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1

Design of a multiregional economic model for forecasting electricity consumption and peak load. Final report  

SciTech Connect

The design of a general multiregional econometric model of the USA and the design of a regional electricity consumption and demand submodel are presented. The multiregional econometric model is intended to provide forecasts of regional population, economic activity by industrial sector, regional wages, and incomes. The electricity submodel is designed to take forecasts of such general economic indicators (together with forecasts of relative electricity and othe energy costs) and to produce forecasts of electricity (kWh) consumption by customer category and forecasts of peak load. While the ultimate purpose of the present effort is regional electricity forecasting, it is clear that the multiregional econometric model which supports the electricity submodel has a great many other uses. The multiregional econometric model design presented in the document represents a natural extension to the regional level of the Wharton Long-Term Annual and Industry Forecasting Model of the USA. The parts of that model that lend themselves to regional disaggregation (employment and wages, for example) are disaggregated. Aggregate US forecasts for such variables are determined by adding up from the bottom. This bottom-up design marks a major departure from earlier regional efforts. In addition to providing a description of the theoretical design of the model, this document provides an extensive review and evaluation of the economic and electricity-energy database needed for its construction.

Adams, F.G.; McCarthy, M.D.; Hill, J.

1982-01-01

2

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables  

Microsoft Academic Search

This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of

Murat Kankal; Adem Akp?nar; Murat ?hsan Kömürcü; Talat ?ükrü Öz?ahin

2011-01-01

3

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

PubMed Central

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

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

2000-01-01

4

The Economic Value Of Ensemble-Based Weather Forecasts  

Microsoft Academic Search

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,

Yuejian Zhu; Zoltan Toth; Richard Wobus; David Richardson; Kenneth Mylne

2002-01-01

5

A Course in Economic Forecasting: Rationale and Content.  

ERIC Educational Resources Information Center

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

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

2000-01-01

6

Aggregate vehicle travel forecasting model  

SciTech Connect

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.

Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)

1995-05-01

7

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

PubMed Central

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

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

2013-01-01

8

A model for disruptive technology forecasting in strategic regional economic development  

Microsoft Academic Search

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

Sul Kassicieh; Nabeel Rahal

2007-01-01

9

Estimating the economic value of wind forecasting to utilities  

SciTech Connect

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

Milligan, M.R.; Miller, A.H. [National Renewable Energy Lab., Golden, CO (United States); Chapman, F. [Environmental Defense Fund, Oakland, CA (United States)

1995-05-01

10

Ups and downs of economics and econophysics — Facebook forecast  

NASA Astrophysics Data System (ADS)

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

Gajic, Nenad; Budinski-Petkovic, Ljuba

2013-01-01

11

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-print Network

, Center for Economic Analysis and Forecasting -- Dean, Mihaylo College of Business and Economics Mira, Department of Economics, Mihaylo College of Business and Economics 1 CSUF MIHAYLO COLLEGE OF BUSINESS-year increase in the debt ceiling -- both of which proceeded without the usual drama. Second, the private sector

de Lijser, Peter

12

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

EPA Science Inventory

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

13

Forecasting the Future Economic Burden of Current Adolescent Overweight: An Estimate of the Coronary Heart Disease Policy Model  

PubMed Central

Objectives. We predicted the future economic burden attributable to high rates of current adolescent overweight. Methods. We constructed models to simulate the costs of excess obesity and associated diabetes and coronary heart disease (CHD) among adults aged 35–64 years in the US population in 2020 to 2050. Results. Current adolescent overweight is projected to result in 161 million life-years complicated by obesity, diabetes, or CHD and 1.5 million life-years lost. The cumulative excess attributable total costs are estimated at $254 billion: $208 billion because of lost productivity from earlier death or morbidity and $46 billion from direct medical costs. Currently available therapies for hypertension, hyperlipidemia, and diabetes, used according to guidelines, if applied in the future, would result in modest reductions in excess mortality (decreased to 1.1 million life-years lost) but increase total excess costs by another $7 billion (increased to $261 billion total). Conclusions. Current adolescent overweight will likely lead to large future economic and health burdens, especially lost productivity from premature death and disability. Application of currently available medical treatments will not greatly reduce these future burdens of increased adult obesity. PMID:19833999

Bibbins-Domingo, Kirsten; Coxson, Pamela; Wang, Y. Claire; Williams, Lawrence; Goldman, Lee

2009-01-01

14

The Record and Improvability of Economic Forecasting  

Microsoft Academic Search

Have macroeconomic forecasts grown more or less accurate over time? This paper assembles, examines, and interprets evidence bearing on this question. Contrary to some critics, there are no indications that U.S. forecasts have grown systematically worse, that is, less accurate, more biased, or both. Neither do any definite trends in a positive direction emerge from comparisons of annual and quarterly

Victor Zarnowitz

1987-01-01

15

Potential Economic Value of Seasonal Hurricane Forecasts  

E-print Network

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 ...

Emanuel, Kerry Andrew

16

Robustness of disaggregate oil and gas discovery forecasting models  

USGS Publications Warehouse

The trend in forecasting oil and gas discoveries has been to develop and use models that allow forecasts of the size distribution of future discoveries. From such forecasts, exploration and development costs can more readily be computed. Two classes of these forecasting models are the Arps-Roberts type models and the 'creaming method' models. This paper examines the robustness of the forecasts made by these models when the historical data on which the models are based have been subject to economic upheavals or when historical discovery data are aggregated from areas having widely differing economic structures. Model performance is examined in the context of forecasting discoveries for offshore Texas State and Federal areas. The analysis shows how the model forecasts are limited by information contained in the historical discovery data. Because the Arps-Roberts type models require more regularity in discovery sequence than the creaming models, prior information had to be introduced into the Arps-Roberts models to accommodate the influence of economic changes. The creaming methods captured the overall decline in discovery size but did not easily allow introduction of exogenous information to compensate for incomplete historical data. Moreover, the predictive log normal distribution associated with the creaming model methods appears to understate the importance of the potential contribution of small fields. ?? 1989.

Attanasi, E.D.; Schuenemeyer, J.H.

1989-01-01

17

Interpretation of Global Forecast Model Flipflops  

NSDL National Science Digital Library

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.

Spangler, Tim

2002-06-04

18

A forecasting model of gaming revenues in Clark County, Nevada  

SciTech Connect

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

Edwards, B.; Bando, A.; Bassett, G.; Rosen, A. [Argonne National Lab., IL (United States); Carlson, J.; Meenan, C. [Science Applications International Corp., Las Vegas, NV (United States)

1992-04-01

19

Forecast of future aviation fuels: The model  

NASA Technical Reports Server (NTRS)

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.

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

1981-01-01

20

Short-Termed Integrated Forecasting System: 1993 Model documentation report  

SciTech Connect

The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

Not Available

1993-05-01

21

Forecasting rates of hydrocarbon discoveries in a changing economic environment  

USGS Publications Warehouse

A method is presented for the estimation of undiscovered oil and gas resources in partially explored areas where economic truncation has caused some discoveries to go unreported; therefore distorting the relationship between the observed discovery size distribution and the parent or ultimate field size distribution. The method is applied to the UK's northern and central North Sea provinces. A discovery process model is developed to estimate the number and size distribution of undiscovered fields in this area as of 1983. The model is also used to forecast the rate at which fields will be discovered in the future. The appraisal and forecasts pertain to fields in size classes as small as 24 million barrels of oil equivalent (BOE). Estimated undiscovered hydrocarbon resources of 11.79 billion BOE are expected to be contained in 170 remaining fields. Over the first 500 wildcat wells after 1 January 1983, the discovery rate in this areas is expected to decline by 60% from 15 million BOE per wildcat well to six million BOE per wildcat well. ?? 1984.

Schuenemeyer, J.H.; Attanasi, E.D.

1984-01-01

22

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

23

Bridge models to forecast the euro area GDP  

Microsoft Academic Search

Quantitative information on the current state of the economy is crucial to economic policy-making and to early understanding of the economic situation, but the quarterly national account (NA) data for GDP in the euro area are released with a substantial delay. The aim of the paper is to examine the forecast ability of bridge models (BM) for GDP growth in

Alberto Baffigi; Roberto Golinelli; Giuseppe Parigi

2004-01-01

24

Economic Perspectives of Technological Progress: New Dimensions for Forecasting Technology  

ERIC Educational Resources Information Center

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;…

Twiss, Brian

1976-01-01

25

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

NASA Astrophysics Data System (ADS)

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

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2013-04-01

26

Forecast of maize dwarf mosaic using growth model forecasting method  

Microsoft Academic Search

Maize dwarf mosaic (MDM) is an important maize viral disease in the world. Disease forecast plays a vital role in controlling it. In this study, three growth models including Logistic growth model, Gompertz model and Weibull model, were used to fit seven groups of MDM data obtained in Chengde, Hebei Province in China. Residual sum of square test showed that

Haiguang Wang; Zhanhong Ma

2010-01-01

27

Economic impact of wind power forecast  

Microsoft Academic Search

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

I. Marti; M. J. San Isidro; M. Gastón; Y. Loureiro; J. Sanz; I. Pérez

28

Economic impacts of advanced weather forecasting on energy system operations  

Microsoft Academic Search

We analyze the impacts of adopting advanced weather forecasting systems at different levels of the decision-making hierarchy of the power grid. Using case studies, we show that state-of-the-art numerical weather prediction (NWP) models can provide high-precision forecasts and uncertainty information that can significantly enhance the performance of planning, scheduling, energy management, and feedback control systems. In addition, we assess the

Victor M. Zavala; Emil M. Constantinescu; Mihai Anitescu

2010-01-01

29

Identifying Effects of Forecast Uncertainty on Flood Control Decision - A Hydro-economic Hedging Framework  

NASA Astrophysics Data System (ADS)

Different from conventional studies developing reservoir operation models and treating forecast as input to obtain operation decisions case by case, this study issues a hydro-economic analysis framework and derives some general relationships between optimal flood control decision and streamflow forecast. By analogy with the hedging rule theory for water supply, we formulate reservoir flood control with a two-stage optimization model, in which the properties of flood damage (i.e., diminishing marginal damage) and the characteristics of forecast uncertainty (i.e., the longer the forecast horizon, the larger the forecast uncertainty) are incorporated to minimize flood risk. We define flood conveying capacity surplus (FCCS) variables to elaborate the trade-offs between the release of current stage (i.e., stage 1) and in the release of future stage (i.e., stage 2). Using Karush-Kuhn-Tucker conditions, the flood risk trade-off between the two stages is theoretically represented and illustrated by three typical situations depending on forecast uncertainty and flood magnitude. The analytical results also show some complicated effects of forecast uncertainty and flood magnitude on real-time flood control decision: 1) When there is a big flood with a small FCCS, the whole FCCS should be allocated to the current stage to hedge against the more certain and urgent flood risk in the current stage; 2) when there is a medium flood with a moderate FCCS, some FCCS should be allocated to the future stage but more FCCS still should be allocated to the current stage; and 3) when there is a small flood with a large FCCS, more FCCS should be allocated to the future stage than the current stage, as a large FCCS in the future stage can still induce some flood risk (distribution of future stage forecast uncertainty is more disperse) while a moderate FCCS in the current stage can induce a small risk. Moreover, this study also presents a hypothetical case study to analyze the flood risk under Pseudo probabilistic streamflow forecast (pPSF, deterministic forecast with variance) and Real probabilistic streamflow forecast (rPSF, ensemble forecast) forecast uncertainties, which shows ensemble forecast techniques are more efficient on mitigating flood risk.

Zhao, T.; Zhao, J.; Cai, X.; Yang, D.

2011-12-01

30

Atmospheric variability: modelling, diagnostics, and forecasting  

NASA Astrophysics Data System (ADS)

The atmosphere: a component of the climate system Modelling: conceptual and comprehensive Modelling strategy A toy climate model Dynamic analysis Stochastic analysis (and the two-time-scale approach) A new general circulation model Numerics, physics, and climatology GCM-experiments Diagnostics: stormtracks and Grosswetter A toy weather model: linear quasi-geostrophic flow Eulerian analysis The textbook linear 2-layer model Energetics Spectral model Rossby waves Baroclinic instability Stochastic forcing Midlatitude cyclone tracks: a Largrangian view Lagrangian analysis Storm track scaling Time series analysis: degrees of freedom and dimension Linear analysis: degrees of freedom Non-linear analysis: dimension Forecasting: persistence, hurricanes, and rain A toy predictability model: persistence in red noise Red noise Chance, climate, and persistence Forecast verification Tropical-cyclone tracks: linear and non-linear forecasts Linear combination of forecasts Non-linear forecasts Probability of precipitation (PoP) Rainfall Markov chain Conclusion

Fraedrich, K.

31

Forecasting Economic and Financial Variables with Global VARs  

E-print Network

and Kulikowski (1991). 3 in more detail in Section 3. The first version of the GVAR (PSW) included real output, real money supply, a price index, exchange rates, a short-term interest rate, and a stock market index. The seventh variable, common to all countries... the forecasts. In this way we ensure that forecasts obtained for different countries are internally coherent within the GVAR modeling framework. The GVAR is composed of individual country vector error correcting models in which the core domestic variables...

Pesaran, M Hashem; Schuermann, Til; Smith, L Vanessa

32

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

ERIC Educational Resources Information Center

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

Donohue, James P.

2006-01-01

33

New Concepts in Wind Power Forecasting Models  

E-print Network

New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, João Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind for more accurate short term wind power forecasting models has led to solid and impressive development

Kemner, Ken

34

Essays on forecasting stationary and nonstationary economic time series  

NASA Astrophysics Data System (ADS)

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.

Bachmeier, Lance Joseph

35

Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics  

E-print Network

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...

Joseph, Andreas; Stanley, Eugene; Chen, Guanrong

2014-01-01

36

Demand forecast model based on CRM  

NASA Astrophysics Data System (ADS)

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

Cai, Yuancui; Chen, Lichao

2006-11-01

37

A univariate model for long-term streamflow forecasting  

NASA Astrophysics Data System (ADS)

This paper, the first in a series of two, employs the principle of maximum entropy (POME) via maximum entropy spectral analysis (MESA) to develop a univariate model for long-term streamflow forecasting. Three cases of streamflow forecasting are investigated: forward forecasting, backward forecasting (or reconstruction) and intermittent forecasting (or filling in missing records). Application of the model is discussed in the second paper.

Krstanovic, P. F.; Singh, V. P.

1991-09-01

38

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

NASA Technical Reports Server (NTRS)

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

1977-01-01

39

Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product  

E-print Network

Numerous time series models are available for forecasting economic output. Autoregressive models were initially applied to US gross national product (GNP), and have been extended to nonlinear structures, such as the ...

Arora, Siddharth

40

Weather Forecaster Understanding of Climate Models  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

41

Pollen Forecast and Dispersion Modelling  

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

42

Influence of Model Physics on NWP Forecasts  

NSDL National Science Digital Library

This module describes model parameterizations of sub-surface, boundary-layer,and free atmospheric processes, such as surface snow processes, soil characteristics, vegetation, evapotranspiration, PBL processes and parameterizations, and trace gases, and their interaction with the radiative transfer process. It specifically addresses how models treat these physical processes and how they can influence forecasts of sensible weather elements.

2007-07-17

43

Technological Forecasting---Model Selection, Model Stability, and Combining Models  

Microsoft Academic Search

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

Nigel Meade; Towhidul Islam

1998-01-01

44

The Economic Value of Temperature Forecasts in Electricity Generation.  

NASA Astrophysics Data System (ADS)

Every day, the U.S. electricity-generating industry decides how to meet the electricity demand anticipated over the next 24 h. Various generating units are available to meet the demand, and each unit may have its own production lead time, start-up cost, and production cost. Total costs can be minimized if electricity demand is accurately forecast. Accurate demand forecasts, in turn, depend on accurate temperature forecasts.This paper estimates the cost savings (i.e., benefits) attributable to temperature forecasts used by the U.S. electricity-generation industry. It does this by establishing the relationship between the quality of temperature forecasts and the quality of electricity demand forecasts at six sites around the United States. It then draws on earlier work by Hobbs et al. on the relationship between the quality of demand forecasts and production costs to estimate the percentage of cost savings from different temperature forecasts. Finally, these cost savings are extrapolated to estimate the total benefits, and incremental benefits, for the United States as a whole.The total benefits of U.S. National Weather Service (NWS) forecasts are estimated to be $166 million. The additional benefits potentially obtainable from a perfect temperature forecast are $75 million per year. It is estimated that an incremental 1% improvement in the forecast quality (from the current NWS forecast) would be worth an additional $1.4 million per year. These numbers do not include other possible benefits of forecasts to the electricity industry, such as those from the improved scheduling of plant maintenance.

Teisberg, Thomas J.; Weiher, Rodney F.; Khotanzad, Alireza

2005-12-01

45

ECONOMIC AND POPULATION FORECASTS FOR BASIN WATER PLANNING IN GEORGIA'S COASTAL REGION: METHODOLOGICAL ISSUES  

Microsoft Academic Search

Abstract The regional planning and management of water resources, whether by water shed or river basin, will require knowing the future demand for water at the sub-state level. This paper argues that population forecasts cannot be reliably developed at the regional level independent of economic,forecasts. Further this paper argues that planning and management,will also require a forecasting tool that can

Phyllis Isley

2003-01-01

46

Modeling and Forecasting Electric Daily Peak Loads  

E-print Network

Modeling and Forecasting Electric Daily Peak Loads Using Abductive Networks R. E. Abdel-Aal Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia techniques including neural networks have been used for this purpose. This paper proposes the alternative

Abdel-Aal, Radwan E.

47

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

SciTech Connect

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.

Cluett, C.; Clark, D.C. (Battelle Human Affairs Research Center, Seattle, WA (USA)); Pittenger, D.B. (Demographics Lab., Olympia, WA (USA))

1990-08-01

48

Modeling, Simulation, and Forecasting of Subseasonal Variability  

NASA Technical Reports Server (NTRS)

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.

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

2003-01-01

49

IRAN WHEAT PRICE FORECASTING PERFORMANCE EVALUATION OF ECONOMIC  

Microsoft Academic Search

Forecasting has been very important in decision making at all levels and sectors of the economy. In agriculture, where the decision environment is characterized by risks and uncertainty largely due to uncertain yields, decision makers require some information about the possible future outcomes. Price forecasts are critical to market participant making production and marketing decisions and to policy makers who

Bita Rahimi Badr

50

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-print Network

. The solar radiation sequence can be seen as a time series, and therefore one can build statistical models1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving

Boyer, Edmond

51

Verification of an Operational Gulf Stream Forecasting Model  

Microsoft Academic Search

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

Scott M. Glenn; Allan R. Robinson

52

Sequential forecast of incident duration using Artificial Neural Network models  

Microsoft Academic Search

This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides multi-period updates of duration time after the incident

Chien-Hung Wei; Ying Lee

2007-01-01

53

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

NASA Astrophysics Data System (ADS)

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.

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

2012-05-01

54

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

NASA Technical Reports Server (NTRS)

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.

Bhattacharyya, R.; Greenberg, J.

1975-01-01

55

Forecasting daily streamflow using hybrid ANN models  

NASA Astrophysics Data System (ADS)

In this paper, the classic 'divide and conquer (DAC)' paradigm is applied as a top-down black-box technique for the forecasting of daily streamflows from the streamflow records alone, i.e. without employing exogenous variables of the runoff generating process such as rainfall. To this end, three forms of hybrid artificial neural networks (ANNs) are used as univariate time series models, namely, the threshold-based ANN (TANN), the cluster-based ANN (CANN), and the periodic ANN (PANN). For the purpose of comparison of forecasting efficiency, the normal multi-layer perceptron form of ANN (MLP-ANN) is selected as the baseline ANN model. Having first applied the MLP-ANN models without any data-grouping procedure, the influence of various data preprocessing procedures on the MLP-ANN model forecasting performance is then investigated. The preprocessing procedures considered are: standardization, log-transformation, rescaling, deseasonalization, and combinations of these. In the context of the single streamflow series considered, deseasonalization without rescaling was found to be the most effective preprocessing procedure. Some discussions are presented (i) on data preprocessing and (ii) on selection of the best ANN model. Overall, among the three variations of hybrid ANNs tested, the PANN model performed best. Compared with the MLP-ANN fitted to the deseasonalized data, the PANN based on the soft seasonal partitioning performed better for short lead times (?3 days), but the advantage vanishes for longer lead times.

Wang, Wen; Gelder, Van Pieter H. A. J. M.; Vrijling, J. K.; Ma, Jun

2006-06-01

56

Identifying and Forecasting Economic Regimes in TAC SCM  

Microsoft Academic Search

\\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

Wolfgang Ketter; John Collins; Maria Gini; Alok Gupta; Paul Schrater

57

Hydro-economic assessment of hydrological forecasting systems  

NASA Astrophysics Data System (ADS)

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.

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

2012-01-01

58

Total Electron Content forecast model over Australia  

NASA Astrophysics Data System (ADS)

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

Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

59

Flood forecasting for River Mekong with data-based models  

NASA Astrophysics Data System (ADS)

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.

Shahzad, Khurram M.; Plate, Erich J.

2014-09-01

60

U.S. Economic Outlook and Forecasts Surviving the Recovery: Shaken, and Stirred...  

E-print Network

Eurozone officials to the area's growing crisis has been slow, half-hearted, and at times discordant5 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

de Lijser, Peter

61

Modeling, Forecasting and Mitigating Extreme Earthquakes  

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

62

Forecast 4d-Var: Exploiting Model Output Statistics  

NASA Astrophysics Data System (ADS)

Forecasts of certain weather elements are improved by linearly relating observed elements to past observations, climatological information, and numerical weather prediction model output. Model output statistics (MOS) is a statistical post-processing of model output that is capable of forecasting sub-grid scale, synoptically-forced events and of correcting some systematic, but state dependent, model bias. We propose to exploit this tendency of accounting for model error by feeding MOS forecasts back into the state estimation problem. MOS forecasts and their associated uncertainty are treated as ``observations'' of the future system state and a four-dimensional variational assimilation procedure is employed to improve the original analysis and resulting model forecast. In a simple-model scenario, it is found that this approach has a small negative impact on the magnitude of forecast errors relative to MOS, but a large positive impact on the variance about the forecast errors: forecast busts are reduced. As a further step, a second round of MOS is performed on the new model forecasts in a manner identical to the original MOS approach. This second application of MOS results in a significant reduction in both the forecast errors and the variance about those errors relative to the first application of MOS.

Hansen, J. A.; Emanuel, K. A.

2001-12-01

63

The NASA Forecast Model Web Map Service  

NASA Astrophysics Data System (ADS)

The NASA Forecast Model WMS (NFMW) provides on-demand visualizations of Earth science data. The current usage focuses on field campaigns and other projects that use the output of the Goddard Earth Observing System (GEOS) and Weather Research Framework (WRF) models, but other models can be supported. The NFMW implements the Open Geospatial Consortium (OGC) Web Map Service (WMS) interoperability specification. WMS provisions for handling time and other dimensions are used extensively to support the multi-dimensional nature of the forecast data. Scientists and other interested parties access the WMS using a variety of clients including our own web-based Viewer, Google Earth, a multi-screen Hyperwall installation, or other WMS-compliant applications. We have found that offering an open-standard interface to our data collection has simplified usage of the data and has permitted users to visualize the data from remote locations using only a web browser. The NFMW and Viewer may be accessed at http://map.nasa.gov/tools.html. The NFMW software is available as open source, and is written in a combination of Perl and Interactive Data Language (IDL). This work is supported by the Geosciences Interoperability Office (GIO) and the Modeling and Analysis Program (MAP) at NASA.

de La Beaujardière, J.

2007-12-01

64

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

PubMed

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

Li, Bai

2014-01-01

65

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

PubMed Central

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

2014-01-01

66

Forecasting electricity demand with end-use\\/econometric models  

Microsoft Academic Search

The Railbelt Electricity Demand (RED) Model, reported in this paper, is a simulation model designed to forecast annual electricity consumption for the residential, commercial-industrial-government and miscellaneous end-use sectors of Alaska's Railbelt region. The model also takes into account government intervention in the energy markets via conservation programs in Alaska and produces forecasts of system annual peak demand. The forecasts of

M. J. King; M. J. Scott

1983-01-01

67

Electricity generation modeling and photovoltaic forecasts in China  

NASA Astrophysics Data System (ADS)

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.

Li, Shengnan

68

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

PubMed Central

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

Bildirici, Melike; Ersin, Ozgur

2014-01-01

69

Development of Ensemble Model Based Water Demand Forecasting Model  

NASA Astrophysics Data System (ADS)

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)

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

2014-05-01

70

Bayesian Model Averaging. An Application to Forecast Inflation in Colombia  

Microsoft Academic Search

An application of Bayesian Model Averaging, BMA, is implemented to construct combined forecasts for the colombian inflation for the short and medium run. A model selection algorithm is applied over a set of linear models with a large dataset of potencial predictors using marginal as well as predictive likelihood. The forecasts obtained when using predictive likelihood outperformed the ones obtained

Eliana González

71

A Stochastic-Dynamic Model for Real Time Flood Forecasting  

NASA Astrophysics Data System (ADS)

A stochastic-dynamic model for real time flood forecasting was developed using Box-Jenkins modelling techniques. The purpose of the forecasting system is to forecast flood levels of the Saint John River at Fredericton, New Brunswick. The model consists of two submodels: an upstream model used to forecast the headpond level at the Mactaquac Dam and a downstream model to forecast the water level at Fredericton. Inputs to the system are recorded values of the water level at East Florenceville, the headpond level and gate position at Mactaquac, and the water level at Fredericton. The model was calibrated for the spring floods of 1973, 1974, 1977, and 1978, and its usefulness was verified for the 1979 flood. The forecasting results indicated that the stochastic-dynamic model produces reasonably accurate forecasts for lead times up to two days. These forecasts were then compared to those from the existing forecasting system and were found to be as reliable as those from the existing system.

Chow, K. C. A.; Watt, W. E.; Watts, D. G.

1983-06-01

72

An operational, global scale spectral ocean wave forecasting model  

Microsoft Academic Search

In late September 1985, the first global scale spectral ocean wave forecasting model designed to provide forecast guidance was placed in an experimental, operational mode at the National Meteorological Center (NMC) in Washington, D.C. The numerical model generates 72 hour forecasts of wave elevation directional frequency spectra in 360 components, and summary statistics at approximately 5000 sea grid points between70degS

Hong Chin

1985-01-01

73

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

EPA Science Inventory

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

74

Multi-model MJO forecasting during DYNAMO/CINDY period  

NASA Astrophysics Data System (ADS)

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

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

2013-08-01

75

Real-time landslide forecasting with the incorporation of landslide modeling and typhoon forecast  

NASA Astrophysics Data System (ADS)

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.

Chiang, Shou-hao; Chang, Kang-Tsung; Chen, Yi-Chin; Chen, Chi-Farn

2014-05-01

76

Automatic time series modeling,intervention analysis, and effective forecasting  

Microsoft Academic Search

The purpose of this study is to investigate the forecasting efficiency of an expert system, an automatic time series modeling system, when applied to a quarterly earnings per share series. The Bethlehem steel quarterly earnings series has a severe outlier problem and the intervention analysis which specifically models the outlier may enhance forecasting efficiency.The purpose of this study is to

John B. Guerard Jr

1989-01-01

77

CSUF Economic Outlook and Forecasts MidYear Update -April 2013  

E-print Network

. On the near-term risks, scal consolidation in the U.S. and the Eurozone crisis continue to remain the two top sequestra- tion took e ect on March 1st. e Eurozone crisis still presents the largest downside riskCSUF Economic Outlook and Forecasts MidYear Update - April 2013 Anil Puri & Mira Farka Mihaylo

de Lijser, Peter

78

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

SciTech Connect

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

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

2009-01-01

79

Regional Model Nesting Within GFS Daily Forecasts Over West Africa  

NASA Technical Reports Server (NTRS)

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

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

2010-01-01

80

A new forecasting model for the diffusion of ISO 9000 standard certifications in European countries  

Microsoft Academic Search

ISO 9000 standards for quality system management are involving a higher and higher number of enterprises and organizations. This paper presents a detailed analysis of certification diffusion in Italy and in some European countries with similar economic structures. Benchmarking and evolution forecasts are based on the “logistic model”, traditionally used for studying biological growth phenomena. The presentation is supported by

Fiorenzo Franceschini; Maurizio Galetto; Giovanni Giannì

2004-01-01

81

Multi-model methods for probabilistic streamflow forecasting  

NASA Astrophysics Data System (ADS)

Streamflow forecasting is important for flood control measures and early warning. Application of a single hydrological model for probabilistic forecasting, based on parameter uncertainty, sometimes does not result in sufficiently reliable forecasts. This can be because a particular model may perform better in rising limp, or peak discharge, or low-flows, and worse in other circumstances. Therefore, this research focuses on combining several models with different characteristics with the objective to produce sharper and more reliable probabilistic streamflow forecasts. Multi-model methods are investigated (committees of models). In particular fuzzy committee method, neural network method and Bayesian model averaging method. These methods are used to combine multiple models, e.g. HBV hydrological model and a Neural Network model. Relationships between applying model-committees for increasing sharpness and for increasing reliability are being analysed. Main case study to be presented is Bagmati river, Nepal. Applicability to MOPEX catchments, USA, will be discussed.

Zhang, Lu; van Andel, Schalk Jan; Solomatine, Dimitri

2013-04-01

82

Economic impact of an improved methanol catalyst. [Forecasting to 2000  

Microsoft Academic Search

The economic future of methanol is reviewed in light of its potential uses as a substitute for traditional hydrocarbon fuels and feedstocks as well as some evolving new uses. Methanol's future market position will depend strongly on its production cost in comparison with competitive products. One promising way to reduce the production cost is by use of an improved catalyst

J. Grens; I. Borg; D. Stephens; C. Colmenares

1983-01-01

83

Wind speed and power forecasting based on spatial correlation models  

SciTech Connect

Wind Energy Conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for systems schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates a technique for forecasting wind speed and power output up to several hours ahead, based on cross correlation at neighboring sites. The authors develop an Artificial Neural Network (ANN) that significantly improves forecasting accuracy comparing to the persistence forecasting model. The method is tested at different sites over a one-year period.

Alexiadis, M.C.; Dokopoulos, P.S.; Sahsamanoglou, H.S.

1999-09-01

84

News-Based Group Modeling and Forecasting  

E-print Network

In this paper, we study news group modeling and forecasting methods using quantitative data generated by our large-scale natural language processing (NLP) text analysis system. A news group is a set of news entities, like top U.S. cities, governors, senators, golfers, or movie actors. Our fame distribution analysis of news groups shows that log-normal and power-law distributions generally could describe news groups in many aspects. We use several real news groups including cities, politicians, and CS professors, to evaluate our news group models in terms of time series data distribution analysis, group-fame probability analysis, and fame-changing analysis over long time. We also build a practical news generation model using a HMM (Hidden Markov Model) based approach. Most importantly, our analysis shows the future entity fame distribution has a power-law tail. That is, only a small number of news entities in a group could become famous in the future. Based on these analysis we are able to answer some interest...

Zhang, Wenbin

2014-01-01

85

Forecasting the Economic Impact of Future Space Station Operations  

NASA Technical Reports Server (NTRS)

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.

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

1967-01-01

86

Integration of DSM technology modeling and long-run forecasting  

SciTech Connect

This paper summarizes the lessons and conclusions from several projects aimed at integrating DSM into long-run forecasting models. The focus of the paper is on the technical issues that arise when attempting to incorporate DSM technology detail directly into end-use forecasting frameworks.

McMenamin, J.S. [Regional Economic Research, Inc., San Diego, CA (United States)

1995-05-01

87

Metropolitan and state economic regions (master) model: regional economic response to national policy  

SciTech Connect

This multiregional economic model, can forecast: (1) regional activity, and (2) regional economic changes caused by economic changes outside the region. Examples forecast industrial output in a Portland, Oregon standard metropolitan statistical area (SMSA) and the change in industrial output in Portland caused by a change in the price of energy or development of a large energy project. The econometric approach was chosen because of flexibiity required for accuracy and its reasonable cost. A typical disadvantage of this approach is offset because of its access to the data collected as a result of an uncompleted READ project. The remainder of this report on the MASTER model is divided into three sections: literature review; model development; and application of the model. MASTER consists of six sets of equations for: (1) industrial and commercial output; (2) employment; (3) wage rages; (4) labor market clearing; (5) population; and (6) income.

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

1982-06-01

88

Evaluation of annual, global seismicity forecasts, including ensemble models  

NASA Astrophysics Data System (ADS)

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.

Taroni, Matteo; Zechar, Jeremy; Marzocchi, Warner

2013-04-01

89

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

Microsoft Academic Search

We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information

Victor M. Zavala; Emil M. Constantinescu; Theodore Krause; Mihai Anitescu

2009-01-01

90

Monthly mean forecast experiments with the GISS model  

NASA Technical Reports Server (NTRS)

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.

Spar, J.; Atlas, R. M.; Kuo, E.

1976-01-01

91

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

92

Analysis of the value for unit commitment of improved load forecasts  

Microsoft Academic Search

Load forecast errors can yield suboptimal unit commitment decisions. The economic cost of inaccurate forecasts is assessed by a combination of forecast simulation, unit commitment optimization, and economic dispatch modeling for several different generation\\/load systems. The forecast simulation preserves the error distributions and correlations actually experienced by users of a neural net-based forecasting system. Underforecasts result in purchases of expensive

Benjamin F. Hobbs; Suradet Jitprapaikulsarn; S. Konda; V. Chankong; K. A. Loparo; D. J. Maratukulam

1999-01-01

93

Spatio-temporal modeling for real-time ozone forecasting  

PubMed Central

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

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

2013-01-01

94

Metropolitan and state economic regions (MASTER) model - overview  

SciTech Connect

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.

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

1983-05-01

95

Value of the GENS Forecast Ensemble as a Tool for Adaptation of Economic Activity to Climate Change  

NASA Astrophysics Data System (ADS)

In an atmosphere of uncertainty as to the magnitude and direction of climate change in upcoming decades, one adaptation mechanism has emerged with consensus support: the upgrade and dissemination of spatially-resolved, accurate forecasts tailored to the needs of users. Forecasting can facilitate the changeover from dependence on climatology that is increasingly out of date. The best forecasters are local, but local forecasters face great constraints in some countries. Indeed, it is no coincidence that some areas subject to great weather variability and strong processes of climate change are economically vulnerable: mountainous regions, for example, where heavy and erratic flooding can destroy the value built up by households over years. It follows that those best placed to benefit from forecasting upgrades may not be those who have invested in the greatest capacity to date. More-flexible use of the global forecasts may contribute to adaptation. NOAA anticipated several years ago that their forecasts could be used in new ways in the future, and accordingly prepared sockets for easy access to their archives. These could be used to empower various national and regional capacities. Verification to identify practical lead times for the economically important variables is a needed first step. This presentation presents the verification that our team has undertaken, a pilot effort in which we considered variables of interest to economic actors in several lower income countries, cf. shepherds in a remote area of Central Asia, and verified the ensemble forecasts of those variables.

Hancock, L. O.; Alpert, J. C.; Kordzakhia, M.

2009-12-01

96

Forecast model applications of retrieved three dimensional liquid water fields  

NASA Technical Reports Server (NTRS)

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.

Raymond, William H.; Olson, William S.

1990-01-01

97

Application of WRF model forecasts and PERSIANN satellite rainfalls for real-time flood forecasting  

NASA Astrophysics Data System (ADS)

This study aims to propose an approach which applies Weather Research and Forecasting (WRF) model forecasts and satellite rainfalls by Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to physiographic inundation-drainage model for real-time flood forecasting. The study area is Dianbao River Basin in southern Taiwan, which is a low-relief area easily suffering flood disasters. Since the study area lacks reliable rainfall forecasting and inundation simulation models, the study proposes an approach to refine WRF model forecasts (abbreviated as WRFMFs hereafter) using satellite rainfalls by PERSIANN (abbreviated as PERSIANN rainfalls hereafter) for enhancing the inundation forecasts and prolonging the lead time. Twenty one sets of on-line WRFMFs under different hypothesized boundary conditions are provided by Taiwan Typhoon and Flood Research Institute. The WRFMFs with a spatial resolution of 5 km*5 km cover the extent of Taiwan (120°E~122°E, 22°N~25°N), which are issued for 72 hours ahead for every 6 hours. However, WRFMFs have a 6-hour delay and are quite different due to their different non-isolated boundary conditions. On the other hand, PERSIANN rainfalls provided by CHRS/UCI are based on the real-time satellite images and can provide real-time global rainfall estimation. Therefore, integrating WRFMFs and PERSIANN rainfalls may be a good approach to provide better rainfall forecasts. The main idea of this approach is to give different WRFMFs different weights by comparing to the PERSIANN rainfalls when a typhoon is formed in the open sea and approaching to Taiwan. Based on the 21 sets of WRFMFs, a pattern recognition method is used to compare the PERSIANN rainfalls to each of the 21 sets of WRFMFs during a same time period for every 6 hours. For example, at a present time (18:00) the WRFMFs are issued with a 6-hour delay from 12:00 for 72 hours ahead. The comparison between each of the 21 sets of WRFMFs and the PERSIANN rainfalls during the past 6 hours (12:00~18:00) is made. Based on the comparisons, 21 errors can be calculated for assigning the weights to the 21 sets of WRFMFs for the 66 hours ahead (herein, six hours ahead are adopted). A set of WRFMF with a smaller error is assigned to have a higher weight. Then, the ensemble approach for the 21 sets of WRFMFs with different weights is performed to obtain more reliable rainfall forecasts. Finally, the study uses physiographic inundation-drainage model for flood inundation simulation. This inundation-drainage model is a pseudo 2-D model which can reasonably simulate flood inundation under the condition of complex topography. By inputting the ensemble of WRFMFs, the inundation-drainage model can forecast the flood extent and depth with less computational time in the study area. These forecasted inundation information can be used to plot the flood inundation maps and help decision makers quickly identify the flood prone areas and make emergency preparedness in advance.

Kuo, C.; Chen, J.; Yang, T.; Lin, Y.; Wang, Y.; Hsu, K.; Sorooshian, S.; Lee, C.; Yu, P.

2013-12-01

98

Stochastic modeling and forecasting of solar radiation data  

Microsoft Academic Search

Modeling of solar radiation data is an essential step in the design and performance prediction of solar energy conversion systems. This paper considers the requirements for solar radiation models from a forecast information user's point of view, and proposes a new modeling approach in which stochastic time series modeling methodology is used to fully extract the statistical properties of solar

T. N. Goh; K. J. Tan

1977-01-01

99

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

SciTech Connect

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

Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

1992-02-01

100

Distributed Hydrologic Models for Flow Forecasts - Part 2  

NSDL National Science Digital Library

Distributed Hydrologic Models for Flow Forecasts Part 2 is the second release in a two-part series focused on the science of distributed models and their applicability to different flow forecasting situations. Presented by Dr. Dennis Johnson, the module provides a more detailed look at the processes and mechanisms involved in distributed hydrologic models. It examines the rainfall/runoff component, snowmelt, overland flow routing, and channel response in a basin as represented in a distributed model. Calibration issues and situations in which distributed hydrologic models might be most appropriate are also addressed.

Comet

2010-09-28

101

Forecasting airplane technologies  

Microsoft Academic Search

Purpose – Airplane technology is undergoing several exciting developments, particularly in avionics, material composites, and design tool capabilities, and, though there are many studies conducted on subsets of airplane technology, market, and economic parameters, few exist in forecasting new commercial aircraft model introduction. In fact, existing research indicates the difficulty in quantitatively forecasting commercial airplanes due in part to the

Ann-Marie Lamb; Tugrul U. Daim; Timothy R. Anderson

2010-01-01

102

An Intelligent Clustering Forecasting System based on Change-Point Detection and Artificial Neural Networks: Application to Financial Economics  

Microsoft Academic Search

This article suggests a new clustering forecasting system to integrate change-point detection and artificial neural networks. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage, the clustering neural network modeling stage,

Kyong Joo Oh; Ingoo Han

2001-01-01

103

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

SciTech Connect

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

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

1992-02-01

104

Short-term flood forecasting with a neurofuzzy model  

NASA Astrophysics Data System (ADS)

This study explores the potential of the neurofuzzy computing paradigm to model the rainfall-runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing technique is a combination of a fuzzy computing approach and an artificial neural network technique. Parameter optimization in the model was performed by a combination of backpropagation and least squares error methods. Performance of the neurofuzzy model was comprehensively evaluated with that of independent fuzzy and neural network models developed for the same basin. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the neurofuzzy model. The value of the relative error in peak flow prediction was within reasonable limits for the neurofuzzy model. The neurofuzzy model forecasted 47.95% of the total number of flow values 1 hour in advance with less than 1% relative error, while for the neural network and fuzzy models the corresponding values were 36.96 and 18.89%, respectively. The forecasts by the neurofuzzy model at higher lead times (up to 6 hours) are found to be better than those from the neural network model or the fuzzy model, implying that the neurofuzzy model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process.

Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

2005-04-01

105

Energy demand forecasting by means of Statistical Modelling: Assessing Benefits of Climate Information  

NASA Astrophysics Data System (ADS)

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.

De Felice, M.; Alessandri, A.; Ruti, P. M.

2012-04-01

106

Industrial end-use planning methodology (INDEPTH): Demonstration of design: Volume 1, Econometric forecasting models: Interim report  

SciTech Connect

The INDEPTH industrial planning methodology will enable utilities to forecast service area electricity demand. The system allows the user to develop energy forecasts for the whole industrial sector, to examine industries most important to the service area, and to study uses of electricity that are of interest in demand-side management programs. The econometric model in this volume forecasts energy use for the entire industrial sector using a set of simultaneous factor demand equations with an imposed structure derived from the economic theory of cost-minimizing behavior.

Andrews, L.M.; King, M.J.; Leary, N.; Perry, D.M.; Snow, C.C.

1986-12-01

107

Research on model for photovoltaic system power forecasting  

Microsoft Academic Search

A model for photovoltaic system power forecasting is developed based on the DC physical model of photovoltaic module, which includes meteorological data processing, photovoltaic system output performance simulating, etc. Based on the model, the photovoltaic system power can be computed at any solar radiation level, ambient temperature, parameters of the photovoltaic module and time duration. Comparing to the real PV

Bo Zhao; Xiaohui Ge; Meidong Xue; Xuesong Zhang; Weiwei Xu

2010-01-01

108

Forecasting risks of natural gas consumption in Slovenia  

Microsoft Academic Search

Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic

Alojz Poredosb

109

Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model  

PubMed Central

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

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

2014-01-01

110

A hybrid linear-neural model for river flow forecasting  

NASA Astrophysics Data System (ADS)

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. The advantage of the algorithm is that the final architecture of the LN model is arrived at during the training process, thus avoiding selection from a class of models. The proposed model has been developed and evaluated for its performance for forecasting the river flow of Kolar basin, in India. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the LN model. The value of the relative error in peak flow prediction was within reasonable limits for the model. The forecasts by the LN model at higher lead times (up to 6 hours) are found to be good. A relative evaluation of LN model performance with that of an ANN model and of a multiple linear regression model indicates that the LN model effectively combines the strength of the other two, implying that the LN model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process.

Chetan, M.; Sudheer, K. P.

2006-04-01

111

Application of nonlinear forecasting techniques for meteorological modeling  

E-print Network

of analyzing meteorological data, namely: the standard atmospheric circulation models and a new approach basedApplication of nonlinear forecasting techniques for meteorological modeling V. PeÃ? rez-MunÃ? uzuri attractor using four years of cloud absorption data obtained from half- hourly Meteosat infrared images from

Boyer, Edmond

112

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

NASA Astrophysics Data System (ADS)

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

Bao, Hongjun; Zhao, Linna

2012-02-01

113

Chaos Models in Economics  

E-print Network

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.

Sorin Vlad; Paul Pascu; Nicolae Morariu

2010-01-20

114

Integration, cointegration and the forecast consistency of structural exchange rate models  

Microsoft Academic Search

We propose an alternative set of criteria for evaluating forecast rationality: the forecast and the actual series (1) have the same order of integration, (2) are cointegrated and (3) have a cointegrating vector consistent with long-run unitary elasticity of expectations. We denote forecasts that meet these criteria as `consistent'. Forecasts generated from monetary models generally pass (1). However, using the

Y.-W. Cheung; M. D. ChinnU

1998-01-01

115

Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting  

E-print Network

Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting SAMUEL RE, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations

Ribes, Aurélien

116

A novel nonlinear ensemble forecasting model incorporating GLAR andANN for foreign exchange rates  

Microsoft Academic Search

In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear auto- regression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ame- liorate forecasting performances. We compare the new model's performance with the two individual forecasting models—GLAR and ANN—as well as with the hybrid model and the linear combination models. Empirical

Lean Yu; Shouyang Wang; K. K. Lai

117

Distributed models for operational river forecasting: research, development, and implementation  

NASA Astrophysics Data System (ADS)

The National Weather Service (NWS) is uniquely mandated amongst federal agencies to provide river forecasts for the United States. To accomplish this mission, the NWS uses the NWS River Forecast System (NWSRFS). The NWSRFS is a collection of hydrologic, hydraulic, data collection, and forecast display algorithms employed at 13 River Forecast Centers (RFCs) throughout the US. Within the NWS, the Hydrology Lab (HL) of the Office of Hydrologic Development conducts research and development to improve the NWS models and products. Areas of current research include, snow, frozen ground, dynamic channel routing, radar and satellite precipitation estimation, uncertainty, and new approaches to rainfall runoff modeling. A prominent area of research lately has been the utility of distributed models to improve the accuracy of NWS forecasts and to provide meaningful hydrologic simulations at ungaged interior nodes. Current river forecast procedures center on lumped applications of the conceptual Sacramento Soil Moisture Accounting (SAC-SMA) model to transform rainfall to runoff. Unit hydrographs are used to convert runoff to discharge hydrographs at gaged locations. Hydrologic and hydraulic routing methods are used to route hydrographs to downstream computational points. Precipitation inputs to the models have been traditionally defined from rain gage observations. With the nationwide implementation of the Next Generation Radar platforms (NEXRAD), the NWS has precipitation estimates of unprecedented spatial and temporal resolution. In order to most effectively use these high resolution data, recent research has been devoted towards the development of distributed hydrologic models to improve the accuracy of NWS forecasts. The development of distributed models in HL is following specific scientific research and implementation strategies, each consisting of several elements. In its science strategy, HL has conducted a highly successful comparison of distributed models (Distributed Model Intercomparison Project- DMIP) in order to identify which model or process algorithms would benefit the NWS mission. DMIP has also been designed to understand issues such as the use of operational data, the amount of calibration required, and methods of deriving initial parameter estimates. DMIP has garnered participation from 12 research institutions in the US and abroad, including China, Canada, and Denmark. Simultaneously, HL has developed a flexible modeling system that can be used to develop and evaluate various rainfall runoff models and modeling approaches (gridded distributed, semi distributed, and lumped). HL has successfully participated in DMIP with a gridded distributed model consisting of the SAC-SMA and kinematic routing in each computational element. As a result of DMIP, the NWS has decided to move ahead with the implementation of the HL distributed model. As with the research effort, a specific implementation plan is being followed. First, a prototype version of the research distributed model is being run at the one RFC for real time operations. Short term software development is being conducted to make this research version more user friendly. Long term software development is planned to derive a system to efficiently support operational distributed modeling. Long term research will also continue into new rainfall/runoff/routing models and well as parameter estimation, calibration and state updating issues. Formal implementation includes a transition phase in which the new distributed model will be run parallel to the current lumped model in selected basins, providing the forecaster with two simulations for decision making. Moreover, such a transition period will provide much needed exposure and training. Problems identified to date with the deployment of distributed models include the addition of a snow model, issues relating to the quality of the NEXRAD data, methods of parameterizing and calibrating a distributed model, methods of state updating, and training of personnel in the transition from lumped to distributed model forecasting.

Smith, M.

2003-04-01

118

Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3 — A nonparametric probabilistic forecast model  

Microsoft Academic Search

This paper is the last in a series of three in the current issue that present a framework for long-term rainfall probabilistic forecasts. A nonparametric probabilistic forecast model is presented. The approach is based on accurate estimation of the conditional probability distribution of rainfall through the use of nonparametric kernel density estimation techniques. The kernel approach is data driven and

A. Sharma

2000-01-01

119

Forecasting Volatility in Stock Market Using GARCH Models  

E-print Network

for dealing with heteroscedastic time series. Keywords: Volatility Heteroscedastic time series GARCH (P,Q) model GJR-GARCH(P,Q) model EGARCH(P,Q) model v contents 1 Introduction 1.1 Volatility ?????????????????????????..1 1.2 Why... Forecasting Volatility is so important????????????..3 1.3 Heteroscedastic time series??????????????????...5 1.4 Some popular Volatility Models????????????????...6 1.4.1 Exponentially Weighted Moving Average (EWMA)???..???..6 1.4.2...

Yang, Xiaorong

2008-01-01

120

Comparison of Conventional and ANN Models for River Flow Forecasting  

NASA Astrophysics Data System (ADS)

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.

Jain, A.; Ganti, R.

2011-12-01

121

Networking Sensor Observations, Forecast Models & Data Analysis Tools  

NASA Astrophysics Data System (ADS)

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.

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

2009-12-01

122

Wavelet regression model for short-term streamflow forecasting  

NASA Astrophysics Data System (ADS)

SummaryWavelet regression (WR) technique is proposed for short-term streamflow forecasting in this study. The WR model is improved combining two methods, discrete wavelet transform and linear regression model. The proposed model is applied to the daily streamflow data of two stations, Karabuk and Derecikviran, on the Filyos River in the Western Black Sea region of Turkey. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient ( R) statistics are used for evaluating the accuracy of the WR models. In the first part of the study, the accuracy of the WR models is compared with the artificial neural network (ANN) and autoregressive moving average (ARMA) models in 1-day ahead streamflow forecasting. Comparison results reveal that the WR model performs better than the ANN and ARMA models. The ARMA model is also found to be slightly better than the ANN. For the Karabuk and Derecikviran stations, it was found that WR models with RMSE = 8.48 m 3/s, MAE = 2.46 m 3/s, R = 0.978 and RMSE = 33.3 m 3/s, MAE = 10.2 m 3/s, R = 0.976 in the validation stage are superior in forecasting 1-day ahead streamflows than the most accurate ARMA models with RMSE = 13.5 m 3/s, MAE = 3.44 m 3/s, R = 0.942 and RMSE = 46.5 m 3/s, MAE = 13.2 m 3/s, R = 0.953, respectively. In the second part of study, WR and ANN models are compared in 2- and 3-day ahead streamflow forecasting. Based on the comparison results, WR models are found to be more accurate than the ANN models.

Kisi, Ozgur

2010-08-01

123

Using Bayesian Model Averaging to Calibrate Forecast Ensembles  

Microsoft Academic Search

Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources. The BMA predictive probability density function (PDF) of any quantity of interest is a weighted average

Adrian E. Raftery; Tilmann Gneiting; Fadoua Balabdaoui; Michael Polakowski

2005-01-01

124

Occupational Shortages Reporting System, Forecasting Model and Correlation Analysis.  

ERIC Educational Resources Information Center

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

Park, Theresa

125

Climate-Based Models for Understanding and Forecasting Dengue Epidemics  

E-print Network

Climate-Based Models for Understanding and Forecasting Dengue Epidemics Elodie Descloux1 indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March­April) lagged the warmest temperature by 1­2 months

Paris-Sud XI, Université de

126

Weather load model for electric demand and energy forecasting  

Microsoft Academic Search

A method of forecasting the heat sensitive portion of electrical demand and energy utilizing a summer weather load model and taking into account probability variation of weather factors is discussed in this paper. The heat sensitive portion of the load is separated from base load and historical data is used to determine the effect of weather on the system load.

C. E. Asbury

1975-01-01

127

Demand Forecasting in Automotive Aftermarket Based on ARMA Model  

Microsoft Academic Search

The rapid development of automobile industry in China promotes the fast growth of the automotive aftermarket. Facing the fierce market competition, it is necessary for a company to forecast the demand for auto spare parts. This paper proposes a method based on ARMA model to fulfill the important task. The accuracy and ease of use of the method is illustrated

Yun Chen; Heng Zhao; Li Yu

2010-01-01

128

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series  

E-print Network

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural Networks R. E. Abdel-Aal Computer Engineering Department, King Fahd University of Petroleum and Minerals Neural networks have been widely used for short-term, and to a lesser degree medium and long term, demand

Abdel-Aal, Radwan E.

129

Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics  

E-print Network

alternative to fossil fuels. However, it is an intermittent source of energy, and its continued spread hingesProbabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics using Heteroskedastic, Washington, USA 2University of Aarhus, Aarhus, Denmark Technical Report no. 546 Department of Statistics

Washington at Seattle, University of

130

Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs  

E-print Network

Production forecasting in shale (ultra-low permeability) gas reservoirs is of great interest due to the advent of multi-stage fracturing and horizontal drilling. The well renowned production forecasting model, Arps? Hyperbolic Decline Model...

Statton, James Cody

2012-07-16

131

Distributed Hydrologic Models for Flow Forecasts - Part 1  

NSDL National Science Digital Library

Distributed Hydrologic Models for Flow Forecasts â 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.

Comet

2009-07-28

132

A Ground-Level Ozone Forecasting Model for Santiago, Chile  

Microsoft Academic Search

A physically based model for ground-level ozone forecasting is evaluated for Santiago, Chile. The model predicts the daily peak ozone concentration, with the daily rise of air temperature as input variable; weekends and rainy days appear as interventions. This model was used to analyse historical data, using the linear transfer function\\/finite impulse response (LTF\\/FIR) formalism; the simultaneous transfer function (STF)

Hector Jorquera; Wilfredo Palma; Jose Tapia

2002-01-01

133

A spreadsheet modeling approach to the Holt-Winters optimal forecasting  

Microsoft Academic Search

The objective of this paper is to determine the optimal forecasting for the Holt–Winters exponential smoothing model using spreadsheet modeling. This forecasting procedure is especially useful for short-term forecasts for series of sales data or levels of demand for goods. The non-linear programming problem associated with this forecasting model is formulated and a spreadsheet model is used to solve the

José Vicente Segura; Enriqueta Vercher

2001-01-01

134

Volcanic ash forecast transport and dispersion (VAFTAD) model  

SciTech Connect

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.

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

135

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect

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.

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

2011-03-28

136

operational modelling and forecasting of the Iberian shelves ecosystem  

NASA Astrophysics Data System (ADS)

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

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

2012-04-01

137

Analysis of the validity of the coefficient estimates and forecasting properties of the RDFOR (Regional Demand FORcasting) models: A summary report: Validation report  

SciTech Connect

The Regional Demand FORcasting model (RDFOR) is a simple econometric model currently used by the DOE within the Midterm Energy Forecasting System. Econometric models are often used to provide baseline forecasts of near- to mid-term economic behavior. From the point of view of the policymaker, it is desirable to ascertain as objectively as possible the degree to which these econometric forecasts can be trusted. This paper illustrates, within the context of the industrial and residential sectors of RDFOR, a number of diagnostic tools of general interest which are useful in assessing model reliability.

Kuh, E.; Lahiri, S.; Minkoff, A.; Swartz, S.; Welsch, R.

1982-11-01

138

Systematic uncertainty reduction in streamflow forecasts development: Importance of Input and Hydrologic Model Uncertainty  

NASA Astrophysics Data System (ADS)

Skillful streamflow forecasts are useful to planners and managers making decisions regarding seasonal water availability and allocation. Seasonal climate forecasts, from atmospheric general circulation models, can be used to obtain the streamflow forecasts. But the skill of these forecasts is very low thus different techniques including better downscaling, using regional climate models and multi-model combinations are investigated to improve the skill. This study focuses on a fundamental research question: given that we have climate forecasts from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to develop multi-model streamflow forecasts? To answer this question, we consider the two possible strategies: (a) reduce the input uncertainty first by combining climate forecasts and then use the multi-model climate forecasts with multiple watershed models (b) ingest the individual climate forecasts (without multi-model combination) with various watershed models and then combine the streamflow predictions that arise from all possible combinations of climate and watershed models. To investigate this, we consider a synthetic streamflow and climate forecasting schemes, so that we will be able to compare all the performance of candidate strategies with the true flows. Preliminary results show that reducing input uncertainty by combining climate forecasts provides greater improvements in streamflow than reducing output uncertainty by combining streamflow predictions which arise from single climate forecasts.

Singh, H.; Arumugam, S.

2011-12-01

139

The SOLAR2000 empirical solar irradiance model and forecast tool  

Microsoft Academic Search

SOLAR2000 is a collaborative project for accurately characterizing solar irradiance variability across the spectrum. A new image- and full-disk proxy empirical solar irradiance model, SOLAR2000, is being developed that is valid in the spectral range of 1–1,000,000 nm for historical modeling and forecasting throughout the solar system. The overarching scientific goal behind SOLAR2000 is to understand how the Sun varies

W. Kent Tobiska; Tom Woods; Frank Eparvier; Rodney Viereck; Linton Floyd; Dave Bouwer; Gary Rottman; O. R. White

2000-01-01

140

Economic Analysis. Computer Simulation Models.  

ERIC Educational Resources Information 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…

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

141

Influence of Model Physics on NWP Forecasts - version 2  

NSDL National Science Digital Library

This module, part of the "NWP Training Series: Effective Use of NWP in the Forecast Process", describes model parameterizations of surface, PBL, and free atmospheric processes, such as surface snow processes, soil thermal and moisture processes, surface vegetation effects such as evapotranspiration, radiative processes involving clouds and trace gases, and turbulent processes in the PBL and free atmosphere. It specifically addresses how models treat these processes, how such processes can potentially interact with each other, and how they can influence forecasts of sensible weather elements. Back in 2000, the subject matter expert for this module was Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC). Revisions to the module were made in 2009 by Drs. Bill Bua and Stephen Jascourt, from the NWP team at UCAR/COMET.

Comet

2009-11-17

142

Daily reservoir inflow forecasting combining QPF into ANNs model  

NASA Astrophysics Data System (ADS)

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

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

2009-01-01

143

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

NASA Astrophysics Data System (ADS)

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

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

2009-09-01

144

The optimal combination forecasting model based on closeness degree and IOWHA operator under the uncertain environment  

Microsoft Academic Search

We propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA) operator under the uncertain environment in which the raw data are expressed as interval numbers. It is a new kind of combination forecasting model with variant weights. We can obtain weighted coefficient vectors of combination forecasting methods by maximizing the closeness degree

Lei Jin; Huayou Chen; Xiang Li; Mengjie Yao

2011-01-01

145

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

Microsoft Academic Search

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,

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

2009-01-01

146

An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008  

ERIC Educational Resources Information Center

Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average…

Chen, Chau-Kuang

2008-01-01

147

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

NASA Astrophysics Data System (ADS)

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.

Ding, Jianli; Li, Huafeng

148

Forecast improvement by interactive ensemble of atmospheric models  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

149

Assessing model state and forecasts variation in hydrologic data assimilation  

NASA Astrophysics Data System (ADS)

Data assimilation (DA) has been widely used in hydrological models to improve model state and subsequent streamflow estimates. However, for poor or non-existent state observations, the state estimation in hydrological DA can be problematic, leading to inaccurate streamflow updates. This study evaluates the soil moisture and flow variations and forecasts by assimilating streamflow and soil moisture. Three approaches of Ensemble Kalman Filter (EnKF) with dual state-parameter estimation are applied: (1) streamflow assimilation, (2) soil moistue assimilation, and (3) combined assimilation of soil moisture and streamflow. The assimilation approaches are evaluated using the Sacramento Soil Moisture Accounting (SAC-SMA) model in the Spencer Creek catchment in southern Ontario, Canada. The results show that there are significant differences in soil moisture variations and streamflow estimates when the three assimilation approaches were applied. In the streamflow assimilation, soil moisture states were markedly distorted, particularly soil moisture of lower soil layer; whereas, in the soil moisture assimilation, streamflow estimates are inaccurate. The combined assimilation of streamflow and soil moisture provides more accurate forecasts of both soil moisture and streamflow, particularly for shorter lead times. The combined approach has the flexibility to account for model adjustment through the time variation of parameters together with state variables when soil moisture and streamflow observations are integrated into the assimilation procedure. This evaluation is important for the application of DA methods to simultaneously estimate soil moisture states and watershed response and forecasts.

Samuel, Jos; Coulibaly, Paulin; Dumedah, Gift; Moradkhani, Hamid

2014-05-01

150

Nonlinear models for ground-level ozone forecasting  

Microsoft Academic Search

One of the main concerns in air pollution is excessive tropospheric ozone concentration. The aim of this work is to develop\\u000a statistical models giving shortterm forecasts of future ground-level ozone concentrations. Since there are few physical insights\\u000a about the dynamic relationship between ozone, precursor emissions and\\/or meteorological factors, a nonparametric and nonlinear\\u000a approach seems promising in order to specify the

Silvano Bordignon; Carlo Gaetan; Francesco Lisi

2002-01-01

151

[Forecasting model of transfer of 137Cs to the plants].  

PubMed

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

Spirin, E V; Anisimov, V S; Dikarev, D B; Kochetkov, I V; Krylenkin, D V

2013-01-01

152

Comparison of modelling techniques for milk-production forecasting.  

PubMed

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

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

2014-06-01

153

FORECAST MODEL FOR MODERATE EARTHQUAKES NEAR PARKFIELD, CALIFORNIA.  

USGS Publications Warehouse

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.

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

1985-01-01

154

Identification and Forecasting in Mortality Models  

PubMed Central

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

Nielsen, Jens P.

2014-01-01

155

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

156

Forecasting tourism demand to Catalonia: Neural networks vs. time series models  

Microsoft Academic Search

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourism demand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time series methods at a regional level. Seasonality and volatility are important features

Oscar Claveria; Salvador Torra

2014-01-01

157

Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models  

Microsoft Academic Search

Despite the massive popularity of probabilistic (association) football forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three probability values corresponding to home win, draw, and away win) there is no agreed scoring rule to determine their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies are inadequate since they

Anthony Costa Constantinou; Norman Elliott Fenton

2012-01-01

158

Forecasting Monthly Sales of Cellphone Companies - the Use of VAR Model  

Microsoft Academic Search

Forecast of earnings is one of the major tasks for financial statement analysts, and sales forecast is the most important step in the process of predicting earnings. Taking one large cell phone company in Taiwan as example and by the use of monthly data to fit a proper VAR model, we found that, when forecasting monthly sales, number of clients

Hsueh-Fang Chien; Shu-Hua Lee; Lee Wen-chih; Yann-ching Tsai

2007-01-01

159

Forecasting with a Bayesian DSGE Model: An Application to the Euro Area  

Microsoft Academic Search

AbstractIn monetary policy strategies geared towards maintaining price stability, conditional and unconditional forecasts of inflation and output play an important role. In this article we illustrate how modern sticky-price dynamic stochastic general equilibrium (DSGE) models, estimated using Bayesian techniques, can become an additional useful tool in the forecasting kit of central banks. First, we show that the forecasting performance of

Frank Smets; Rafael Wouters

2004-01-01

160

A new car-following model with the consideration of the driver's forecast effect  

NASA Astrophysics Data System (ADS)

In this Letter, we develop a new car-following model with the consideration of the driver's forecast effect (DFE). The analytical and numerical results show that the stability of traffic flow will gradually be enhanced with the increase of the forecast effect coefficient and the forecast time.

Tang, T. Q.; Li, C. Y.; Huang, H. J.

2010-08-01

161

The skill of seasonal ensemble low flow forecasts for four different hydrological models  

NASA Astrophysics Data System (ADS)

This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.

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

2014-05-01

162

SolarAnywhere forecast (Perez & Hoff) This chapter describes, and presents an evaluation of, the forecast models imbedded in the  

E-print Network

) to animate satellite images between consecutive half hourly native frames and produce one minute irradiances of, the forecast models imbedded in the SolarAnywhere platform. The models include satellite derived from the US geostationary weather satellites using a semi empirical model of the type described

Perez, Richard R.

163

Economic Models as Analogies Itzhak Gilboa  

E-print Network

analyze models that are "theoretical cases", which help understand economic problems by drawing analogies between the model and the problem. According to this view, economic models, empirical data, experimentalEconomic Models as Analogies Itzhak Gilboa , Andrew Postlewaite , Larry Samuelson,§ and David

Schmeidler, David

164

A feature fusion based forecasting model for financial time series.  

PubMed

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

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

2014-01-01

165

A Feature Fusion Based Forecasting Model for Financial Time Series  

PubMed Central

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

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

2014-01-01

166

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

PubMed Central

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

Dai, Yonghui; Han, Dongmei; Dai, Weihui

2014-01-01

167

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

SciTech Connect

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.

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

2013-12-18

168

Economic model of OPEC coalition  

SciTech Connect

Efforts to formulate the economic behavior of OPEC are complicated because the coalition is made up of member countries, each of which pursues a different objective, arrives at its own optimum price and quantity, and has an opportunity to participate in a group consensus. This requires a new cartel model that will explain the coalitionary process, yet remain flexible enough to consider the diverse objectives of individual members. The author analyzes the possibility of realizing this consensus in relation to the differing politico-economic structures of the members. He concludes that the long-run stability of OPEC requires reversing some historical patterns, although offsetting effects may avoid a short-term instability. 19 references, 3 figures, 2 tables.

Razavi, H.

1984-10-01

169

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

NASA Astrophysics Data System (ADS)

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.

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

2014-05-01

170

The strategy of building a flood forecast model by neuro-fuzzy network  

NASA Astrophysics Data System (ADS)

A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model.

Chen, Shen-Hsien; Lin, Yong-Huang; Chang, Li-Chiu; Chang, Fi-John

2006-04-01

171

A neural network short term load forecasting model for the Greek power system  

SciTech Connect

This paper presents the development of an Artificial Neural Network (ANN) based short-term load forecasting model for the Energy Control Center of the Greek Public Power Corporation (PPC). The model can forecast daily load profiles with a lead time of one to seven days. Attention was paid for the accurate modeling of holidays. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set are described in the paper. The results indicate that the load forecasting model developed provides accurate forecasts.

Bakirtzis, A.G.; Petridis, V.; Kiartzis, S.J.; Alexiadis, M.C. [Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering] [Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering; Maissis, A.H.

1996-05-01

172

Modeling and Forecasting the Malawi Kwacha-US Dollar Nominal Exchange Rate  

Microsoft Academic Search

This study develops a blended version of the monetary and portfolio models for the MK\\/USD exchange rate, and assesses the forecasting performance of the model against a simple random walk. The results indicate that the model performs better than the simple random walk on the 6, 12 and 24 months forecasting horizons. However, the model does not perform well on

Kisu Simwaka

2007-01-01

173

Lightning forecasting in southeastern Brazil using the WRF model  

NASA Astrophysics Data System (ADS)

This paper introduces a lightning forecasting method called Potential Lightning Region (PLR), which is the probability of the occurrence of lightning over a region of interest. The PLR was calculated using a combination of meteorological variables obtained from high-resolution Weather Research and Forecasting (WRF) model simulations during the summer season in southeastern Brazil. The model parameters used in the PLR definition were: surface-based Convective Available Potential Energy (SBCAPE), Lifted Index (LI), K-Index (KI), average vertical velocity between 850 and 700 hPa (w), and integrated ice-mixing ratio from 700 to 500 hPa (QICE). Short-range runs of twelve non-severe thunderstorm cases were performed with the WRF model, using different convective and microphysical schemes. Through statistical evaluations, the WRF cloud parameterizations that best described the convective thunderstorms with lightning in southeastern Brazil were the combination of Grell-Devenyi and Thompson schemes. Two calculation methods were proposed: the Linear PLR and Normalized PLR. The difference between them is basically how they deal with the influence of lightning flashes over the WRF domain's grid points for the twelve thunderstorms analyzed. Three case studies were used to test both methods. A statistical evaluation lowering the spatial resolution of the WRF grid into larger areas was performed to study the behavior and accuracy of the PLR methods. The Normalized PLR presented the most suitable one, predicting flash occurrence appropriately.

Zepka, G. S.; Pinto, O.; Saraiva, A. C. V.

2014-01-01

174

Middle Atlantic Bight Marine Ecosystem: A Regional Forecast Model Study  

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

175

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

Makarov, Yuri V.; Reyes Spindola, Jorge F.; Samaan, Nader A.; Diao, Ruisheng; Hafen, Ryan P.

2010-11-02

176

Forecasting the Market Penetration of Energy Conservation Technologies: The Decision Criteria for Choosing a Forecasting Model  

E-print Network

., and Sahal, D., eds., Technological Substitution: Forecasting Techniques and Applications, American Elsevier Publishing Company, 197-217, 1976. [10] Lenz, R.C., Jr. and Lanford, H.I'I. "The Substi tution Phenomenon tV", Business Horizons, 63-67, 1972..., Derendra, "The Multidimensional Diffu sion of Technology", in Linstone, H., and Sahal, D., eds., Technological Substitution: Forecasting Techniques and Applicat~ons, Amer ican Elsevier Publishing Company, Inc., 223-234, 1976 [18] Sahal, Derendra...

Lang, K.

1982-01-01

177

A high resolution WRF model for wind energy forecasting  

NASA Astrophysics Data System (ADS)

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

Vincent, Claire Louise; Liu, Yubao

2010-05-01

178

A Capacity Forecast Model for Volatile Data in Maintenance Logistics  

NASA Astrophysics Data System (ADS)

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

Berkholz, Daniel

2009-05-01

179

ASSESSMENT OF ECONOMIC PERFORMANCE OF MODEL PREDICTIVE  

E-print Network

ASSESSMENT OF ECONOMIC PERFORMANCE OF MODEL PREDICTIVE CONTROL THROUGH VARIANCE/CONSTRAINT TUNING advanced process control (APC) strategies to deal with multivariable constrained control problems with an ultimate objective towards economic optimization. Any attempt to evaluate MPC performance should therefore

Huang, Biao

180

Peak Flow Forecasting with Radar Precipitation and the Distributed Model CASC2D  

Microsoft Academic Search

The processing speed of computers and availability of spatial hydrologic data make distributed watershed models a viable approach for many applications, including peak flow forecasting. This study demonstrates the feasibility of using radar precipitation data with a distributed watershed model to obtain increased lead-time for peak flow forecasting. The CASC2D watershed model is applied to the Hassyampa River watershed in

Jeff Jorgeson; Pierre Julien

2005-01-01

181

An artificial neural network model for rainfall forecasting in Bangkok, Thailand  

NASA Astrophysics Data System (ADS)

This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

Hung, N. Q.; Babel, M. S.; Weesakul, S.; Tripathi, N. K.

2009-08-01

182

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam  

Microsoft Academic Search

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results\\u000a in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes\\u000a crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based\\u000a on stochastic modeling or artificial intelligence (AI)

Ahmed El-Shafie; Mahmoud Reda Taha; Aboelmagd Noureldin

2007-01-01

183

Forecasting Ability of a Multi-Renewal Seismicity Model  

NASA Astrophysics Data System (ADS)

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

Molchan, George; Romashkova, Leontina

2014-09-01

184

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

USGS Publications Warehouse

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

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

2014-01-01

185

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

USGS Publications Warehouse

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

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

2004-01-01

186

Modeling the Hudson River PlumeModeling the Hudson River Plume Forecast plume variabilityForecast plume variability equivalent freshwater distributionequivalent freshwater distribution  

E-print Network

Hudson River flow from USGS Mohawk and Fort Edward gauges + persistence (for forecast) accessed via webModeling the Hudson River PlumeModeling the Hudson River Plume Forecast plume variability L CE A N B S E R V A T IO N AB R U U T G E R S N I V E R S I T Y The Hudson River plume and adjacent

Wilkin, John

187

A 30-day forecast experiment with the GISS model and updated sea surface temperatures  

NASA Technical Reports Server (NTRS)

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.

Spar, J.; Atlas, R.; Kuo, E.

1975-01-01

188

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

NASA Astrophysics Data System (ADS)

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

Weng, Weifeng

189

ARTIFICIAL NEURAL NETWORK MODELS INVESTIGATION FOR EUPHRATES RIVER FORECASTING &BACK CASTING  

Microsoft Academic Search

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

Cheleng A. Arslan

2013-01-01

190

Every cloud has a silver lining: Weather forecasting models could predict brain tumor  

E-print Network

Every cloud has a silver lining: Weather forecasting models could predict brain tumor growth Ever prediction ? a modern state estimation algorithm known as a Local Ensemble Transform Kalman Filter: An Application of Data "Every cloud has a silver lining: Weather forecasting models could predict brain tumor

Kuang, Yang

191

THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION  

EPA Science Inventory

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

192

New Regional Airport Passenger Throughput Forecast Model Based on Airport Groups  

Microsoft Academic Search

In order to solve the problem of the new airport passenger throughput forecast, a new forecast model which based on airport groups is put forward in this paper. In this model, the competition ability of airports and the important factors which affect passengers travel choice are introduced into it. Then by constructing passenger utility function, the new regional airport passenger

Hong jun Xu; Jun gai Tian; Hui tao Ma

2011-01-01

193

Optimization of NWP model closure parameters using total energy norm of forecast error as a target  

NASA Astrophysics Data System (ADS)

We explore the use of dry total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM (global circulation models) closure parameters related to clouds and precipitation. The target criterion in the optimization is the dry total energy norm of 3-day forecast error with respect to the ECMWF (European Centre for Medium-Range Weather Forecasts) operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day 10 forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into midlatitudes at longer ranges and appears as a smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

Ollinaho, P.; Järvinen, H.; Bauer, P.; Laine, M.; Bechtold, P.; Susiluoto, J.; Haario, H.

2014-09-01

194

Tampa Bay Operational Forecast System (TBOFs): Model Development and Skill Assessment.  

National Technical Information Service (NTIS)

The Tampa Bay Operational Forecast System (TBOFS) has been developed based on a hydrodynamic model system, Regional Ocean Model System (ROMS, Haidvogel, 2008). The curvilinear model grid was constructed and populated with bathymetry obtained from NOS surv...

A. Zhang, E. Wei

2011-01-01

195

Predictive models for forecasting hourly urban water demand  

NASA Astrophysics Data System (ADS)

SummaryOne of the goals of efficient water supply management is the regular supply of clean water at the pressure required by consumers. In this context, predicting water consumption in urban areas is of key importance for water supply management. This prediction is also relevant in processes for reviewing prices; as well as for operational management of a water network. In this paper, we describe and compare a series of predictive models for forecasting water demand. The models are obtained using time series data from water consumption in an urban area of a city in south-eastern Spain. This includes highly non-linear time series data, which has conditioned the type of models we have included in our study. Namely, we have considered artificial neural networks, projection pursuit regression, multivariate adaptive regression splines, random forests and support vector regression. Apart from these models, we also propose a simple model based on the weighted demand profile resulting from our exploratory analysis of the data. In our comparative study, all predictive models were evaluated using an experimental methodology for hourly time series data that detailed water demand in a hydraulic sector of a water supply network in a city in south-eastern Spain. The accuracy of the obtained results, together with the medium size of the demand area, suggests that this was a suitable environment for making adequate management decisions.

Herrera, Manuel; Torgo, Luís; Izquierdo, Joaquín; Pérez-García, Rafael

2010-06-01

196

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

SciTech Connect

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.

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

2013-07-25

197

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

NASA Astrophysics Data System (ADS)

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

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

2010-09-01

198

Evaluation of spatially distributed snow models for streamflow forecasting  

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

199

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

NASA Astrophysics Data System (ADS)

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

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

2014-11-01

200

Model climate as a function of forecast lead time in an imperfect model scenario  

NASA Astrophysics Data System (ADS)

Numerical weather and climate models constitute the best available tools to tackle the problems of weather prediction and climate projection. These models have played a key role in the attribution of the observed climate change to anthropogenic causes. However, a better understanding of the current models and the development of improved models are still required to address issues such as the interpretation of climate projections and the large uncertainties still present in regional climate change studies. Two assumptions lie at the heart the climate model suitability: (1) a climate attractor exists, and (2) the model attractor lies on the climate attractor, or at least on the projection of the climate attractor onto model space. In this contribution, two versions of the Lorenz '96 system are used, one as a prototype system and another as an imperfect model, to investigate the implications of assumption (2). In particular, the dependence of model-generated climate on forecast lead time is examined. It is shown that forecasts produced by the imperfect model rapidly diverge from the system's orbit and that this divergence is mainly due to model error. As a result, climatologies produced from these divergent forecasts show a dependence on forecast lead time. This dependence is characterised by an initial rapid bias growth with respect to the system's climatology. The initial bias growth ends at a saturation level which is reached as the transient period in individual forecasts dies out (spin-up period). Furthermore, it is shown that, once the spin-up period is over, climatologies generated with long-term integrations of both the prototype system and the imperfect model are essentially the same as climatologies generated from short-term forecasts from a perfect and an imperfect model, respectively. Despite its simplicity with respect to the actual climate system, this study about the Lorenz '96 system shows features that are relevant for climate studies and the understanding of climate models. In order to show this, two examples using real-world data from operational forecasting systems and climate experiments are also discussed.

Martinez-Alvarado, Oscar

2014-05-01

201

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

SciTech Connect

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

Edwards, B.K.; Bando, A.

1992-07-01

202

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

SciTech Connect

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

Edwards, B.K.; Bando, A.

1992-01-01

203

Estimating demand for industrial and commercial land use given economic forecasts.  

PubMed

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

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

2014-01-01

204

Ensemble Volcanic Aerosol Forecasting in Hawaii using a Parallelized version of the Hysplit Dispersion Model  

NASA Astrophysics Data System (ADS)

A transition from deterministic to probabilistic forecasts of the dispersion of emissions from the Kilauea Volcano on the Island of Hawaii is under way. Operational forecasts of volcanic smog (vog) have been produced for 3 years by a custom version of NOAA's Hysplit dispersion model (vog model hereafter), a Lagrangian transport model that uses high-resolution WRF-ARW model output for initial conditions run at the University of Hawaii at Manoa. The vog model has been successful in predicting which locations in the State of Hawaii will be impacted by the vog plume. Initial concentrations of emissions from the volcano are set empirically based on downstream observations provided by the Hawaiian Volcano Observatory. Fast changing meteorological conditions and/or rapid variations in emissions rates cause forecast errors to increase. Recent efforts aim to leverage the parallelism of Hysplit to run ensemble forecasts with various initial condition configurations to better quantify the forecast uncertainty. The ensemble will contain 28 members each with perturbed heights and locations of initial aerosol concentrations. Forecast sulfur dioxide and sulfate aerosol concentrations follow Air Resources Laboratory's Air Quality Index (AQI). The resulting probabilistic forecasts will provide probability of exceedance plots and concentration-probability plots for each AQI level. Because some people are extremely sensitive to low concentrations of sulfate aerosols, the lowest AQI levels will be distinguished in the exceedance map output. Downstream observations at Pahala and Kona will be used to validate the ensemble results, which will also be compared to the results of deterministic forecasts.

Pattantyus, A.; Businger, S.

2013-12-01

205

A Forecasting Metric for Evaluating DSGE Models for Policy Analysis  

E-print Network

Wouters for providing the matlab codes and posterior draws for Smets and Wouters (2007). Address: Abhishek. The news is defined as the one-step ahead forecast errors and the first and second moments of this news for the observed news. The paper then evaluates the first and second moments of both the forecast errors

Niebur, Ernst

206

A Recent Forecast Failure  

Microsoft Academic Search

Failures are not rare in economic forecasting, probably due to the high incidence of unpredictable shocks and regime shifts in the economy. Thus, there is a premium on adaptation in the forecast process, in order to avoid se- quences of forecast failure. This paper evaluates a sequence of poor inflation forecasts in the Norges Bank Inflation Report, and we present

Ragnar Nymoen

207

A Kp forecast model based on neural network  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

208

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

NASA Technical Reports Server (NTRS)

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

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

2003-01-01

209

Threat Level Forecast for Ship's Oil Spill - Based on BP Neural Network Model  

Microsoft Academic Search

It's very important to assess the threat level in time when the ship's oil spill occurred, because the threat level forecast will help to come to a decision when dealing with the accident. BP neural network model is proposed in this paper to build a thread level forecast method for ship's oil spill accident. Train the BP neural network first

Cai Wenxue; Zheng Yanwu; Shi Yongqiang; Zhong Huiling

2009-01-01

210

Electricity demand load forecasting of the Hellenic power system using an ARMA model  

Microsoft Academic Search

Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject

S. Sp. Pappas; L. Ekonomou; P. Karampelas; D. C. Karamousantas; S. K. Katsikas; G. E. Chatzarakis; P. D. Skafidas

2010-01-01

211

A new marked point process model for the federal funds rate target: Methodology and forecast evaluation  

Microsoft Academic Search

Forecasts of key interest rates set by central banks are of paramount concern for investors and policy makers. Recently it has been shown that forecasts of the federal funds rate target, the most anticipated indicator of the Federal Reserve Bank's monetary policy stance, can be improved considerably when its evolution is modeled as a marked point process (MPP). This is

Joachim Grammig; Kerstin Kehrle

2008-01-01

212

Forecasting of Chaotic Cloud Absorption Time Series for Meteorological and Plume Dispersion Modeling  

Microsoft Academic Search

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

V. P EREZ-MUNUZURI

213

Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow  

Microsoft Academic Search

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%

Martyn P. Clark; Lauren E. Hay

2004-01-01

214

Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States  

E-print Network

suppression require training before they can be put to work suppressing wildland fires, but may be retainedStatistical Model for Forecasting Monthly Large Wildfire Events in Western United States HAIGANOUSH to forecast the number and location of large wildfire events (with specified confidence bounds) is important

Westerling, Anthony L.

215

T GREAT ALASKA WEATHER MODELING SYMPOSIUM WHAT: About 50 scientists, forecasters, decision  

E-print Network

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

Moelders, Nicole

216

Modeling, History Matching, Forecasting and Analysis of Shale Reservoirs Performance Using Artificial Intelligence  

E-print Network

SPE 143875 Modeling, History Matching, Forecasting and Analysis of Shale Reservoirs Performance Using Artificial Intelligence Shahab D. Mohaghegh, Intelligent Solutions, Inc. & West Virginia formations. In this paper we discuss using a new and completely different approach to modeling, history

Mohaghegh, Shahab

217

Monitoring and forecast of hydro meteorological hazards basing on data of distant assay and mathematical modeling  

NASA Astrophysics Data System (ADS)

Russian Federation having giant area has low concentration of land meteorological check points. Net of monitoring is not enough for effective forecast and prediction of weather dynamics and extremely situations. Under increase of extremely situations and incidents - hurricanes et al (two times from begin of XXI century) reconstruction and "perestroika" of monitoring net is needful and necessary. The basis of such a progress is distant monitoring using planes and satellites adding land contact monitoring base on efforts of existed points and stations. Interaction of contact and distant views may make hydro meteorological data and prediction more fine and significant. Tradition physical methods must be added by new biological methods of modern study. According to gotten researches animal are able to predict extremely hazards of natural and anthropogenic nature basing of interaction between biological matter and probable physical field that is under primary study. For example it was animals which forecasted dropping of Chelyabinsk meteorite of 2013. Adding of biological indication with complex of meteorological data may increase significance of hazard prediction. The uniting of all data and approaches may become basis of proposed mathematical hydro meteorological weather models. Introduction to practice reported complex methods may decrease of loss from hydro meteorological risks and hazards and increase stability of country economics.

Sapunov, Valentin; Dikinis, Alexandr; Voronov, Nikolai

2014-05-01

218

A short-run monetary model of exchange rate determination: Stability tests and forecasting  

Microsoft Academic Search

The paper develops a short-run econometric monetary model of exchange rate determination. The model assumes a conventional money demand function, markets which are linked by interest arbitrage, adaptive expectations formation, and parameters which are stable over time. One-period-ahead forecasts of the mark\\/pound rate generated by the model compare favorably with naive model forecasts using monthly data. Stability tests provided evidence

D. H. Richardson; M. T. C. Wu

1988-01-01

219

Risk of model weighting in multi-model climate projections - lessons learnt from seasonal forecasting  

NASA Astrophysics Data System (ADS)

During the past decade, seasonal forecasting has become a well-established technique, and dynamical seasonal prediction systems are now in operational use at a range of climate prediction centers. In the wake of these developments, an enormous data-base of climate model simulations has been created, which has not only advanced our knowledge about seasonal predictability per se. Given that these data allow for systematic and statistically robust verification, plenty has also been learnt about technical and conceptual issues with relevance to other time-scales, e.g. questions regarding the interpretation and post-processing of ensemble forecasts. This presentation focuses on the issue of multi-model combination - an issue which is also highly relevant in the context of climate change projections. From the evaluation of seasonal forecasts, it has been demonstrated that multi-models on average outperform any single model strategy. Moreover, seasonal forecasts have helped us to understand the underlying mechanisms and reasons for the success of multi-model combination. In particular, it has been possible to resolve the seeming paradox as to why, and under what conditions, a multi-model can outperform the best participating single model. While the potential benefits of multi-models are now widely accepted on essentially all time-scales, there is so far no consensus on what is the best way to construct a multi-model. The simplest way is to give one vote to each model ("equal weighting"), while more sophisticated approaches suggest to apply model-weights according to some measure of performance ("optimum weighting"). Seasonal forecasts have revealed that model weighting indeed can improve the forecasts, but only if the optimum model weights are accurately known and truly represent the underlying model uncertainties. Otherwise, equal weighting on average yields better results. These findings have major implications for the context of climate change, where - mainly due to the long time-scales involved - the determination of optimum weights is still an unresolved issue, and the "risk" of inadvertently applying wrong weights is high. In fact, with a conceptual model of climate change it can be shown that more information may actually be lost by wrong weights than could potentially be gained by optimum weights, particularly if internal variability is high. These results do not imply that the derivation of performance-based weights for climate projections is impossible by principle. However, they do imply that a decision to weight climate models should be taken with great care, and that for many applications equal weighting may well be the better and more transparent way to go.

Weigel, Andreas; Liniger, Mark; Appenzeller, Christof; Fischer, Andreas

2010-05-01

220

Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework  

NASA Astrophysics Data System (ADS)

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

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

2014-10-01

221

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

PubMed

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

Qi, Cheng; Chang, Ni-Bin

2011-06-01

222

Predicting offshore temperatures in Monterey Bay based on coastal observations using linear forecast models  

NASA Astrophysics Data System (ADS)

The linear systems approach is used to forecast offshore near-surface and subsurface temperatures in Monterey Bay and further offshore based on coastal sea surface temperatures (SSTs) at Pacific Grove. SST from Pacific Grove provided the input to the system and the forecast parameters or outputs were temperature at 1 m and 100 m at the M1 buoy located 20 km from Pacific Grove near the center of Monterey Bay, and temperature at 1 m at the M2 buoy located 55 km from Pacific Grove. To forecast temperatures at the M1 and M2 buoys, Box-Jenkins, State-Space, ARX, and ARMAX models were employed. Model formulation, implementation, forecasting procedures, and methods of evaluation are presented. Seven and 30-day forecasts were routinely made for the daily observations although other forecast horizons were employed. For all models and variables, RMS differences between the forecasts and the observations increased rapidly between 1 and 15 days. Beyond about 30 days, RMS differences tended to remain almost constant with increasing forecast horizon. Overall, model forecasts were best for temperature at 100 m at the M1 buoy, due to the fact that temperature is well conserved at depth. Differences in performance between the models were small but the ARMAX model often produced forecasts that were slightly better than the rest, a result that we attribute to a more complete specification of the noise. Although the Box-Jenkins and State-Space models have the potential to produce better forecasts, because more terms must be specified to implement them, the opportunity to produce less-than-optimal results is also greater. Finally, because of seasonal changes in the circulation of Monterey Bay, it is possible that causality was violated, upon occasion, placing certain constraints on the results. Models based on the linear systems approach, where they can be implemented, could serve as a useful adjunct to hydrodynamic ocean circulation models by providing additional information for model initialization, evaluation, and data assimilation. Using the same approach, operational forecasts of the coastal circulation could be made by including forecast winds and the predicted tides as inputs, and CODAR-observed surface currents as the output. In a less glamorous but still useful role, they could be used to fill significant gaps in offshore records where data continuity and quality are important.

Breaker, Laurence C.; Brewster, Jodi K.

223

An artificial neural network model for rainfall forecasting in Bangkok, Thailand  

NASA Astrophysics Data System (ADS)

The present study developed an artificial neural network (ANN) model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), and the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input for training of the model was found most satisfactory in forecasting rainfall in Bangkok, Thailand. The developed ANN model was applied to derive rainfall forecast from 1 to 6 h ahead at 75 rain gauge stations in the study area as forecast point from the data of 3 consecutive years (1997-1999). Results were highly satisfactory for rainfall forecast 1 to 3 h ahead. Sensitivity analysis indicated that the most important input parameter beside rainfall itself is the wet bulb temperature in forecasting rainfall. Based on these results, it is recommended that the developed ANN model can be used for real-time rainfall forecasting and flood management in Bangkok, Thailand.

Hung, N. Q.; Babel, M. S.; Weesakul, S.; Tripathi, N. K.

2008-01-01

224

Systemic change increases forecast uncertainty of land use change models  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

225

Use of a snowmelt model for weekly flood forecast for a major reservoir in Lithuania  

NASA Astrophysics Data System (ADS)

A snowmelt model is used for the weekly forecast of daily discharges in the Kaunas reservoir, Lithuania. The results are used to feed a risk-based decision-making model developed by the first author for dam operation during floods. Physically based calibration of a degree-day model is carried out and coupled with flow routing using Nash's instantaneous unit hydrograph theory. Temperature forecast is used as the driving variable. Due to the relative smoothness of snowmelt over time and the considerable basin size, the model provides acceptable results. Kalman filtering is then used to merge the estimates from the snowmelt model with those from an ARIMA flow model, resulting in better forecasting than that using each method alone. Uncertainty analysis of the snowmelt-model results is then carried out, showing considerable influence of the main parameter degree-day and of soil moisture conditions. Therefore these must be accurately estimated for forecasting purposes during flood events.

Simaityte, Jurgita; Bocchiola, Daniele; Augutis, Juozas; Rosso, Renzo

226

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran  

NASA Astrophysics Data System (ADS)

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.

Soltanzadeh, I.; Azadi, M.; Vakili, G. A.

2011-07-01

227

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.  

PubMed

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. PMID:24845950

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

228

Forecasting exposure to volcanic ash based on ash dispersion modeling  

NASA Astrophysics Data System (ADS)

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

Peterson, Rorik A.; Dean, Ken G.

2008-03-01

229

Microcomputers and Dynamic Economic Models.  

ERIC Educational Resources Information Center

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)

Taylor, Peter

1985-01-01

230

An application of ensemble/multi model approach for wind power production forecasting  

NASA Astrophysics Data System (ADS)

The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

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

2011-02-01

231

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

NASA Astrophysics Data System (ADS)

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.

Shukla, Shraddhanand; Funk, Christopher; Hoell, Andrew

2014-09-01

232

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

NASA Technical Reports Server (NTRS)

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

Hesse, Michael

2009-01-01

233

HTGR Application Economic Model Users' Manual  

SciTech Connect

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

A.M. Gandrik

2012-01-01

234

Residential Saudi load forecasting using analytical model and Artificial Neural Networks  

NASA Astrophysics Data System (ADS)

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

Al-Harbi, Ahmad Abdulaziz

235

A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models  

Microsoft Academic Search

In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, among other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the 'non-linearity' characterizes the future, and compare the forecast

Michael P. Clements; Jeremy Smith

1999-01-01

236

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

NASA Astrophysics Data System (ADS)

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.

Carver, Glenn; Vá?a, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah

2014-05-01

237

Determining Plausible Forecast Outcomes  

NSDL National Science Digital Library

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.

Comet

2010-08-17

238

A simple model for forecast of coastal algal blooms  

NASA Astrophysics Data System (ADS)

In eutrophic sub-tropical coastal waters around Hong Kong and South China, algal blooms (more often called red tides) due to the rapid growth of microscopic phytoplankton are often observed. Under favourable environmental conditions, these blooms can occur and subside over rather short time scales—in the order of days to a few weeks. Very often, these blooms are observed in weakly flushed coastal waters under calm wind conditions—with or without stratification. Based on high-frequency field observations of harmful algal blooms at two coastal mariculture zones in Hong Kong, a mathematical model has been developed to forecast algal blooms. The model accounts for algal growth, decay, settling and vertical turbulent mixing, and adopts the same assumptions as the classical Riley, Stommel and Bumpus model (Riley, G.A., Stommel, H., Bumpus, D.F., 1949. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection Yale University 12, 1-169). It is shown that for algal blooms to occur, a vertical stability criterion, E < 4 ?l2/ ?2, must be satisfied, where E, ?, l are the vertical turbulent diffusivity, algal growth rate, and euphotic layer depth respectively. In addition, a minimum nutrient threshold concentration must be reached. Moreover, with a nutrient competition consideration, the type of bloom (caused by motile or non-motile species) can be classified. The model requires as input simple and readily available field measurements of water column transparency and nutrient concentration, and representative maximum algal growth rate of the motile and non-motile species. In addition, with the use of three-dimensional hydrodynamic circulation models, simple relations are derived to estimate the vertical mixing coefficient as a function of tidal range, wind speed, and density stratification. The model gives a quick assessment of the likelihood of algal bloom occurrence, and has been validated against field observations over a 4-year period. The model helps to explain the observed spatial and temporal patterns of bloom occurrences in relation to the vertical turbulence and nutrient condition. The success of the model points the way to the development of real time management models for disaster mitigation.

Wong, Ken T. M.; Lee, Joseph H. W.; Hodgkiss, I. J.

2007-08-01

239

Modeling OPEC behavior: economic and political alternatives  

Microsoft Academic Search

The predominant approach to modeling OPEC behavior depends upon the assumption that economic self-interest provides the best predictor of the cartel's price and production strategy. With rational monopoly behavior, the exogenous characteristics of the oil market determine an optimal price path for the group. But OPEC members have diverse economic as well as political goals. And uncertainty about oil market

2009-01-01

240

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

NASA Technical Reports Server (NTRS)

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.

Shafer, B.; Jones, E. B.; Frick, D. M. (principal investigators)

1982-01-01

241

Toward Improving Water Supply Forecasts on the Carson River with a Physically Based Hydrologic Model  

NASA Astrophysics Data System (ADS)

Researchers at the Desert Research Institute and the USBR are conducting research aimed at improving water supply forecasts on the Carson River as part of the Water 2025 initiative. The primary goal of the effort is to improve short, seasonal, and long term streamflow forecasts through the use of a physically based hydrologic model (MMS-PRMS) coupled with an operational river routing model (Riverware). Streamflow from high-altitude headwater basins is simulated with MMS-PRMS model and routed with the Riverware model through the Carson valley where a number of ungauged agricultural diversions and returns complicate the real system. The water supply forecasts made with the coupled model are evaluated through comparison with forecasts made by the National Weather Service, the Natural Resources Conservation Service, and historic streamflow using multiple objective measures

Rajagopal, S.; Boyle, D. P.; Lamorey, G.; Bassett, S.; Coors, S.; Mann, M.

2005-12-01

242

Heterogeneous Agent Models in Economics and Finance  

Microsoft Academic Search

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,

Cars H. Hommes

2005-01-01

243

Ensemble forecasting  

Microsoft Academic Search

Numerical weather prediction models as well as the atmosphere itself can be viewed as nonlinear dynamical systems in which the evolution depends sensitively on the initial conditions. The fact that estimates of the current state are inaccurate and that numerical models have inadequacies, leads to forecast errors that grow with increasing forecast lead time. The growth of errors depends on

M. Leutbecher; T. N. Palmer

2008-01-01

244

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

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

245

Survey of Economic Models of Criminal Behavior.  

National Technical Information Service (NTIS)

Economic theories of criminal behavior premise the criminal as rational actor engaged in a calculus of incentives and disincentives. The fundamental model assumes the actor allocates time between legitimate and illegitimate activities, with time allocatio...

W. J. Haga

1987-01-01

246

Earthquake Forecasting in Northeast India using Energy Blocked Model  

NASA Astrophysics Data System (ADS)

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

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

2009-12-01

247

THE GLOBAL IMPACT OF SATELLITE-DERIVED POLAR WINDS ON MODEL FORECASTS  

E-print Network

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

Wisconsin at Madison, University of

248

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

PubMed

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

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

2007-09-01

249

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

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

250

Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting  

Microsoft Academic Search

A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain

V. A. Bell; R. J. Moore

2000-01-01

251

The Leading Indicator Approach to Economic Forecasting--Retrospect and Prospect  

Microsoft Academic Search

For many years a system of leading, coincident, and lagging economic indicators, first developed in the 1930s by the National Bureau of Economic Research (NBER), has been widely used in the United States to appraise the state of the business cycle. Since 1961 the current monthly figures for these indicators have been published by the U.S. Department of Commerce in

Philip A. Klein; Geoffrey H. Moore

1982-01-01

252

Serving Collections of Forecast Model Runs with the THREDDS Data Server  

NASA Astrophysics Data System (ADS)

The THREDDS Data Server (TDS) is a web server that provides metadata and data access for scientific datasets. It provides OPeNDAP, WCS, HTTP and netCDF subsetting services for a number of data formats, including netCDF, HDF5, GRIB, BUFR, etc. The TDS is 100% Java, and runs within the Tomcat web server. We have added a new way to serve model data, which takes a collection of Forecast Model Run datasets, and constructs a single dataset with a 2D time coordinate (run time, forecast time). In the case of Unidata's server, these are collections of GRIB files, and we deal correctly with missing data records by using the forecast and run dates, rather than array indices. The TDS also creates various other "synthetic" datasets from the collection: 1) all data from one analysis run; 2) data with the same forecast offset hour (eg all the 3 hour forecasts, from different runs); 3) data with a constant forecast date (eg all the data with forecast/valid time of 2006-08-08T12:00:00Z, from different runs); and 4) the "best" time series, taking the data from the most recent run available. We are currently working with a number of data partners to test and extend this functionality.

Caron, J.

2006-12-01

253

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

254

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

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

255

Econometric model for the disaggregation of state-level electricity demand forecasts to the service area  

SciTech Connect

An econometric model for the disaggregation of state-level electricity demand forecasts to the service area developed by Oak Ridge National Laboratory (ORNL) is presented. Based on demand models for the service area and the remainder of the state in which it is located, a model which explains the service area's share of the state's demand is developed and estimated for six service areas using econometric techniques. The share is then forecasted and combined with the forecasts for state demand presented in Regional Econometric Model for Forecasting Electricity Demand by Sector and by State (ORNL/NUREG-49) to obtan service-area forecasts to 1990. Results indicate that some service areas differ dramatically from the state and the region of which they are a part. The historic difference in the growth rate of electricity demand is reflected in the forecasted future growth rates generated by the model. This implies that the application of regional or state growth rates to some particular service areas may be inappropriate. The major causes of the difference between the areas seem to be differences in the responsiveness and growth of prices, income, populatzon, and industrial activity.

Tepel, R.C.; Alvic, D.R.; Jay, J.M.; Thorne, A.D.

1980-02-01

256

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

NASA Astrophysics Data System (ADS)

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

Kuznetsova, Maria

257

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

NASA Astrophysics Data System (ADS)

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.

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

2014-07-01

258

Presentation to the Forecasters Club: The Economic Outlook and Options for Fiscal Policy.  

National Technical Information Service (NTIS)

Congressional Budget Office (CBO) expects that the economic recovery will proceed at a modest pace, leaving the unemployment rate above 8 percent until 2012. There are monetary and fiscal policy options that, if applied at a sufficient scale, would increa...

D. W. Elmendorf

2010-01-01

259

Evaluation of the Weather Research and Forecasting Model on  

E-print Network

the prospect of improving the accuracy of wind resource estimates and short-term wind energy forecasts. However: Implications for Wind Energy Brandon Storm*, Wind Science and Engineering Research Center, Texas Tech this region more favorable for wind energy production. At the same time, the presence of LLJs can

Basu, Sukanta

260

On Modeling and Forecasting Time Series of Smooth Curves  

E-print Network

and dynamically updating the forecasts. The re- search problem is motivated by efficient operations management updat- ing using penalized least squares. The proposed methods are illustrated via the motivating of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

Shen, Haipeng

261

Forecasting oil price trends using wavelets and hidden Markov models  

Microsoft Academic Search

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

Edmundo G. de Souza e Silva; Luiz F. L. Legey

2010-01-01

262

Radiation fog forecasting using a 1-dimensional model  

E-print Network

The importance of fog forecasting to the aviation community, to road transportation and to the public at large is irrefutable. The deadliest aviation accident in history was in fact partly a result of fog back on 27 March 1977. This has, along...

Peyraud, Lionel

2012-06-07

263

Alaska North Slope regional gas hydrate production modeling forecasts  

USGS Publications Warehouse

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

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

2011-01-01

264

A combined wavelet - ARFIMA model for daily streamflow forecasting considering long range dependence  

NASA Astrophysics Data System (ADS)

Short term streamflow forecasting is of importance in water resources management, especially from the point of view of operational flow control and risk management. Beside deterministic rainfall runoff and flow routing models, stochastic time series models are also in operational use for this purpose. The fitting of such stochastic models is preceded, when suitable, by removing the systematic components in the time series (such as trends, seasonality). Usually the interest of practitioners lies in the fitting of the stochastic part of the time series model and removing the systematic components is considered rather a routine task. However, each deseasonalization method has an effect on time series analyzed, affecting the autocorrelation structure and thus influencing the following model choice and the fitted model parameters. When choosing an appropriate stochastic model the practitioners often neglect the presence of long range dependence when considering short term forecasting. This, however, might have an effect on the forecasts even in short term horizon. The autoregressive integrated moving average models (ARFIMA) are often used for modelling of time series displaying long range dependence in hydrology. In hydrology, wavelets are mostly applied for feature extraction and process description rather then modelling and forecasting. In this work we attempted to improve the deseasonalization step of the modelling process by using wavelet analysis. We proposed to combine an ARFIMA model with a wavelet transform used for deseasonalization. The quality of the model is tested on one to ten days ahead forecasts of mean daily runoffs from the Danube River measured at Kienstock in Lower Austria. A comparison with two other models - an ARFIMA model combined with moving average deseasonalization and a linear wavelet based model was performed. The results of the model comparison showed that use of wavelets provides a suitable alternative to the moving average deseasonalization. For one and two days forecasting horizon the new approach did not show improvement in the forecasting performance over the other tested models. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models, thus offering improvement compared to the usual moving average deseasonalization. Since none of the three models was able to remove autocorrelation from the squared residuals, usually indicating heteroscedasticity in the time series, the concept of the wavelet deseasonalization may be explored further in combination of other possibly suitable model, such as a fractionally integrated generalized autoregressive conditional heteroscedasticity model type.

Szolgayová, Elena; Arlt, Josef; Blöschl, Günter; Szolgay, Ján

2013-04-01

265

Use of Seasonal Climate Forecasts in Rangeland-Based Livestock Operations in West Texas.  

NASA Astrophysics Data System (ADS)

The potential for west Texas ranchers to increase the profitability of their enterprises by becoming more proactive in their management practices by using seasonal climate forecasts is investigated using a focus group and ecological-economic modeling. The focus group felt forecasts could potentially be used in making decisions concerning stocking rates, brush control, and deer herd management. Further, the focus group raised concerns about the potential misuse of `value-added' forage forecasts. These concerns necessitate a revisiting of the value-added concept by the climate-forecasting community. Using only stocking-rate decisions, the potential value of seasonal forage forecasts is estimated. As expected, the economic results suggest the value of the forecasts depends on the restocking and destocking price along with other economic factors. More important, the economic results and focus-group reactions to these results suggest the need for multiyear modeling when examining the potential impact of using improved climate forecasts.

Jochec, Kristi G.; Mjelde, James W.; Lee, Andrew C.; Conner, J. Richard

2001-09-01

266

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

NASA Astrophysics Data System (ADS)

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

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

2013-09-01

267

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

NASA Astrophysics Data System (ADS)

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

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

2014-04-01

268

Application of a regional hurricane wind risk forecasting model for wood-frame houses.  

PubMed

Hurricane wind risk in a region changes over time due to changes in the number, type, locations, vulnerability, and value of buildings. A model was developed to quantitatively estimate changes over time in hurricane wind risk to wood-frame houses (defined in terms of potential for direct economic loss), and to estimate how different factors, such as building code changes and population growth, contribute to that change. The model, which is implemented in a simulation, produces a probability distribution of direct economic losses for each census tract in the study region at each time step in the specified time horizon. By changing parameter values and rerunning the analysis, the effects of different changes in the built environment on the hurricane risk trends can be estimated and the relative effectiveness of hypothetical mitigation strategies can be evaluated. Using a case study application for wood-frame houses in selected counties in North Carolina from 2000 to 2020, this article demonstrates how the hurricane wind risk forecasting model can be used: (1) to provide insight into the dynamics of regional hurricane wind risk-the total change in risk over time and the relative contribution of different factors to that change, and (2) to support mitigation planning. Insights from the case study include, for example, that the many factors contributing to hurricane wind risk for wood-frame houses interact in a way that is difficult to predict a priori, and that in the case study, the reduction in hurricane losses due to vulnerability changes (e.g., building code changes) is approximately equal to the increase in losses due to building inventory growth. The potential for the model to support risk communication is also discussed. PMID:17362399

Jain, Vineet Kumar; Davidson, Rachel Ann

2007-02-01

269

Demand Forecasting Using Bayesian Experiment with Non-homogenous Poisson Process Model  

Microsoft Academic Search

This study presents a novel mathematical model using Bayesian model for demand forecasting with non-homogenous Poisson process model. This study aims to construct a framework to minimize the overproduction and underproduction costs by using the time-dependent uncertainty of accumulative demand curve. Specific models were derived as the fundamentals of this approach. Furthermore, this study also proposed a method to evaluate

Hung-Ju Wang; Chen-Fu Chien; Ching-Fang Liu

2005-01-01

270

STAR and ANN models: forecasting performance on the Spanish “Ibex35” stock index  

Microsoft Academic Search

This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions)

Jorge V. Pérez-Rodríguez; Salvador Torra; Julián Andrada-Félix

2005-01-01

271

Ecological economic modeling and valuation of ecosystems  

Microsoft Academic Search

We are attempting to integrate ecological and economic modeling and analysis in order to improve our understanding of regional systems, assess potential future impacts of various land-use, development, and agricultural policy options, and to better assess the value of ecological systems. Starting with an existing spatially articulated ecosystem model of the Patuxent River drainage basin in Maryland, we are adding

N. Bockstael; R. Costanza; I. Strand; W. Boynton; K. Bell; L. Wainger

1995-01-01

272

MODELLING WITH ECONOMICS Ivan Meznik, Miloslav Kerkovsky  

E-print Network

variable , four basic "qualitative" values are considered - positive(+), negative(-), zero(0) and unknown171 MODELLING WITH ECONOMICS Ivan Meznik, Miloslav Kerkovsky Brno University of Technology, Czech] ). Several research studies have been carried out dealing with the impact of different types of modelling

Spagnolo, Filippo

273

Economic models of employee motivation  

Microsoft Academic Search

Workers present employers with a range of tricky problems. They can be crooked, subversive, surly, or indolent, even if they are paid on time. Joseph A. Ritter and Lowell J. Taylor explore economists' main theories of how compensation is used to address employee motivation and how these models help to explain puzzling features of labor market. Although these theories are

Joseph A. Ritter; Lowell J. Taylor

1997-01-01

274

A Real-Time Operational Forecast Model for Meteorology and Air Quality During Peak Air Pollution Episodes in Oslo, Norway  

Microsoft Academic Search

A real-time operational forecast model for meteorology and air quality for Oslo, Norway is presented. The model systemconsists of an operational meteorological forecasts modeland an air quality model. A non-hydrostatic model operatedon two different domains with 1 and 3 km horizontalresolution is nested within the routine meteorologicalforecast model, which is run for North West Europe with 10 kmhorizontal resolution. The

Erik Berge; Sam-Erik Walker; Asgeir Sorteberg; Mothei Lenkopane; Steinar Eastwood; Hildegunn I. Jablonska; Morten Ødegaard Køltzow

2002-01-01

275

Risks of model weighting in multi-model climate projections - lessons learnt from seasonal forecasting  

NASA Astrophysics Data System (ADS)

During the past decade, seasonal forecasting has become a well-established technique, and dynamical seasonal prediction systems are now in operational use at a range of climate prediction centers. In the wake of these developments, an enormous data-base of climate model simulations has been created, which has not only advanced our knowledge about seasonal predictability per se. Given that these data allow for systematic and statistically robust verification, plenty has also been learnt about technical and conceptual issues with relevance to other time-scales, e.g. questions regarding the interpretation and post-processing of ensemble forecasts. This presentation focuses on the issue of multi-model combination - an issue which is also highly relevant in the context of climate change projections. From the evaluation of seasonal forecasts, it has been demonstrated that multi-models on average outperform any single model strategy. Moreover, seasonal forecasts have helped us to understand the underlying mechanisms and reasons for the success of multi-model combination. In particular, it has been possible to resolve the seeming paradox as to why, and under what conditions, a multi-model can outperform the best participating single model. While the potential benefits of multi-models are now widely accepted on essentially all time-scales, there is so far no consensus on what is the best way to construct a multi-model. The simplest way is to give one vote to each model ("equal weighting"), while more sophisticated approaches suggest to apply model-weights according to some measure of performance ("optimum weighting"). Seasonal forecasts have revealed that model weighting indeed can improve the forecasts, but only if the optimum model weights are accurately known and truly represent the underlying model uncertainties. Otherwise, equal weighting on average yields better results. These findings have major implications for the context of climate change, where - mainly due to the long time-scales involved - the determination of optimum weights is still an unresolved issue, and the "risk" of inadvertently applying wrong weights is high. In fact, with a conceptual model of climate change we show that more information may actually be lost by wrong weights than could potentially be gained by optimum weights, particularly if internal variability is high. These results do not imply that the derivation of performance-based weights for climate projections is impossible by principle. However, they do imply that a decision to weight climate models should be taken with great care, and that for many applications equal weighting may well be the better and more transparent way to go. Reference: Weigel A.P., R. Knutti, M.A. Liniger and C. Appenzeller (2010). Risks of model weighting in multi-model climate projections. J. Clim. in press

Weigel, A. P.; Knutti, R.; Liniger, M. A.; Appenzeller, C.

2010-09-01

276

Forecasting scheme for swan coastal river streamflow using combined model of IOHLN and Niño4  

NASA Astrophysics Data System (ADS)

The study aims to investigate the possible relationship between Niño 4 and Indian Ocean high longitude (IOHLN) with the Swan coastal river flow by constructing a regression model which predict streamflow patterns and which enables to obtain long time lead to forecasting, in a period when there was not much rainfall. Many streamflow forecast models use rainfall and runoff relationship, which is dependent on basin response time and hence cannot provide large forecasting lead time. For water resource management, this lead time of predictability is not capable for a long period of drying trend. Significant findings of this study suggest that Niño 4 and Indian Ocean high pressure longitude (IOHLN) can be used for forecasting of flow in Swan river. In this study not only qualitative forecast of Swan coastal river is presented based on the conditional probability, but also a quantitative forecast is done by combining Niño.4 and IOHLN indices using multiple regression, which shows enhancement over other climate indicators when used alone. The Conditional probability model correctly predict 7 years category of flow out of 8 years flow.

Rehman, Saqib Ur; Saleem, Kashif

2014-02-01

277

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

NASA Astrophysics Data System (ADS)

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.

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

2009-12-01

278

Multi-model calibration and combination of tropical seasonal sea surface temperature forecasts  

NASA Astrophysics Data System (ADS)

Different combination methods based on multiple linear regression are explored to identify the conditions that lead to an improvement of seasonal forecast quality when individual operational dynamical systems and a statistical-empirical system are combined. A calibration of the post-processed output is included. The combination methods have been used to merge the ECMWF System 4, the NCEP CFSv2, the Météo-France System 3, and a simple statistical model based on SST lagged regression. The forecast quality was assessed from a deterministic and probabilistic point of view. SSTs averaged over three different tropical regions have been considered: the Niño3.4, the Subtropical Northern Atlantic and Western Tropical Indian SST indices. The forecast quality of these combinations is compared to the forecast quality of a simple multi-model (SMM) where all single models are equally weighted. The results show a large range of behaviours depending on the start date, target month and the index considered. Outperforming the SMM predictions is a difficult task for linear combination methods with the samples currently available in an operational context. The difficulty in the robust estimation of the weights due to the small samples available is one of the reasons that limit the potential benefit of the combination methods that assign unequal weights. However, these combination methods showed the capability to improve the forecast reliability and accuracy in a large proportion of cases. For example, the Forecast Assimilation method proved to be competitive against the SMM while the other combination methods outperformed the SMM when only a small number of forecast systems have skill. Therefore, the weighting does not outperform the SMM when the SMM is very skilful, but it reduces the risk of low skill situations that are found when several single forecast systems have a low skill.

Rodrigues, Luis Ricardo Lage; Doblas-Reyes, Francisco Javier; Coelho, Caio Augusto dos Santos

2013-04-01

279

Multi-model calibration and combination of tropical seasonal sea surface temperature forecasts  

NASA Astrophysics Data System (ADS)

Different combination methods based on multiple linear regression are explored to identify the conditions that lead to an improvement of seasonal forecast quality when individual operational dynamical systems and a statistical-empirical system are combined. A calibration of the post-processed output is included. The combination methods have been used to merge the ECMWF System 4, the NCEP CFSv2, the Météo-France System 3, and a simple statistical model based on SST lagged regression. The forecast quality was assessed from a deterministic and probabilistic point of view. SSTs averaged over three different tropical regions have been considered: the Niño3.4, the Subtropical Northern Atlantic and Western Tropical Indian SST indices. The forecast quality of these combinations is compared to the forecast quality of a simple multi-model (SMM) where all single models are equally weighted. The results show a large range of behaviours depending on the start date, target month and the index considered. Outperforming the SMM predictions is a difficult task for linear combination methods with the samples currently available in an operational context. The difficulty in the robust estimation of the weights due to the small samples available is one of the reasons that limit the potential benefit of the combination methods that assign unequal weights. However, these combination methods showed the capability to improve the forecast reliability and accuracy in a large proportion of cases. For example, the Forecast Assimilation method proved to be competitive against the SMM while the other combination methods outperformed the SMM when only a small number of forecast systems have skill. Therefore, the weighting does not outperform the SMM when the SMM is very skilful, but it reduces the risk of low skill situations that are found when several single forecast systems have a low skill.

Rodrigues, Luis Ricardo Lage; Doblas-Reyes, Francisco Javier; Coelho, Caio Augusto dos Santos

2014-02-01

280

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

NASA Astrophysics Data System (ADS)

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

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

2014-08-01

281

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

NASA Technical Reports Server (NTRS)

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

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

2008-01-01

282

A new lattice model of traffic flow with the consideration of the driver's forecast effects  

NASA Astrophysics Data System (ADS)

In this Letter, a new lattice model is presented with the consideration of the driver's forecast effects (DFE). The linear stability condition of the extended model is obtained by using the linear stability theory. The analytical results show that the new model can improve the stability of traffic flow by considering DFE. The modified KdV equation near the critical point is derived to describe the traffic jam by nonlinear analysis. Numerical simulation also shows that the new model can improve the stability of traffic flow by adjusting the driver's forecast intensity parameter, which is consistent with the theoretical analysis.

Peng, G. H.; Cai, X. H.; Liu, C. Q.; Cao, B. F.

2011-05-01

283

GEM-MACH15 Operational Air Quality Forecast Model: An Evaluation of the First Year's Performance  

NASA Astrophysics Data System (ADS)

GEM-MACH was implemented by Environment Canada as a new multi-scale in-line air quality (AQ) forecast system in late 2009. The operational version, GEM-MACH15, is a limited-area model with 15-km horizontal grid spacing and 58 vertical levels extending from the surface to 0.1 hPa. The model is run twice daily at the Canadian Meteorological Centre to produce 48-hour forecasts over a continental-scale domain. In this presentation, the first operational performance evaluation of GEM-MACH15 predictions for a one-year period (Aug. 2009-July 2010) will be described. Model forecasts of O3, PM2.5, and NO2 will be compared against available surface measurements from Canadian and U.S. real-time monitoring networks. A statistical analysis will be presented showing monthly forecast performance and geographical variability across Canadian and U.S. regions. A kriging technique will be applied to show the spatial distribution of model statistical performance measures. Model strengths and weaknesses will also be identified.GEM-MACH15 Operational Air Quality Forecast Model: An Evaluation of the First Year’s Performance

Pavlovic, R.; Menard, S.; Moran, M. D.; Beaulieu, P.; Gilbert, S.; Chen, J.; Makar, P.; Morneau, G.

2010-12-01

284

Forecasting Artificial Intelligence Demand  

NASA Astrophysics Data System (ADS)

Forecasts are major components of the decision analysis process. When accurate, estimates of future economic activity associated with specific courses of action can correctly set corporate strategy in an uncertain environment. When inaccurate, they can lead to bankruptcy. The basic trouble with most forecasts is that they are not made by forecasters.

Wheeler, David R.; Shelley, Charles

1986-03-01

285

The global impact of satellite-derived polar winds on model forecasts  

NASA Astrophysics Data System (ADS)

The use of Atmospheric Motion Vectors (AMVs) in Numerical Weather Prediction (NWP) models continues to be an important source of information in data sparse regions. These AMVs are derived from a time-sequence of images from geostationary and polar orbiting satellites. NWP centers have documented positive impact on model forecasts not only in regions where the AMVs are measured, but elsewhere as well. One example is the positive impact that the Moderate Resolution Imaging Spectroradiometer (MODIS) polar winds have on forecasts in the middle and subtropical latitudes, especially in 3 to 5 day forecasts and forecast bust situations. The MODIS winds are only derived poleward of 65° latitude. What are possible explanations for this global impact? This study investigates the hypothesis that the assimilation of polar winds modifies the flow in high latitudes near the polar jet stream and that this effect propagates to lower latitudes in extended forecasts. Using a pre-operational version of the National Centers for Environmental Prediction's (NCEP) Global Forecast System (GFS), a side-by-side experiment was run for a six week period during the late summer of 2004, with and without the MODIS polar winds. Five forecast cases within this period were examined to determine how winds in the polar regions affect the wind and geopotential height fields in the jet stream region, resulting in changes in wave propagation speed. From the five cases examined, it was determined that the addition of the polar winds modifies the mass balance in synoptic-scale waves near the polar jet streams, more consistently in data void regions. This change in mass balance is evident in differences in the ageostrophic wind, which has an effect on the speed and amplitude of baroclinic waves that extends from the jet stream into lower latitudes in later forecast times. These results reveal the substantial impact that polar-only observations have on the predictability of global weather systems.

Santek, David A.

286

Modelling and forecasting the diffusion of innovation – A 25-year review  

Microsoft Academic Search

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

Nigel Meade; Towhidul Islam

2006-01-01

287

Identification of seasonal water supply forecasting models using Akaike's information criterion  

Microsoft Academic Search

A streamflow model representative of basins subject to seasonal snowcover is developed using Akaike's information criterion for structure determination. A seasonal water supply forecasting function based on the model is given and its performance is evaluated. Extensions to include hydrometeorological inputs in the model and to make it self-tuning to changes in streamflow dynamics are discussed.

T. B. Cline

1979-01-01

288

An Enhancement to the Linear Dynamic System Model for Air Traffic Forecasting  

E-print Network

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

Sun, Dengfeng

289

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

NASA Astrophysics Data System (ADS)

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.

Yost, Charles

290

Multi and Single Model Ensemble Forecasting in the Gulf of Mexico  

NASA Astrophysics Data System (ADS)

The Navy Coastal Ocean Model (NCOM) has been configured for the Gulf of Mexico and used to investigate forecast error via ensemble forecasting methods. The models assimilate observations via the Navy Coupled Ocean Data Assimilation (NCODA) system. The model has ~3 km horizontal grid resolution, 46 levels in the vertical, boundary forcing from a global ocean model also based on NCOM, surface forcing from the Navy's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), as well as tidal forcing and river runoff. A deterministic control run provides the forecast error which is used (via an ensemble transform) to perturb the ensemble members. The atmospheric forcing is also perturbed via a space-time deformation technique. 32 ensemble members are generated and each produces a 72 hours forecast. These are the so called single model ensembles. Other Navy forecast systems that include the Gulf of Mexico (global and regional) that differ primarily in horizontal and vertical resolution and boundary conditions (surface and lateral) are used to calculate the so called multi (or super) ensemble. For both cases statistics calculated across the ensemble members are shown and discussed. Limits of predictability are described and discussed, especially with respect to the Loop Current Eddy Shedding episode of early July 2010 (Eddy Franklin). Overall system performance is quantified and discussed, with emphasis on (but not limited to) the Deep Water Horizon oil spill timeframe. Longer term predictability (30 day) is also investigated and discussed.

Hogan, P.; Thoppil, P.; Rowley, C.; Coelho, E.

2012-04-01

291

Modeling and Forecasting Livestock Feed Resources in India Using Climate Variables  

PubMed Central

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

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

2012-01-01

292

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

NASA Astrophysics Data System (ADS)

High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of incorporating subgrid-scale cloud radiation interactions using the Kain-Fritsch (KF) and Rapid Radiation Transfer Model Global schemes. However, it is still unclear to what extent the KF convection scheme could be modified to improve high resolution precipitation forecasts with the WRF model. In this numerical study, we have made several changes to the KF scheme (i.e. inclusion of subgrid-scale cloud radiation interactions, a dynamic adjustment timescale, cloud updraft mass fluxes impact on grid-scale vertical velocity and a LCL-based entrainment methodology). These science updates introduce scale dependency for some of these parameters in KF scheme and makes the upgraded KF scheme usable at 9km and 3km grid resolutions in the WRF-ARW 3.4.1. The WRF model convection forecast experiments are performed over US Southern Great Plains in 2002 summer, during which the International H2O Project (IHOP 2002) measurements are used for model forecast validations. The evaluation also uses MET tool which is widely used for model performances to provide some statistical verification. Results indicate that (1) the initial conditions play a key role in the high resolution weather forecasting; and (2) our modified KF scheme is able to alleviate the excessive precipitation in 9km resolution and improve the precipitation forecasts in 3km resolution simulations.

Zheng, Y.; Alapaty, K. V.; Kumar, A.; Niyogi, D. S.

2013-12-01

293

Weather Research and Forecasting Model Goals: Develop an advanced mesoscale forecast  

E-print Network

and manage data and I/O #12;Software Architecture OMP Solve DM comm Threads Data formats, Parallel I/O Message Passing · Driver: I/O, communication, multi-nests, state data · Model routines computational, tile versus cost #12;Eulerian Nonhydrostatic Model Solvers Full conservation of variables in flux form

294

Forecasting quantitative rainfall over India using multi-model ensemble technique  

NASA Astrophysics Data System (ADS)

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.

Durai, V. R.; Bhardwaj, Rashmi

2014-10-01

295

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

PubMed Central

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

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

2011-01-01

296

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

NASA Technical Reports Server (NTRS)

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.

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

1998-01-01

297

A physical and economic model of the nuclear fuel cycle  

NASA Astrophysics Data System (ADS)

A model of the nuclear fuel cycle that is suitable for use in strategic planning and economic forecasting is presented. The model, to be made available as a stand-alone software package, requires only a small set of fuel cycle and reactor specific input parameters. Critical design criteria include ease of use by nonspecialists, suppression of errors to within a range dictated by unit cost uncertainties, and limitation of runtime to under one minute on a typical desktop computer. Collision probability approximations to the neutron transport equation that lead to a computationally efficient decoupling of the spatial and energy variables are presented and implemented. The energy dependent flux, governed by coupled integral equations, is treated by multigroup or continuous thermalization methods. The model's output includes a comprehensive nuclear materials flowchart that begins with ore requirements, calculates the buildup of 24 actinides as well as fission products, and concludes with spent fuel or reprocessed material composition. The costs, direct and hidden, of the fuel cycle under study are also computed. In addition to direct disposal and plutonium recycling strategies in current use, the model addresses hypothetical cycles. These include cycles chosen for minor actinide burning and for their low weapons-usable content.

Schneider, Erich Alfred

298

A new hybrid Coulomb/statistical model for forecasting aftershock rates  

NASA Astrophysics Data System (ADS)

Forecasting the spatial and temporal distribution of aftershocks is of great importance to earthquake scientists, civil protection authorities and the general public as these events cause disproportionate damage and consternation relative to their size. At present, there are two main approaches to such forecasts-purely statistical methods based on observations of the initial portions of aftershock sequences and a physics-based approach based on Coulomb stress changes caused by the main shock. Here we develop a new method which combines the spatial constraints from the Coulomb model with the statistical power of the STEP (short-term earthquake probability) approach. We test this pseudo prospectively and retrospectively on the Canterbury sequence against the STEP model and a Coulomb rate-state method, using data from the first 10 d following each main event to forecast the rate of M ? 4 events in the following 100 d. We find that in retrospective tests the new model outperforms STEP for two events in the sequence but this is not the case for pseudo-prospective tests. Further, the Coulomb rate-state approach never performs better than STEP. Our results suggest that incorporating the physical constraints from Coulomb stress changes can increase the forecasting power of statistical models and clearly show the importance of good data quality if prospective forecasts are to be implemented in practice.

Steacy, Sandy; Gerstenberger, Matt; Williams, Charles; Rhoades, David; Christophersen, Annemarie

2014-02-01

299

Model developed for economic gas dispatch  

SciTech Connect

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.

Leary, A. (Essex County Gas Co., Amesbury, MA (United States)); Curtis, E.J. (Curtis Associates Inc., York Harbor, ME (United States))

1993-12-01

300

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation  

NASA Technical Reports Server (NTRS)

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.

Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

2007-01-01

301

An economic-demographic model of the United States labor market.  

PubMed

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

Anderson, J M

1982-01-01

302

Kinetic Exchange Models in Economics and Sociology  

E-print Network

In this article, we briefly review the different aspects and applications of kinetic exchange models in economics and sociology. Our main aim is to show in what manner the kinetic exchange models for closed economic systems were inspired by the kinetic theory of gas molecules. The simple yet powerful framework of kinetic theory, first proposed in 1738, led to the successful development of statistical physics of gases towards the end of the 19th century. This framework was successfully adapted to modeling of wealth distributions in the early 2000's. In later times, it was applied to other areas like firm dynamics and opinion formation in the society, as well. We have tried to present the flavour of the several models proposed and their applications, intentionally leaving out the intricate mathematical and technical details.

Goswami, Sanchari

2014-01-01

303

Fog forecasting: ``old fashioned'' semi-empirical methods from radio sounding observations versus ``modern'' numerical models  

NASA Astrophysics Data System (ADS)

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.

Holtslag, M. C.; Steeneveld, G. J.; Holtslag, A. A. M.

2010-07-01

304

Improving a stage forecasting Muskingum model by relating local stage and remote discharge  

NASA Astrophysics Data System (ADS)

Following the parsimonious concept of parameters, simplified models for flood forecasting based only on flood routing have been developed for flood-prone sites located downstream of a gauged station and at a distance allowing an appropriate forecasting lead-time. In this context, the Muskingum model can be a useful tool. However, critical points in hydrological routing are the representation of lateral inflows contribution and the knowledge of stage-discharge relationships. As regards the former, O'Donnell (O'Donnell, T., 1985. A direct three-parameter Muskingum procedure incorporating lateral inflow, Hydrol. Sci. J., 30[4/12], 479-496) proposed a three-parameter Muskingum procedure assuming the lateral inflows proportional to the contribution entering upstream. Using this approach, Franchini and Lamberti (Franchini, M. & Lamberti, P., 1994. A flood routing Muskingum type simulation and forecasting model based on level data alone, Water Resour. Res., 30[7], 2183-2196) presented a simple model Muskingum type to provide forecast water levels at the downstream end by selecting a routing time interval and, hence, a forecasting lead-time allowing to express the forecast stage as a function of only observed quantities. Moramarco et al. (Moramarco, T., Barbetta, S., Melone, F. & Singh, V.P., 2006. A real-time stage Muskingum forecasting model for a site without rating curve, Hydrol. Sci. J., 51[1], 66-82) enhanced the modeling scheme incorporating a procedure for adapting the parameter linked to lateral inflows. This last model, called STAFOM (STAge FOrecasting Model), was also extended to a two connected river branches schematization in order to improve significantly the forecasting lead-time. The STAFOM model provided satisfactory results for most of the analysed flood events observed in different river reaches in the Upper-Middle Tiber River basin in Central Italy. However, the analysis highlighted that the stage forecast should be enhanced when sudden modifications occur in the upstream and downstream hydrographs recorded in real-time. Moramarco et al. (Moramarco, T., Barbetta, S., F. Melone, F. & Singh, V.P., 2005. Relating local stage and remote discharge with significant lateral inflow, J. Hydrol. Engng ASCE, 10[1], 58-69) showed that for any flood condition at ends of a river reach, a direct proportionality between the upstream and downstream mean velocity can be found. This insight was the basis for developing the Rating Curve Model (RCM) that allows to also accommodate significant lateral inflow contributions, permitting, without using a flood routing procedure and without the need of a rating curve at a local site, to relate the local hydraulic conditions with those at a remote gauged section. Therefore, to improve the STAFOM performance mainly for highly varying flood conditions, the model has been here modified by coupling it with a procedure based on the RCM approach. Several flood events occurred along different equipped river reaches of the Upper Tiber River basin have been used as case study. Results showed that the new model, named STAFOM-RCM, apart from to improve the stage forecast accuracy in terms of error on peak stage, Nash-Sutcliffe efficiency coefficient and the coefficient of persistence, allowed to use a larger lead time thus avoiding the two-river branches cascade schematization where fluctuations in stage forecasting occur more frequently.

Barbetta, S.; Moramarco, T.; Melone, F.; Brocca, L.

2009-04-01

305

Hydrological forecasting  

SciTech Connect

The authors present a report on the technologies now available in the rapidly growing field of hydrological forecasting. The volume covers the major hydrological areas relevant to the watershed, as opposed to the hillslope, scale, and discusses the latest simulation models in use. Included is material on modeling strategies, soil water modeling, the use of radar for precipitation measurements, remote sensing of soil moisture, modeling changes in forest evapotranspiration, and snow and ice. Also presented is information on groundwater forecasting, water quality, lumped catchment models, variable source area models, and distributed models.

Anderson, M.G.; Burt, T.P.

1985-01-01

306

Interevent times in a new alarm-based earthquake forecasting model  

NASA Astrophysics Data System (ADS)

This study introduces a new earthquake forecasting model that uses the moment ratio (MR) of the first to second order moments of earthquake interevent times as a precursory alarm index to forecast large earthquake events. This MR model is based on the idea that the MR is associated with anomalous long-term changes in background seismicity prior to large earthquake events. In a given region, the MR statistic is defined as the inverse of the index of dispersion or Fano factor, with MR values (or scores) providing a biased estimate of the relative regional frequency of background events, here termed the background fraction. To test the forecasting performance of this proposed MR model, a composite Japan-wide earthquake catalogue for the years between 679 and 2012 was compiled using the Japan Meteorological Agency catalogue for the period between 1923 and 2012, and the Utsu historical seismicity records between 679 and 1922. MR values were estimated by sampling interevent times from events with magnitude M ? 6 using an earthquake random sampling (ERS) algorithm developed during previous research. Three retrospective tests of M ? 7 target earthquakes were undertaken to evaluate the long-, intermediate- and short-term performance of MR forecasting, using mainly Molchan diagrams and optimal spatial maps obtained by minimizing forecasting error defined by miss and alarm rate addition. This testing indicates that the MR forecasting technique performs well at long-, intermediate- and short-term. The MR maps produced during long-term testing indicate significant alarm levels before 15 of the 18 shallow earthquakes within the testing region during the past two decades, with an alarm region covering about 20 per cent (alarm rate) of the testing region. The number of shallow events missed by forecasting was reduced by about 60 per cent after using the MR method instead of the relative intensity (RI) forecasting method. At short term, our model succeeded in forecasting the occurrence region of the 2011 Mw 9.0 Tohoku earthquake, whereas the RI method did not. Cases where a period of quiescent seismicity occurred before the target event often lead to low MR scores, meaning that the target event was not predicted and indicating that our model could be further improved by taking into account quiescent periods in the alarm strategy.

Talbi, Abdelhak; Nanjo, Kazuyoshi; Zhuang, Jiancang; Satake, Kenji; Hamdache, Mohamed

2013-09-01

307

Current challenges using models to forecast seawater intrusion: lessons from the Eastern Shore of Virginia, USA  

USGS Publications Warehouse

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.

Sanford, Ward E.; Pope, Jason P.

2010-01-01

308

Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean  

E-print Network

Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean David H over the Arctic Ocean with a western Arctic grid using 25-km resolution. The model is based upon WRF tool for studies of Arctic Ocean meteorology. Citation: Bromwich, D. H., K. M. Hines, and L.-S. Bai

Howat, Ian M.

309

Field Significance Revisited: Spatial Bias Errors in Forecasts as Applie dt o the Eta Model  

Microsoft Academic Search

The spatial structure of bias errors in numerical model output is valuable to both model developers and operational forecasters, especially if the field containing the structure itself has statistical significance in the face of naturally occurring spatial correlation. A semiparametric Monte Carlo method, along with a moving blocks bootstrap method is used to determine the field significance of spatial bias

KIMBERLY L. ELMORE; MICHAEL E. BALDWIN; DAVID M. SCHULTZ

2006-01-01

310

Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics  

E-print Network

Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output of (se- vere) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands thunderstorm indices, computed from the High-Resolution Limited-Area Model (HIRLAM), and (post- processed

Schmeits, Maurice

311

J Syst Sci Complex (2012) 25: 641674 MODELING AND FORECASTING OF STOCK  

E-print Network

J Syst Sci Complex (2012) 25: 641­674 MODELING AND FORECASTING OF STOCK MARKETS UNDER A SYSTEM specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal

Benmei, Chen

312

A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures  

Microsoft Academic Search

The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to provide insights into the impact of distribution policies on the adoption of new products. The model is intended to assist motion picture exhibitor chains (retailers) in managing

Mohanbir S. Sawhney; Jehoshua Eliashberg

1996-01-01

313

Evaluation of Advanced Wind Power Forecasting Models Results of the Anemos Project  

E-print Network

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

Paris-Sud XI, Université de

314

Interactive dust-radiation modeling: A step to improve weather forecasts  

Microsoft Academic Search

Inclusion of mineral dust radiative effects could lead to a significant improvement in the radiation balance of numerical weather prediction models with subsequent improvements in the weather forecast itself. In this study the radiative effects of mineral dust have been fully incorporated into a regional atmospheric dust model. Dust affects the radiative fluxes at the surface and the top of

Carlos Pérez; Slobodan Nickovic; Goran Pejanovic; José María Baldasano; Emin Özsoy

2006-01-01

315

Comparison of precipitable water vapor (PWV) maps derived by GPS, SAR interferometry, and numerical forecasting models  

NASA Astrophysics Data System (ADS)

In this work we present the results of an experiment aiming to measure and model atmospheric delay by means of GPS, Weather Research and Forecasting (WRF) model and Synthetic Aperture Radar Interferometry (InSAR). Examples of maps of the atmospheric delay over the region of Lisbon are shown.

Mateus, P.; Nico, G.; Tomé, R.; Catalão, J.; Miranda, P.

2010-10-01

316

Weather Forecasting  

NSDL National Science Digital Library

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.

Nielsen-Gammon, John

1996-09-01

317

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation  

NASA Technical Reports Server (NTRS)

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.

Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

2007-01-01

318

Evaluation of a 3-D hydrodynamic model and atmospheric forecast forcing using observations in Lake Ontario  

Microsoft Academic Search

Six-month observations of surface meteorology, water temperature, and currents in Lake Ontario are used to evaluate a high-resolution, three-dimensional hydrodynamic model and the forecasted forcing from a regional version of the Canadian operational global environmental multiscale (GEM) model. The hydrodynamic model is based on the Princeton Ocean Model (POM). Driven by both the observed and modeled surface wind stress and

Anning Huang; Yerubandi R. Rao; Youyu Lu

2010-01-01

319

Hydrological Forecasting in the Oder Estuary using a ThreeDimensional Hydrodynamic Model  

Microsoft Academic Search

A three-dimensional operational hydrodynamic model, developed at the Institute of Oceanography, University of Gda?sk was used to forecast hydrological conditions in the Oder Estuary. The model was based on the coastal ocean circulation model known as the Princeton Ocean Model (POM). Because of wind-driven water backup in the Oder mouth, a simplified operational model of river discharge, based on water

Halina Kowalewska-Kalkowska; Marek Kowalewski

2006-01-01

320

The data-based mechanistic approach to the modelling, forecasting and control of environmental systems  

Microsoft Academic Search

The paper presents a unified approach to the modelling, forecasting and control of natural and man-made environmental systems. The modelling approach exploits the author’s Data-Based Mechanistic (DBM) modelling philosophy, combined with powerful methods of recursive statistical estimation. These provide the basis for two major stages of model building: first, the critical evaluation of the over-parametrized simulation models that are currently

Peter C. Young

2006-01-01

321

Determining economic benefits of satellite data in short-range forecasting  

NASA Technical Reports Server (NTRS)

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

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

1981-01-01

322

Drought Monitoring and Forecasting for the U.S. Using Climate Model Seasonal Forecast  

Microsoft Academic Search

Drought is the most costly natural hazard to the U.S. economy. Drought preparation and mitigation require skillful predictions of drought on-set, development, and recovery. A model-based Drought Monitor and Prediction System (DMAPS) is presented, and it provides a real-time quantitative drought assessment and prediction capability for the U.S. Using the North America Land Data Assimilation System (NLDAS) realtime meteorological forcing

L. Luo; H. Li; J. Sheffield; E. F. Wood

2007-01-01

323

Wheat forecast economics effect study. [value of improved information on crop inventories, production, imports and exports  

NASA Technical Reports Server (NTRS)

A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function.

Mehra, R. K.; Rouhani, R.; Jones, S.; Schick, I.

1980-01-01

324

Low flow forecasting with data driven models that include and models that do not include hydrological knowledge - a comparison study  

NASA Astrophysics Data System (ADS)

Good and accurate long-term low flow forecasting is important in the fields of sustainable water management, water rights, water supply management, industrial use of freshwater, optimization of the reservoir operations for the production of electric energy and other water-related disciplines. Today, low flow forecasting is usually performed as an integrated part of calibrated rainfall-runoff models, but in our research we developed two types of simple empirical 7-day ahead low flow forecasting models by using the M5 machine learning method for the generation of regression and model trees. Development of the first type of models was based solely on the application of the M5 machine learning method (1-, 2-, 3-, 4-, 5-, 6-and 7-day lead time low flow forecasting model trees were developed from using only past flow data and then combined to produce 7-day ahead forecast curve), while the development of the other type of models included the conceptual knowledge of linear reservoir recession functions AND application of the M5 machine learning method (we modelled the streamflow recession coefficient k as a function of the flow rate at which the 7-day low flow forecast is made and the decrease in the flow rate from the previous day). Both types of 7-day ahead low flow forecasting models were developed by using the same type and amount of data and were built for the Podhom gauging station on the Radovna River and the Medvode gauging station on the Sora River (both are Slovenian tributaries of the Sava River, which itself is a Danube River tributary). The results were compared and tested both visually and numerically.

Stravs, L.; Brilly, M.

2009-04-01

325

The clinical burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model  

PubMed Central

OBJECTIVES: The incidence of prostate cancer is increasing, as is the number of diagnostic and therapeutic interventions to manage this disease. We developed a Markov state-transition model--the Montreal Prostate Cancer Model--for improved forecasting of the health care requirements and outcomes associated with prostate cancer. We then validated the model by comparing its forecasted outcomes with published observations for various cohorts of men. METHODS: We combined aggregate data on the age-specific incidence of prostate cancer, the distribution of diagnosed tumours according to patient age, clinical stage and tumour grade, initial treatment, treatment complications, and progression rates to metastatic disease and death. Five treatments were considered: prostatectomy, radiation therapy, hormonal therapies, combination therapies and watchful waiting. The resulting model was used to calculate age-, stage-, grade- and treatment-specific clinical outcomes such as expected age at prostate cancer diagnosis and death, and metastasis-free, disease-specific and overall survival. RESULTS: We compared the model's forecasts with available cohort data from the Surveillance, Epidemiology and End Results (SEER) Program, based on over 59,000 cases of localized prostate cancer. Among the SEER cases, the 10-year disease-specific survival rates following prostatectomy for tumour grades 1, 2 and 3 were 98%, 91% and 76% respectively, as compared with the model's estimates of 96%, 92% and 84%. We also compared the model's forecasts with the grade-specific survival among patients from the Connecticut Tumor Registry (CTR). The 10-year disease-specific survival among the CTR cases for grades 1, 2 and 3 were 91%, 76% and 54%, as compared with the model's estimates of 91%, 73% and 37%. INTERPRETATION: The Montreal Prostate Cancer Model can be used to support health policy decision-making for the management of prostate cancer. The model can also be used to forecast clinical outcomes for individual men who have prostate cancer or are at risk of the disease. PMID:10763395

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

2000-01-01

326

ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting  

NASA Astrophysics Data System (ADS)

This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of ARIMA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.

Wang, Hongjie; Zhao, Weigang

327

Forecasting the High Energy Electron Radiation Belts Using Physics Based Models  

NASA Astrophysics Data System (ADS)

Wave-particle interactions waves play an important role in the loss and acceleration of electrons in the radiation belts. Here we present results from the SPACECAST project to forecast the high energy electron radiation belts using physics based models in the UK and France. The forecasting models include wave-particle interactions, radial diffusion, and losses by Coulomb collisions, and highlight the importance of various types of wave-particle interactions. The system is driven by a time series of the Kp index derived from solar wind data and ground based magnetometers and provides a forecast of the radiation belts up to 3 hours ahead, updated every hour. We show that during the storm of 8-9 March, 2012 the forecasts were able to reproduce the electron flux at geostationary orbit measured by GOES 13 to within a factor of two initially, and to within a factor of 10 later on during the event. By including wave-particle interactions between L* = 6.5 and 8 the forecast of the electron flux at geostationary orbit was significantly improved for the month of March 2012. We show examples of particle injection into the slot region, and relativistic flux drop-outs and suggest that flux drop outs are more likely to be associated with magnetopause motion than losses due to wave-particle interactions. To improve the forecasts we have developed a new database of whistler mode chorus waves from 5 different satellite missions. We present data on the power spectra of the waves as a function of magnetic local time, latitude and radial distance, and present pitch angle and energy diffusion coefficients for use in global models. We show that waves at different latitudes result in structure in the diffusion rates and we illustrate the effects on the trapped electron flux. We present forecasting skill scores which show quantitatively that including wave-particle interactions improves our ability to forecast the high energy electron radiation belt. Finally we suggest several areas where more data and more research are needed from missions such as RBSP to reduce uncertainty and improve forecasting skill.

Horne, R. B.

2012-12-01

328

The economic mobility in money transfer models  

NASA Astrophysics Data System (ADS)

In this paper, we investigate the economic mobility in four money transfer models which have been applied into the research on wealth distribution. We demonstrate the mobility by recording the time series of agents’ ranks and observing their volatility. We also compare the mobility quantitatively by employing an index, “the per capita aggregate change in log-income”, proposed by economists. Like the shape of distribution, the character of mobility is also decided by the trading rule in these transfer models. It is worth noting that even though two models have the same type of distribution, their mobility characters may be quite different.

Ding, Ning; Xi, Ning; Wang, Yougui

2006-07-01

329

Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon 2011  

NASA Astrophysics Data System (ADS)

The atmospheric motion vectors (AMVs) retrieved from multi-spectral geostationary satellites form a very crucial input to improve the initial conditions of numerical weather prediction (NWP) models at all operational agencies throughout the globe. With the recent update of operational AMV retrieval algorithm using infrared, water vapor, and visible channels of Indian geostationary meteorological satellite Kalpana-1, an attempt has been made to assess the impact of AMVs in the NWP models. In this study, the impact of Kalpana-1 AMVs is assessed by assimilating them in the Weather Research and Forecasting (WRF) model using three-dimensional variational data assimilation method during the entire month of July 2011 over the Indian Ocean region. Apart from Kalpana-1 AMVs, the other AMVs available from Global Telecommunications System (GTS) are also assimilated to generate the WRF model analyses. After the initial verification of WRF model analyses, the 12-h wind forecasts from the WRF model are compared with National Centers for Environmental Prediction Global Data Assimilation System final analyses. The assimilation of Kalpana-1 AMVs shows positive impact in 12-h wind forecast over the tropical region in the upper troposphere. Similar results are obtained when other AMVs available through GTS are used for assimilation, though the magnitude of positive impact of Kalpana-1 AMVs is slightly higher over tropical region. The 24-h rainfall forecasts are also improved over the Western India and the Bay of Bengal region, when Kalpana-1 AMVs are used for assimilation against control experiments.

Kaur, Inderpreet; Kumar, Prashant; Deb, S. K.; Kishtawal, C. M.; Pal, P. K.; Kumar, Raj

2014-06-01

330

SOM-based Hybrid Neural Network Model for Flood Inundation Extent Forecasting  

NASA Astrophysics Data System (ADS)

In recent years, the increasing frequency and severity of floods caused by climate change and/or land overuse has been reported both nationally and globally. Therefore, estimation of flood depths and extents may provide disaster information for alleviating risk and loss of life and property. The conventional inundation models commonly need a huge amount of computational time to carry out a high resolution spatial inundation map. Moreover, for implementing appropriate mitigation strategies of various flood conditions, different flood scenarios and the corresponding mitigation alternatives are required. Consequently, it is difficult to reach real-time forecast of the inundation extent by conventional inundation models. This study proposed a SOM-RNARX model, for on-line forecasting regional flood inundation depths and extents. The SOM-RNARX model is composed of SOM (Self-Organizing Map) and RNARX (recurrent configuration of nonlinear autoregressive with exogenous inputs). The SOM network categorizes various flood inundation maps of the study area to produce a meaningful regional flood topological map. The RNARX model is built to forecast the total flooded volume of the study area. To find the neuron with the closest total inundated volume to the forecasted total inundated volumes, the forecasted value is used to adjust the weights (inundated depths) of the closest neuron and obtain a regional flood inundation map. The proposed methodology was trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model in Yilan County, Taiwan. For comparison, the CHIM (clustering-based hybrid inundation model) model which was issued by Chang et al. (2010) was performed. The major difference between these two models is that CHIM classify flooding characteristics, and SOM-RNARX extracts the relationship between rainfall pattern and flooding spatial distribution. The results show that (1)two models can adequately provide on-line forecasts of 3-h-ahead flood inundation depths in the study area; and (2)SOM-RNARX consistently outperform CHIM in online multistep-ahead inundation forecasts, while SOM-RNARX needs more storage for model parameters than CHIM and increases the loading of database as well.

Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John

2014-05-01

331

Model-based approach to seasonal ensemble forecast of snowmelt water inflow into a reservoir  

NASA Astrophysics Data System (ADS)

An approach to seasonal ensemble forecast of snowmelt runoff has been developed and applied for forecasting lateral water inflow into the Cheboksary Reservoir (the watershed area is 374,000 km2) located in the middle Volga River basin. The approach combines a physically-based semi-distributed hydrological ECOMAG model with ensembles of future weather scenarios for a specified lead-time of the forecast, which are then used as inputs for a hydrological model. The ECOMAG model describes processes of snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface and channel flow. The hydrological model is forced using daily meteorological variables (precipitation, air temperature, and air humidity) taken from the available observation data prior to the forecast date. Using these datasets, the initial watershed state (primarily, areal distribution of snow water equivalent, soil moisture content and soil freezing depth) as well as the initial river channel state are simulated by the model. Results from these spin-up simulations are routinely controlled by comparing them with observations from snow and agricultural surveys and streamflow observations. To assign ensemble of weather scenarios for the specified lead-time of the forecast (3 months ahead in this study), two approaches are applied: (1) the historical, observed daily weather patterns are utilized which assumed to be representative of possible future weather conditions; and (2) the artificial daily weather patterns Monte-Carlo are simulated by a stochastic weather generator. Being forced by the assigned ensembles of weather patterns for the forecast lead time, the ECOMAG model produces ensembles of hydrographs of inflow into the Cheboksary Reservoir. Using the developed approach, hindcasts have been produced for 30 spring seasons beginning from the filling of the reservoir in 1982 and the statistical properties of the obtained ensembles of runoff characteristics (volume and peak discharge) have been evaluated. The median forecast traces have been analyzed using the traditional Nash-and-Sutcliffe criterion as well as the distribution-oriented verification measures have been utilized to assess the probabilistic information contained in both forecast ensembles.

Gelfan, Alexander; Motovilov, Yuri; Moreido, Vsevolod

2014-05-01

332

Coupling Climate Models and Forward-Looking Economic Models  

NASA Astrophysics Data System (ADS)

Authors: Dr. Kenneth L. Judd, Hoover Institution, and Prof. William A. Brock, University of Wisconsin Current climate models range from General Circulation Models (GCM’s) with millions of degrees of freedom to models with few degrees of freedom. Simple Energy Balance Climate Models (EBCM’s) help us understand the dynamics of GCM’s. The same is true in economics with Computable General Equilibrium Models (CGE’s) where some models are infinite-dimensional multidimensional differential equations but some are simple models. Nordhaus (2007, 2010) couples a simple EBCM with a simple economic model. One- and two- dimensional ECBM’s do better at approximating damages across the globe and positive and negative feedbacks from anthroprogenic forcing (North etal. (1981), Wu and North (2007)). A proper coupling of climate and economic systems is crucial for arriving at effective policies. Brock and Xepapadeas (2010) have used Fourier/Legendre based expansions to study the shape of socially optimal carbon taxes over time at the planetary level in the face of damages caused by polar ice cap melt (as discussed by Oppenheimer, 2005) but in only a “one dimensional” EBCM. Economists have used orthogonal polynomial expansions to solve dynamic, forward-looking economic models (Judd, 1992, 1998). This presentation will couple EBCM climate models with basic forward-looking economic models, and examine the effectiveness and scaling properties of alternative solution methods. We will use a two dimensional EBCM model on the sphere (Wu and North, 2007) and a multicountry, multisector regional model of the economic system. Our aim will be to gain insights into intertemporal shape of the optimal carbon tax schedule, and its impact on global food production, as modeled by Golub and Hertel (2009). We will initially have limited computing resources and will need to focus on highly aggregated models. However, this will be more complex than existing models with forward-looking economic modules, and the initial models will help guide the construction of more refined models that can effectively use more powerful computational environments to analyze economic policies related to climate change. REFERENCES Brock, W., Xepapadeas, A., 2010, “An Integration of Simple Dynamic Energy Balance Climate Models and Ramsey Growth Models,” Department of Economics, University of Wisconsin, Madison, and University of Athens. Golub, A., Hertel, T., etal., 2009, “The opportunity cost of land use and the global potential for greenhouse gas mitigation in agriculture and forestry,” RESOURCE AND ENERGY ECONOMICS, 31, 299-319. Judd, K., 1992, “Projection methods for solving aggregate growth models,” JOURNAL OF ECONOMIC THEORY, 58: 410-52. Judd, K., 1998, NUMERICAL METHODS IN ECONOMICS, MIT Press, Cambridge, Mass. Nordhaus, W., 2007, A QUESTION OF BALANCE: ECONOMIC MODELS OF CLIMATE CHANGE, Yale University Press, New Haven, CT. North, G., R., Cahalan, R., Coakely, J., 1981, “Energy balance climate models,” REVIEWS OF GEOPHYSICS AND SPACE PHYSICS, Vol. 19, No. 1, 91-121, February Wu, W., North, G. R., 2007, “Thermal decay modes of a 2-D energy balance climate model,” TELLUS, 59A, 618-626.

Judd, K.; Brock, W. A.

2010-12-01

333

SOLERAS - Solar-Powered Water Desalination Project at Yanbu: Forecasting models for operating and maintenance cost of the pilot plant  

SciTech Connect

This study was conducted in cooperation with the Department of Industrial Engineering of King Abdulaziz University. The main objective of this study is to meet some of the goals of the Solar Energy Water Desalination Plant (SEWDP) plan in the area of economic evaluation. The first part of this project focused on describing the existing trend in the operation and maintenance (OandM) cost for the SOLERAS Solar Energy Water Desalination Plant in Yanbu. The second part used the information obtained on existing trends to find suitable forecasting models. These models, which are found here, are sensitive to changes in costs trends. Nevertheless, the study presented here has established the foundation for (OandM) costs estimating in the plant. The methodologies used in this study should continue as more data on operation and maintenance costs become available, because, in the long run, the trend in costs will help determine where cost effectiveness might be improved. 7 refs., 24 figs., 15 tabs.

Al-Idrisi, M.; Hamad, G.

1987-04-01

334

Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling  

SciTech Connect

Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach - system dynamics modeling - for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool - Stella[reg]. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.

Dyson, Brian [Department of Environmental Engineering, Texas A and M University-Kingsville, MSC 213, Kingsville, TX 78363 (United States); Chang, N.-B. [Department of Environmental Engineering, Texas A and M University-Kingsville, MSC 213, Kingsville, TX 78363 (United States)]. E-mail: nchang@even.tamuk.edu

2005-07-01

335

Economic evolutions and their resilience: a model  

SciTech Connect

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.)

Breitenecker, M.; Gruemm, H.

1981-04-01

336

Working Paper, draft 5, February 2012 pi-football: A Bayesian network model for forecasting Association Football  

E-print Network

1 Working Paper, draft 5, February 2012 pi-football: A Bayesian network model for forecasting Association Football match outcomes ANTHONY C. CONSTANTINOU* , NORMAN E. FENTON AND MARTIN NEIL Risk be both objective and subjective. We present a Bayesian network model for forecasting Association Football

Fenton, Norman

337

Forecast of geomagnetic storms using CME parameters and the WSA-ENLIL model  

NASA Astrophysics Data System (ADS)

Intense geomagnetic storms are caused by coronal mass ejections (CMEs) from the Sun and their forecast is quite important in protecting space- and ground-based technological systems. The onset and strength of geomagnetic storms depend on the kinematic and magnetic properties of CMEs. Current forecast techniques mostly use solar wind in-situ measurements that provide only a short lead time. On the other hand, techniques using CME observations near the Sun have the potential to provide 1-3 days of lead time before the storm occurs. Therefore, one of the challenging issues is to forecast interplanetary magnetic field (IMF) southward components and hence geomagnetic storm strength with a lead-time on the order of 1-3 days. We are going to answer the following three questions: (1) when does a CME arrive at the Earth? (2) what is the probability that a CME can induce a geomagnetic storm? and (3) how strong is the storm? To address the first question, we forecast the arrival time and other physical parameters of CMEs at the Earth using the WSA-ENLIL model with three CME cone types. The second question is answered by examining the geoeffective and non-geoeffective CMEs depending on CME observations (speed, source location, earthward direction, magnetic field orientation, and cone-model output). The third question is addressed by examining the relationship between CME parameters and geomagnetic indices (or IMF southward component). The forecast method will be developed with a three-stage approach, which will make a prediction within four hours after the solar coronagraph data become available. We expect that this study will enable us to forecast the onset and strength of a geomagnetic storm a few days in advance using only CME parameters and the physics-based models.

Moon, Y.; Lee, J.; Jang, S.; Na, H.; Lee, J.

2013-12-01

338

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

PubMed Central

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

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

2014-01-01

339

Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics  

PubMed Central

A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters—a basic particle filter (PF) with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)—and three ensemble filters—the ensemble Kalman filter (EnKF), the ensemble adjustment Kalman filter (EAKF), and the rank histogram filter (RHF)—were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS) model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003–2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1–5 weeks in the future; the ensemble filters are more accurate predicting peaks in the past. PMID:24762780

Yang, Wan; Karspeck, Alicia; Shaman, Jeffrey

2014-01-01

340

Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction  

E-print Network

directions, with 0, 90, 180 and 270 degrees denoting a northerly, easterly, southerly and westerly windBias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction Le of Statistics Technical Report no. 557 Abstract Wind direction is an angular variable, as opposed to weather

Washington at Seattle, University of

341

Regional demand forecasting and simulation model: user's manual. Task 4, final report  

Microsoft Academic Search

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

Parhizgari

1978-01-01

342

Novel Modeling Tools for Propagating Climate Change Variability and Uncertainty into Hydrodynamic Forecasts  

EPA Science Inventory

Understanding impacts of climate change on hydrodynamic processes and ecosystem response within the Great Lakes is an important and challenging task. Variability in future climate conditions, uncertainty in rainfall-runoff model forecasts, the potential for land use change, and t...

343

Current challenges using models to forecast seawater intrusion: lessons from the Eastern Shore of Virginia, USA  

E-print Network

be obtained at that local scale. Keywords Numerical modeling . Salt-water/fresh-water relations . USA of the Eastern Shore of Virginia, USA was calibrated to reproduce historical water levels and forecast, and that global warming may create noticeable sea-level rises over the next century, water-resource managers face

344

Integrating water data, models and forecasts - the Australian Water Resources Information System (Invited)  

Microsoft Academic Search

Under the Commonwealth Water Act 2007 the Bureau of Meteorology was given a new national role in water information, encompassing standards, water accounts and assessments, hydrological forecasting, and collecting, enhancing and making freely available Australia's water information. The Australian Water Resources Information System (AWRIS) is being developed to fulfil part of this role, by providing foundational data, information and model

R. Argent; P. Sheahan; N. Plummer

2010-01-01

345

Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information  

Microsoft Academic Search

This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to

Enric Monte; Salvador Torra

2014-01-01

346

Probabilistic Quantitative Precipitation Forecasting using a Two-Stage Spatial Model  

E-print Network

Gaussian processes drive precipitation occurrence and precipitation amount, respectively. The first process is latent and governs precipitation occurrence via a truncation. The second process explains the spatialProbabilistic Quantitative Precipitation Forecasting using a Two-Stage Spatial Model Veronica J

Washington at Seattle, University of

347

Fuzzy modelling of basin saturation state and neural networks for flood forecasting  

E-print Network

Fuzzy modelling of basin saturation state and neural networks for flood forecasting G. Corani a , G and rainfalls, without providing a description of the saturation state of the basin, which in contrast plays a description of the basin saturation state; the basin state is classified as belonging with different degrees

Corani, Giorgio

348

Seasonal Maize Forecasting for South Africa and Zimbabwe Derived from an Agroclimatological Model  

E-print Network

Seasonal Maize Forecasting for South Africa and Zimbabwe Derived from an Agroclimatological Model, with a hindcast correlation over 16 seasons of 0.92 for South Africa and 0.62 for Zimbabwe. Over 17 seasons and actual maize water-stress in South Africa, and a correlation of 0.79 for the same relationship

Martin, Randall

349

RESERVOIR RELEASE FORECAST MODEL FOR FLOOD OPERATION OF THE FOLSOM PROJECT INCLUDING PRE-RELEASES  

E-print Network

and Environmental Engineering, Institute for Dam Safety Risk Management, Utah Water Research Laboratory, Utah State-line Planning Mode, the Reservoir Release Forecast Model (RRFM) is being used to test alternatives operating in addition to developing and testing operating rule changes, including possible pre-release strategies

Bowles, David S.

350

Daily air quality forecast (gases and aerosols) over Switzerland. Modeling tool description and first results analysis.  

NASA Astrophysics Data System (ADS)

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.

Couach, O.; Kirchner, F.; Porchet, P.; Balin, I.; Parlange, M.; Balin, D.

2009-04-01

351

Ecological forecasting in Chesapeake Bay: Using a mechanistic-empirical modeling approach  

NASA Astrophysics Data System (ADS)

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.

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

352

APPLICATION AND EVALUATION OF CMAQ IN THE UNITED STATES: AIR QUALITY FORECASTING AND RETROSPECTIVE MODELING  

EPA Science Inventory

Presentation slides provide background on model evaluation techniques. Also included in the presentation is an operational evaluation of 2001 Community Multiscale Air Quality (CMAQ) annual simulation, and an evaluation of PM2.5 for the CMAQ air quality forecast (AQF) ...

353

Identification of seasonal short-term load forecasting models using statistical decision functions  

SciTech Connect

After the seasonal variation of the daily electric load has been identified with statistical decision functions, accurate short-term forecasts may be produced using rather simple models. A hierarchical classification algorithm is applied to hourly temperature readings to divide the historical database into seasonal subsets. These subsets are used to statistically identify and fit a response function for each season. These functional models constitute a library of models useful to the power scheduler. For a particular day, the appropriate model is selected by performing discriminant analysis. This approach is illustrated using data from a summer peaking utility. This application demonstrates that an entire procedure for specifying forecasting models may be formed with currently available statistical software. Furthermore, the models may be implemented on a microcomputer spreadhseet.

Hubele, N.F.; Cheng, C.S. (Arizona State Univ., Tempe, AZ (USA). Dept. of Industrial Engineering)

1990-02-01

354

Value of operational forecasts of seasonal average sea surface temperature anomalies for selected rain-fed agricultural locations of Chile  

Microsoft Academic Search

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

Francisco J. Meza; Daniel S. Wilks

2003-01-01

355

Diamond Model of National Economic Competitive Advantage Based on National Economic Security  

Microsoft Academic Search

\\u000a This paper firstly analyzes China’s national economic competitive advantage (NECA) using method of principal components analysis.\\u000a The result presents that the level of Chinese economic competitive advantage is low but increases continuously. Based on empirical\\u000a study, it builds a Diamond Model of National Economic Competitive Advantage based on national economic security and regards\\u000a factors of external risk, economic performance, motivation

Siyi Qin; Genhua Hu

356

Forecast uncertainty in semi-arid flash flood modeling using radar rain input  

Microsoft Academic Search

Flash floods are extremely dangerous hazards in the semi-arid southwest US at short temporal scales, posing a significant danger to life and property. Attempts to mitigate this flood risk using model-based forecasting are subject to uncertainties in the model and the data. This study reports on such an attempt using the distributed, semi-arid mechanistic rainfall-runoff model KINEROS2 driven by the

C. Unkrich; S. Yatheendradas; H. Gupta; T. Wagener; D. Goodrich; M. Schaffner; A. Stewart

2007-01-01

357

Numerical Forecasting of Radiation Fog. Part I: Numerical Model and Sensitivity Tests  

Microsoft Academic Search

To improve the forecast of dense radiative fogs, a method has been developed using a one-dimensional model of the nocturnal boundary layer forced by the mesoscale fields provided by a 3D limited-area operational model. The 1D model involves a treatment of soil-atmosphere exchanges and a parameterization of turbulence in stable layers in order to correctly simulate the nocturnal atmospheric cooling.

Thierry Bergot; Daniel Guedalia

1994-01-01

358

Evaluation of boundary-layer type forecasts 1 Evaluation of boundary-layer type in a weather forecast model  

E-print Network

affect the forecasts of weather phenomenon such as tornadoes (Stensrud and Weiss 2002), hurricanes formation. Therefore, there is a strong need for accurate and comprehensive methods for the evaluation

Hogan, Robin

359

Predictions of zonal wind and angular momentum by the NMC medium-range forecast model during 1985-89  

NASA Technical Reports Server (NTRS)

This paper investigates the quality of weather predictions of the atmosphere's relative angular momentum (M) made by the most recent version of the NMC medium-range forecast model (MRF88) during December 1985-1989. It was found that, compared with older versions of MRF, bias errors in the MRF88 forecasts of M became more prominent, while random errors were not affected. Both types of errors in the M forecasts could be traced to problems with forecasts in the zonal mean zonal wind in the tropics.

Rosen, Richard D.; Salstein, David A.; Nehrkorn, Thomas

1991-01-01

360

Retrospective Forecast Test for the 1989 Loma Prieta Sequence Based on Physical and Statistical Modeling  

NASA Astrophysics Data System (ADS)

We implement a retrospective forecast test specific to the 1989 Loma Prieta sequence and we focus on the comparison between two realizations of the epidemic-type aftershock sequence (ETAS) model and twenty-one models based on Coulomb stress change calculations and rate-and-state theory (CRS). We find that: (1) ETAS models forecast the spatial evolution of seismicity better in the near-source region, (2) CRS models can compete with ETAS models at off-fault regions and short-periods after the mainshock, (3) adopting optimally oriented planes as receivers could lead to better performance for short-time period up to a few days, whereas geologically specified planes should be implemented at long-term forecasting, and (4) CRS models based on shear stress have comparable performance with other CRS models, with the benefit of fewer free parameters involved in the stress calculations. The above results show that physics-based and statistical forecast models are complimentary, and that future forecasts should be combinations of ETAS and CRS models in space and time. We note that the realization in time and space of the CRS models involves a number of critical parameters ('learning' phase seismicity rates, regional stress field, loading rates on faults), which should be retrospectively tested to improve the predictive power of physics-based models.During our experiment the forecast covers Northern California [123.0-121.3°W in longitude 36.4-38.2°N in latitude] in a 2.5 km spatial grid within a 10-day interval following a mainshock, but here we focus on the results related with the post-seismic period of Loma Prieta earthquake. We consider for CRS models a common learning phase (1974-1980) to ensure consistency in our comparison, and we take into consideration stress perturbations imparted by 9 M>5.0 earthquakes between 1980-1989 in Northern California, including the 1988-1989 Lake Ellsman events. ETAS parameters correspond to the maximum likelihood estimations derived after inversion using a stochastic declustering algorithm that is fit to the entire Northern California earthquake catalog rather than to the particular aftershock sequence. We evaluate the forecasts with likelihood tests, suggested by the Collaboratory for the Study of Earthquake Predectability (CSEP) initiative, to detect any spatial inconsistency (L-test), as well as for the total amount of predicted versus observed events (N-test) in specific testing areas along San Andreas fault. These tests are performed in two testing areas, one at the off-fault and the other at the near-source region of Loma Prieta event. Our evaluation focuses approximately on the first year after Loma Prieta, including the Watsonville seismic sequence beginning 180 days after the mainshock. We also study the short (up to 10 days) and long-term (>10 days) performance of our forecasting models by implementing the aforementioned tests inside these time classes. We effectively combine the best performing models in order to provide an ensemble forecast based on the Bayesian approach, recently suggested within the Collaboratory for the Study of Earthquake Predictability (CSEP) framework.

Segou, M.; Parsons, T.; Ellsworth, W. L.

2012-12-01

361

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications  

NASA Technical Reports Server (NTRS)

The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

2009-01-01

362

Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models  

SciTech Connect

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 from ECMWF analyses and each model is run for 3 days to determine the differences with the EPIC field data. Observations during the EPIC cruise show a stable and well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the four models is that the forecasted planetary boundary layer (PBL) height is too low when compared to EPIC observations. All the models produce a strong diurnal cycle in the Liquid Water Path (LWP) but there are large differences in the amplitude and the phase compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface. There is a large spread in the surface energy budget terms amongst the models and large discrepancies with observational estimates. Single Column Model (SCM) experiments with the CAM show that the vertical pressure velocity has a large impact on the PBL height and LWP. Both the amplitude of the vertical pressure velocity field and its vertical structure play a significant role in the collapse or the maintenance of the PBL.

Hannay, C; Williamson, D L; Hack, J J; Kiehl, J T; Olson, J G; Klein, S A; Bretherton, C S; K?hler, M

2008-01-24

363

Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling  

SciTech Connect

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.

Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao

2011-09-29

364

EVALUATION OF SEVERAL PM 2.5 FORECAST MODELS USING DATA COLLECTED DURING THE ICARTT/NEAQS 2004 FIELD STUDY  

EPA Science Inventory

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...

365

Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters  

NASA Technical Reports Server (NTRS)

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.

Hesse, Michael

2009-01-01

366

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

PubMed

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

Marzocchi, Warner; Jordan, Thomas H

2014-08-19

367

Modelling UK regional airport traffic: the relevance of the civil aviation authority's traffic distribution model to UK regional airports’ forecasting  

Microsoft Academic Search

The Civil Aviation Authority's (CAA) modelling of UK airport traffic intentionally concentrates on the South East capacity issues. Its treatment of regional airports therefore is less rigorous, yet attempts have been made by others to use the regional forecasts as a basis for planning regional airports. A review of the CAA's modelling shows that there are characteristics, in the form

Robert E. Caves

1992-01-01

368

An economic model of the manufacturers' aircraft production and airline earnings potential, volume 3  

NASA Technical Reports Server (NTRS)

A behavioral explanation of the process of technological change in the U. S. aircraft manufacturing and airline industries is presented. The model indicates the principal factors which influence the aircraft (airframe) manufacturers in researching, developing, constructing and promoting new aircraft technology; and the financial requirements which determine the delivery of new aircraft to the domestic trunk airlines. Following specification and calibration of the model, the types and numbers of new aircraft were estimated historically for each airline's fleet. Examples of possible applications of the model to forecasting an individual airline's future fleet also are provided. The functional form of the model is a composite which was derived from several preceding econometric models developed on the foundations of the economics of innovation, acquisition, and technological change and represents an important contribution to the improved understanding of the economic and financial requirements for aircraft selection and production. The model's primary application will be to forecast the future types and numbers of new aircraft required for each domestic airline's fleet.

Kneafsey, J. T.; Hill, R. M.

1978-01-01

369

Corruption and Religion. Adding to the Economic Model?  

Microsoft Academic Search

The cross-country pattern in the 1998 corruption index from Transparency International is explained by a mixed economic-cultural model of corruption: (1) The economic model uses the level of real income per capita, the rate of inflation and the level of economic freedom. (2) The cultural model uses a set of variables giving the shares of 11 religions in each country,

Martin Paldam

2001-01-01

370

Diabatic dynamic initialization. [method for different large scale and mesoscale forecast models  

NASA Technical Reports Server (NTRS)

A version of the dynamical adjustment procedure is generalized in order to make the procedure applicable to a diabatic model. In this new initialization procedure, backward adiabatic model integration is followed by forward diabatic model integration, with a high-frequency (low-pass) time filter in the form of the Euler backward time-differencing scheme applied throughout the integration. The new procedure was applied to the Goddard Laboratory for Atmospheres 4D data-assimilation system. It was found that, right from the beginning of the forecast integration, the forecast tendencies (and fields) were free of any noise due to imbalance in initial conditions, and the shocks related to an initial imbalance between model physics and dynamics, or the initial spinup effect, were practically removed.

Fox-Rabinovitz, Michael S.; Gross, Brian D.

1993-01-01

371

An analysis of the weather research and forecasting model for wind energy applications in Wyoming  

NASA Astrophysics Data System (ADS)

Determination of wind speeds at the hub height of wind turbines is an important focus of wind energy studies. Standard extrapolation methods are unable to accurately estimate 50-m winds from standard 10-m winds under stable conditions. Modeling of winds is an alternative. Daily numerical simulations from December 2011-November 2012 have been conducted using the Weather Research and Forecasting model (WRF) to evaluate its potential for determining wind speeds at hub height. Model simulations have been validated with data collected at the University of Wyoming Wind Tower (UWT). WRF was superior to operational models in predicting 10-m wind speeds at surface stations and at the UWT. Results from WRF also showed that biases are present; WRF tends to overestimate winds during low-wind events and underestimate winds during high-wind events. WRF has demonstrated skill in hub height wind forecasts for Wyoming that can be of use for wind farm planning and operation.

Siuta, David

372

Numerical forecasting of radiation fog. Part I: Numerical model and sensitivity tests  

SciTech Connect

To improve the forecast of dense radiative fogs, a method has been developed using a one-dimensional model of the nocturnal boundary layer forced by the mesoscale fields provided by a 3D limited-area operational model. The 1D model involves a treatment of soil-atmosphere exchanges and a parameterization of turbulence in stable layers in order to correctly simulate the nocturnal atmospheric cooling. Various sensitivity tests have been carried out to evaluate the influence of the main input parameters of the model (geostrophic wind, horizontal advections, cloud cover, soil moisture, etc.) on the predicted fog characteristics. The principal result concerns the difficulty of obtaining accurate forecasts in the case of fog appearing in the middle or at the end of the night, when the local atmospheric cooling is weak. 33 refs., 13 figs.

Bergot, T.; Guedalia, D. (Universite Paul Sabatier, Toulouse (France))

1994-06-01

373

Regional forecasting with global atmospheric models; Fourth year report  

SciTech Connect

The scope of the report is to present the results of the fourth year`s work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals.

Crowley, T.J.; North, G.R.; Smith, N.R. [Applied Research Corp., College Station, TX (United States)

1994-05-01

374

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation  

NASA Technical Reports Server (NTRS)

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two 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). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

Watson, Leela R.

2007-01-01

375

Municipal water consumption forecast accuracy  

NASA Astrophysics Data System (ADS)

Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

Fullerton, Thomas M.; Molina, Angel L.

2010-06-01

376

Home Economics Education Career Path Guide and Model Curriculum Standards.  

ERIC Educational Resources Information Center

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…

California State Univ., Northridge.

377

New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India  

Microsoft Academic Search

Summary   New models based on (a) Multivariate Principal Component Regression (PCR) (b) Neural Network (NN) and (c) Linear Discriminant\\u000a Analysis (LDA) techniques were developed for long-range forecasts of summer monsoon (June–September) rainfall over two homogeneous\\u000a regions of India, viz., North West India and Peninsular India. The PCR and NN models were developed with two different data\\u000a sets. One set consisted

M. Rajeevan; Pulak Guhathakurta; V. Thapliyal

2000-01-01

378

Development of a regression model to forecast ground-level ozone concentration in Louisville, KY  

Microsoft Academic Search

To support ozone forecasting and episodic air pollution control initiatives in the Louisville metropolitan area, a multiple-linear regression model to predict daily domain-peak ground-level ozone concentration [O3] has been developed and validated. Using only surface meteorological data from 1993–1996 and making extensive use of parametric transformations to improve accuracy, the ten parameter model has a standard error of prediction of

Milton C. Hubbard; W. Geoffrey Cobourn

1998-01-01

379

A hybrid dynamic forecast model for analyzing celebrity endorsement effects on consumer attitudes  

Microsoft Academic Search

This study investigates the time-varying effects of celebrity endorsements on consumer purchase attitudes toward promoted products using a novel dynamic hierarchical multi-attribute attitude forecast model. The induced direct and indirect effects via constructs of product attributes and net product value are then incorporated into the proposed conceptual model, which is formulated with a discrete-time nonlinear stochastic system. An empirical study

Jiuh-Biing Sheu

2010-01-01

380

Simulation studies of the application of SEASAT data in weather and state of sea forecasting models  

NASA Technical Reports Server (NTRS)

The design and analysis of SEASAT simulation studies in which the error structure of conventional analyses and forecasts is modeled realistically are presented. The development and computer implementation of a global spectral ocean wave model is described. The design of algorithms for the assimilation of theoretical wind data into computers and for the utilization of real wind data and wave height data in a coupled computer system are presented.

Cardone, V. J.; Greenwood, J. A.

1979-01-01

381

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions  

NASA Astrophysics Data System (ADS)

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

Pérez, B.; Brouwer, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hackett, B.; Verlaan, M.; Fanjul, E. A.

2012-03-01

382

Modeling and forecasting MODIS-based Fire Potential Index on a pixel basis using time series models  

NASA Astrophysics Data System (ADS)

The aim of this research was to model and forecast MODIS-based Fire Potential Index (FPI), implemented with Normalized Difference Water Index (NDWI), as a proxy of forest fire risk, in Navarre (Spain) on a pixel basis using time series models with a forecasting horizon of one year. We forecast FPINDWI for 2009 based on time series from 2001 to 2008. In the modeling process, the Box and Jenkins methodology was applied in two consecutive stages. First, several generic models based on average FPINDWI time series from different “fuel type-ecoregion” combinations were developed. In a second stage, the generic models were implemented at the pixel level for the entire study region. The usefulness of the proposed autoregressive (AR) model, using the original data and introducing significant seasonal AR parameters, was demonstrated. Results show that 93.18% of the estimated models (EMs) are highly accurate and present good forecasting ability, precisely reproducing the original FPINDWI dynamics. Best results were found in the Mediterranean areas dominated by grasslands; slightly lower accuracies were found in the temperate and alpine regions, and especially in the transition areas between them and the Mediterranean region.

Huesca, Margarita; Litago, Javier; Merino-de-Miguel, Silvia; Cicuendez-López-Ocaña, Victor; Palacios-Orueta, Alicia

2014-02-01

383

A spatial-temporal projection model for extended-range forecast in the tropics  

NASA Astrophysics Data System (ADS)

An extended singularity value decomposition based statistical model, namely the spatial-temporal projection model (STPM), was constructed for the extended-range (10-30-day) forecast of tropical outgoing longwave radiation anomalies (OLRA). The special feature of this empirical model is using the spatial and temporal information of predictor-predictand coupled patterns to predict the temporally varying predictand field at all-time leads (i.e., 10-35 days) at once. A 10-year hindcast result shows that, different from previous statistical models, the skill scores of the STPM dropped slowly with forecast lead times. Useful skills can be detected at 30-35 day leads over most tropical regions. The highest temporal correlation coefficient of 0.4-0.5 appears over the Maritime Continent (Indian and western North Pacific monsoon regions) in boreal winter (summer), exceeding a 99 % confidence level. The STPM is also capable in predicting the spatial evolutions of convective anomalies, including the zonal and meridional propagation of OLRA. The forecast of the Real-time Multivariate MJO indices shows that the STPM attains a higher skill than previous statistical models. The STPM also shows comparable skills with the state-of-the-art dynamic models during the Dynamics of the Madden-Julian Oscillation campaign period, especially at 15-day and longer leads.

Zhu, Zhiwei; Li, Tim; Hsu, Pang-chi; He, Jinhai

2014-10-01

384

Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts  

NASA Astrophysics Data System (ADS)

In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological dynamic and processes, i. e. sample heterogeneity. For a same streamflow range corresponds different processes such as rising limbs or recession, where uncertainties are different. The dynamical approach improves reliability, skills and sharpness of forecasts and globally reduces confidence intervals width. When compared in details, the dynamical approach allows a noticeable reduction of confidence intervals during recessions where uncertainty is relatively lower and a slight increase of confidence intervals during rising limbs or snowmelt where uncertainty is greater. The dynamic approach, validated by forecaster's experience that considered the empirical approach not discriminative enough, improved forecaster's confidence and communication of uncertainties. Montanari, A. and Brath, A., (2004). A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., (2007). Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.

Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

2012-04-01

385

Forecasting alpine vegetation change using repeat sampling and a novel modeling approach.  

PubMed

Global change affects alpine ecosystems by, among many effects, by altering plant distributions and community composition. However, forecasting alpine vegetation change is challenged by a scarcity of studies observing change in fixed plots spanning decadal-time scales. We present in this article a probabilistic modeling approach that forecasts vegetation change on Niwot Ridge, CO using plant abundance data collected from marked plots established in 1971 and resampled in 1991 and 2001. Assuming future change can be inferred from past change, we extrapolate change for 100 years from 1971 and correlate trends for each plant community with time series environmental data (1971-2001). Models predict a decreased extent of Snowbed vegetation and an increased extent of Shrub Tundra by 2071. Mean annual maximum temperature and nitrogen deposition were the primary a posteriori correlates of plant community change. This modeling effort is useful for generating hypotheses of future vegetation change that can be tested with future sampling efforts. PMID:21954731

Johnson, David R; Ebert-May, Diane; Webber, Patrick J; Tweedie, Craig E

2011-09-01

386

Air pollution forecasting by coupled atmosphere-fire model WRF and SFIRE with WRF-Chem  

E-print Network

Atmospheric pollution regulations have emerged as a dominant obstacle to prescribed burns. Thus, forecasting the pollution caused by wildland fires has acquired high importance. WRF and SFIRE model wildland fire spread in a two-way interaction with the atmosphere. The surface heat flux from the fire causes strong updrafts, which in turn change the winds and affect the fire spread. Fire emissions, estimated from the burning organic matter, are inserted in every time step into WRF-Chem tracers at the lowest atmospheric layer. The buoyancy caused by the fire then naturally simulates plume dynamics, and the chemical transport in WRF-Chem provides a forecast of the pollution spread. We discuss the choice of wood burning models and compatible chemical transport models in WRF-Chem, and demonstrate the results on case studies.

Kochanski, Adam K; Mandel, Jan; Clements, Craig B

2013-01-01

387

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models  

NASA Astrophysics Data System (ADS)

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19-25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5-11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8-10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.

Pattanaik, D. R.; Kumar, Arun

2014-10-01

388

Cloudiness forecast with WRF mesoscale model: Validation from BLLAST 2011 field campaign  

NASA Astrophysics Data System (ADS)

Cloud cover is one of the most difficult meteorological variables to predict by weather forecasting meteorological models. However it is a very important element to determine because it has multiple applications, not only in weather forecasting but also in other issues as those related to renewable energy, and particularly to those related to solar radiation, as can be solar thermal or photovoltaic power, where the passage of a cloud across the fields of solar panels can reduced energy production. Cloudiness forecasting is clearly a challenge for this field, where we can achieve a significant reduction in production costs of this energy if an accurate cloud cover forecasting is available. The processes involved in the formation and organization of clouds and precipitation extend from physical and chemical processes involved in small-scale nucleation and growth of cloud particles to the large-scale dynamic processes that are associated with synoptic weather systems. It is important to consider an appropriate scale, not only in determining the effects of a particular phenomenon but also in planning experimental campaigns. The objective of this work is to analyze the ability of the a mesoscale prediction model (WRF) to simulate cloud cover for three different days of the BLLAST 2011 field campaign, recently performed at the south of France, near the Pyrenees: a day with clear skies, an overcast day, and finally a day with clouds of evolution including some scattered showers. Sensitivity experiments using different PBL, Microphysics and Cumulus parameterizations have been carried out, and the simulations have been analyzed in order to establish the best configuration to accurate forecast the cloudiness and meteorological variables associated to it (T2m, longwave and shortwave incoming radiation at surface).

González-Zamora, Ángel; Yagüe, Carlos; Román-Cascon, Carlos; Sastre, Mariano

2013-04-01

389

Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models  

NASA Astrophysics Data System (ADS)

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

390

The efficiency of the Weather Research and Forecasting (WRF) model for simulating typhoons  

NASA Astrophysics Data System (ADS)

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.

Haghroosta, T.; Ismail, W. R.; Ghafarian, P.; Barekati, S. M.

2014-08-01

391

Hurricane Intensity Forecasts with a Global Mesoscale Model on the NASA Columbia Supercomputer  

NASA Technical Reports Server (NTRS)

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.

Shen, Bo-Wen; Tao, Wei-Kuo; Atlas, Robert

2006-01-01

392

AN EARLY WARNING SYSTEM FOR TURKEY: THE FORECASTING OF ECONOMIC CRISIS BY USING THE ARTIFICIAL NEURAL NETWORKS  

Microsoft Academic Search

An economic crisis is typically a rare kind of an event but it impedes monetary stability, fiscal stability, financial stability, price stability, and sustainable economic development when it appears. Economic crises have huge adverse effects on economic and social system. This study uses an artificial neural network learning paradigm to predict economic crisis events for early warning aims. This paradigm

Fuat Sekmen; Murat Kurkcu

2014-01-01

393

Correction methods for statistical models in tropospheric ozone forecasting  

NASA Astrophysics Data System (ADS)

This study proposes two methods to enhance the performance of statistical models for prediction tropospheric ozone concentrations. The first method corrects the statistical model based on the average daily profile of the model errors in training set. The second method estimates the model error by making the analogy with three basic modes of feedback control: proportional, integral and derivative. These correction methods were tested with multiple linear regression (MLR) and artificial neural networks (ANN) for prediction of hourly average tropospheric ozone (O 3) concentrations. The inputs of the models were the hourly average concentrations of sulphur dioxide (SO 2), carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO 2) and O 3, and some meteorological variables (temperature - T; relative humidity - RH; and wind speed - WS) measured 24 h before. The analysed period was from May to June 2003 divided in training and test periods. ANN presented slightly better performance than MLR model for prediction of O 3 concentrations. Both models presented improvements with the proposed correction methods. The first method achieved the highest improvements with ANN model. However, the second method was the one that obtained the best predictions of hourly average O 3 concentrations with the correction of MLR model.

Pires, J. C. M.; Martins, F. G.

2011-05-01