynamic CGE Models
Shih-Mo Lin; Ping-Cheng Li; Shih-Hsun Hsu; Chung-Huang Huang
The purpose of this paper is to forecast energy demand and CO 2 emissions for Taiwan over the period 1999-2015. Two models are constructed for this purpose: one is a state space model and the other is a dynamic computable general equilibrium (CGE) model. The state space model is built based on detailed time series data on energy consumption spanning
Application of the NCEP Regional Spectral Model to Improve Mesoscale Weather Forecasts in Hawaii
Jian-Jian Wang; Hann-Ming Henry Juang; Kevin Kodama; Steve Businger; Yi-Leng Chen; James Partain
1998-01-01
The operational implementation of the National Centers for Environmental Prediction (NCEP) Regional Spec- tral Model (RSM) in Hawaii is the first application of a mesoscale model to improve weather forecasts in the Pacific region. The primary model guidance for the National Weather Service Pacific region has been provided by the NCEP Aviation (AVN) run of the Global Spectral Model (GSM).
Forecasting oil price trends using wavelets and hidden Markov models
Edmundo G. de Souza e Silva; Luiz F. L. Legey
2010-01-01
The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we
Satellite data input to Windy Gap computerized streamflow forecasting model
JOHN R. ECKHARDT
1986-01-01
The basis for timely residual streamflow forecasts for the recently completed Windy Gap Project in Colorado is a remote hydrologie data collection network which utilizes the GOES Satellite for data transmission to project headquarters in Loveland, Colorado. Snowpack, soil moisture, streamflow, precipitation, temperature, and wind data collected at 15 remote sites in the Fraser River Basin are used to update
Development of a real-time quantitative hydrologic forecasting model
Bell, John Frank
1986-01-01
TIME ) Ftgure 2. porno(lal f loni daaaga reduction aada possible hy a gives ruspanav ties (acr rrata forecast Ivad (Iacl. The do(ted ll?es In tha figure gives wt essnple uhlclt ahoun that as tha fnrecast land tlaa Incrva. as frua 4 to l4 hours...
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.
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.
for electricity to support a given level of economic activity will also depend on the cost of electricity, and high) based on different assumptions about the key determinants of electricity demand. Much efficiency will tend to increase the construction cost of electrically heated homes. This relative increase
An Economic Model for Pricing Digital Products
Gerald V. Post
2009-01-01
An economic model is created that emphasizes the characteristics of digital products. By focusing on price instead of quantity, the model examines the impact of low marginal costs and advertising revenue. The conditions for producing negative prices are explored along with the limitations of assuming linear demand curves in an e-commerce setting. The model explains observed phenomenon in Web examples
A Four-Stage Hybrid Model for Hydrological Time Series Forecasting
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
Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets
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.
Verification of Categorical Probability Forecasts
H. Zhang; T. Casey
2000-01-01
This paper compares a number of probabilistic weather forecasting verification approaches. Forecasting skill scores from linear error in probability space and relative operating characteristics are compared with results from an alternative approach that first transforms probabilistic forecasts to yes\\/no form and then assesses the model forecasting skill. This approach requires a certain departure between the categorical probability from forecast models
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.
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.
NSDL National Science Digital Library
John Nielsen-Gammon
1996-09-01
Weather Forecasting is a set of computer-based learning modules that teach students about meteorology from the point of view of learning how to forecast the weather. The modules were designed as the primary teaching resource for a seminar course on weather forecasting at the introductory college level (originally METR 151, later ATMO 151) and can also be used in the laboratory component of an introductory atmospheric science course. The modules assume no prior meteorological knowledge. In addition to text and graphics, the modules include interactive questions and answers designed to reinforce student learning. The module topics are: 1. How to Access Weather Data, 2. How to Read Hourly Weather Observations, 3. The National Collegiate Weather Forecasting Contest, 4. Radiation and the Diurnal Heating Cycle, 5. Factors Affecting Temperature: Clouds and Moisture, 6. Factors Affecting Temperature: Wind and Mixing, 7. Air Masses and Fronts, 8. Forces in the Atmosphere, 9. Air Pressure, Temperature, and Height, 10. Winds and Pressure, 11. The Forecasting Process, 12. Sounding Diagrams, 13. Upper Air Maps, 14. Satellite Imagery, 15. Radar Imagery, 16. Numerical Weather Prediction, 17. NWS Forecast Models, 18. Sources of Model Error, 19. Sea Breezes, Land Breezes, and Coastal Fronts, 20. Soundings, Clouds, and Convection, 21. Snow Forecasting.
An Enhancement to the Linear Dynamic System Model for Air Traffic Forecasting
Sun, Dengfeng
functionalities of air traffic controls. The Linear Dynamic System Model that predicts the traffic demand within the Air Route Traffic Control Centers serves well for this purpose. This model formulates inflowsAn Enhancement to the Linear Dynamic System Model for Air Traffic Forecasting Yi Cao School
Shu-Xia Yang
2008-01-01
This paper proposes a organic hybrid model of the genetic algorithm and the particle swarm algorithm firstly, then establishes the multi-factor time series forecasting model, designs the BP neural networks, adopts the organic hybrid model of genetic algorithm and particle swarm algorithm to optimize the weight from the input layer to the hidden layer, the weight from the hidden layer
Regional Demand Forecasting Model: 1977 and 1978 versions. Task 3, final documentation report
Parhizgari
1978-01-01
This report documents the demand forecasting model, RDFOR. Chapter I presents an overview of the structure of RDFOR as well as its linkages with other models within the PIES model. An important link between RDFOR and PIES is the Demand Interface System (DFACE) which prepares the output of RDFOR for input into PIES. Chapter II provides an in-depth analysis of
Double Branching model to forecast the next M ? 5.5 earthquakes in Italy
Anna Maria Lombardi; Warner Marzocchi
2009-01-01
The purpose of this work is to set up a new forecasting model, named Double Branching, for large earthquakes in Italy. The model is time-dependent, since it assumes that each earthquake can generate, or is correlated to, other earthquakes through different physical mechanisms. In a recent paper [Marzocchi, W., Lombardi, A.M., 2008. A Double Branching model for earthquake occurrence, J.
Modelling and forecasting the diffusion of innovation – A 25-year review
Nigel Meade; Towhidul Islam
2006-01-01
The wealth of research into modelling and forecasting the diffusion of innovations is impressive and confirms its continuing importance as a research topic. The main models of innovation diffusion were established by 1970. (Although the title implies that 1980 is the starting point of the review, we allowed ourselves to relax this constraint when necessary.) Modelling developments in the period
A Novel Nonlinear Neural Network Ensemble Model for Financial Time Series Forecasting
Kin Keung Lai; Lean Yu; Shouyang Wang; Wei Huang
2006-01-01
In this study, a new nonlinear neural network ensemble model is pro- posed for financial time series forecasting. In this model, many different neural network models are first generated. Then the principal component analysis technique is used to select the appropriate ensemble members. Finally, the sup- port vector machine regression method is used for neural network ensemble. For further illustration,
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.
Francisco J. Meza; Daniel S. Wilks
2003-01-01
This study investigates the economic value of several simple forecasts of 3-month average eastern tropical Pacific sea surface temperature anomalies (SSTA). Two Chilean agricultural regions were selected and the value of information for the main crops is obtained using an integrated model. The value of perfect forecasts is computed along with several simply formulated imperfect seasonal forecasts using a classification
Economic Modeling in Chronic Obstructive Pulmonary Disease
Maureen Rutten-van Molken; Todd A. Lee
2006-01-01
Calculating the cost-effectiveness of interventions is an important step in accurately assessing the health and financial burdens of a disease. Although clinical trials that include cost data can be used to compare the cost-effectiveness of specific interventions, they only deal with outcomes within the time frame of the trial. Health economic models can synthesize epidemiologic, clinical, economic, andquality-of-lifedatafrommanydifferentsourcesandextrapolate results to
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
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.
NASA Astrophysics Data System (ADS)
Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.
2013-12-01
One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.
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.
NASA Astrophysics Data System (ADS)
Kumar, Prashant; Kishtawal, C. M.; Pal, P. K.
2014-03-01
Rainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four-dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.
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.
Compositional Security Modelling Structure, Economics, and Behaviour
Pym, David J.
Compositional Security Modelling Structure, Economics, and Behaviour Tristan Caulfield1 , David Pym and the behavioural choices of agents operating within the system. Models are executable, so allowing system- atic- ingful way with other stakeholders, such as operations managers, finance managers, or senior strategists
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.
An economic-demographic model of the United States labor market.
Anderson, J M
1982-01-01
An econometric model that has been developed to investigate the effects of demographic change on the US economy is described. The specific demographic features examined are the sizes of age sex groups in the US working age population. The size of these groups from now through the end of the 20th century will be determined primarily by past and current levels of fertility so they can be forecast with some degree of confidence. The model expands both the domain and accuracy of longterm economic forecasting by making use of the considerable quantity of demographic information that can be forecast, at least through this century, with a fairly great degree of confidence. In addition to economic forecasting, this study of the impact of demographic changes on the US labor market contributes to the investigation of the interrelationships among economic and demographic changes. The task of the model is as follows: given an exogenous projection of fertility and mortality rates and net immigration and given exogenous forecasts of variables such as rates of technical change, government demand for goods and services, and tax rates, the model forecasts variables characterizing the labor market and the macroeconomy. The model uses the fundamental principles of supply and demand, the economic theory of production, and the theory of household allocation of time and income to draw the implications of changes in demographic variables for the labor market and the economy. The crux of the model is a set of relationships depicting the behavior of the US labor market. In the labor market submodel, the input of labor of each of 16 age sex groups and its piece in each period is determined by the interaction of supply and demand. The 16 demographic groups are males and females, respectively, of ages 14-15, 16-17, 18-24, 25-34, 35-44, 45-54, 55-64, and 65 and over. Equations depicting the supply of and demand for labor of various demographic groups are estimated and provide the behavioral relationships of the labor market submodel. The following description of the model is in 3 parts: the demand for labor; the labor supply equations; and the intergration of the 2 and the complete growth model. Some illustrative forecasts are included. In all 3 forecasts, the proportion of the labor force accounted for by workers in the middle age groups, 25-54, increases, reaching the highest levels in the post World War 2 period in the 1990-2000 decade. The proportion accounted for by males in that age group does not rise notably and remains lower than it was in the 1950s and 1960s. The proportion accounted for by women age 25-54 rises markedly. This trend is possible the most salient feature of the forecasts. PMID:12264899
Initial methodology for forecasting vehicle modes of activity as input to modal emissions models
Washington, S.P.; Leonard, J.D. II; Roberts, C.A. [Georgia Inst. of Tech., Atlanta, GA (United States). School of Civil and Environmental Engineering
1997-12-31
Researchers at Georgia Inst. of Technology and others have been developing emissions models based on modes of vehicle activity over the past two years. However, in current regional modeling practice, there are no tools for forecasting vehicle activity modes which are needed as input to these modal emissions models. In order to address this shortcoming, researchers at Georgia Tech, UC Davis, San Jose University, and the California Polytechnic University in San Luis Obispo have been developing a method to forecast modes of vehicle activity. The authors first provide the general framework used to link forecasted vehicle activity with forecasted emissions of CO, NO{sub x} and HC from light-duty motor vehicles. They then develop algorithms to estimate modal activity on freeways using a statistical modeling procedure called hierarchical tree-based regression (HTBR). A brief explanation of the statistical theory is provided, followed by presentation of general modeling results for forecasting vehicle modes of activity including idle, acceleration, power and positive kinetic energy. To generate traffic data for the statistical analysis, a full factorial data set containing 1600 observations was developed using a modification of the FRESIM simulation modeling software. A freeway interchange was simulated using five sections consistent with the Highway Capacity Manual consisting of three basic freeway sections and two weaving sections delineated at ramp junctions. The authors demonstrate how modes of vehicle activity could be forecast using variables predicted by current travel demand models. They also demonstrate how the simulation results will help reduce the number of factors that affect modal activity on freeways. Finally, the authors present limitations of this approach and anticipated future research directions including the collection of a data set aided by these results using a simulated data set.
Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies
Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.; Diao, Ruisheng; Lu, Ning
2014-04-14
To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.
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.
Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao
2011-09-29
This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Granada, Universidad de
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models JoseÂ´ Luis, few works have used more sophisticated tools based in Artificial Intelligence, as are neural or neuro on each interval. Experimental results show an advantage of the neuro-fuzzy models against classical
Integrated forecasting model synthetic fuels study. Volume 1. Overview and findings. Final report
Marshalla
1982-01-01
The future of a synthetic fuels industry in the United States, with particular emphasis on the consequences for the electric power industry, is assessed in this study. The assessment is based on use of the Integrated Forecasting Model (IFM), a technology based integrated system model of the national energy economy. The study was performed under the general direction and advice
An implementation of a neural network based load forecasting model for the EMS
A. D. Papalexopoulos; Shangyou Hao; Tie-Mao Peng
1994-01-01
This paper presents the development and implementation of an artificial neural network (ANN) based short-term system load forecasting model for the energy control center of the Pacific Gas and Electric Company (PG&E). Insights gained during the development of the model regarding the choice of the input variables and their transformations, the design of the ANN structure, the selection of the
Evaluation of Advanced Wind Power Forecasting Models Results of the Anemos Project
Paris-Sud XI, Université de
1 Evaluation of Advanced Wind Power Forecasting Models Results of the Anemos Project I. Martí1.kariniotakis@ensmp.fr Abstract An outstanding question posed today by end-users like power system operators, wind power producers or traders is what performance can be expected by state-of-the-art wind power prediction models. This paper
An Aggregate Air Traffic Forecasting Model subject to Stochastic Christabelle S. Bosson
Sun, Dengfeng
. There have also been several algorithms developed for air traffic control. In 1993, Delahaye and OdoniAn Aggregate Air Traffic Forecasting Model subject to Stochastic Inputs Christabelle S. Bosson-2045 This paper introduces an aggregate air traffic model that calculates the number of aircraft in each Air Route
3D City modeling for urban scale heating energy demand forecasting
Aneta Strzalka; Jürgen Bogdahn; Volker Coors; Ursula Eicker
2011-01-01
An urban energy management tool was developed, which is able to predict the heating energy demand of urban districts and analyze strategies for improving building standards. Building models of different Levels of Detail are investigated and analyzed according to their suitability for forecasting energy demand. Based on the specific 3D city model, an input file is generated, which can be
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer
Paris-Sud XI, Université de
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We show that this methodology can improve the accuracy of meteorological data estimation compared
Modeling and Forecasting the Onset and Duration of a Severe Dutch Fog Event
NASA Astrophysics Data System (ADS)
van der Velde, I. R.; Steeneveld, G. J.; Wichers Schreurs, B. G. J.; Holtslag, A. A. M.
2009-09-01
A case of severe radiation fog in the Netherlands is analyzed as a benchmark for the development of a very high resolution NWP model for airport capacity prognoses. Simulations of the mesoscale models WRF and Hirlam are evaluated to determine the state of the art in fog forecasting and to derive requirements for further research and development. For this case, WRF is unable to simulate the fog for most of the permutations of parameterizations selectable in its framework. Hirlam does model the onset of fog but is unable to let the fog grow beyond the lowest model layer, leading to an early dispersal of fog at the morning transition. The sensitivity of fog forecasts to model formulation is further analyzed with a high resolution single column version of Hirlam, and with an additional single column research model, which was specifically designed for fog forecasting. The single column results are found to be sensitive to the proper specification of initial conditions and external forcings. High vertical resolution is essential for the formation and growth of the fog layer and when the fog lifts for the maintenance of a stratus deck. The properly configured column models are able to accurately model the onset of fog and its maturation, but fail in the simulation of fog persistence and subsequent dispersal. Details of the turbulence parameterization appear to be important in this process. It is concluded that, despite advances in numerical weather prediction, fog forecasting is still a challenge.
Forecasting the Market for Cellular Mobile Services by Linear Regression Models
ÅKE ARVIDSSON
2009-01-01
We consider the problems of explaining and forecasting the penetration and the traffic in cellular mobile networks. To this end, we create two regression models, viz. one to predict the penetration from service charges and network effects and another one to predict the traffic from service charges and diffusion and adoption effects. The results of the models can also be
Maren Outwater; Steve Castleberry; Yoram Shiftan; Moshe Ben-Akiva; Yu Shuang Zhou; Arun Kuppam
2003-01-01
The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process. The focus of this work was on expanding the mode choice model to
Estimating and Forecasting Asset Volatility and Its Volatility: A Markov-Switching Range Model
Jan Piplack
2009-01-01
This paper proposes a new model for modeling and forecasting the volatility of asset markets. We suggest to use the log range defined as the natural logarithm of the difference of the maximum and the minimum price observed for an asset within a certain period of time, i.e. one trading week. There is clear evidence for a regime-switching behavior of
Using neural networks and GIS to forecast land use changes: a Land Transformation Model
Bryan C. Pijanowski; Daniel G. Brown; Bradley A. Shellito; Gaurav A. Manik
The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTMparameterized for Michigan's Grand Traverse Bay Watershed and explores how factors
Medium term system load forecasting with a dynamic artificial neural network model
M. Ghiassi; David K. Zimbra; H. Saidane
2006-01-01
This paper presents the development of a dynamic artificial neural network model (DAN2) for medium term electrical load forecasting (MTLF). Accurate MTLF provides utilities information to better plan power generation expansion (or purchase), schedule maintenance activities, perform system improvements, negotiate forward contracts and develop cost efficient fuel purchasing strategies. We present a yearly model that uses past monthly system loads
A novel nonlinear RBF neural network ensemble model for financial time series forecasting
Donglin Wang; Yajie Li
2010-01-01
In this paper, a novel nonlinear Radial Basis Function Neural Network (RBF-NN) ensemble model based on ?-Support Vector Machine (SVM) regression is presented for financial time series forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets
Classical Mathematical Models for Description and Forecast of Preclinical Tumor Growth
Boyer, Edmond
! 1! Classical Mathematical Models for Description and Forecast of Preclinical Tumor Growth2013 #12;! 2! Abstract Tumor growth is a complex process involving a large number of biological formalized with the help of mathematical models. Based on experimental data of in vivo syngeneic tumor growth
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.
NASA Astrophysics Data System (ADS)
Xu, Wei; Zhang, Chi; Peng, Yong; Fu, Guangtao; Zhou, Huicheng
2014-12-01
This paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process.
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.
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.
Censored regression modeling in agricultural economics
Khee-Guan Tan, Andrew
1991-01-01
a large number of observations reporting zero expenditures. The literature on censored regression modeling is quite extensive. Previous research focused on the development of various estimation techniques and tests for model adequacy. More... data, it is typical to encounter problems with non-reporting 27 Table 1. Agricultural Economic Studies Employing Censored Regression Models Author(s) Estimation Techniques S 'Sc tion Tests Non-Norm, Heterosced. Omit. Var. Consumer Demand Capps...
Application of a new phenomenological coronal mass ejection model to space weather forecasting
NASA Astrophysics Data System (ADS)
Howard, T. A.; Tappin, S. J.
2010-07-01
Recent work by the authors has produced a new phenomenological model for coronal mass ejections (CMEs). This model, called the Tappin-Howard (TH) Model, takes advantage of the breakdown of geometrical linearity when CMEs are observed by white-light imagers at large distances from the Sun. The model extracts 3-D structure and kinematic information on the CME using heliospheric image data. This can estimate arrival times of the CME at 1 AU and impact likelihood with the Earth. Hence the model can be used for space weather forecasting. We present a preliminary evaluation of this potential with three mock trial forecasts performed using the TH Model. These are already-studied events from 2003, 2004 and 2007 but we performed the trials assuming that they were observed for the first time. The earliest prediction was made 17 hours before impact and predicted arrival times reached differences within one hour for at least one forecast for all three events. The most accurate predicted arrival time was 15 min from the actual, and all three events reach accuracies of the order of 30 min. Arrival speeds were predicted to be very similar to the bulk plasma speed within the CME near 1 AU for each event, with the largest difference around 300 km/s and the least 40 km/s. The model showed great potential and we aspire to fully validate it for integration with existing tools for space weather forecasting.
ENERGY DEMAND FORECAST METHODS REPORT
....................................................................................1-5 Sectoral Energy Demand Forecast ModelsCALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400
NASA Astrophysics Data System (ADS)
Pérez-Muñuzuri, V.
1998-11-01
A nonlinear forecasting method based on the reconstruction of a chaotic strange attractor from about 1.5 years of cloud absorption data obtained from half-hourly Meteosat infrared images was used to predict the behavior of the time series 24 h in advance. The forecast values are then used by a meteorological model for daily prediction of plume transport from the As Pontes 1400-MW power plant in northwestern Spain. Results from the meteorological model, using the cloud absorption predictions, are compared with measurements obtained from meteorological towers and a Remtech PA-3 sodar. The effects of cloud absorption on SO2 ground-level concentration forecasts are analyzed for two different days.
Verleysen, Michel
Time series forecasting with SOM and local non-linear models - Application to the DAX30 index.blayo@prefigure.com, verleysen@dice.ucl.ac.be Keywords: time series forecasting, local models, financial prediction, returns Abstract-- A general method for time series fore- casting is presented. Based on the splitting of the past
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.
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.
Applied Welfare Economics with Discrete Choice Models
Harvey Rosen; Kenneth A. Small
1981-01-01
Economists have been paying increasing attention to the study of situations in which consumers face a discrete rather than a continuous set of choices. Such models are potentially very important in evaluating the impact of government programs upon consumer welfare. But very little has been said in general regarding the tools of applied welfare economics indiscrete choice situations. This paper
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.
Applications of AR*-GRNN model for financial time series forecasting
Weimin Li; Yishu Luo; Qin Zhu; Jianwei Liu; Jiajin Le
2008-01-01
AR* models contain Autoregressive Moving Average and Generalized Autoregressive Conditional Heteroscedastic class model which\\u000a are widely used in time series. Recent researches in forecasting with Generalized Regression Neural Network (GRNN) suggest\\u000a that GRNN can be a promising alternative to the linear and nonlinear time series models. In this paper, a model composed of\\u000a AR* and GRNN is proposed to take
Evaluation of Different Model-Error Schemes in Mesoscale Ensemble Forecasts (Invited)
NASA Astrophysics Data System (ADS)
Berner, J.; Smith, K. R.; Ha, S.; Hacker, J.; Snyder, C.
2013-12-01
The performance of several different model-error schemes and selected combinations is verified for probabilistic forecasts with the WRF-ARW mesoscale ensemble system over the Contiguous United States. Including a model-error representation leads to more spread and small, but significant increases in forecast skill. In the free atmosphere, a stochastic kinetic-energy backscatter scheme performs best, while multiple-physics schemes tend to be superior near the surface. Combing multiple stochastic and deterministic parameterizations results in the biggest improvement throughout. To investigate if the model-error schemes are able to represent structural uncertainty or if the improved skill is solely the result of an increase in ensemble spread, two additional computations were performed: First, the Brier score is decomposed into reliability, resolution and uncertainty, which have different sensitivities to spread. Secondly, all forecasts are calibrated to have the same variance as the observations, which results in similar ensemble spreads. In the raw and re-calibrated ensemble systems, the decomposition of the Brier score improves both, the resolution and reliability component, indicating that the benefits of including a model-error scheme goes beyond increasing the ensemble spread. The improvements are quantified for biased and de-biased forecast. We find that the relative performance of the different model-error schemes remains similar in the raw and postprocessed ensemble experiments.
Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic
2014-01-01
The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region. PMID:24842026
Model developed for economic gas dispatch
Leary, A. (Essex County Gas Co., Amesbury, MA (United States)); Curtis, E.J. (Curtis Associates Inc., York Harbor, ME (United States))
1993-12-01
Essex County Gas Co. is using at new, highly efficient approach to simulate the daily economic dispatch of gas supplies to meet market requirements under FERC Order 636. Although sophisticated in its design, the modeling environment permits straightforward model construction with richly detailed components that can be readily changed as needed by gas utility personnel after only a short training period. It provides a mechanism for very detailed simulation of the market and supply balances governing LDC operations. The model serves as Essex County Gas' primary what if tool for testing the operational and economic consequences of a wide variety of supply and demand-side-management alternatives. The model, developed by consultant E.J. Curtis, is driven by Effective Heating Degree-Day daily weather patterns, such as design, normal, warm and extreme. The model is driven by weather patterns input as time series, so other independent variables such as general inflation factors, energy cost projections and economic model results can also be input as time-series data. Alternatively, detailed submodels for such components can be imbedded within the model to automatically generate this information. It incorporates supply and market simulation elements, permitting ready adaptation for use not only in conventional supply planning but also integrated resource management. Comparative what if ' cases can be run with specific demand-side management initiatives toggled on and off.
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.
NASA Astrophysics Data System (ADS)
Smith, Paul; Pappenberger, Florian
2015-04-01
Bayesian Model Averaging and Non-homogeneous Gaussian Regression have been proposed as techniques for post-processing ensemble forecasts into predictive probability distributions. Both methods make use of past forecast data for which observations are available to propose weights for the ensemble members along with bias and dispersion corrections. The mathematical basis and application of these methods though differs significantly. In this work we contrast the forecast results derived using these methods within the European Flood Awareness System, an operational flood forecasting system covering Europe. The performance of the different methods at lead times up to 15 days is compared at multiple sites and for notable flood events.
Hybrid grey theories and BP algorithm neural network forecast model in logistics park
Zhan-gen Wang
2011-01-01
Hybrid grey theories and BP algorithm neural network forecast model(GBPNN) is established based on the season influence factor from the logistics park side and chose the 'trainlm' to practice from a comparison and analysis of three BP algorithm function which named 'traingd, trainlm, thaingdx'. The season influence factor which is used as the GBPNN input layer provided a expert evaluation
NASA Astrophysics Data System (ADS)
Couach, O.; Kirchner, F.; Porchet, P.; Balin, I.; Parlange, M.; Balin, D.
2009-04-01
Map3D, the acronym for "Mesoscale Air Pollution 3D modelling", was developed at the EFLUM laboratory (EPFL) and received an INNOGRANTS awards in Summer 2007 in order to move from a research phase to a professional product giving daily air quality forecast. It is intended to give an objective base for political decisions addressing the improvement of regional air quality. This tool is a permanent modelling system which provides daily forecast of the local meteorology and the air pollutant (gases and particles) concentrations. Map3D has been successfully developed and calculates each day at the EPFL site a three days air quality forecast over Europe and the Alps with 50 km and 15 km resolution, respectively (see http://map3d.epfl.ch). The Map3D user interface is a web-based application with a PostgreSQL database. It is written in object-oriented PHP5 on a MVC (Model-View-Controller) architecture. Our prediction system is operational since August 2008. A first validation of the calculations for Switzerland is performed for the period of August 2008 - January 2009 comparing the model results for O3, NO2 and particulates with the results of the Nabel measurements stations. The subject of air pollution regimes (NOX/VOC) and specific indicators application with the forecast will be also addressed.
Dynamic generalized linear models for non-Gaussian time series forecasting
K. Triantafyllopoulos
2008-01-01
The purpose of this paper is to provide a discussion, with illustrating examples, on Bayesian forecasting for dynamic generalized linear models (DGLMs). Adopting approximate Bayesian analysis, based on conjugate forms and on Bayes linear estimation, we describe the theoretical framework and then we provide detailed examples of response distributions, including binomial, Poisson, negative binomial, geometric, normal, log-normal, gamma, exponential, Weibull,
Modeling and forecasting hourly electric load by multiple linear regression with interactions
Tao Hong; Min Gui; Mesut E. Baran; H. Lee Willis
2010-01-01
Short-term electric load modeling and forecasting has been intensively studied during the past 50 years. With the emerging development of smart grid technologies, demand side management (DSM) starts to attract the attention of electric utilities again. To perform a decent DSM, beyond when and how much the demand will be, the utilities are facing another question: why is the electricity
Philip Doganis; Eleni Aggelogiannaki; Haralambos Sarimveis
2008-01-01
Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored,
Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach
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.
Regional demand forecasting and simulation model: user's manual. Task 4, final report
Parhizgari
1978-01-01
The Department of Energy's Regional Demand Forecasting Model (RDFOR) is an econometric and simulation system designed to estimate annual fuel-sector-region specific consumption of energy for the US. Its purposes are to (1) provide the demand side of the Project Independence Evaluation System (PIES), (2) enhance our empirical insights into the structure of US energy demand, and (3) assist policymakers in
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.
Frank W. Ciarallo; Raymond R. Hill; Sriram Mahadevan; Vikrant Chopra; Patrick J. Vincent; Christoper S. Allen
2005-01-01
The Mobility Aircraft Availability Forecasting (MAAF) model prototype development and study effort was initiated to help the United States Air Force Air Mobility Command (AMC) answer the question, “How can we accurately predict mission capable (MC) rates?” While perfect prediction of aircraft MC rates is not possible, we investigate a simulation-based risk analysis approach. Current prediction methods utilize “after the
Building the Undergraduate Knowledge Base in Observing, Modeling and Forecasting Space Weather
D. J. Knipp; M. G. McHarg
2006-01-01
Although much has been written about space weather, there is little information available at the undergraduate level about the fundamental processes involved in dealing with such a complex system. In this talk we discuss the contents of three chapters in a new undergraduate space weather textbook that deal specifically with the role of observations, models and forecasts in space weather.
? ADAPTING SOIL MOISTURE RETRIEVAL ALGORITHM FOR DATA ASSIMILATION INTO WEATHER FORECASTING MODELS
F. Posa; C. Notarnicola
Soil moisture prediction is of utmost importance also in relation to data assimilation in weather forecasting model. In this case soil moisture retrieval algorithms need to adapt in order to meet resolution, spatial coverage and time computation requirements. These problems have to be faced differently as they are pertinent to different aspects of the inversion algorithms. In this paper we
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.
A Comparative Analysis of Load Balancing Algorithms Applied to a Weather Forecast Model
Eduardo R. Rodrigues; P. O. A. Navaux; J. Panetta; A. Fazenda; C. L. Mendes; Laxmikant V. Kale
2010-01-01
Among the many reasons for load imbalance in weather forecasting models, the dynamic imbalance caused by localized variations on the state of the atmosphere is the hardest one to handle. As an example, active thunderstorms may substantially increase load at a certain time step with respect to previous time steps in an unpredictable manner - after all, tracking storms is
Tourism Demand Modeling and Forecasting: A Review of Literature Related to Greater China
Gang Li
2009-01-01
Greater China, including Mainland China, Hong Kong, Macau, and Taiwan, contributes significantly to both regional and global tourism developments. Empirical research on tourism demand modeling and forecasting has attracted increasing attention of scholars both within and beyond this region. One hundred eighty articles are identified that were published in both English? and Chinese?language journals since the beginning of the 1990s.
Ecological forecasting in Chesapeake Bay: Using a mechanistic-empirical modeling approach
NASA Astrophysics Data System (ADS)
Brown, C. W.; Hood, R. R.; Long, W.; Jacobs, J.; 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.
Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling
Weaver, Harold A. "Hal"
time series are difference stationary, and occasional mean shifts, parameter instability, stochasticFacts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling Serena Ng-war recessions in the US in being driven by deleveraging and financial market factors. We document how recessions
A Novel Hybrid Intelligent Model for Financial Time Series Forecasting and Its Application
Wei Wang; Hong Zhao; Qiang Li; Zhixiong Liu
2009-01-01
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data into
Volatility Forecasts in Financial Time Series with HMM-GARCH Models
Chen, Yiling
Volatility Forecasts in Financial Time Series with HMM-GARCH Models Xiong-Fei Zhuang and Lai. 1 Introduction Volatility analysis of financial time series is an important aspect of many financial]. Their success stems from their ability to capture some stylized facts of financial time series, such as time
Brad Seely; Clive Welham; Hamish Kimmins
2002-01-01
The effect of alternative harvesting practices on long-term ecosystem productivity and carbon sequestration was investigated with the ecosystem simulation model, FORECAST. Three tree species, white spruce (Picea glauca), trembling aspen (Populus tremuloides), and lodgepole pine (Pinus contorta var. latifolia), were each used in combination with different rotation lengths. An additional run was conducted to investigate the effect of nitrogen addition
with detectable salinity increases before 2050. The objective was to examine whether salinity increases can algorithms and solution methods were developed to solve the equations for variable-density groundwater flow to use a mathematical model to try to forecast the increases in salinity that might occur in a particular
Development and evaluation of the operational Air-Quality forecast model for Austria ALARO-CAMx
NASA Astrophysics Data System (ADS)
Flandorfer, Claudia; Hirtl, Marcus; Krüger, Bernd C.
2014-05-01
The Air-Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Life Sciences (BOKU) in Vienna by order of the regional governments since 2005. The modeling system is currently a combination of the meteorological model ALARO and the photochemical dispersion model CAMx. Two modeling domains are used with the highest resolution (5 km) in the alpine region. Various extensions with external data sources have been conducted in the past to improve the daily forecasts of the model. Since 2013 O3- and PM10-observations from the Austrian measurement network have been assimilated daily using optimum interpolation. Dynamic chemical boundary conditions are obtained from Air-Quality forecasts provided by ECMWF in the frame of MACC-II. Additionally the latest available high resolved emission inventories for Austria are combined with TNO and EMEP data. The biogenic emissions are provided by the SMOKE model. ZAMG provides daily forecasts of O3, PM10 and NO2 to the regional governments of Austria. The evaluation of these forecasts is done for the summer 2013 with the main focus on the forecasts of ozone. The measurements of the Air-Quality stations are compared with the punctual forecasts at the sites of the station and with the area forecasts for every province of Austria. In the summer of 2013, two heat waves occurred. The first very short heat wave was in June 2013. During this period one exceedance of the alert threshold value for ozone occurred. The second heat wave took place from the end of July to the mid of August. Due to very high temperatures (new temperature record for Austria measured in Bad Deutsch-Altenburg with 40.5°C) and long dryness episodes the information threshold value has been exceeded several times in the eastern regions of Austria. The alert threshold value has been exceeded one time in this period. For the evaluation, the results for the second heat wave episode in Eastern Austria will be discussed in detail.
A multi-model Python wrapper for operational oil spill transport forecasts
NASA Astrophysics Data System (ADS)
Hou, X.; Hodges, B. R.; Negusse, S.; Barker, C.
2015-01-01
The Hydrodynamic and oil spill modeling system for Python (HyosPy) is presented as an example of a multi-model wrapper that ties together existing models, web access to forecast data and visualization techniques as part of an adaptable operational forecast system. The system is designed to automatically run a continual sequence of hindcast/forecast hydrodynamic models so that multiple predictions of the time-and-space-varying velocity fields are already available when a spill is reported. Once the user provides the estimated spill parameters, the system runs multiple oil spill prediction models using the output from the hydrodynamic models. As new wind and tide data become available, they are downloaded from the web, used as forcing conditions for a new instance of the hydrodynamic model and then applied to a new instance of the oil spill model. The predicted spill trajectories from multiple oil spill models are visualized through Python methods invoking Google MapTM and Google EarthTM functions. HyosPy is designed in modules that allow easy future adaptation to new models, new data sources or new visualization tools.
W. M. McHugh; J. M. Storie; J. W. Lockett; S. G. Scott; E. A. Holt
1977-01-01
Operating instructions and system documentation for a computerized energy demand forecasting model are presented. The model has the capability to forecast energy demand for four fuel types for the three Northwest states, in five-year steps, from 1980 through the year 2000. The forecasts were further broken down into the residential, commercial, industrial, transportation, and other sectors. The model written in
Use of Ceres-Wheat Model for Wheat Yield Forecast in Beijing
Xian Wang; Chunjiang Zhao; Cunjun Li; Liangyun Liu; Wenjiang Huang; Pengxin Wang
2009-01-01
\\u000a The CERES-Wheat model was applied to simulate yields from 2005 to 2007 at Xiaotangshan of northern Beijing. Experiment datum\\u000a required by CERESWheat model were all collected and checked. In addition, 1974-2004 climate records were taken and calculated\\u000a as predictive weather scenario used for yield forecasting. The model calibration adopted simulation results of 2005 and which\\u000a of the other two years
Downey, P.C.; Klontz, G.W.
1983-03-01
Computer implementation of the mathematical models of quantitative relationships in aquaculture systems is a dynamic process which provides a conceptual framework for understanding systems behavior. These models can provide useful information on variable significance to systems functioning. This computer-implemented mathematical model addresses one of the significant limitations of aquaculture systems management, namely, production forecasting, by providing a method of using current technology to predict Allowable Growth Rate (AGR).
8:00 Tsunami Overview Eddie Bernard 8:30 Tsunami Forecast Modeling and Discussion Vasily Titov
8:00 Tsunami Overview Eddie Bernard 8:30 Tsunami Forecast Modeling and Discussion Vasily Titov 9:15 Tsunami Hazard Assessment and Discussion Diego Arcas 10:00 Break 10:15 Tsunami Measurements: Tour and Discussion Chris Meinig 11:30 Tsunami Forecast System Demonstration Don Denbo, Chris Moore 12:15 Tsunami Wrap
Real-time forecasts of PM2.5 aerosol mass from seven air-quality forecast models (AQFMs) are statistically evaluated against observations collected in the northeastern U.S. and southeastern Canada from two surface networks and aircraft data during the summer of 2004 IC...
A Versatile Model for Evaluating Thermal EOR Production Economics
Scott H. Stevens; Vello A. Kuuskraa
1998-01-01
California currently has over 20,000 wells producing heavy oil within thermal enhanced oil recovery (TEOR) projects, producing 410,000 BOPD or about 7% of total U.S. oil pro- duction. Forecasting future TEOR production under alterna- tive economic scenarios is a daunting task but important to predicting future heavy oil supplies in the U.S. In support of the U.S. Department of Energy
Sparse High Dimensional Models in Economics
Fan, Jianqing; Lv, Jinchi; Qi, Lei
2010-01-01
This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed. PMID:22022635
NASA Astrophysics Data System (ADS)
Mendoza, Pablo; McPhee, James; Vargas, Ximena
2010-05-01
This paper focuses on the application of Topnet, a physically based distributed hydrological model, for real - time flood forecasting purposes. The area of interest is the Cautin River basin, located in Southern Chile (38° 29' S and 72° 00' W). The catchment area is 2688 km2 and the annual mean rainfall is 2346 mm. After calibration, the model is able to reproduce hourly streamflow at the basin outlet successfully. However, it is impossible to get reliable simulations for all flood events analyzed using the same set of parameters. In order to reduce model uncertainty, an ensemble Kalman filter implementation was calibrated and applied, demonstrating that model simulations can improve significantly. Furthermore, Talagrand histograms and Q-Q plots indicate that it is possible to get good ensemble properties in a rainy period. Model calibration and assimilation results suggest that lack of information about the spatial variability of model parameters hinders our ability to obtain a reliable propagation of information from the outlet to a gauged upstream point, in opposition to results obtained assimilating streamflows only at an interior location. Finally, we combine the hydrological model with a 5-day weather forecast based on the WRF model, and show the skill of the proposed framework in forecasting maximum flows in a basin with limited basic information.
Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay
Jacobs, John M.; Rhodes, M.; Brown, C. W.; Hood, Raleigh R.; Leight, A.; Long, Wen; Wood, R.
2014-11-01
The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.
NASA Astrophysics Data System (ADS)
Xia, J.; O'Connor, K. M.; Kachroo, R. K.; Liang, G. C.
1997-12-01
A non-linear perturbation model for river flow forecasting is developed, based on consideration of catchment wetness using an antecedent precipitation index (API). Catchment seasonality, of the form accounted for in the linear perturbation model (the LPM), and non-linear behaviour both in the runoff generation mechanism and in the flow routing processes are represented by a constrained non-linear model, the NLPM-API. A total of ten catchments, across a range of climatic conditions and catchment area magnitudes, located in China and in other countries, were selected for testing daily rainfall-runoff forecasting with this model. It was found that the NLPM-API model was significantly more efficient than the original linear perturbation model (the LPM). However, restriction of explicit non-linearity to the runoff generation process, in the simpler LMP-API form of the model, did not produce a significantly lower value of the efficiency in flood forecasting, in terms of the model efficiency index R2.
A Hidden Markov Model for avalanche forecasting on Chowkibal-Tangdhar road axis in Indian Himalayas
NASA Astrophysics Data System (ADS)
Joshi, Jagdish Chandra; Srivastava, Sunita
2014-12-01
A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal-Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992-2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008-2009, 2009-2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.
Economic evolutions and their resilience: a model
Breitenecker, M.; Gruemm, H.
1981-04-01
The report designs a highly aggregated macroeconomic model that can be formulated in terms of a system of ordinary differential equations. The report consists of two parts supplementing each other in a sort of symbiosis. One part is the abstract structure of the equations - that is, the individual dependence of the time variations of the state variables (which span the state space) on the variables themselves (which in this model are E, K, and L). The other part is the parameter space, each point of which is a set of parameter values that have a well-defined economic meaning and thereby endow the system with economic content. (Copyright (c) 1981, International Institute for Applied Systems Analysis.)
Enhanced seasonal forecast skill following stratospheric sudden warmings
NASA Astrophysics Data System (ADS)
Sigmond, M.; Scinocca, J. F.; Kharin, V. V.; Shepherd, T. G.
2013-02-01
Advances in seasonal forecasting have brought widespread socio-economic benefits. However, seasonal forecast skill in the extratropics is relatively modest, prompting the seasonal forecasting community to search for additional sources of predictability. For over a decade it has been suggested that knowledge of the state of the stratosphere can act as a source of enhanced seasonal predictability; long-lived circulation anomalies in the lower stratosphere that follow stratospheric sudden warmings are associated with circulation anomalies in the troposphere that can last up to two months. Here, we show by performing retrospective ensemble model forecasts that such enhanced predictability can be realized in a dynamical seasonal forecast system with a good representation of the stratosphere. When initialized at the onset date of stratospheric sudden warmings, the model forecasts faithfully reproduce the observed mean tropospheric conditions in the months following the stratospheric sudden warmings. Compared with an equivalent set of forecasts that are not initialized during stratospheric sudden warmings, we document enhanced forecast skill for atmospheric circulation patterns, surface temperatures over northern Russia and eastern Canada and North Atlantic precipitation. We suggest that seasonal forecast systems initialized during stratospheric sudden warmings are likely to yield significantly greater forecast skill in some regions.
NASA Astrophysics Data System (ADS)
Chen, L. C.; Mo, K. C.; Zhang, Q.; Huang, J.
2014-12-01
Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Starting in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the North American Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the predictive skill of meteorological drought using real-time NMME forecasts for the period from May 2012 to May 2014. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation coefficient and root-mean-square errors against the observations, are used to evaluate forecast skill.Similar to the assessment based on NMME retrospective forecasts, predictive skill of monthly-mean precipitation (P) forecasts is generally low after the second month and errors vary among models. Although P forecast skill is not large, SPI predictive skill is high and the differences among models are small. The skill mainly comes from the P observations appended to the model forecasts. This factor also contributes to the similarity of SPI prediction among the six models. Still, NMME SPI ensemble forecasts have higher skill than those based on individual models or persistence, and the 6-month SPI forecasts are skillful out to four months. The three major drought events occurred during the 2012-2014 period, the 2012 Central Great Plains drought, the 2013 Upper Midwest flash drought, and 2013-2014 California drought, are used as examples to illustrate the system's strength and limitations. For precipitation-driven drought events, such as the 2012 Central Great Plains drought, NMME SPI forecasts perform well in predicting drought severity and spatial patterns. For fast-developing drought events, such as the 2013 Upper Midwest flash drought, the system failed to capture the onset of the drought.
Testing for ontological errors in probabilistic forecasting models of natural systems.
Marzocchi, Warner; Jordan, Thomas H
2014-08-19
Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265
Testing for ontological errors in probabilistic forecasting models of natural systems
Marzocchi, Warner; Jordan, Thomas H.
2014-01-01
Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265
NASA Astrophysics Data System (ADS)
Adamowski, J. F.; Khalil, B. E.; Broda, S.; Ozga-Zielinski, B.
2014-12-01
In this study, five data-driven models were evaluated for groundwater level short-term forecasting under tailings recharge from an abandoned mine in Quebec, Canada. Multiple linear regression (MLR) models were used as a linear model, while artificial neural network (ANN) models were used as a non-linear model. In addition, two hybrid models that utilize wavelet transforms for data preprocessing with MLR or ANN models (W-MLR, W-ANN) were considered for the evaluation of the usefulness of wavelet analysis with linear and nonlinear models. The fifth model was a wavelet bootstrap ANN (W-B-ANN) model. Three predictors were considered as inputs: the tailing recharge, total precipitation, and mean air temperature. Results showed that models using wavelets for data preprocessing (W-MLR and W-ANN) performed better than their corresponding basic models (MLR and ANN), which highlights the ability of wavelet transforms to decompose non-stationary data into discrete wavelet components, highlighting cyclic patterns and trends in the time series at varying temporal scales, rendering the data usable in forecasting. In general, with or without wavelets, ANN models performed better than MLR models; this indicates the nonlinear relationship between the three predictors and the groundwater level. Overall, the W-B-ANN model outperformed all models for each of the three lead-times, which highlights the usefulness of bootstrap modeling, and ensuring model robustness along with improved reliability by reducing variance.
EC-EARTH: an Earth System Model based on the ECWMF Integrated Forecasting System
NASA Astrophysics Data System (ADS)
Selten, F.; Bintanja, R.; Yang, S.; Severijns, C.; Semmler, T.; Wyser, K.; Wang, X.; Hazeleger, W.
2009-04-01
EC-EARTH is the name of an Earth system model that is being developed by a number of institutes in Europe. It is based on the Integrated Forecast System of the European Centre for Medium Range Weather Forecasts (ECWMF). The ECMWF model delivers the best weather forecasts in the world by an objective measure. However, when applied to climate time scales, the performance is not better than the state-of-art climate models by an objective metrics. In the Numerical Weather Prediction version, the top of the atmosphere fluxes (TOA) are not balanced with observed sea surface temperatures as a lower boundary condition. After consultation of experts at ECMWF, a set of parameters was identified that could be used to reduce the model biases and close the TOA heat budget. We describe a set of tuning experiments and show the subsequent improvements in the simulated climate by an objective metrics. The adjusted model at T159L62 resolution coupled to the NEMO2/ORCA1 ocean model outperforms the mean CMIP3 model using this metrics. Additional transient integrations show the extent to which 'fast processes' contribute to the errors in the mean state and variance.
Forecasting the behaviour of complex landslides with a spatially distributed hydrological model
NASA Astrophysics Data System (ADS)
Malet, J.-P.; van Asch, Th. W. J.; van Beek, R.; Maquaire, O.
2005-01-01
The relationships between rainfall, hydrology and landslide movement are often difficult to establish. In this context, ground-water flow analyses and dynamic modelling can help to clarify these complex relations, simulate the landslide hydrological behaviour in real or hypothetical situations, and help to forecast future scenarios based on environmental change. The primary objective of this study is to investigate the possibility of including more temporal and spatial information in landslide hydrology forecasting, by using a physically based spatially distributed model. Results of the hydrological and geomorphological investigation of the Super-Sauze earthflow, one of the persistently active landslide occurring in clay-rich material of the French Alps, are presented. Field surveys, continuous monitoring and interpretation of the data have shown that, in such material, the groundwater level fluctuates on a seasonal time scale, with a strong influence of the unsaturated zone. Therefore a coupled unsaturated/saturated model, incorporating Darcian saturated flow, fissure flow and meltwater flow is needed to adequately represent the landslide hydrology. The conceptual model is implemented in a 2.5-D spatially distributed hydrological model. The model is calibrated and validated on a multi-parameters database acquired on the site since 1997. The complex time-dependent and three-dimensional groundwater regime is well described, in both the short- and long-term. The hydrological model is used to forecast the future hydrological behaviour of the earthflow in response to potential environmental changes.
Álvaro González; Miguel Vázquez-Prada; Javier B. Gómez; Amalio F. Pacheco
2006-01-01
Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example,
G. Perona; R. Notarpietro; M. Gabella; A. Speranza
2003-01-01
In this paper we report the results of feasibility studies concerning the use of radars on polar orbiting space-platforms for the direct observation of meteorological fields (in particular large-scale vertical velocity) that are crucial in the initialisation and verification of models for NWF (Numerical Weather Forecast). Specifically, we have made reference to LAM (Limited Area Models) with horizontal grid-size of
A Modeling Approach for Flash Flood Forecasting for Small Watersheds in Iowa
W. S. Lincoln; K. J. Franz
2008-01-01
Current flood predictions are limited by often out-dated statistical guidance and a rigid modeling system that seldom accounts for basin-specific hydrologic response times. The National Weather Service (NWS) SACramento Soil Moisture Accounting Model (SACSMA), which is used to generate short-range (1-7 days) streamflow forecasts, is most commonly run at a 6-hour timestep. The 6-hour timestep can be inadequate for capturing
Wensheng Dai; Chi-Jie Lu
2008-01-01
In this paper, a financial time series forecasting model based on wavelet frame and support vector regression is proposed. In the proposed model, wavelet frame is first used to decompose the predicting variables into sub-series with different scales. The hidden information of the predicting variables could be discovered in these sub-series. The SVR then uses the sub-series to build the
Time-Series Forecasting by Means of Linear and Nonlinear Models
Janset Kuvulmaz; Serkan Usanmaz; Seref Naci Engin
2005-01-01
\\u000a The main objective of this paper is two folds. First is to assess some well-known linear and nonlinear techniques comparatively\\u000a in modeling and forecasting financial time series with trend and seasonal patterns. Then to investigate the effect of pre-processing\\u000a procedures, such as seasonal adjustment methods, to the improvement of the modeling capability of a nonlinear structure implemented\\u000a as ANNs in
Forecasting, Structural Time Series Models and the Kalman Filter
Andrew C. Harvey
1989-01-01
In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology.
A hybrid linear-neural model for river flow forecasting
M. Chetan; K. P. Sudheer
2006-01-01
This paper presents a novel hybrid linear-neural (LN) model formulation to effectively model rainfall-runoff processes. The central idea of the proposed model framework is that the hidden layer of an artificial neural network (ANN) model be designed with a combination of linear and nonlinear neurons. A training algorithm for the proposed model is designed based on minimum description length criteria.
W. M. McHugh; J. M. Storie; J. W. Lockett; S. G. Scott; E. A. Holt
1977-01-01
This document contains operating instructions and system documentation for a computerized energy demand forecasting model. The model has the capability to forecast energy demand for four fuel types (electricity, gas, oil, and coal), for the three Northwest states (Washington, Oregon, and Idaho), in five-year steps, from 1980 through the year 2000. The forecasts are further broken down into the Residential,
NASA Astrophysics Data System (ADS)
Hong, Mei; Zhang, Ren; Wang, Dong; Feng, Mang; Wang, Zhengxin; Singh, Vijay P.
2015-07-01
To address the inaccuracy of long-term El Niño-Southern Oscillation (ENSO) forecasts, a new dynamical-statistical forecasting model of the ENSO index was developed based on dynamical model reconstruction and improved self-memorization. To overcome the problem of single initial prediction values, the largest Lyapunov exponent was introduced to improve the traditional self-memorization function, thereby making it more effective for describing chaotic systems, such as ENSO. Equation reconstruction, based on actual data, was used as a dynamical core to overcome the problem of using a simple core. The developed dynamical-statistical forecasting model of the ENSO index is used to predict the sea surface temperature anomaly in the equatorial eastern Pacific and El Niño/La Niña events. The real-time predictive skills of the improved model were tested. The results show that our model predicted well within lead times of 12 months. Compared with six mature models, both temporal correlation and root mean square error of the improved model are slightly worse than those of the European Centre for Medium-Range Weather Forecasts model, but better than those of the other five models. Additionally, the margin between the forecast results in summer and those in winter is not great, which means that the improved model can overcome the "spring predictability barrier", to some extent. Finally, a real-time prediction experiment is carried out beginning in September 2014. Our model is a new exploration of the ENSO forecasting method.
Role of multiple-scale modeling of epilepsy in seizure forecasting.
Kuhlmann, Levin; Grayden, David B; Wendling, Fabrice; Schiff, Steven J
2015-06-01
Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here, we review the literature on the multiple-scale neural modeling of epilepsy and the use of such models to infer physiologic changes underlying epilepsy and epileptic seizures. The authors argue how these methods can be applied to advance the state-of-the-art in seizure forecasting. PMID:26035674
Home Economics Education Career Path Guide and Model Curriculum Standards.
ERIC Educational Resources Information Center
California State Univ., Northridge.
This curriculum guide developed in California and organized in 10 chapters, provides a home economics education career path guide and model curriculum standards for high school home economics programs. The first chapter contains information on the following: home economics education in California, home economics careers for the future, home…
Robert Alan Clements; Frederic Paik Schoenberg; Danijel Schorlemmer
2011-01-01
Modern, powerful techniques for the residual analysis of spatial-temporal point process models are reviewed and compared. These methods are applied to California earthquake forecast models used in the Collaboratory for the Study of Earthquake Predictability (CSEP). Assessments of these earthquake forecasting models have previously been performed using simple, low-power means such as the L-test and N-test. We instead propose residual
Hurricane Intensity Forecasts with a Global Mesoscale Model on the NASA Columbia Supercomputer
NASA Technical Reports Server (NTRS)
Shen, Bo-Wen; Tao, Wei-Kuo; Atlas, Robert
2006-01-01
It is known that General Circulation Models (GCMs) have insufficient resolution to accurately simulate hurricane near-eye structure and intensity. The increasing capabilities of high-end computers (e.g., the NASA Columbia Supercomputer) have changed this. In 2004, the finite-volume General Circulation Model at a 1/4 degree resolution, doubling the resolution used by most of operational NWP center at that time, was implemented and run to obtain promising landfall predictions for major hurricanes (e.g., Charley, Frances, Ivan, and Jeanne). In 2005, we have successfully implemented the 1/8 degree version, and demonstrated its performance on intensity forecasts with hurricane Katrina (2005). It is found that the 1/8 degree model is capable of simulating the radius of maximum wind and near-eye wind structure, and thereby promising intensity forecasts. In this study, we will further evaluate the model s performance on intensity forecasts of hurricanes Ivan, Jeanne, Karl in 2004. Suggestions for further model development will be made in the end.
Optimizing numerical weather forecasting models for the CRAY-1 and cyber 205 computers
NASA Astrophysics Data System (ADS)
Dickinson, A.
1982-06-01
The Meteorological Office has recently acquired a Cyber 205 computer system which will be used to run large numerical models of the atmosphere and weather forecasting models. In particular a new operational forecast model has been developed for this computer and this is being coded to take full advantage of the vector capabilities of this machine. Development versions of this model have also been run on a Cray-1 at the European Centre for Medium Range Weather Forecasts. This has presented the opportunity to compare some of the features of these two computers. It is shown that in general numerical weather prediction models can be split into two sections, the dynamical and physical processes. In the dynamical processes the same operations are applied at every point in the integration domain and so these processes are readily vectorizable. However, the physical processes are conditional and intermittent in nature and use must be made of the mask/merge, compress/expand and gather/scatter class of instructions in order to vectorize these processes.
The efficiency of the Weather Research and Forecasting (WRF) model for simulating typhoons
NASA Astrophysics Data System (ADS)
Haghroosta, T.; Ismail, W. R.; Ghafarian, P.; Barekati, S. M.
2014-08-01
The Weather Research and Forecasting (WRF) model includes various configuration options related to physics parameters, which can affect the performance of the model. In this study, numerical experiments were conducted to determine the best combination of physics parameterization schemes for the simulation of sea surface temperatures, latent heat flux, sensible heat flux, precipitation rate, and wind speed that characterized typhoons. Through these experiments, several physics parameterization options within the Weather Research and Forecasting (WRF) model were exhaustively tested for typhoon Noul, which originated in the South China Sea in November 2008. The model domain consisted of one coarse domain and one nested domain. The resolution of the coarse domain was 30 km, and that of the nested domain was 10 km. In this study, model simulation results were compared with the Climate Forecast System Reanalysis (CFSR) data set. Comparisons between predicted and control data were made through the use of standard statistical measurements. The results facilitated the determination of the best combination of options suitable for predicting each physics parameter. Then, the suggested best combinations were examined for seven other typhoons and the solutions were confirmed. Finally, the best combination was compared with other introduced combinations for wind-speed prediction for typhoon Washi in 2011. The contribution of this study is to have attention to the heat fluxes besides the other parameters. The outcomes showed that the suggested combinations are comparable with the ones in the literature.
NASA Astrophysics Data System (ADS)
Schmalwieser, Alois W.; Schauberger, Günther
2000-11-01
Since October 1995, a daily forecast of the UV index, as the irradiance of the biologically effective ultraviolet radiation, for the next day is published for Austria, Europe, and world wide. The Austrian forecast model as well as the input parameters are described. By connecting the UV index with the sensitivity of the photobiological skin types, a recommendation is given to select an appropriate sun protection factor of a sunscreen to avoid overexposure of the skin. The validation of the Austrian forecast model is done by measurements of the biologically effective ultraviolet radiation collected between July 1996 and July 1998 at Vienna (48°N, 16°E), Austria. The forecast quality is evaluated by comparing the Austrian model with two statistical models used in Canada and the Netherlands. By using the underestimation of the UV index as criteria in the sense of radiation protection, the Austrian model shows a 12% underestimation over the whole year.
NASA Astrophysics Data System (ADS)
Baroncini, F.; Castelli, F.
2009-09-01
Data assimilation techniques based on Ensemble Filtering are widely regarded as the best approach in solving forecast and calibration problems in geophysics models. Often the implementation of statistical optimal techniques, like the Ensemble Kalman Filter, is unfeasible because of the large amount of replicas used in each time step of the model for updating the error covariance matrix. Therefore the sub optimal approach seems to be a more suitable choice. Various sub-optimal techniques were tested in atmospheric and oceanographic models, some of them are based on the detection of a "null space". Distributed Hydrologic Models differ from the other geo-fluid-dynamics models in some fundamental aspects that make complex to understanding the relative efficiency of the different suboptimal techniques. Those aspects include threshold processes , preferential trajectories for convection and diffusion, low observability of the main state variables and high parametric uncertainty. This research study is focused on such topics and explore them through some numerical experiments on an continuous hydrologic model, MOBIDIC. This model include both water mass balance and surface energy balance, so it's able to assimilate a wide variety of datasets like traditional hydrometric "on ground" measurements or land surface temperature retrieval from satellite. The experiments that we present concern to a basin of 700 kmq in center Italy, with hourly dataset on a 8 months period that includes both drought and flood events, in this first set of experiment we worked on a low spatial resolution version of the hydrologic model (3.2 km). A new Kalman Filter based algorithm is presented : this filter try to address the main challenges of hydrological modeling uncertainty. The proposed filter use in Forecast step a COFFEE (Complementary Orthogonal Filter For Efficient Ensembles) approach with a propagation of both deterministic and stochastic ensembles to improve robustness and convergence proprieties. After, through a P.O.D. Reduction from control theory, we compute a Reduced Order Forecast Covariance matrix . In analysis step the filter uses a LE (Local Ensemble) Kalman Filter approach. We modify the LE Kalman Filter assimilation scheme and we adapt its formulation to the P.O.D. Reduced sub-space propagated in forecast step. Through this, assimilation of observations is made only in the maximum covariance directions of the model error. Then the efficiency of this technique is weighed in term of hydrometric forecast accuracy in a preliminary convergence test of a synthetic rainfall event toward a real rain fall event.
Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification
Olalotiti-Lawal, Feyisayo
2013-05-17
Rapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make future forecasts. It is often important to also quantify inherent risks...
Empirical model for forecasting of Total Electron content over Europe
A. Krankowski; L. W. Baran; I. I. Shagimuratov; I. I. Ephishov
2003-01-01
A simple empirical model for prediction of ionospheric total electron content at European Latitudes is presented. To develop the TEC model the permanent GPS observations with middle latitudes stations: Matera, Boroviec, Lamkowko and Onsala from years 1996 to 2001 were used. When estimating of TEC from GPS measurements we used one layer approximation of ionosphere and local model of TEC
NASA Astrophysics Data System (ADS)
Xia, Daqing; Xu, Youping
1998-06-01
In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in which the dynamic framework of hydrostatic equilibrium is taken. The main contributions are: the mixing ratios of all species of hydrometeors are added as the prognostic variables of model, the prognostic equations of these hydrometeors are introduced, the cloud physical framework is specially designed, some technical measures are used to resolve a series of physical, mathematical and computational problems arising from water-bearing; and so on. The various problems (in such aspects as the designs of physical and calculating schemes and the composition of computational programme) which are exposed in feasibility test, in sensibility test, and especially in operational forecasting experiments are successfully resolved using a lot of technical measures having been developed from researches and tests. Finally, the operational forecasting running of the water-bearing numerical model and its forecasting system is realized stably and reliably, and the fine forecasts are obtained. All of these mentioned above will be described in second paper.
The Economics of Digital Business Models: A Framework for Analyzing the Economics of Platforms
Eric Brousseau; Thierry Penard
2007-01-01
The paper proposes an analytical framework for comparing different business models for producing information goods and digital services. It is based on three dimensions that also refer to contrasted literature: the economics of matching, the economics of assembling and the economics of knowledge management. Our framework attempts to identify the principal trade-offs at the core of choices among alternative digital
NASA Astrophysics Data System (ADS)
Sharapatov, Abish
2015-04-01
Standard models of skarn-magnetite deposits in folded regions of Kazakhstan, is made by using generalized geological and geophysical parameters of the similar existing deposits. Such models might be Sarybay, Sokolovskoe and other deposits of Valeryanovskaya structural-facies zone (SFZ) in Torgay paleorifts structure. They are located in the north of SFZ. Forecasting area located in the south of SFZ - in the North of Aral Sea region. These models are outlined from the study of deep structure of the region using geophysical data. Upper and deep zones were studied by separating gravity and magnetic fields on the regional and local components. Seismic and geoelectric data of region were used in interpretation. Thus, the similarity between northern and southern part of SFZ has been identified in geophysical aspects, regional and local geophysical characteristics. Creation of standard models of scarn-magnetite deposits for GIS database allows highlighting forecast criteria of such deposits type. These include: - the presence of fault zones; - thickness of volcanic strata - about 2 km or more, the total capacity of circum-ore metasomatic rocks - about 1.5 km and more; - spatial positions and geometric data of the ore bodies - steeply dipping bodies in the medium gabbroic intrusions and their contact with carbonate-dolomitic strata; - presence in the geological section of the near surface zone with the electrical resistance of 200 Om*m, corresponding to the Devonian, Early Carboniferous volcanic sediments and volcanics associated with subvolcanic bodies and intrusions; - a relatively shallow depth of the zone at a rate of Vp = 6.4-6.8 km/s - uplifting Conrad border, thickening of the granulite-basic layer; - positive values of magnetic (high-amplitude) and gravitational field. A geological forecast model is carried out by structuring geodata based on detailed analysis and aggregation of geological and formal knowledge bases on standard targets. Aggregation method of geological knowledge constitutes development of bank models of the analyzed geological structures within various ranges, ore-bearing features described by numerous prospecting indicators. Created standard models are entered into database of specialized GIS-technology. Models are used for solving forecasting tasks on the principle of comparing the formalized features of the standard with the forecast objects. Quantitative estimation is the similarity coefficient. Database is necessary in the planning methodology for conducting field research, and in subsurface resource management in the region.
A component time-series model for SO 2 data: Forecasting, interpretation and modification
Gerd Tetzlaff
1997-01-01
A time-series forecasting method is developed to enable advance warning of smog in winter. A component model for the time series of SO2 concentration essentially using a recursive Kalman algorithm is constructed on the basis of spectral analysis. It is found that the smog episodes with low frequencies and time-dependent power spectra are solely represented by the trend component. This
P. Guhathakurta; M. Rajeevan; V. Thapliyal
1999-01-01
Summary ?The existing methods based on statistical techniques for long range forecasts of Indian monsoon rainfall have shown reasonably\\u000a accurate performance, for last 11 years. Because of the limitation of such statistical techniques, new techniques may have\\u000a to be tried to obtain better results. In this paper, we discuss the results of an artificial neural network model by combining\\u000a two different
Ralf Giering; Thomas Kaminski; Ricardo Todling; Ronald Errico; Ronald Gelaro; Nathan Winslow
The NASA finite volume General Circulation Model (fvGCM) is a three-dimensional Navier-Stokes solver that is being used for quasi-operational weather forecasting at NASA-GSFC. By means of TAF, ecient tangent linear and adjoint versions are generated from the Fortran-90 source code of fvGCM's dynamical core. fvGCM's parallelisa- tion capabilities based on OpenMP and MPI have been transferred to the tangent linear
Tangent Linear and Adjoint Versions of NASA\\/GMAO’s Fortran 90 Global Weather Forecast Model
Ralf Giering; Thomas Kaminski; Ricardo Todling; Ronald Errico; Ronald Gelaro; Nathan Winslow
The NASA finite-volume General Circulation Model (fvGCM) is a three-dimensional Navier-Stokes solver being used for quasi-operational weather forecasting at NASA\\/GMAO. We use the automatic differentiation tool TAF to generate eficient tangent linear and adjoint versions from the Fortran 90 source code of fvGCM’s dynamical core. fvGCM’s parallelisation capabilities based on OpenMP and MPI have been transferred to the tangent linear
Alfred J. Cavallo
2004-01-01
Following Hubbert’s successful prediction of the timing of US peak oil production, Hubbert’s model has been used extensively to predict peak oil production elsewhere. However, forecasts of world and regional peak oil and natural gas production using Hubbert’s methodology usually have failed, leading to the implicit belief that such predictions always will fail and that we need not worry about
H. Rott; M. Baumgartner; R. Ferguson; G. Glendinning; B. Johansson; T. Nagler; O. Pirker; S. Quegan; G. Wright
1999-01-01
The HYDALP project is aimed at the application of Earth observation data to improve modelling and forecasting of daily runoff in alpine and high latitude basins. SAR from ERS-1,-2 and Radarsat, together with optical images from SPOT, Landsat, IRS, NOAA, and RESURS are used for snow cover monitoring. Automatic and semi-automatic tools have been developed for generating snow maps from
A new model for time-series forecasting using radial basis functions and exogenous data
Juan Manuel Górriz Sáez; Carlos García Puntonet; Moisés Salmerón; Juan José González De La Rosa
2004-01-01
In this paper, we present a new model for time-series forecasting using radial basis functions (RBFs) as a unit of artificial neural networks (ANNs), which allows the inclusion of exogenous information (EI) without additional pre-processing. We begin by summarizing the most well-known EI techniques used ad hoc, i.e., principal component analysis (PCA) and independent component analysis (ICA). We analyze the
NASA Astrophysics Data System (ADS)
Sullivan, Z.; Fan, X.
2014-12-01
Karst is defined as a landscape that contains especially soluble rocks such as limestone, gypsum, and marble in which caves, underground water systems, over-time sinkholes, vertical shafts, and subterranean river systems form. The cavities and voids within a karst system affect the hydrology of the region and, consequently, can affect the moisture and energy budget at surface, the planetary boundary layer development, convection, and precipitation. Carbonate karst landscapes comprise about 40% of land areas over the continental U.S east of Tulsa, Oklahoma. Currently, due to the lack of knowledge of the effects karst has on the atmosphere, no existing weather model has the capability to represent karst landscapes and to simulate its impact. One way to check the impact of a karst region on the atmosphere is to check the performance of existing weather models over karst and non-karst regions. The North American Mesoscale (NAM) operational forecast is the best example, of which historical forecasts were archived. Variables such as precipitation, maximum/minimum temperature, dew point, evapotranspiration, and surface winds were taken into account when checking the model performance over karst versus non-karst regions. The forecast verification focused on a five-year period from 2007-2011. Surface station observations, gridded observational dataset, and North American Regional Reanalysis (for certain variables with insufficient observations) were used. Thirteen regions of differing climate, size, and landscape compositions were chosen across the Contiguous United States (CONUS) for the investigation. Equitable threat score (ETS), frequency bias (fBias), and root-mean-square error (RMSE) scores were calculated and analyzed for precipitation. RMSE and mean bias (Bias) were analyzed for other variables. ETS, fBias, and RMSE scores show generally a pattern of lower forecast skills, a greater magnitude of error, and a greater under prediction of precipitation over karst than non-karst regions. In addition, standardized data was used to eliminate differences from varying climates across CONUS. The metrics derived from the standardized data shows further evidence that the NAM forecast had lower forecast skills and an overall higher magnitude of error over karst than non-karst regions.
Gas-field deliverability forecasting: A coupled reservoir simulator and surface facilities model
Trick, M.D. [Neotechnology Consultants Ltd. (United States); Agarwal, R. [Computer Modelling Group (United States); Ammer, J.R.; Mercer, J.C. [USDOE Morgantown Energy Technology Center, WV (United States); Harris, R.P. [National Fuel Gas Supply Corp. (United States)
1994-08-01
To determine if a gas contract can be satisified now and in the future, it is necessary to forecast the performance of the gas reservoir, the gas inflow into the sandface, the multiphase pressure losses in the wellbore and gathering system and the field facilities. Surface production models which rigorously model from the sandface to the plant gate are available. However, these surface packages model reservoirs simply, in most cases as tank-type reservoirs. Comprehensive 3 dimensional reservoir simulators are available, but typically only include simple surface networks which don`t adequately model multiphase flow in complex gathering systems. This paper describes the procedures used in a joint venture by two software vendors to combine an existing reservoir simulator and an existing surface facilities model into a single forecasting tool. Relatively small changes were made to each program. In the new model, the black oil reservoir simulator provides the formation pressure and water to gas ratio for each well. The surface facilities model then calculates the multiphase flow pressure losses in the wellbore and gathering system, plus the corresponding flow rates for each well. The actual production required from each well to satisfy the pipeline contractual requirements, over each time step, is computed by the surface facilities model and relayed back to the reservoir simulator. The time step is determined dynamically according to the requirements of each program. The performance and results from the coupled model are compared to that of running each model separately for a gas storage field in the USA and for a gas production field with bottom-water. It is shown that running each model separately does not account for all the factors affecting the forecast.
Forecasting the distribution of multi-step in?ation: do macro variables matter?
Sebastiano Manzana; Dawit Zerom
The evidence in the in?ation forecasting literature suggests that simple time series models are typically hard to outperform in predicting the dynamics of the flrst moment, and that using information about indicators of economic activity does not lead to out-of-sample forecasting gains. While most of the earlier literature focused on the ability of leading indicators (via the Phillips Curve -
Jiansheng Wu; Jie Zhou; Yuelin Gao
2009-01-01
Accurate rainfall forecasting has been one of the most important role in order to reduce the risk to life and to alleviate economic losses by natural disasters. Recently, support vector regression (SVR) provides an alternative approach for developing rainfall forecasting model due to the use of a risk function consisting of the empirical error and a regularized term which is
Regional forecasting with global atmospheric models; Final report
T. J. Crowley; N. R. Smith
1994-01-01
The purpose of the project was to conduct model simulations for past and future climate change with respect to the proposed Yucca Mtn. repository. The authors report on three main topics, one of which is boundary conditions for paleo-hindcast studies. These conditions are necessary for the conduction of three to four model simulations. The boundary conditions have been prepared for
NASA Astrophysics Data System (ADS)
Halliwell, G. R.; Srinivasan, A.; Kourafalou, V. H.; Yang, H.; Le Henaff, M.; Atlas, R. M.
2012-12-01
The accuracy of hurricane intensity forecasts produced by coupled forecast models is influenced by errors and biases in SST forecasts produced by the ocean model component and the resulting impact on the enthalpy flux from ocean to atmosphere that powers the storm. Errors and biases in fields used to initialize the ocean model seriously degrade SST forecast accuracy. One strategy for improving ocean model initialization is to design a targeted observing program using airplanes and in-situ devices such as floats and drifters so that assimilation of the additional data substantially reduces errors in the ocean analysis system that provides the initial fields. Given the complexity and expense of obtaining these additional observations, observing system design methods such as OSSEs are attractive for designing efficient observing strategies. A new fraternal-twin ocean OSSE system based on the HYbrid Coordinate Ocean Model (HYCOM) is used to assess the impact of targeted ocean profiles observed by hurricane research aircraft, and also by in-situ float and drifter deployments, on reducing errors in initial ocean fields. A 0.04-degree HYCOM simulation of the Gulf of Mexico is evaluated as the nature run by determining that important ocean circulation features such as the Loop Current and synoptic cyclones and anticyclones are realistically simulated. The data-assimilation system is run on a 0.08-degree HYCOM mesh with substantially different model configuration than the nature run, and it uses a new ENsemble Kalman Filter (ENKF) algorithm optimized for the ocean model's hybrid vertical coordinates. The OSSE system is evaluated and calibrated by first running Observing System Experiments (OSEs) to evaluate existing observing systems, specifically quantifying the impact of assimilating more than one satellite altimeter, and also the impact of assimilating targeted ocean profiles taken by the NOAA WP-3D hurricane research aircraft in the Gulf of Mexico during the Deepwater Horizon oil spill. OSSE evaluation and calibration is then performed by repeating these two OSEs with synthetic observations and comparing the resulting observing system impact to determine if it differs from the OSE results. OSSEs are first run to evaluate different airborne sampling strategies with respect to temporal frequency of flights and the horizontal separation of upper-ocean profiles during each flight. They are then run to assess the impact of releasing multiple floats and gliders. Evaluation strategy focuses on error reduction in fields important for hurricane forecasting such as the structure of ocean currents and eddies, upper ocean heat content distribution, and upper-ocean stratification.
Kuang Yu Huang; J. Chuen-jiuan; Ting-cheng Chang
2008-01-01
In this study, the weight clustering model which consists of GM(1,N) with K-means Clustering is combined with grey systems theory and rough set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM(1,1) predicting model to forecast the future trends of
M. Verdecchia; E. Coppola; C. Faccani; R. Ferretti; A. Memmo; M. Montopoli; G. Rivolta; T. Paolucci; E. Picciotti; A. Santacasa; B. Tomassetti; G. Visconti; F. S. Marzano
2008-01-01
Summary A flood forecast chain, developed at the Centre of Excellence for Remote Sensing and Hydro-Meteorology (CETEMPS) and based\\u000a on coupled mesoscale atmospheric and a newly developed distributed hydrological model with in-situ and remote sensing data\\u000a integration, is illustrated. The focus is on small-catchment flood forecast in complex topography in Central Italy, but the\\u000a developed modelling and processing integrated tools may
R. Console; M. Murru; G. Falcone
2010-01-01
A stochastic triggering (epidemic) model incorporating short-term clustering was fitted to the instrumental earthquake catalog\\u000a of Italy for event with local magnitudes 2.6 and greater to optimize its ability to retrospectively forecast 33 target events\\u000a of magnitude 5.0 and greater that occurred in the period 1990–2006. To obtain an unbiased evaluation of the information value\\u000a of the model, forecasts of
Data sensitivity in a hybrid STEP/Coulomb model for aftershock forecasting
NASA Astrophysics Data System (ADS)
Steacy, S.; Jimenez Lloret, A.; Gerstenberger, M.
2014-12-01
Operational earthquake forecasting is rapidly becoming a 'hot topic' as civil protection authorities seek quantitative information on likely near future earthquake distributions during seismic crises. At present, most of the models in public domain are statistical and use information about past and present seismicity as well as b-value and Omori's law to forecast future rates. A limited number of researchers, however, are developing hybrid models which add spatial constraints from Coulomb stress modeling to existing statistical approaches. Steacy et al. (2013), for instance, recently tested a model that combines Coulomb stress patterns with the STEP (short-term earthquake probability) approach against seismicity observed during the 2010-2012 Canterbury earthquake sequence. They found 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. They suggested that the major reason for this discrepancy was uncertainty in the slip models and, in particular, in the geometries of the faults involved in each complex major event. Here we test this hypothesis by developing a number of retrospective forecasts for the Landers earthquake using hypothetical slip distributions developed by Steacy et al. (2004) to investigate the sensitivity of Coulomb stress models to fault geometry and earthquake slip, and we also examine how the choice of receiver plane geometry affects the results. We find that the results are strongly sensitive to the slip models and moderately sensitive to the choice of receiver orientation. We further find that comparison of the stress fields (resulting from the slip models) with the location of events in the learning period provides advance information on whether or not a particular hybrid model will perform better than STEP.
FINANCIAL REPORTS AND FORECASTING ACCURACY
GIAMPAOLO GABBI
The research is aimed at verifying the reliability of real forecasts periodically published by international financial intermediaries. During the last years we have recorded a sensible increase of analysis and research papers to support investment decision centres, both for real and financial economics. Firstly, our research will show an analysis of the database provided by 13 banks and economic forecasting
NASA Astrophysics Data System (ADS)
Chang, W.; Tsai, W.; Lin, F.; Lin, S.; Lien, H.; Chung, T.; Huang, L.; Lee, K.; Chang, C.
2008-12-01
During a typhoon or a heavy storm event, using various forecasting models to predict rainfall intensity, and water level variation in rivers and flood situation in the urban area is able to reveal its capability technically. However, in practice, the following two causes tend to restrain the further application of these models as a decision support system (DSS) for the hazard mitigation. The first one is due to the difficulty of integration of heterogeneous models. One has to take into consideration the different using format of models, such as input files, output files, computational requirements, and so on. The second one is that the development of DSS requires, due to the heterogeneity of models and systems, a friendly user interface or platform to hide the complexity of various tools from users. It is expected that users can be governmental officials rather than professional experts, therefore the complicated interface of DSS is not acceptable. Based on the above considerations, in the present study, we develop an open system for integration of several simulation models for flood forecasting by adopting the FEWS (Flood Early Warning System) platform developed by WL | Delft Hydraulics. It allows us to link heterogeneous models effectively and provides suitable display modules. In addition, FEWS also has been adopted by Water Resource Agency (WRA), Taiwan as the standard operational system for river flooding management. That means this work can be much easily integrated with the use of practical cases. In the present study, based on FEWS platform, the basin rainfall-runoff model, SOBEK channel-routing model, and estuary tide forecasting model are linked and integrated through the physical connection of model initial and boundary definitions. The work flow of the integrated processes of models is shown in Fig. 1. This differs from the typical single model linking used in FEWS, which only aims at data exchange but without much physical consideration. So it really makes the tighter collaboration work among these hydrological models. In addition, in order to make communication between system users and decision makers efficient and effective, a real-time and multi-user communication platform, designated as Co-life, is incorporated in the present study. Through its application sharing function, the flood forecasting results can be displayed for all attendees situated at different locations to help the processes of decision making for hazard mitigation. Fig. 2 shows the cyber-conference of WRA officials with the Co-life system for hazard mitigation during the typhoon event.
NASA Astrophysics Data System (ADS)
Artan, G. A.; Shrestha, M.; Tokar, S.; Rowland, J.; Verdin, J. P.; Amer, S.
2012-12-01
Floods are the most common and widespread climate-related hazards throughout the globe. Most human losses due to floods occur in the tropical regions of Africa, Asia, and Central America. The use of flood forecasting can reduce the death toll associated with floods. Recent research suggests that the frequency and severity of extreme rainfall events will increase; therefore, there is an urgent need for timely flood forecasting. In those tropical regions, a paucity of the ground-based precipitation data collection networks and the lack of data sharing across international borders for trans-boundary basins have made it impractical to use traditional flood forecasting that relies on station-measured precipitation data. Precipitation estimated from satellite data offers an effective means for calculating areal precipitation estimates in sparsely gauged regions. Because of the apparent uncertainty associated with satellite-based precipitation estimates, the use of such data in hydrologic modeling has been limited in the past. We will present results from our research on the utility of precipitation estimates from satellite data for flood forecasting and snowpack monitoring purposes. We found that remotely sensed precipitation data in combination with distributed hydrologic models can play an important role in early warning and monitoring of floods. For large basins the results of hydrologic models forced with satellite-based precipitation were comparable those the stream flow simulated stream using precipitation measured with ground-based networks. Snowpack simulated with precipitation estimates from satellite data underestimated the snow water content compared with snow water recorded by the SNOTEL network or simulated by SNODAS system; nevertheless, the estimates were found to be useful in mapping the snowpack.
Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting
NASA Astrophysics Data System (ADS)
Owens, M. J.; Horbury, T. S.; Wicks, R. T.; McGregor, S. L.; Savani, N. P.; Xiong, M.
2014-06-01
Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.
Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir
Almohammadi, Hisham
2013-07-26
................................................................................................ xi 1. INTRODUCTION AND LITERATURE REVIEW ...................................... 1 1.1 Introduction ......................................................................................... 1 1.2 Background... ......................................................................................... 3 1.3 Research Objectives .......................................................................... 21 2. METHODOLOGY AND MODEL DESCRIPTION ................................... 22 2.1 Introduction...
Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir
Almohammadi, Hisham
2013-07-26
................................................................................................ xi 1. INTRODUCTION AND LITERATURE REVIEW ...................................... 1 1.1 Introduction ......................................................................................... 1 1.2 Background... ......................................................................................... 3 1.3 Research Objectives .......................................................................... 21 2. METHODOLOGY AND MODEL DESCRIPTION ................................... 22 2.1 Introduction...
NASA Astrophysics Data System (ADS)
Ismail, A.; Hassan, Noor I.
2013-09-01
Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.
GMRVVm-SVR model for financial time series forecasting
Hui Jiang; Zhizhong Wang
2010-01-01
The complex model GMRVVm–SVR has been adopted to predict financial time series with such characteristics as small sample size, poor information, non-stationary, high noise and non-linearity. In order to construct GMRVVm–SVR, the m-root grey model with revised verge value (GMRVVm) has been introduced and modified by support vector regression based on the calculation of the residual error sequence between predicted
Effect of data quality on a hybrid Coulomb/STEP model for earthquake forecasting
NASA Astrophysics Data System (ADS)
Steacy, Sandy; Jimenez, Abigail; Gerstenberger, Matt; Christophersen, Annemarie
2014-05-01
Operational earthquake forecasting is rapidly becoming a 'hot topic' as civil protection authorities seek quantitative information on likely near future earthquake distributions during seismic crises. At present, most of the models in public domain are statistical and use information about past and present seismicity as well as b-value and Omori's law to forecast future rates. A limited number of researchers, however, are developing hybrid models which add spatial constraints from Coulomb stress modeling to existing statistical approaches. Steacy et al. (2013), for instance, recently tested a model that combines Coulomb stress patterns with the STEP (short-term earthquake probability) approach against seismicity observed during the 2010-2012 Canterbury earthquake sequence. They found 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. They suggested that the major reason for this discrepancy was uncertainty in the slip models and, in particular, in the geometries of the faults involved in each complex major event. Here we test this hypothesis by developing a number of retrospective forecasts for the Landers earthquake using hypothetical slip distributions developed by Steacy et al. (2004) to investigate the sensitivity of Coulomb stress models to fault geometry and earthquake slip. Specifically, we consider slip models based on the NEIC location, the CMT solution, surface rupture, and published inversions and find significant variation in the relative performance of the models depending upon the input data.
Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece
NASA Astrophysics Data System (ADS)
Voukantsis, Dimitris; Niska, Harri; Karatzas, Kostas; Riga, Marina; Damialis, Athanasios; Vokou, Despoina
2010-12-01
Airborne pollen have been associated with allergic symptoms in sensitized individuals, having a direct impact on the overall quality of life of a considerable fraction of the population. Therefore, forecasting elevated airborne pollen concentrations and communicating this piece of information to the public are key issues in prophylaxis and safeguarding the quality of life of the overall population. In this study, we adopt a data-oriented approach in order to develop operational forecasting models (1-7 days ahead) of daily average airborne pollen concentrations of the highly allergenic taxa: Poaceae, Oleaceae and Urticaceae. The models are developed using a representative dataset consisting of pollen and meteorological time-series recorded during the years 1987-2002, in the city of Thessaloniki, Greece. The input variables (features) of the models have been optimized by making use of genetic algorithms, whereas we evaluate the performance of three algorithms: i) multi-Layer Perceptron, ii) support vector regression and iii) regression trees originating from distinct domains of Computational Intelligence (CI), and compare the resulting models with traditional multiple linear regression models. Results show the superiority of CI methods, especially when forecasting several days ahead, compared to traditional multiple linear regression models. Furthermore, the CI models complement each other, resulting to a combined model that performs better than each one separately. The overall performance ranges, in terms of the index of agreement, from 0.85 to 0.93 clearly suggesting the potential operational use of the models. The latter ones can be utilized in provision of personalized and on-time information services, which can improve quality of life of sensitized citizens.
NASA Astrophysics Data System (ADS)
Di, Zhenhua; Duan, Qingyun; Gong, Wei; Wang, Chen; Gan, Yanjun; Quan, Jiping; Li, Jianduo; Miao, Chiyuan; Ye, Aizhong; Tong, Charles
2015-01-01
global sensitivity analysis method was used to identify the parameters of the Weather Research and Forecasting (WRF) model that exert the most influence on precipitation forecasting. Twenty-three adjustable parameters were selected from seven physical components of the WRF model. The sensitivity was evaluated based on skill scores calculated over nine 5 day precipitation forecasts during the summer seasons from 2008 to 2010 in the Greater Beijing Area in China. We found that eight parameters are more sensitive than others. Storm type seems to have no impact on the list of sensitive parameters but does influence the degree of sensitivity. We also examined the physical interpretation of parameter sensitivity. This analysis is useful for further optimization of the WRF model parameters to improve precipitation forecasting.
Development of a dust deposition forecast model for a mine tailings impoundment
NASA Astrophysics Data System (ADS)
Stovern, Michael
Wind erosion, transport and deposition of particulate matter can have significant impacts on the environment. It is observed that about 40% of the global land area and 30% of the earth's population lives in semiarid environments which are especially susceptible to wind erosion and airborne transport of contaminants. With the increased desertification caused by land use changes, anthropogenic activities and projected climate change impacts windblown dust will likely become more significant. An important anthropogenic source of windblown dust in this region is associated with mining operations including tailings impoundments. Tailings are especially susceptible to erosion due to their fine grain composition, lack of vegetative coverage and high height compared to the surrounding topography. This study is focused on emissions, dispersion and deposition of windblown dust from the Iron King mine tailings in Dewey-Humboldt, Arizona, a Superfund site. The tailings impoundment is heavily contaminated with lead and arsenic and is located directly adjacent to the town of Dewey-Humboldt. The study includes in situ field measurements, computational fluid dynamic modeling and the development of a windblown dust deposition forecasting model that predicts deposition patterns of dust originating from the tailings impoundment. Two instrumented eddy flux towers were setup on the tailings impoundment to monitor the aeolian and meteorological conditions. The in situ observations were used in conjunction with a computational fluid dynamic (CFD) model to simulate the transport of windblown dust from the mine tailings to the surrounding region. The CFD model simulations include gaseous plume dispersion to simulate the transport of the fine aerosols, while individual particle transport was used to track the trajectories of larger particles and to monitor their deposition locations. The CFD simulations were used to estimate deposition of tailings dust and identify topographic mechanisms that influence deposition. Simulation results indicated that particles preferentially deposit in regions of topographic upslope. In addition, turbulent wind fields enhanced deposition in the wake region downwind of the tailings. This study also describes a deposition forecasting model (DFM) that can be used to forecast the transport and deposition of windblown dust originating from a mine tailings impoundment. The DFM uses in situ observations from the tailings and theoretical simulations of aerosol transport to parameterize the model. The model was verified through the use of inverted-disc deposition samplers. The deposition forecasting model was initialized using data from an operational Weather Research and Forecasting (WRF) model and the forecast deposition patterns were compared to the inverted-disc samples through gravimetric, chemical composition and lead isotopic analysis. The DFM was verified over several month-long observing periods by comparing transects of arsenic and lead tracers measured by the samplers to the DFM PM27 forecast. Results from the sampling periods indicated that the DFM was able to accurately capture the regional deposition patterns of the tailings dust up to 1 km. Lead isotopes were used for source apportionment and showed spatial patterns consistent with the DFM and the observed weather conditions. By providing reasonably accurate estimates of contaminant deposition rates, the DFM can improve the assessment of human health impacts caused by windblown dust from the Iron King tailings impoundment.
James H. Stock; Mark W. Watson
1999-01-01
This paper investigates forecasts of US inflation at the 12-month horizon. The starting point is the conventional unemployment rate Phillips curve, which is examined in a simulated out-of-sample forecasting framework. Inflation forecasts produced by the Phillips curve generally have been more accurate than forecasts based on other macroeconomic variables, including interest rates, money and commodity prices. These forecasts can however
A study of forecast growth with a barotropic model of the atmosphere
NASA Technical Reports Server (NTRS)
Halberstam, I. M.
1978-01-01
A barotropic model of the atmosphere was used to test various sources of forecast error. These errors are classified as truncation error, physical error, or initial error. It was shown that growth patterns due to each category differ significantly. Initial errors were shown not to grow in a barotropic model contrary to reports of other studies which indicate that they basically do grow. Also, random initial errors were shown to decrease due to the filtering effect of the model itself. Results seem to indicate that instabilities are required for error growth, be they barotropic or baroclinic, and that random errors are not representative of true initial conditions.
Model selection forecasts for the spectral index from the Planck satellite
Cédric Pahud; Andrew R Liddle; Pia Mukherjee; David Parkinson
2006-06-19
The recent WMAP3 results have placed measurements of the spectral index n_S in an interesting position. While parameter estimation techniques indicate that the Harrison-Zel'dovich spectrum n_S=1 is strongly excluded (in the absence of tensor perturbations), Bayesian model selection techniques reveal that the case against n_S=1 is not yet conclusive. In this paper, we forecast the ability of the Planck satellite mission to use Bayesian model selection to convincingly exclude (or favour) the Harrison-Zel'dovich model.
Quantifying the forecasting of characteristic-earthquake occurrence in a minimalist model
Vázquez-Prada, M; Gómez, J B; Pacheco, A F; Vazquez-Prada, Miguel; Gonzalez, Alvaro; Gomez, Javier B.; Pacheco, Amalio F.
2003-01-01
Using error diagrams "a la Molchan", we quantify the forecasting of characteristic-earthquake occurrence in a recently introduced minimalist model. A general strategy of optimization is tried out in order to improve the simple hypothesis of quasiperiodic behaviour for the time of return of the characteristic earthquake. This strategy consists in finding a property, related to the occurrence of earthquakes in the cycle, that divides the probability distribution of the time of return of the characteristic earthquake in two distinct distributions. These distributions should be clearly separated in time, and both must contain a sizeable part of the total probability. Developing this idea and combining retarding and advancing effects, an improvement in the forecasts is attained.
Growth Diagnostics for Dark Energy models and EUCLID forecast
Sampurnanand; Anjan A. Sen
2013-12-23
In this work we introduce a new set of parameters $(r_{g}, s_{g})$ involving the linear growth of matter perturbation that can distinguish and constrain different dark energy models very efficiently. Interestingly, for $\\Lambda$CDM model these parameters take exact value $(1,1)$ at all red shifts whereas for models different from $\\Lambda$CDM, they follow different trajectories in the $(r_{g}, s_{g})$ phase plane. By considering the parametrization for the dark energy equation of state ($w$) and for the linear growth rate ($f_{g}$), we show that different dark energy behaviours with similar evolution of the linear density contrast, can produce distinguishable trajectories in the $(r_{g}, s_{g})$ phase plane. Moreover, one can put stringent constraint on these phase plane using future measurements like EUCLID ruling out some of the dark energy behaviours.
González, A; Gómez, J B; Pacheco, A F; Gonzalez, Alvaro; Vazquez-Prada, Miguel; Gomez, Javier B.; Pacheco, Amalio F.
2005-01-01
Numerical models of seismic faults are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the accurate synchronization of the models. The rupture area is one of the measurable parameters of actual earthquakes. Here we explore how this can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forec...
Low flow forecasting for the Austrian Danube
NASA Astrophysics Data System (ADS)
Nester, Thomas; Kirnbauer, Robert; Blöschl, Günter; Viglione, Alberto; Kickinger, Peter
2013-04-01
Cargo traffic on the Danube depends on the water level of the Danube. If the water level is too low, heavy loaded push convoys could strand. Therefore, in the case of low flow periods, push convoys either need to unload parts of their cargo or wait for a higher water level. Both these options are not economic for the shippers. To give the shippers a better possibility to plan their cargo, via donau has commisioned a forecasting system to forecast low flows with a lead time of up to 168 hours. This paper gives a general overview of the low flow forecasting model for the Danube and its tributaries. Runoff is estimated for all tributaries to the Austrian Danube and the Danube (with a total size of more than 100.000 km²). The model is based on a conceptual water balance model. The catchments are divided into sub-basins with sizes ranging from 69 km² to 15.000 km² according to on-line available gauging stations. Hourly data from 91 discharge gauges as well as precipitation and temperature data from more than 170 stations were used to calibrate the runoff formation in the catchments. Results from different calibration periods are shown. Meteorological forecasts are used as input for the hydrologic forecasts. Both deterministic and ensemble forecasts cover a time span of 168 hours. A real time updating procedure based on ensemble Kalman filtering is implemented to have the best initial conditions.
NASA Astrophysics Data System (ADS)
Cole, Steven J.; Moore, Robert J.; Robson, Alice J.; Mattingley, Paul S.
2014-05-01
Across Britain, floods in rapidly responding catchments are a major concern and regularly cause significant damage (e.g. Boscastle 2004, Morpeth 2008, Cornwall 2010 and Comrie 2012). Typically these catchments have a small area and are characterised by steep slopes and/or significant suburban/urban land-cover. The meteorological drivers can be of convective origin or frontal with locally intense features (e.g. embedded convection or orographic enhancement); saturated catchments can amplify the flood response. Both rainfall and flood forecasting for Rapid Response Catchments (RRCs)are very challenging due to the often small-scale nature of the intense rainfall which is of most concern, the small catchment areas, and the short catchment response times. Over the last 3 to 4 years, new countrywide Flood Forecasting Systems based on the Grid-to-Grid (G2G) distributed hydrological (rainfall-runoff and routing) model have been implemented across Britain for use by the Flood Forecasting Centre and Scottish Flood Forecasting Service. This has achieved a step-change in operational capability with forecasts of flooding several days ahead "everywhere" on a 1 km grid now possible. The modelling and forecasting approach underpins countrywide Flood Guidance Statements out to 5 days which are used by emergency response organisations for planning and preparedness. The initial focus of these systems has been to provide a countrywide overview of flood risk. However, recent research has explored the potential of the G2G approach to support more frequent and detailed alerts relevant to flood warning in RRCs. Integral to this activity is the use of emerging high-resolution (~1.5km) rainfall forecast products, in deterministic and ensemble form. High spatial resolutions are required to capture some of the small-scale processes and intense rainfall features such as orographic enhancement and convective storm evolution. Even though a deterministic high-resolution numerical weather prediction (NWP) model can provide realistic looking rainfall forecasts, significant uncertainties remain in timing, location and whether a particular feature develops or not. Generally the smaller the scale of the rainfall feature, the shorter the lead-time at which these uncertainties become important. Therefore ensembles are needed to provide uncertainty context for longer lead-time G2G flow forecasts, particularly for small-scale RRCs. A systematic assessment framework has been developed for exploring and understanding the utility of G2G flood forecasts for RRCs. Firstly perfect knowledge of rainfall observations is assumed for past and future times, so as not to confound the hydrological model analysis with errors from rainfall forecasts. Secondly an assessment is made of using deterministic rainfall forecasts (from NWP UKV) in a full emulation of real-time G2G forecasts, and using foreknowledge of rainfall observations as a reference baseline. Finally use of rainfall forecast ensembles with G2G to produce probabilistic flood forecasts is considered, empploying a combination of case-study and longer-term analyses. Blended Ensemble rainfall forecasts (combining radar ensemble nowcast and NWP rainfalls) are assessed in two forms: forecasts out to 24 hours updated 4 times a day, and nowcasts out to 7 hours updated every 15 minutes. Results from the assessment will be presented along with candidates for new operational products and tools that can support flood warning for RRCs, taking account of the inherent uncertainty in the forecasts.
1998-01-01
The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.
JEDI: Jobs and Economic Development Impacts Model The Jobs and Economic Development Impact (JEDI America program to model wind energy jobs and impacts, JEDI has been expanded to biofuels, concentrating from industry norms), JEDI estimates the number of jobs and economic impacts to a local area (usually
Water Demand Forecasting in Umm Al-Quwain (UAE) Using the IWR-MAIN Specify Forecasting Model
Mohamed M. Mohamed; Aysha A. Al-Mualla
2010-01-01
IWR-MAIN software is used in this paper to forecast water demand in the Emirate of Umm Al-Quwain (UAQ), located in the northern\\u000a part of the United Arab Emirates (UAE), for the next twenty 5 years. Two different databases are used. The first one provides\\u000a average yearly water consumptions since 1980, while the second provides more detailed monthly water consumptions from 2000.
NASA Astrophysics Data System (ADS)
Choi, Hyun-Joo; Hong, Song-You
2015-04-01
The subgrid orographic parameterization scheme implemented in Global/Regional Integrated Model system (GRIMs), which is used as the reference in developing physics schemes of KIAPS Integrated Model, is updated by including effects of flow blocking and orographic anisotropy in addition to existing orographic gravity wave (GW) drag parameterization. The formula of the additional flow-blocking stress follows bulk aerodynamic drag form based in part on scale analysis, and the height of blocked layer is determined according to the dividing streamline theory. The formula of the GW stress is modified by including the effect of orographic anisotropy. To investigate impacts of the updated orographic parameterization scheme, short- and medium-range forecasts for heavy rainfall case over Korea (12 UTC 25 July-12 UTC 4 August 2011) and seasonal simulations (December-February 1996/97) are performed using the GRIMs with the updated scheme. The updated orographic parameterization scheme contributes to alleviate 10m wind speed overestimated over the land in the short- and medium-range forecasts due to the additional flow-blocking drag. In addition, the alleviated wind speed reduces surface fluxes by decreasing exchange coefficients which in turn affects surface temperature and precipitation. The wind forecasts are improved throughout the entire atmosphere from the troposphere to the stratosphere as well as near the surface, which is directly due to the modified GW drag and indirectly due to the interaction of the GW drag with the flow-blocking drag. In particular, the stratospheric winter polar night jet is simulated more realistically in the seasonal forecasts.
Post Audit of Lake Michigan Lake Trout PCB Model Forecasts
The Lake Michigan (LM) Mass Balance Study was conducted to measure and model polychlorinated biphenyls (PCBs) and other anthropogenic substances to gain a better understanding of the transport, fate, and effects of these substances within the system and to aid managers in the env...
Integrated Modeling for Watershed Ecosystem Services Assessment and Forecasting
Regional scale watershed management decisions must be informed by the science-based relationship between anthropogenic activities on the landscape and the change in ecosystem structure, function, and services that occur as a result. We applied process-based models that represent...
Lance Gentry
There are many good articles on various forecasting models. There is consensus that no single diffusion model is best for every situation. Experts in the field have asked for studies to provide empirical-based guidelines for recommending when various models should be used. This research investigates multiple diffusion models and provides recommendations for which diffusion models are appropriate for radical and
ERIC Educational Resources Information Center
Geroy, Gary D.
The validity of the University of Minnesota Skills Training Cost-Benefit Forecasting Model (STCBFM) in a corporate setting was studied. Research and related literature suggested that a model for forecasting the economic benefits of training should include facility to identify and summarize costs and provide an assessment of the value of the…
Modeling of High-altitude Atmospheric Dispersion Using Climate and Meteorological Forecast Data
Glascoe, L G; Chin, H S
2005-03-30
The overall objective of this study is to provide a demonstration of capability for importing both high altitude meteorological forecast and climatological datasets from NRL into the NARAC modeling system to simulate high altitude atmospheric droplet release and dispersion. The altitude of release for the proposed study is between 60 and 100km altitude. As either standard climatological data (over a period of 40 years) or daily meteorological forecasts can drive the particle dispersion model, we did a limited comparison of simulations with meteorological data and simulations with climatological data. The modeling tools used to address this problem are the National Atmospheric Release Advisory Center (NARAC) modeling system at LLNL which are operationally employed to assist DOE/DHS/DOD emergency response to an atmospheric release of chemical, biological, and radiological contaminants. The interrelation of the various data feeds and codes at NARAC are illustrated in Figure 1. The NARAC scientific models are all verified to both analytic solutions and other codes; the models are validated to field data such as the Prairie Grass study (Barad, 1958). NARAC has multiple real-time meteorological data feeds from the National Weather Service, from the European Center for Medium range Weather Forecasting, from the US Navy, and from the US Air Force. NARAC also keeps a historical archive of meteorological data partially for research purposes. The codes used in this effort were the Atmospheric Data Assimilation and Parameterization Techniques (ADAPT) model (Sugiyama and Chan, 1998) and a development version of the Langrangian Operational Dispersion Integrator (LODI) model (Nasstrom et al., 2000). The use of the NASA GEOS-4 dataset required the use of a development version of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model (Hodur, 1997; Chin and Glascoe, 2004). The specific goals of this study are the following: (1) Confirm data compatibility of NRL meteorological and climatological data for NARAC models. Import both high altitude meteorological forecasts and high altitude climatological data provided by NRL into the NARAC system. (2) Run ADAPT and LODI transport/dispersion codes for one scenario on imported meteorological forecast and climatological data. (3) Provide documentation of the effort. The following tasking description gives both the context and manner in which the goals listed above were accomplished: (A) We had discussions with NRL personnel, notably Stefan Thonnard and Doug Drob, to confirm the data compatibility of the data that we will be importing for use. Data up to 100km in altitude was provided and imported into the NARAC modeling system. (B) The ADAPT atmospheric data assimilation model was used to take data from NRL and provide mass-consistent three-dimensional time-varying wind fields for the NARAC Langrangian particle tracking code, LODI. A test version of LODI, developed to consider rarefied conditions, higher altitude turbulence, and high initial particle speeds, was used run on the ADAPT output. (C) The results of the proof-of-concept simulations under time-varying meteorological forecasts and under climatological wind fields are compared and documented in this brief report discussing the capability of the NARAC modeling system for importing and using the high altitude datasets from NRL. A limited assessment of the difference between dispersion results on the different data sets is made.
Application of hydrological models for flood forecasting and flood control in India and Bangladesh
NASA Astrophysics Data System (ADS)
Refsgaard, J. C.; Havnø, K.; Ammentorp, H. C.; Verwey, A.
A general mathematical modelling system for real-time flood forecasting and flood control planning is described. The system comprises a lumped conceptual rainfall-runoff model, a hydrodynamic model for river routing, reservoir and flood plain simulation, an updating procedure for real-time operation and a comprehensive data management system. The system is presently applied for real-time forecasting of the two 20 000 km 2 (Yamuna and Damodar) catchments in India as well as for flood control modelling at the same two catchments in India. In another project the system is being established for the entire Bangladesh with a coarse discretization and for the South East Region of Bangladesh with a fine model discretization. The objectives of the modelling application in Bangladesh are to enable predictions of the effects of alternative river regulation structures in terms of changes in water levels, inundations, siltration and salinity. The modelling system has been transferred to the Central Water Commission of India and the Master Plan Organization of Bangladesh in connection with comprehensive training programmes. The models are presently being operated by Indian and Bangladeshi engineers in the two countries.
NASA Astrophysics Data System (ADS)
Larnier, K.; Roux, H.; Garambois, P.; Dartus, D.
2012-04-01
The MARINE model (Roux et al, 2011) is a physically based distributed model dedicated to real time flash flood forecasting on small to medium catchments. The infiltration capacity is evaluated by the Green and Ampt equation and the surface runoff calculation is divided into two parts: the land surface flow and the flow in the drainage network both based on kinematic wave hypothesis. In order to take into account rainfall spatial-temporal variability as well as the various behaviours of soil types among the catchment, the model is spatially distributed, which can also help to understand the flood driving processes. The model integrates remote sensing data such as the land coverage map with spatial resolution adapted to hydrological scales. Minimal data requirements for the model are: the Digital Elevation Model describing catchment topography and the location and description of the drainage network. Moreover some parameters are not directly measurable and need to be calibrated. Most of the sources of uncertainties can be propagated thanks to variational method (Castaings et al, 2009) and finally help to determine time dependent uncertainty intervals. This study also investigates the methodology developed for real-time flash flood forecasting using the MARINE model and data assimilation techniques. According to prior sensitivity analyses and calibrations, parameters values were determined as constants or initial guess. Then a data assimilation method called the adjoint state method is used to update some of the most sensitive parameters to improve accuracy of discharges predictions. The forecast errors are evaluated as a function of lead time and discussed from an operational point of view. Multiple strategies in term of updatable parameters set, length of time window, parameters bounds and observation threshold used to trigger the assimilation method are discussed regarding accuracy, robustness and real-time feasibility.
NASA Astrophysics Data System (ADS)
Trendafiloski, G.; Gaspa Rebull, O.; Ewing, C.; Podlaha, A.; Magee, B.
2012-04-01
Calibration and validation are crucial steps in the production of the catastrophe models for the insurance industry in order to assure the model's reliability and to quantify its uncertainty. Calibration is needed in all components of model development including hazard and vulnerability. Validation is required to ensure that the losses calculated by the model match those observed in past events and which could happen in future. Impact Forecasting, the catastrophe modelling development centre of excellence within Aon Benfield, has recently launched its earthquake model for Algeria as a part of the earthquake model for the Maghreb region. The earthquake model went through a detailed calibration process including: (1) the seismic intensity attenuation model by use of macroseismic observations and maps from past earthquakes in Algeria; (2) calculation of the country-specific vulnerability modifiers by use of past damage observations in the country. The use of Benouar, 1994 ground motion prediction relationship was proven as the most appropriate for our model. Calculation of the regional vulnerability modifiers for the country led to 10% to 40% larger vulnerability indexes for different building types compared to average European indexes. The country specific damage models also included aggregate damage models for residential, commercial and industrial properties considering the description of the buildings stock given by World Housing Encyclopaedia and the local rebuilding cost factors equal to 10% for damage grade 1, 20% for damage grade 2, 35% for damage grade 3, 75% for damage grade 4 and 100% for damage grade 5. The damage grades comply with the European Macroseismic Scale (EMS-1998). The model was validated by use of "as-if" historical scenario simulations of three past earthquake events in Algeria M6.8 2003 Boumerdes, M7.3 1980 El-Asnam and M7.3 1856 Djidjelli earthquake. The calculated return periods of the losses for client market portfolio align with the repeatability of such catastrophe losses in the country. The validation process also included collaboration between Aon Benfield and its client in order to consider the insurance market penetration in Algeria estimated approximately at 5%. Thus, we believe that the applied approach led towards the production of an earthquake model for Algeria that is scientifically sound and reliable from one side and market and client oriented on the other side.
NASA Astrophysics Data System (ADS)
Makkeasorn, A.; Chang, N. B.; Zhou, X.
2008-05-01
SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.
Making the Best Use of Cybersecurity Economic Models
Rachel Rue; Shari Lawrence Pfleeger
2009-01-01
This article describes an analysis of several representative cybersecurity economic models, where the authors seek to determine whether each model's underlying assumptions are realistic and useful. They find that many of the assumptions are the same across disparate models, and most assumptions are far from realistic. They recommend several changes so that the predictions from economic models can be more
NASA Astrophysics Data System (ADS)
Pike, A.; Danner, E.; Lindley, S.; Melton, F. S.; Nemani, R. R.; Hashimoto, H.; Rajagopalan, B.; Caldwell, R. J.
2009-12-01
In the Central Valley of California, stream 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. However, many water resource-related decisions in regulated rivers rely upon models using a daily-to-monthly mean temperature standard. Furthermore, current water temperature models are limited by the lack of spatially detailed meteorological forecasts. To address this issue, we utilize the coupled TOPS-WRF (Terrestrial Observation and Prediction System - Weather Research and Forecasting) framework—a high-resolution (15min, 1km) assimilation of satellite-derived meteorological observations and numerical weather forecasts— to improve the spatial and temporal resolution of stream temperature predictions. In this study, we developed a high-resolution mechanistic 1-dimensional stream temperature model (sub-hourly time step, sub-kilometer spatial resolution) for the Upper Sacramento River in northern California. The model uses a heat budget approach to calculate the rate of heat transfer to/from the river. Inputs for the heat budget formulation are atmospheric variables provided by the TOPS-WRF model. The hydrodynamics of the river (flow velocity and channel geometry) are characterized using densely-spaced channel cross-sections and flow data. Water temperatures are calculated by considering the hydrologic and thermal characteristics of the river and solving the advection-diffusion equation in a mixed Eulerian-Lagrangian framework. Modeled hindcasted temperatures for a test period (May - November 2008) substantially improve upon the existing daily-to-monthly mean temperature standards. Modeled values closely approximate both the magnitude and the phase of measured water temperatures. Furthermore, our model results reveal important longitudinal patterns in diel temperature variation that are unique to regulated rivers, and may be critical to salmon habitat. Ultimately, end users will be able to access the model online, run various scenarios of water discharge and temperature under forecasted weather conditions (3-5 days, and seasonal), and inform decisions about water releases to maintain optimal temperatures for fishery health.
A high resolution Adriatic-Ionian Sea circulation model for operational forecasting
NASA Astrophysics Data System (ADS)
Ciliberti, Stefania Angela; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Vukicevic, Tomislava; Lecci, Rita; Verri, Giorgia; Kumkar, Yogesh; Creti', Sergio
2015-04-01
A new numerical regional ocean model for the Italian Seas, with focus on the Adriatic-Ionian basin, has been implemented within the framework of Technologies for Situational Sea Awareness (TESSA) Project. The Adriatic-Ionian regional model (AIREG) represents the core of the new Adriatic-Ionian Forecasting System (AIFS), maintained operational by CMCC since November 2014. The spatial domain covers the Adriatic and the Ionian Seas, extending eastward until the Peloponnesus until the Libyan coasts; it includes also the Tyrrhenian Sea and extends westward, including the Ligurian Sea, the Sardinia Sea and part of the Algerian basin. The model is based on the NEMO-OPA (Nucleus for European Modeling of the Ocean - Ocean PArallelise), version 3.4 (Madec et al. 2008). NEMO has been implemented for AIREG at 1/45° resolution model in horizontal using 121 vertical levels with partial steps. It solves the primitive equations using the time-splitting technique for solving explicitly the external gravity waves. The model is forced by momentum, water and heat fluxes interactively computed by bulk formulae using the 6h-0.25° horizontal-resolution operational analysis and forecast fields from the European Centre for Medium-Range Weather Forecast (ECMWF) (Tonani et al. 2008, Oddo et al. 2009). The atmospheric pressure effect is included as surface forcing for the model hydrodynamics. The evaporation is derived from the latent heat flux, while the precipitation is provided by the Climate Prediction Centre Merged Analysis of Precipitation (CMAP) data. Concerning the runoff contribution, the model considers the estimate of the inflow discharge of 75 rivers that flow into the Adriatic-Ionian basin, collected by using monthly means datasets. Because of its importance as freshwater input in the Adriatic basin, the Po River contribution is provided using daily average observations from ARPA Emilia Romagna observational network. AIREG is one-way nested into the Mediterranean Forecasting System (MFS, http://medforecast.bo.ingv.it/) using daily means fields computed from daily outputs of the 1/16° general circulation model. One-way nesting is done by a novel pre-processing tool for an on-the-fly computation of boundary datasets compatible with BDY module provided by NEMO. It imposes the interpolation constraint and correction as in Pinardi et al. (2003) on the total velocity, ensuring that the total volume transport across boundaries is preserved after the interpolation procedures. In order to compute the lateral open boundary conditions, the model applies the Flow Relaxation Scheme (Engerdhal, 1995) for temperature, salinity and velocities and the Flather's radiation condition (Flather, 1976) for the depth-mean transport. Concerning the forecasting production cycle, AIFS produces 9-days forecast every day, producing hourly and daily means of temperature, salinity, surface currents, heat flux, water flux and shortwave radiation fields. AIREG model performances have been verified by using statistics (root mean square errors and BIAS) with respect to observed data (ARGO and CDT datasets)
NASA Astrophysics Data System (ADS)
Gronewold, A. D.; Ritzenthaler, A.; Fry, L. M.; Anderson, E. J.
2012-12-01
There is a clear need in the water resource and public health management communities to develop and test modeling systems which provide robust predictions of water quality and water quality standard violations, particularly in coastal communities. These predictions have the potential to supplement, or even replace, conventional human health protection strategies which (in the case of controlling public access to beaches, for example) are often based on day-old fecal indicator bacteria monitoring results. Here, we present a coupled modeling system which builds upon recent advancements in watershed-scale hydrological modeling and coastal hydrodynamic modeling, including the evolution of the Huron-Erie Connecting Waterways Forecasting System (HECWFS), developed through a partnership between NOAA's Great Lakes Environmental Research Laboratory (GLERL) and the University of Michigan Cooperative Institute for Limnology and Ecosystems Research (CILER). Our study is based on applying the modeling system to a popular beach in the metro-Detroit (Michigan, USA) area and implementing a routine shoreline monitoring program to help assess model forecasting skill. This research presents an important stepping stone towards the application of similar modeling systems in frequently-closed beaches throughout the Great Lakes region.
NASA Astrophysics Data System (ADS)
Benmouiza, Khalil; Cheknane, Ali
2015-04-01
This paper aims to introduce an approach for multi-hour forecasting (915 h ahead) of hourly global horizontal solar radiation time series and forecasting of a small-scale solar radiation database (30- and 1-s scales) for a period of 1 day (47,000 s ahead) using commonly and available measured meteorological solar radiation. Three methods are considered in this study. First, autoregressive-moving-average (ARMA) model is used to predict future values of the global solar radiation time series. However, because of the non-stationarity of solar radiation time series, a phase of detrending is needed to stationarize the irradiation data; a 6-degree polynomial model is found to be the most stationary one. Secondly, due to the nonlinearity presented in solar radiation time series, a nonlinear autoregressive (NAR) neural network model is used for prediction purposes. Taking into account the advantages of both models, the goodness of ARMA for linear problems and NAR for nonlinear problems, a hybrid method combining ARMA and NAR is introduced to produce better results. The validation process for the site of Ghardaia in Algaria shows that the hybrid model gives a normalized root mean square error (NRMSE) equals to 0.2034 compared to a NRMSE equal to 0.2634 for NAR model and 0.3241 for ARMA model.
NASA Astrophysics Data System (ADS)
Nayak, M. A.; Villarini, G.; Lavers, D. A.
2013-12-01
Atmospheric Rivers (ARs) are filamentary regions of enhanced moisture transport in the lower troposphere and occur in the pre-cold frontal region (within the warm sector) of extra-tropical cyclones. They can be responsible for heavy rainfall and flooding over large areas of the mid-latitude regions, with major impacts on public safety and economic activity. They also transport and deliver atmospheric moisture that is essential for water resources and supply. The focus of this work is examination of the skill of several global Numerical Weather Prediction (NWP) models in identifying the occurrence of ARs over the central United States. This is an area affected by extreme floods, many of which have recently been related to the occurrence of ARs. Although these catastrophic events cannot be prevented, it is possible to be better prepared. Improved readiness relies on the availability of information that would allow making better-informed decisions about the most suitable water management strategies. Assessment of the NWP forecast skill is also an important way of testing whether the processes responsible for these events are captured by NWP models. Analyses cover the period from October 2006 to present. Different identification algorithms are implemented based on column-integrated water vapor and integrated water vapor transport. Multiple lead times are examined, ranging from 1 to 16 days, and a number of skill scores are considered to verify the skill of these NWP models.
Impact of CAMEX-4 Data Sets for Hurricane Forecasts using a Global Model
NASA Technical Reports Server (NTRS)
Kamineni, Rupa; Krishnamurti, T. N.; Pattnaik, S.; Browell, Edward V.; Ismail, Syed; Ferrare, Richard A.
2005-01-01
This study explores the impact on hurricane data assimilation and forecasts from the use of dropsondes and remote-sensed moisture profiles from the airborne Lidar Atmospheric Sensing Experiment (LASE) system. We show that the use of these additional data sets, above those from the conventional world weather watch, has a positive impact on hurricane predictions. The forecast tracks and intensity from the experiments show a marked improvement compared to the control experiment where such data sets were excluded. A study of the moisture budget in these hurricanes showed enhanced evaporation and precipitation over the storm area. This resulted in these data sets making a large impact on the estimate of mass convergence and moisture fluxes, which were much smaller in the control runs. Overall this study points to the importance of high vertical resolution humidity data sets for improved model results. We note that the forecast impact from the moisture profiling data sets for some of the storms is even larger than the impact from the use of dropwindsonde based winds.
Chih-Chiang Lu; Chu-Hui Chen; Tian-Chyi J. Yeh; Cheng-Mau Wu; I-Fang Yau
2006-01-01
Typhoons and storms have often brought heavy rainfalls and induced floods that have frequently caused severe damage and loss\\u000a of life in Taiwan. Our ability to predict sewer discharge and forecast floods in advance during storm seasons plays an important\\u000a role in flood warning and flood hazard mitigation. In this paper, we develop an integrated model (TFMBPN) for forecasting\\u000a sewer
NASA Astrophysics Data System (ADS)
Erasmus, D. Andre; Sarazin, Marc S.
2001-01-01
Astronomical observatories are extremely dependent on sky transparency. As the expensive new very large telescopes enter into operation, flexible observing modes are being introduced, which allow each 'observing block' to be scheduled at the most appropriate time. In such modes, it makes sense to develop tools for forecasting ambient conditions. We present here the operational water vapor and cirrus cloud forecast model developed for ESO observatories in Northern Chile.
Phan, Thao P; Alkema, Leontine; Tai, E Shyong; Tan, Kristin H X; Yang, Qian; Lim, Wei-Yen; Teo, Yik Ying; Cheng, Ching-Yu; Wang, Xu; Wong, Tien Yin; Chia, Kee Seng; Cook, Alex R
2014-01-01
Objective Singapore is a microcosm of Asia as a whole, and its rapidly ageing, increasingly sedentary population heralds the chronic health problems other Asian countries are starting to face and will likely face in the decades ahead. Forecasting the changing burden of chronic diseases such as type 2 diabetes in Singapore is vital to plan the resources needed and motivate preventive efforts. Methods This paper describes an individual-level simulation model that uses evidence synthesis from multiple data streams—national statistics, national health surveys, and four cohort studies, and known risk factors—aging, obesity, ethnicity, and genetics—to forecast the prevalence of type 2 diabetes in Singapore. This comprises submodels for mortality, fertility, migration, body mass index trajectories, genetics, and workforce participation, parameterized using Markov chain Monte Carlo methods, and permits forecasts by ethnicity and employment status. Results We forecast that the obesity prevalence will quadruple from 4.3% in 1990 to 15.9% in 2050, while the prevalence of type 2 diabetes (diagnosed and undiagnosed) among Singapore adults aged 18–69 will double from 7.3% in 1990 to 15% in 2050, that ethnic Indians and Malays will bear a disproportionate burden compared with the Chinese majority, and that the number of patients with diabetes in the workforce will grow markedly. Conclusions If the recent rise in obesity prevalence continues, the lifetime risk of type 2 diabetes in Singapore will be one in two by 2050 with concomitant implications for greater healthcare expenditure, productivity losses, and the targeting of health promotion programmes. PMID:25452860