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1

A Linear Forecasting Model and Its Application to Economic Data

We present a forecasting model based on fuzzy pattern recognition and weighted linear regression. In this model fuzzy pattern recognition is used to find homogeneous fuzzy classes in a heterogeneous data set. It is assumed that the classes represent typical situations. For each class a weighted regression analysis is conducted. The forecasting results obtained by the class regression analysis are

Georg Peters

2001-01-01

2

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

3

Safe-economical route model of a ship to avoid tropical cyclones using dynamic forecast environment

NASA Astrophysics Data System (ADS)

In heavy sea conditions related to tropical cyclones (TCs), losses to shipping caused by capsizing are greater than other kinds of accidents. Therefore, it is important to consider capsizing risk in the algorithms used to generate safe-economic routes that avoid tropical cyclones (RATC). A safe-economic routing and assessment model for RATC, based on a dynamic forecasting environment, is presented in this paper. In the proposed model, a ship's risk is quantified using its capsizing probability caused by heavy wave conditions. Forecasting errors in the numerical models are considered in the ship risk assessment according to their distribution characteristics. A case study shows that: the economic cost of RATCs is associated not only to the ship's speed, but also to the acceptable capsizing probability which is related with the ship's characteristic and the cargo loading condition. Case study results demonstrate that the optimal routes obtained from the model proposed in this paper are superior to those produced by traditional methods.

Wu, L.; Wen, Y.; Wu, D.; Zhang, J.; Xiao, C.

2014-08-01

4

ERIC Educational Resources Information Center

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

Blond, David L.

5

NASA Astrophysics Data System (ADS)

In heavy sea conditions related to tropical cyclones (TCs), losses to shipping caused by capsizing are greater than other kinds of accidents. Therefore, it is important to consider capsizing risk in the algorithms used to generate safe-economic routes that avoid tropical cyclones (RATC). A safe-economic routing and assessment model for RATC, based on a dynamic forecasting environment, is presented in this paper. In the proposed model, a ship's risk is quantified using its capsizing probability caused by heavy wave conditions. Forecasting errors in the numerical models are considered according to their distribution characteristics. A case study shows that: the economic cost of RATCs is associated not only to the ship's speed and the acceptable risk level, but also to the ship's wind and wave resistance. Case study results demonstrate that the optimal routes obtained from the model proposed in this paper are significantly superior to those produced by traditional methods.

Wu, L. C.; Wen, Y. Q.; Wu, D. Y.

2013-05-01

6

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

7

Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times.

S. Wing; J. R. Johnson; J. Jen; C.-I. Meng; D. G. Sibeck; K. Bechtold; J. Freeman; K. Costello; M. Balikhin; K. Takahashi

2005-01-01

8

Aggregate vehicle travel forecasting model

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

9

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

10

Estimating the economic value of wind forecasting to utilities

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

11

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

12

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

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

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

2012-09-01

13

Three Essays on Energy Economics and Forecasting

This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil...

Shin, Yoon Sung

2012-02-14

14

Computerized Enrollment Driven Financial Forecasting Model.

ERIC Educational Resources Information Center

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

Sarvella, John R.

15

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

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...

16

By examining disaggregate state-level data, we address two weaknesses of prior estimates of economic voting models in U.S. Presidential elections. First, our disaggregate approach substantially improves statistical power, thus reducing the danger of â€œover- fitting.â€ Second, our analysis demonstrates systematic differences in voting behavior across states, which have been ignored: voters in higher-income states respond significantly to inflation, changes in

Stephen Haynes; Joe Stone

2008-01-01

17

Economic indicators selection for crime rates forecasting using cooperative feature selection

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

18

Economic benefits of improved meteorological forecasts - The construction industry

NASA Technical Reports Server (NTRS)

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

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

1976-01-01

19

A forecasting model of gaming revenues in Clark County, Nevada

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

1992-11-01

20

A forecasting model of gaming revenues in Clark County, Nevada

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

21

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.

Comet

2002-06-04

22

Short-Termed Integrated Forecasting System: 1993 Model documentation report

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

23

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

24

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

25

A survey of 124 users of externally produced financial and economic forecasts in Turkey investigated their expectations and perceptions of forecast quality and their reasons for judgmentally adjusting forecasts. Expectations and quality perceptions mainly related to the timeliness of forecasts, the provision of a clear justifiable rationale and accuracy. Cost was less important. Forecasts were frequently adjusted when they lacked

Sinan Gönül; Dilek Önkal; Paul Goodwin

2009-01-01

26

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

27

Revenue forecasting using a least-squares support vector regression model in a fuzzy environment

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with genetic algorithms (FLSSVRGA) to forecast seasonal revenues.

Kuo-Ping Lin; Ping-Feng Pai; Yu-Ming Lu; Ping-Teng Chang

28

Forecasting electricity usage using univariate time series models

NASA Astrophysics Data System (ADS)

Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

Hock-Eam, Lim; Chee-Yin, Yip

2014-12-01

29

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

30

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

31

UNCERTAINTY, OPTIMAL USE, AND ECONOMIC VALUE OF WEATHER FORECASTS

in the definition of the event being forecast." #12;"New ways for displaying and communicating probabilistic prediction." "NOAA should improve its product development process by collaborating with users and partners Â· Cost-Loss Decision-Making Model -- Static decision-making structure -- Only two possible actions (a

Katz, Richard

32

AIR QUALITY MODEL EVALUATION - FORECASTING AND RETROSPECTIVES

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

33

Nambe Pueblo Water Budget and Forecasting model.

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

Brainard, James Robert

2009-10-01

34

Tropical Cyclone Track Forecasts Using an Ensemble of Dynamical Models

The relative independence of the tropical cyclone track forecasts produced by regional and global numerical weather prediction models suggests that a simple ensemble average or consensus forecast derived from a com- bination of these models may be more accurate, on average, than the forecasts of the individual models. Forecast errors of a simple ensemble average of three models for the

James S. Goerss

2000-01-01

35

On the Economic Value of Probability of Precipitation Forecasts in Canada.

NASA Astrophysics Data System (ADS)

Canadian weather offices have recently begun to receive probability of precipitation information estimated from numerical weather prediction guidance, but this information is not given in the public forecast. This paper uses a well-known procedure to estimate the increased economic value of these probability forecasts over equivalent categorical forecasts for Toronto. The cost-loss matrix given by Epstein (1969) is generalized and applied in economic value calculations of a Probability of Precipitation Amount (POPA) procedure for the same Toronto data set.The increased economic value of these probability forecasts is a substantial fraction of the value of categorical forecasts and appears to justify the expense involved in a marketing/education program necessary for the public to understand these forecasts.

Stuart, Ambury

1982-04-01

36

Enhancing model based forecasting of geomagnetic storms

NASA Astrophysics Data System (ADS)

Modern society is increasingly dependent on the smooth operation of large scale technology supporting Earth based activities such as communication, electricity distribution, and navigation. This technology is potentially threatened by global geomagnetic storms, which are caused by the impact of plasma ejected from the Sun upon the protective magnetic field that surrounds the Earth. Forecasting the timing and magnitude of these geomagnetic storms is part of the emerging discipline of space weather. The most severe geomagnetic storms are caused by magnetic clouds, whose properties and characteristics are important variables in space weather forecasting systems. The methodology presented here is the development of a new statistical approach to characterize the physical properties (variables) of the magnetic clouds and to examine the extent to which theoretical models can be used in describing both of these physical properties, as well as their evolution in space and time. Since space weather forecasting is a complex system, a systems engineering approach is used to perform analysis, validation, and verification of the magnetic cloud models (subsystem of the forecasting system) using a model-based methodology. This research demonstrates that in order to validate magnetic cloud models, it is important to categorize the data by physical parameters such as velocity and distance travelled. This understanding will improve the modeling accuracy of magnetic clouds in space weather forecasting systems and hence increase forecasting accuracy of geomagnetic storms and their impact on earth systems.

Webb, Alla G.

37

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

38

Crude Oil Price Forecasting with an Improved Model Based on Wavelet Transform and RBF Neural Network

The fluctuation of oil price decides the security of energy and economics. So the crude oil price forecasting performs importantly. In the paper, we apply the improved model based on wavelet transform and radial basis function (RBF) neural network to forecast the future oil price. Wavelet transform decomposes the original price which is used as the output layer of RBF

Wu Qunli; Hao Ge; Cheng Xiaodong

2009-01-01

39

Nonlinear Forecasting Using Nonparametric Transfer Function Models

The focus of this paper is using nonparametric transfer function models in forecasting. Nonparametric smoothing methods are used to model the relationship between variables (the transfer function) and the noise is modeled as an Autoregressive Moving Average (ARMA) process. The transfer function is estimated jointly with the ARMA parameters. Nonparametric smoothing methods are flexible thus can be used to model

Jun M. Liu

2009-01-01

40

Forecast-based interventions can reduce the health and economic burden of wildfires.

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 county wide intervention advisories were designed to occur when fine particulate matter (PM2.5) from smoke, forecasted 24 or 48 h in advance, was expected to exceed a predetermined threshold. Three different thresholds were considered in simulations, each with three different levels of adherence to the advisories. Interventions were simulated in the adult population susceptible to health exacerbations related to the chronic conditions of asthma and congestive heart failure. Associations between Emergency Department (ED) visits for these conditions and daily PM2.5 concentrations under each intervention were evaluated. Triggering interventions at lower PM2.5 thresholds (? 20 ?g/m(3)) with good compliance yielded the greatest risk reduction. At the highest threshold levels (50 ?g/m(3)) interventions were ineffective in reducing health risks at any level of compliance. The economic benefit of effective interventions exceeded $1 M in excess ED visits for asthma and heart failure, $2 M in loss of productivity, $100 K in respiratory conditions in children, and $42 million due to excess mortality. PMID:25123711

Rappold, Ana G; Fann, Neal L; Crooks, James; Huang, Jin; Cascio, Wayne E; Devlin, Robert B; Diaz-Sanchez, David

2014-09-16

41

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

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

42

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator November 2013 Â© Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

43

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator February 2014 Â© Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

44

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator Aug 2013 Â© Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

45

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator May 2014 Â© Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

46

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator August 2014 Â© Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

47

Forecasting Turbulent Modes with Nonparametric Diffusion Models

This paper presents a nonparametric diffusion modeling approach for forecasting partially observed noisy turbulent modes. The proposed forecast model uses a basis of smooth functions (constructed with the diffusion maps algorithm) to represent probability densities, so that the forecast model becomes a linear map in this basis. We estimate this linear map by exploiting a previously established rigorous connection between the discrete time shift map and the semi-group solution associated to the backward Kolmogorov equation. In order to smooth the noisy data, we apply diffusion maps to a delay embedding of the noisy data, which also helps to account for the interactions between the observed and unobserved modes. We show that this delay embedding biases the geometry of the data in a way which extracts the most predictable component of the dynamics. The resulting model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and in the observation noise limit. We will show numerical examples on a wide-range of well-studied turbulent modes, including the Fourier modes of the energy conserving Truncated Burgers-Hopf (TBH) model, the Lorenz-96 model in weakly chaotic to fully turbulent regimes, and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. In these examples, forecasting skills of the nonparametric diffusion model are compared to a wide-range of stochastic parametric modeling approaches, which account for the nonlinear interactions between the observed and unobserved modes with white and colored noises.

Tyrus Berry; John Harlim

2015-01-27

48

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

49

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

50

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

51

Applications products of aviation forecast models

NASA Technical Reports Server (NTRS)

A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.

Garthner, John P.

1988-01-01

52

In the last ten years, with the help of satellite remote sensing, we build up a huge database of fire points in China. The remote sensing data that we used to do the fire monitoring include NOAA, FY-1, FY-3 and MODIS. In this paper, we present a new model for fire forecast base on the former database and NCEP reanalysis

Shengli Wu; Cheng Liu

2010-01-01

53

Hyperparameter estimation in forecast models

A large number of non-linear time series models can be more easily analyzed using traditional linear methods by considering explicitly the difference between parameters of interest, or just parameters, and hyperparameters. One example is the class of conditionally Gaussian dynamic linear models. Bayesian vector autoregressive models and non-linear transfer function models are also important examples in the literature. Until recently,

Hedibert Freitas Lopes; Ajax R. Bello Moreira; Alexandra Mello Schmidt

1999-01-01

54

Application of a Combination Forecasting Model in Logistics Parks' Demand

Logistics parks' demand is an important basis of establishing the development policy of logistics industry and logistics infrastructure for planning. In order to improve the forecast accuracy of logistics parks' demand, a combination forecasting model is proposed in this paper. Firstly, we use grey forecast model and exponential smoothing method to predict the demand respectively, then we combine the two

Chen Qin; Qi Ming

2010-01-01

55

Probabilistic Yield Forecast Based on Aproduction Process Model

NASA Astrophysics Data System (ADS)

A method for probabilistic forecast of agricultural yield depending on meteorological variability, i.e. forecast of agrometeorological resources, is discussed. Forecast is based on the category of meteorologically possible yield (MPY)-the maximum possible yield for a given variety in the existing meteorological conditions. The forecasting process is realized by a potato production process model POMOD, which applies the principle of maximum plant productivity and method of reference yields. The yield diversity, granting probabilistic distribution was obtained from series of model calculations,whereby the weather realizations for post-forecast period were gained from a century-long meteorological data series. Three examples realized for extremely different years are discussed. The results of such forecast, presented as a cumulative distribution, allow user to adjust and plan activities to thesufficiently assured yield level. Forecast of agrometeorological resources can be transformed to the forecast of real commercial yield (CY) by incorporating the efficiency coefficient of using meteorological conditions (CY/MPY).

Kadaja, Jüri; Saue, Triin; Vii, Peeter

56

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

57

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

58

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

59

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

60

Guidance on the choice of threshold for binary forecast modeling

NASA Astrophysics Data System (ADS)

This paper proposes useful guidance on the choice of threshold for binary forecasts. In weather forecast systems, the probabilistic forecast cannot be used directly when estimated too smoothly. In this case, the binary forecast, whether a meteorological event will occur or not, is preferable to the probabilistic forecast. A threshold is needed to generate a binary forecast, and the guidance in this paper encompasses the use of skill scores for the choice of threshold according to the forecast pattern. The forecast pattern consists of distribution modes of estimated probabilities, occurrence rates of observations, and variation modes. This study is performed via Monte-Carlo simulation, with 48 forecast patterns considered. Estimated probabilities are generated by random variate sampling from five distributions separately. Varying the threshold from 0 to 1, binary forecasts are generated by threshold. For the assessment of binary forecast models, a 2 × 2 contingency table is used and four skill scores (Heidke skill score, hit rate, true skill statistic, and threat score) are compared for each forecast pattern. As a result, guidance on the choice of skill score to find the optimal threshold is proposed.

Sohn, Keon Tae; Park, Sun Min

2008-01-01

61

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

62

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, Özgür

2014-01-01

63

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, Özgür

2014-01-01

64

From Social Data Mining to Forecasting SocioEconomic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It

Dirk Helbing; Stefano Balietti

2010-01-01

65

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

66

The potential economic benefits of improvements in weather forecasting

NASA Technical Reports Server (NTRS)

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

Thompson, J. C.

1972-01-01

67

Merging Multiple Climate Model Forecasts for Seasonal Hydrologic Predictions

NASA Astrophysics Data System (ADS)

Skillful seasonal hydrologic predictions are required in water resource management, preparation for drought and its impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the Eastern U.S., with focus on the Ohio River basin. The seasonal hydrologic prediction system utilizes a hydrologic model (in this case the Variable Infiltration Capacity model) as the central element for producing ensemble hydrologic predictions of soil moisture, snow and streamflow with lead times up to 6 months. The uniqueness of this forecast system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate predictions from multiple climate models with observed climatology in a Bayesian framework such that the uncertainties related to the atmospheric forcings can be reduced and better quantified. This framework also downscales the climate model forecasts to scales appropriate for hydrologic prediction and uses a rank structure of selected historical forcings to ensure that generated ensembles of daily meteorological forcings have reasonable patterns in space and time. Three types of forecasts were performed in the study: those using information from NCEP's Climate Forecast System (CFS), those using information from CFS and the European Union funded multi-model prediction project called DEMETER, and those based the Extended Streamflow Prediction (ESP) approach. Forecasts (CFS, CFS+DEMETER and ESP) were made with the system for the summer periods (May to October) for 1981 - 1999, and represent forecast information from one climate model, eight climate models and none, respectively. The differences in forecast skills between CFS, CFS+DEMETER and ESP reflect the improvement with the new forecast method against the current hydrological operational approach, which is based on ESP. The forecast for the summer 1988 shows very promising skill in precipitation, soil moisture and streamflow forecast over the Ohio river basin, especially with the CFS+DEMETER forecast. The evaluation over all 19 summer forecasts shows significant skill improvement with the new multi-model method during the first two months of the forecasts. The improvement is marginal to moderate when only CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions. It also shows the need for international collaborations to develop multi-model seasonal predictions.

Luo, L.; Wood, E. F.; Pan, M.; Li, H.

2007-12-01

68

Forecast of the output value of Taiwan's opto-electronics industry using the Grey forecasting model

This article applies the Grey forecasting model from Grey theory to forecast accurately the output value of Taiwan's opto-electronics industry from 2000 to 2005. The 2005 output value of Taiwan's opto-electronics industry will be NT$2216.954 billion; of opto-electronics components, NT$150.995 billion; of computer peripherals, NT$1993 billion; of optical devices and equipment, NT$24.664 billion; of opto-electronics applications, NT$17.374 billion; and of

Chin-Tsai Lin; Shih-Yu Yang

2003-01-01

69

A recurrent support vector regression model in rainfall forecasting

NASA Astrophysics Data System (ADS)

To minimize potential loss of life and property caused by rainfall during typhoon seasons, precise rainfall forecasts have been one of the key subjects in hydrological research. However, rainfall forecast is made difficult by some very complicated and unforeseen physical factors associated with rainfall. Recently, support vector regression (SVR) models and recurrent SVR (RSVR) models have been successfully employed to solve time-series problems in some fields. Nevertheless, the use of RSVR models in rainfall forecasting has not been investigated widely. This study attempts to improve the forecasting accuracy of rainfall by taking advantage of the unique strength of the SVR model, genetic algorithms, and the recurrent network architecture. The performance of genetic algorithms with different mutation rates and crossover rates in SVR parameter selection is examined. Simulation results identify the RSVR with genetic algorithms model as being an effective means of forecasting rainfall amount. Copyright

Pai, Ping-Feng; Hong, Wei-Chiang

2007-03-01

70

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

71

Modelling and forecasting snowmelt floods for operational forecasting in Finland

A modified version of HBV-3 model is in operational use on nine river basins ranging from 300 to 30 000 km2 in Finland. The snowmelt model used is a modified degree-day method with temperature and precipi tation as input data. For one experimental area (21 km2) different types of snowmelt models are tested including degree-day models, energy balance models and

BERTEL VEHVILAINEN

72

Operational forecasting based on a modified Weather Research and Forecasting model

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

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18

73

Time dependent Directional Profit Model for Financial Time Series Forecasting

Time dependent Directional Profit Model for Financial Time Series Forecasting Jingtao YAO Chew Lim their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training

Yao, JingTao

74

Multilayer Stock Forecasting Model Using Fuzzy Time Series

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

Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

2014-01-01

75

Multilayer stock forecasting model using fuzzy time series.

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

Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

2014-01-01

76

Visibility Parameterization For Forecasting Model Applications

NASA Astrophysics Data System (ADS)

In this study, the visibility parameterizations developed during Fog Remote Sensing And Modeling (FRAM) projects, conducted in central and eastern Canada, will be summarized and their use for forecasting/nowcasting applications will be discussed. Parameterizations developed for reductions in visibility due to 1) fog, 2) rain, 3) snow, and 4) relative humidity (RH) during FRAM will be given and uncertainties in the parameterizations will be discussed. Comparisons made between Canadian GEM NWP model (with 1 and 2.5 km horizontal grid spacing) and observations collected during the Science of Nowcasting Winter Weather for Vancouver 2010 (SNOW-V10) project and FRAM projects, using the new parameterizations, will be given Observations used in this study were obtained using a fog measuring device (FMD) for fog parameterization, a Vaisala all weather precipitation sensor called FD12P for rain and snow parameterizations and visibility measurements, and a total precipitation sensor (TPS), and distrometers called OTT ParSiVel and Laser Precipitation Measurement (LPM) for rain/snow particle spectra. The results from the three SNOW-V10 sites suggested that visibility values given by the GEM model using the new parameterizations were comparable with observed visibility values when model based input parameters such as liquid water content, RH, and precipitation rate for visibility parameterizations were predicted accurately.

Gultepe, I.; Milbrandt, J.; Binbin, Z.

2010-07-01

77

DESCRIPTION OF THE UNITED STATES SORGHUM SUBSECTOR AND THE DEVELOPMENT OP A SEASONAL SORGHUM CASH PRICE FORECASTING MODEL A Thesis by DAVID MICHAEL JACKSON Submitted to the Graduate College of Texas A&M University in partial fulfillment... of the requirement for the degree of MASTER OF SCIENCE December 1978 Major Subject: Agricultural Economics DESCRIPTION OF THE UNITED STATES SORGHUM SUBSECTOR AND THE DEVELOPMENT OF A SEASONAL SORGHUM CASH PRICE FORECASTING MODEL A Thesis by DAVID MICHAEL...

Jackson, David Michael

2012-06-07

78

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

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

2014-01-01

79

Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

80

Electric vehicle charge planning using Economic Model Predictive Control

Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide grid services, both for peak reduction and for ancillary services, by absorbing short term

Rasmus Halvgaard; Niels K. Poulsen; Henrik Madsen; John B. Jorgensen; Francesco Marra; Daniel Esteban Morales Bondy

2012-01-01

81

Hybrid deterministic - stochastic model for forecasting of monthly river flows

NASA Astrophysics Data System (ADS)

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

Svetlíková, D.; Szolgay, J.; Kohnová, S.; Komorníková, M.; Szökeová, D.

2009-04-01

82

A hybrid spatiotemporal drought forecasting model for operational use

NASA Astrophysics Data System (ADS)

Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

Vasiliades, L.; Loukas, A.

2010-09-01

83

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

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

Yoo, Wucherl; Sim, Alex

2014-07-07

84

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

Reister, D.B. [Oak Ridge National Lab., TN (United States)

1985-08-01

85

Combining regional forecast and crop yield models for the USDA

NASA Astrophysics Data System (ADS)

Besides the risk of different economic and market conditions, large agricultural interests face the risk of crop losses from a number of weather-related perils including drought and heat, excess moisture, hail, frost and freeze, and wind. In a joint project, AIR Worldwide and Agrilogic are teamed with the RMA(Risk Management Agency) component of the USDA (United States Department of Agriculture) in developing InsuranceVision, a tool to support the producer in crop insurance decision-making. The tool will use available climatic, agronomic and econometric models to analyze likely scenarios over the growing season and project probable yields and prices by harvest. The tool will ultimately assist growers in deciding what insurance products will best minimize their market risk. This presentation focuses on the weather/climate related models based on the NCAR-NCEP Global Reanalysis Project data set, the NCAR Community Climate Model (CCM 3.6) and the 5th generation NCAR-Penn State University Mesoscale Model (MM5). A method will be discussed that derives crop yield probability distributions from historical detrended yield data, numerical weather model climatologies, climate projections and locally refined forecasts.

Zuba, G.; Gibbas, M.; Lee, M.; Dailey, P.; Keller, J.

2003-04-01

86

A logistics demand forecasting model based on Grey neural network

Logistics demand forecasting is important for investment decision-making of infrastructure and strategy programming of the logistics industry. In this paper, a hybrid method which combines the Grey Model, artificial neural networks and other techniques in both learning and analyzing phases is proposed to improve the precision and reliability of forecasting. After establishing a learning model GNNM(1,8) for road logistics demand

Fangzhong Qi; Da Yu; Bernhard Holtkamp

2010-01-01

87

Inaccurate forecasts of the logistic growth model for Nobel Prizes

Modis [Technol. Forecast. Soc. Change 34 (1988) 95] reports that a logistic growth (LG) model of the number of U.S. Nobel Prize recipients provides an excellent fit for the period 1901–1987. This model forecasts that approximately 235 Americans will receive a Nobel Prize by year-end 2002 and that a total of 283 Americans will eventually receive a Nobel Prize. We

Bruce L. Golden; Paul F. Zantek

2004-01-01

88

Forthcoming: Journal of Applied Business and Economics (2011) Integrating Financial Statement Modeling and Sales Forecasting Using EViews John T. Cuddington Colorado School of Mines Irina Khindanova of the financial forecasts. INTRODUCTION In most business school programs students are exposed to financial

89

NSDL National Science Digital Library

This site is a joint effort of NOAA Research and the College of Education at the University of South Alabama. The goal of the site is to provide middle school science students and teachers with research and investigation experiences using on-line resources. In this unit students look at the science of weather forecasting as a science by exploring cloud, temperatures, and air pressure data and information. Students apply this information to interpret and relate meteorological maps to each other. Parts of the unit include gathering information from other websites, applying the data gathered, and performing enrichment exercises. This site contains a downloadable teachers guide, student guide, and all activity sheets to make the unit complete.

90

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

91

A Fuzzy Logic Fog Forecasting Model for Perth Airport

NASA Astrophysics Data System (ADS)

Perth Airport is a major airport along the southwest coast of Australia. Even though, on average, fog only occurs about twelve times a year, the lack of suitable alternate aerodromes nearby for diversion makes fog forecasts for Perth Airport very important to long-haul international flights. Fog is most likely to form in the cool season between April and October. This study developed an objective fuzzy logic fog forecasting model for Perth Airport for the cool season. The fuzzy logic fog model was based on outputs from a high-resolution operational NWP model called LAPS125 that ran twice daily at 00 and 12 UTC, but fuzzy logic was employed to deal with the inaccuracy of NWP prediction and uncertainties associated with relationships between fog predictors and fog occurrence. The outcome of the fuzzy logic fog model is in one of the four categories from low to high fog risk as FM0, FM5, FM15 or FM30, intended to map to approximate fog probability of 0, 5, 15 and 30%, respectively. The model was found useful in its 5 year performance in the cool seasons between 2004 and 2008 and required little recalibration if mist was treated as if it were also a fog event in the skill evaluation. To generate an operational fog forecast for Perth Airport, the outcome of the fuzzy logic fog model was averaged with the outcomes of two other fog forecasting methods using a simple consensus approach. Fog forecast so generated is known as the operational consensus forecast. Skill assessment using frequency distribution diagram, Hansen and Kuiper skill score, and Relative Operating Characteristic curve showed that the operational consensus forecast outperformed all three individual methods. Out of the three methods, the fuzzy logic fog model ranked second. It performed better than the other objective method called GASM but worse than the subjective method which relied on forecaster's subjective assessment. The skills of the fuzzy logic fog model can be further improved with the tuning of fuzzy functions. In addition, similar models can be customised for other airports. The study also suggested the use of the simple consensus approach to enhance forecasting skills for other stations or weather phenomena if there were two or more independent forecasting methods available.

Miao, Y.; Potts, R.; Huang, X.; Elliott, G.; Rivett, R.

2012-05-01

92

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

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

93

The quest for physically realistic streamflow forecasting models

NASA Astrophysics Data System (ADS)

The current generation of time stepping hydrological models used by operational forecasting agencies are process-weak, where model parameters are often assigned unrealistic values to compensate for model structural weaknesses. These time stepping simulation models are therefore subject to the same stationarity predicament that plagues statistical streamflow forecasting systems. Consequently, the operational forecasting community has similar research priorities to the science community, that is, to develop physically realistic hydrological models. This paper describes development of a new modeling framework to improve the representation of hydrological processes within operational streamflow forecasting models. The framework recognizes that the majority of process-based models use the same set of physics - most models use Darcy's Law to represent the flow of water through the soil matrix and Fourier's Law for thermodynamics. The new modeling framework uses numerically robust solutions of the hydrology and thermodynamic governing equations as the structural core, and incorporates multiple options to represent the impact of different modeling decisions, including different methods to represent spatial variability and different parameterizations of surface fluxes and shallow groundwater. Use of multivariate research data to evaluate these different modeling options reveals that the new modeling framework can provide realistic simulations of both point-scale measurements of hydrologic states and fluxes as well as realistic simulations of streamflow in headwater catchments, with minimal calibration. Moreover, the availability of multiple modeling options improves representation of model uncertainty.

Restrepo, Pedro; Wood, Andy; Clark, Martyn

2013-04-01

94

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

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

95

Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

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

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

2013-01-01

96

Wind energy forecasting for the Netherlands using the WRF atmosphere model

BMT ARGOSS operates the WRF atmosphere model for regional weather forecasts and long-term historical analyses across the globe. Operational forecasts for the Netherlands are provided to an energy company to obtain power output forecasts up to 5 days ahead. The WRF model is operated at resolutions of 3 km and 9 km. Forecasts are provided 4 times per day, up

H. Zelle; C. Calkoen; P. Groenewoud; S. Hulst; Á. Mika

2010-01-01

97

The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows

The effect of imposing different numbers of unit roots on forecasting accuracy is examined using univariate ARMA models. To see whether additional information improves forecasting accuracy and increases the informative forecast horizon, we cross-relate the time series for inbound tourism in Sweden for different visitor categories and estimate vector ARMA models. The mean-squared forecast error for different filters indicates that

Patrik Gustavsson; Jonas Nordström

1999-01-01

98

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

99

Validation of Model Forecasts of the Ambient Solar Wind

NASA Technical Reports Server (NTRS)

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

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

2009-01-01

100

Representing Hurricanes with a Nested Global Forecast Model

NASA Astrophysics Data System (ADS)

A global forecast model is essential for predicting hurricane tracks beyond a period of ~2 days since global processes that may influence the longer-term storm tracks can be represented explicitly and there are no errors from the lateral boundary conditions that can propagate into the model domain and diminish the accuracy of the track forecasts. However, global models usually do not have enough horizontal and vertical resolution to produce meaningful hurricane intensity forecasts. Most current operational global forecast models represent the atmosphere horizontally using spherical harmonic basis functions with an equivalent resolution of ~40-50 km. The NOAA Science Advisory Board Hurricane Intensity Research Working Group recommends approximately 1-km-resolution hurricane forecasts in order to represent the important physical processes in the core region of hurricanes that are important to accurately predict hurricane intensity. Even with state-of-the-art computers, it will be many years before global forecasts with 1-km horizontal resolution are practical. To predict both hurricane tracks and intensity well, a nested global model is necessary. Large-scale processes are represented on a coarser, computationally-efficient grid while features such as hurricanes are represented on a high-resolution nest. The global model used in this study is the Ocean-Land-Atmosphere Model (OLAM) being developed at Duke University. OLAM is the global successor to the Regional Atmospheric Modeling System (RAMS), which originated at Colorado State University in 1986. OLAM uses the same physics parameterizations as RAMS, but it solves the governing equations by discretizing the atmosphere on an unstructured triangular finite-volume grid. The triangular grid uses the Arakawa-C staggering and is fully mass conservative. Since the triangular mesh is unstructured, the mesh can be refined to produce much higher horizontal resolution in areas of interest such as near hurricanes. Here, we examine hurricane track and intensity forecasting in a global nested model using a real-data case. Using a high-resolution nest in the vicinity of a hurricane, we examine how well the inner core hurricane structure can be resolved in order to produce meaningful intensity forecasts. We also determine if the better representation of hurricanes also leads to better longer-term hurricane track predictions.

Otte, M. J.; Walko, R. L.; Avissar, R.

2007-12-01

101

Forecasting comparison between two nonlinear models: fuzzy regression versus SETAR

In this article, we compare the forecasting performances of the Self-Exciting Threshold Autoregressive (SETAR) model and a fuzzy clustering regression model. The series used in this study are high-frequency financial data in the form of seven major stock prices in the US stock markets; the stock indices from seven world stock trading centres; the daily prices for two important commodities,

Hui Feng

2011-01-01

102

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

103

The study describes the development, testing and applications of site-specific tsunami inundation models (forecast models) for use in NOAA's tsunami forecast and warning system. The model development process includes sensitivity studies of tsunami wave characteristics in the nearshore and inundation, for a range of model grid setups, resolutions and parameters. To demonstrate the process, four forecast models in Hawaii, at

L. Tang; V. V. Titov; C. D. Chamberlin

2009-01-01

104

Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

NASA Astrophysics Data System (ADS)

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

105

Forecasting the conditional volatility of oil spot and futures prices with structural breaks of oil spot and futures prices using three GARCH-type models, i.e., linear GARCH, GARCH with structural that oil price fluctuations influence economic activity and financial sector (e.g., Jones and Kaul, 1996

Paris-Sud XI, UniversitÃ© de

106

A national econometric forecasting model of the dental sector.

The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974

Feldstein, P J; Roehrig, C S

1980-01-01

107

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

108

Abstract Objective To develop an integrated health forecasting model as part of the International Futures (IFs) modelling system. Methods The IFs model begins with the historical relationships between economic and social development and cause-specific mortality used by the Global Burden of Disease project but builds forecasts from endogenous projections of these drivers by incorporating forward linkages from health outcomes back to inputs like population and economic growth. The hybrid IFs system adds alternative structural formulations for causes not well served by regression models and accounts for changes in proximate health risk factors. Forecasts are made to 2100 but findings are reported to 2060. Findings The base model projects that deaths from communicable diseases (CDs) will decline by 50%, whereas deaths from both non-communicable diseases (NCDs) and injuries will more than double. Considerable cross-national convergence in life expectancy will occur. Climate-induced fluctuations in agricultural yield will cause little excess childhood mortality from CDs, although other climate?health pathways were not explored. An optimistic scenario will produce 39 million fewer deaths in 2060 than a pessimistic one. Our forward linkage model suggests that an optimistic scenario would result in a 20% per cent increase in gross domestic product (GDP) per capita, despite one billion additional people. Southern Asia would experience the greatest relative mortality reduction and the largest resulting benefit in per capita GDP. Conclusion Long-term, integrated health forecasting helps us understand the links between health and other markers of human progress and offers powerful insight into key points of leverage for future improvements. PMID:21734761

Hughes, Barry B; Peterson, Cecilia M; Rothman, Dale S; Solórzano, José R; Mathers, Colin D; Dickson, Janet R

2011-01-01

109

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

110

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

111

CCPP-ARM Parameterization Testbed Model Forecast Data

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

Klein, Stephen

112

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

113

Adaptation of Mesoscale Weather Models to Local Forecasting

NASA Technical Reports Server (NTRS)

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

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

2003-01-01

114

Forecast and virtual weather driven plant disease risk modeling system

Technology Transfer Automated Retrieval System (TEKTRAN)

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

115

Weather load model for electric demand and energy forecasting

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

116

Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting

NASA Astrophysics Data System (ADS)

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

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

2008-05-01

117

Emphasis is placed on the nature and magnitude of socio-economic impacts of fossil-fuel development. A model is described that identifies and estimates the magnitude of the economic impacts of anticipated energy resource development in site-specific areas and geographically contiguous areas of unspecified size. The modeling methodology was designed to assist industries and government agencies complying with recent federal and state

Stenehjem

1975-01-01

118

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

119

Distributed Hydrologic Models for Flow Forecasts - Part 1

NSDL National Science Digital Library

Distributed Hydrologic Models for Flow Forecasts – Part 1 provides a basic description of distributed hydrologic models and how they work. This module is the first in a two-part series focused on the science of distributed models and their applicability in different situations. Presented by Dr. Dennis Johnson, the module begins with a review of hydrologic models, and then examines the differences between lumped and distributed models. It explains how lumped models may be distributed by subdividing the basin and suggests when distributed hydrologic models are most appropriate. Other topics covered include the advantages of physically-based versus conceptual approaches and some strengths and challenges associated with distributed modeling.

2014-09-14

120

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

NASA Astrophysics Data System (ADS)

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

Franchini, Marco; Lamberti, Paolo

1994-07-01

121

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

A numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), has been developed to run at Canadian weather centers. METRo uses roadside observations from road weather information systems stations as input, together with meteorological forecasts from the operational Global Environmental Multiscale (GEM) model of the Canadian Meteorological Centre; the meteorologist can modify this forecast

Louis-Philippe Crevier; Yves Delage

2001-01-01

122

National Weather Service (NWS) forecasters currently have access to a limited set of models that may not be suitable for all Iowa basins or forecasting situations, such as small, fast responding streams. Flexible modeling systems that allow model configurations to change according to the watershed characteristics may provide useful predictive information to supplement existing forecast products. The United States Army

William Scott Lincoln

2009-01-01

123

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

124

Forecasting Lightning Threat using Cloud-resolving Model Simulations

NASA Technical Reports Server (NTRS)

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

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

2009-01-01

125

Forecasting wind-driven wildfires using an inverse modelling approach

NASA Astrophysics Data System (ADS)

A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event) in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

Rios, O.; Jahn, W.; Rein, G.

2014-06-01

126

Review of Wind Energy Forecasting Methods for Modeling Ramping Events

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

127

A Dirichlet process model for classifying and forecasting epidemic curves

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

2014-01-01

128

Towards Operational Modeling and Forecasting of the Iberian Shelves Ecosystem

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

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

2012-01-01

129

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

ERIC Educational Resources Information Center

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

Lefberg, Irv; And Others

130

The SOLAR2000 empirical solar irradiance model and forecast tool

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

131

Real-Business-Cycle Models and the Forecastable Movements in Output, Hours, and Consumption

The authors study the movements in output, consumption, and hours that are forecastable from a vector autoregression and analyze how they differ from those predicted by standard real-business-cycle models. They show that actual forecastable movements in output have a variance about one hundred times larger than those predicted by the model. The authors also find that forecastable changes in the

Julio J. Rotemberg; Michael Woodford

1996-01-01

132

Forecasting methods are routinely employed to predict the outcome of competitive events (CEs) and to shed light on the factors that influence participants’ winning prospects (e.g., in sports events, political elections). Combining statistical models’ forecasts, shown to be highly successful in other settings, has been neglected in CE prediction. Two particular difficulties arise when developing model-based composite forecasts of CE

Stefan Lessmann; Ming-Chien Sung; Johnnie E. V. Johnson; Tiejun Ma

2012-01-01

133

A transactions choice model for forecasting demand for alternative-fuel vehicles

The vehicle choice model developed here is one component in a micro-simulation demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in California. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. The choice model

David Brownstone; David S. Bunch; Thomas F. Golob; Weiping Ren

1996-01-01

134

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

135

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.

2014-09-14

136

NASA Astrophysics Data System (ADS)

A recently implemented real-time ocean prediction system for the western North Atlantic based on the physical circulation model component of the Harvard Ocean Prediction System (HOPS) was used during an observation simulation experiment (OSE) in November 2009. The modeling system was built to capture the mesoscale dynamics of the Gulf Stream (GS), its meanders and rings, and its interaction with the shelf circulation. To accomplish this, the multiscale velocity-based feature models for the GS region are melded with the water-mass-based feature model for the Gulf of Maine and shelf climatology across the shelf/slope front for synoptic initialization. The feature-based initialization scheme was utilized for 4 short-term forecasts of varying lengths during the first two weeks of November 2009 in an ensemble mode with other forecasts to guide glider control.A reanalysis was then carried out by sequentially assimilating the data from three gliders (RU05, RU21 and RU23) for the two-week period. This two-week-long reanalysis framework was used to (i) study model sensitivity to SST and glider data assimilation; and (ii) analyze the impact of assimilation in space and time with patchy glider data. The temporal decay of salinity assimilation is found to be different than that of temperature. The spatial footprint of assimilated temperature appears to be more defined than that of salinity. A strategy for assimilating temperature and salinity in an SST-glider phased manner is then offered. The reanalysis results point to a number of new research directions for future sensitivity and quantitative studies in modeling and data assimilation.

Gangopadhyay, Avijit; Schmidt, Andre; Agel, Laurie; Schofield, Oscar; Clark, Jenifer

2013-07-01

137

NASA Astrophysics Data System (ADS)

A recently implemented real-time ocean prediction system for the western North Atlantic based on the physical circulation model component of the Harvard Ocean Prediction System (HOPS) was used during an observation simulation experiment (OSE) in November 2009. The modeling system was built to capture the mesoscale dynamics of the Gulf Stream (GS), its meanders and rings, and its interaction with the shelf circulation. To accomplish this, the multiscale velocity-based feature models for the GS region are melded with the water-mass-based feature model for the Gulf of Maine and shelf climatology across the shelf/slope front for synoptic initialization. The feature-based initialization scheme was utilized for 4 short-term forecasts of varying lengths during the first two weeks of November 2009 in an ensemble mode with other forecasts to guide glider control. A reanalysis was then carried out by sequentially assimilating the data from three gliders (RU05, RU21 and RU23) for the two-week period. This two-week-long reanalysis framework was used to (i) study model sensitivity to SST and glider data assimilation; and (ii) analyze the impact of assimilation in space and time with patchy glider data. The temporal decay of salinity assimilation is found to be different than that of temperature. The spatial footprint of assimilated temperature appears to be more defined than that of salinity. A strategy for assimilating temperature and salinity in an SST-glider phased manner is then offered. The reanalysis results point to a number of new research directions for future sensitivity and quantitative studies in modeling and data assimilation.

Gangopadhyay, Avijit; Schmidt, Andre; Agel, Laurie; Schofield, Oscar; Clark, Jenifer

2013-07-01

138

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

139

Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data.

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

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

2014-11-20

140

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

141

Application of data assimilation to solar wind forecasting models

NASA Astrophysics Data System (ADS)

Data Assimilation through Kalman filtering [1,2] is a powerful statistical tool which allows to combine modeling and observations to increase the degree of knowledge of a given system. We apply this technique to the forecast of solar wind parameters (proton speed, proton temperature, absolute value of the magnetic field and proton density) at 1 AU, using the model described in [3] and ACE data as observations. The model, which relies on GOES 12 observations of the percentage of the meridional slice of the sun covered by coronal holes, grants 1-day and 6-hours in advance forecasts of the aforementioned quantities in quiet times (CMEs are not taken into account) during the declining phase of the solar cycle and is tailored for specific time intervals. We show that the application of data assimilation generally improves the quality of the forecasts during quiet times and, more notably, extends the periods of applicability of the model, which can now provide reliable forecasts also in presence of CMEs and for periods other than the ones it was designed for. Acknowledgement: The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement N. 218816 (SOTERIA project: http://www.soteria-space.eu). References: [1] R. Kalman, J. Basic Eng. 82, 35 (1960); [2] G. Welch and G. Bishop, Technical Report TR 95-041, University of North Carolina, Department of Computer Science (2001); [3] B. Vrsnak, M. Temmer, and A. Veronig, Solar Phys. 240, 315 (2007).

Innocenti, M.; Lapenta, G.; Vrsnak, B.; Temmer, M.; Veronig, A.; Bettarini, L.; Lee, E.; Markidis, S.; Skender, M.; Crespon, F.; Skandrani, C.; Soteria Space-Weather Forecast; Data Assimilation Team

2010-12-01

142

A model for short term electric load forecasting

and residual component. This basic load component is defined as the load twenty-four hours preceding the prediction hour. The residual term includes a weather- correlated load component which is the incremental weather sensitive load between the base day... and the prediction day at any hour. This value is determined from the non-linear curve of weather sensitive load. vs. dry bulb temperature. Most forecasting techniques are inadequate in modelling a weather- correlated. load component, and this limitation is very...

Tigue, John Robert

2012-06-07

143

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

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

144

Wind energy forecasting for the Netherlands using the WRF atmosphere model

NASA Astrophysics Data System (ADS)

BMT ARGOSS operates the WRF atmosphere model for regional weather forecasts and long-term historical analyses across the globe. Operational forecasts for the Netherlands are provided to an energy company to obtain power output forecasts up to 5 days ahead. The WRF model is operated at resolutions of 3 km and 9 km. Forecasts are provided 4 times per day, up to 120 hours into the future. To estimate an accurate power output forecast based on a single weather forecast, wind speed, direction and air density are computed at specific wind farm locations, at hub height. Additionally, an uncertainty interval for the wind speed forecast is estimated based on several components: a multi-year hindcast validation study, a model forecast skill validation study, ensemble data from a global model and the spatial wind speed variability around the location of interest. Using the 4 parameters wind speed, wind speed uncertainty, wind direction and air mass, a statistical model provides power output forecasts based on a historical database of power output and modeled wind forecasts. The presentation will focus on the methods applied for model validation and estimating the wind speed uncertainty interval for a single model forecast. Model improvements related to topography and land use data sets are also discussed.

Zelle, H.; Calkoen, C.; Groenewoud, P.; Hulst, S.; Mika, Á.

2010-09-01

145

Solar activity forecast with a dynamo model

Although systematic measurements of the solar polar magnetic field exist only from mid 1970s, other proxies can be used to infer the polar field at earlier times. The observational data indicate a strong correlation between the polar field at a sunspot minimum and the strength of the next cycle, although the strength of the cycle is not correlated well with the polar field produced at its end. This suggests that the Babcock Leighton mechanism of poloidal field generation from decaying sunspots involves randomness, whereas the other aspects of the dynamo process must be reasonably ordered and deterministic. Only if the magnetic diffusivity within the convection zone is assumed to be high, we can explain the correlation between the polar field at a minimum and the next cycle. We give several independent arguments that the diffusivity must be of this order. In a dynamo model with diffusivity like this, the poloidal field generated at the mid latitudes is advected toward the poles by the meridional circulation and simultaneously diffuses towards the tachocline, where the toroidal field for the next cycle is produced. To model actual solar cycles with a dynamo model having such high diffusivity, we have to feed the observational data of the poloidal field at the minimum into the theoretical model. We develop a method of doing this in a systematic way. Our model predicts that cycle 24 will be a very weak cycle. Hemispheric asymmetry of solar activity is also calculated with our model and compared with observational data.

Jie Jiang; Piyali Chatterjee; Arnab Rai Choudhuri

2007-07-16

146

A first large-scale flood inundation forecasting model

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

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

2013-11-04

147

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

Seasonal Maize Forecasting for South Africa and Zimbabwe Derived from an Agroclimatological Model to clearly link climate and maize forecasts. The forecasts utilize global sea surface temperature (SST and actual maize water-stress in South Africa, and a correlation of 0.79 for the same relationship

Martin, Randall

148

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

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

149

Solar activity forecast with a dynamo model

NASA Astrophysics Data System (ADS)

Although systematic measurements of the Sun's polar magnetic field exist only from mid-1970s, other proxies can be used to infer the polar field at earlier times. The observational data indicate a strong correlation between the polar field at a sunspot minimum and the strength of the next cycle, although the strength of the cycle is not correlated well with the polar field produced at its end. This suggests that the Babcock-Leighton mechanism of poloidal field generation from decaying sunspots involves randomness, whereas the other aspects of the dynamo process must be reasonably ordered and deterministic. Only if the magnetic diffusivity within the convection zone is assumed to be high (of order 1012cm2s-1), we can explain the correlation between the polar field at a minimum and the next cycle. We give several independent arguments that the diffusivity must be of this order. In a dynamo model with diffusivity like this, the poloidal field generated at the mid-latitudes is advected toward the poles by the meridional circulation and simultaneously diffuses towards the tachocline, where the toroidal field for the next cycle is produced. To model actual solar cycles with a dynamo model having such high diffusivity, we have to feed the observational data of the poloidal field at the minimum into the theoretical model. We develop a method of doing this in a systematic way. Our model predicts that cycle 24 will be a very weak cycle. Hemispheric asymmetry of solar activity is also calculated with our model and compared with observational data.

Jiang, Jie; Chatterjee, Piyali; Choudhuri, Arnab Rai

2007-11-01

150

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

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

151

FORECAST MODEL FOR MODERATE EARTHQUAKES NEAR PARKFIELD, CALIFORNIA.

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

152

Draft Report A Forecast Model of Long-Term PCB

...................................................................................................................26 Local Watershed (Urban Runoff) Load Reductions ..............................................................................26 Recovery Forecasts Under Different Management Scenarios

153

A monthly crude oil spot price forecasting model using relative inventories

This paper presents a short-term forecasting model of monthly West Texas Intermediate crude oil spot prices using readily available OECD industrial petroleum inventory levels. The model provides good in-sample and out-of-sample dynamic forecasts for the post-Gulf War time period. In-sample and out-of-sample forecasts from the model are compared with those derived from other models. The model is intended for the

Michael Ye; John Zyren; Joanne Shore

2005-01-01

154

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

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

155

A toy model for monthly river flow forecasting

NASA Astrophysics Data System (ADS)

SummaryRiver flow forecasting depends on land-atmosphere coupled processes, and is relevant to hydrological applications and land-ocean coupling. A toy model is developed here for monthly river flow forecasting using the river flow and river basin averaged precipitation in prior month. Model coefficients are calibrated for each month using historical data. The toy model is based on water balance, easy to use and reproduce, and robust to calibrate with a short period of data. For five major rivers in the world, its results agree with observations very well. Its prediction uncertainty can be quantified using the model's error statistics or using a dynamic approach, but not by the dispersion of 10,000 ensemble members with different sets of coefficients in the model. Its results are much better than those from a physically based land model even after the mean bias correction. The toy model and a standard neural network available from the MATLAB give similar results, but the latter is more sensitive to the length of calibration period. For the monthly prediction of river flow with a strong seasonal cycle, a modified Nash-Sutcliffe coefficient of efficiency is introduced and is found to be more reliable in model evaluations than the original coefficient of efficiency or the correlation coefficient.

Zeng, Xubin; Kiviat, Kira L.; Sakaguchi, Koichi; Mahmoud, Alaa M. A.

2012-07-01

156

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

157

A Feature Fusion Based Forecasting Model for Financial Time Series

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

158

A feature fusion based forecasting model for financial time series.

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

159

Traffic congestion forecasting model for the INFORM System. Final report

This report describes a computerized traffic forecasting model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM system roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information system. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical forecasting of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy forecasted (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.

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

1995-05-01

160

Evaluation of boundary-layer type forecasts 1 Evaluation of boundary-layer type in a weather Many studies evaluating model boundary-layer schemes focus either on near-surface parameters, combined in a way to match model representation of the boundary layer as closely as possible, can be used

Hogan, Robin

161

Linear regression has been used for many years for forecasting in marketing, management, sales and energy. In this paper, a fuzzy-based approach is applied for the transport energy demand forecasting using socio-economic and transport related indicators. This forecasting is analyzed based on gross domestic product (GDP), population and the number of vehicles together with historical energy data from 1993 to

M. R. Taghizadeh; H. G. Shakouri; M. B. Menhaj; M. R. Mehregan; A. Kazemi

2009-01-01

162

Forecasts of crude oil prices' volatility are important inputs to many decision making processes in application areas such as macroeconomic policy making, risk management, options pricing, and portfolio management. Despite the fact that a large number of forecasting models have been designed to forecast crude oil prices' volatility, so far the relative performance evaluation of competing forecasting models remains an

Bing Xu; Jamal Ouenniche

2012-01-01

163

Lightning Forecasts and Data Assimilation into Numerical Weather Prediction Models

NASA Astrophysics Data System (ADS)

This presentation reviews two aspects of lightning in numerical weather prediction (NWP) models: forecasting lightning and assimilating lightning data into NWP models to improve weather forecasts. One of the earliest routine forecasts of lightning was developed for fire weather operations. This approach used a multi-parameter regression analysis of archived cloud-to-ground (CG) lightning data and archived NWP data to optimize the combination of model state variables to use in forecast equations for various CG rates. Since then, understanding of how storms produce lightning has improved greatly. As the treatment of ice in microphysics packages used by NWP models has improved and the horizontal resolution of models has begun approaching convection-permitting scales (with convection-resolving scales on the horizon), it is becoming possible to use this improved understanding in NWP models to predict lightning more directly. An important role for data assimilation in NWP models is to depict the location, timing, and spatial extent of thunderstorms during model spin-up so that the effects of prior convection that can strongly influence future thunderstorm activity, such as updrafts and outflow boundaries, can be included in the initial state of a NWP model run. Radar data have traditionally been used, but systems that map lightning activity with varying degrees of coverage, detail, and detection efficiency are now available routinely over large regions and reveal information about storms that is complementary to the information provided by radar. Because data from lightning mapping systems are compact, easily handled, and reliably indicate the location and timing of thunderstorms, even in regions with little or no radar coverage, several groups have investigated techniques for assimilating these data into NWP models. This application will become even more valuable with the launch of the Geostationary Lightning Mapper on the GOES-R satellite, which will extend routine coverage even farther into remote regions and provides the most promising means for routine thunderstorm detection over oceans. On-going research is continually expanding the methods used to assimilate lightning data, which began with simple techniques for assimilating CG data and now are being extended to assimilate total lightning data. Most approaches either have used the lightning data simply to indicate where the subgrid scale convective parameterization of a model should produce deep convection or have used the lightning data to indicate how to modify a model variable related to thunderstorms, such as rainfall rate or water vapor mixing ratio. The developing methods for explicitly predicting lightning activity provide another, more direct means for assimilating total lightning data, besides providing information valuable to the general public and to many governmental and commercial enterprises. Such a direct approach could be particularly useful for ensemble techniques used to produce probabilistic thunderstorm forecasts.

MacGorman, D. R.; Mansell, E. R.; Fierro, A.; Ziegler, C.

2012-12-01

164

A New Forecasting Model for USD/CNY Exchange Rate

” in nonparametric regressions. A functional-coefficient regression model can be defined by µt = E(Yt | Ut,Xt) = p ? j=1 aj(Ut)Xtj, (3.2) where, Yt ? R1 is a dependent variable, Xt ? Rp are explanatory variables and Ut ? Rk are smoothing variables. We assume that {Yt...,Xt,Ut}?t=?? are 5Cai et al.: Forecasting USD/CNY Exchange Rate Published by De Gruyter, 2012 strictly stationary and {aj(·)pj=1} are measurable functions mapping from Rk to R1; see Cai, Fan and Yao (2000) for details. For the conditional volatility part ?t, a GARCH...

Cai, Zongwu; Chen, Linna; Fang, Ying

2012-09-18

165

Interpretation, modeling and forecasting runoff of regional hydrogeologic systems

NASA Astrophysics Data System (ADS)

Long-range modeling of a precipitation-runoff process has become indispensable to predict/forecast runoff and study the impact of modern anthropogenic factors and land change use on watersheds. The purpose of this thesis research is to interpret, model and forecast complex drainage basins using advanced signal processing technique and a physically-based low-dimensional dynamic model. The first emphasis is placed on a hydrogeologic interpretation of a complex drainage basin. The space- time patterns of annual, interannual, and decadal components of precipitation, temperature, and runoff (P- T-R) using long-record time series across the steep topographic gradient of the Wasatch Front in northern Utah, are examined. The singular spectrum analysis is used to detect dominant oscillations and spatial patterns in the data and to discuss the relation to the unique mountain and basin hydrologic setting. For precipitation and temperature, only the annual/seasonal spectral peaks were found to be significantly different from the underlying noise floor. Spectral peaks in runoff show increasing low-frequency components at intermediate and low elevation. A conceptual hydrogeologic model for the mountain and basin system proposes how losing streams and deep upwelling groundwater in the alluvial aquifer could explain the strong low-frequency component in streams. The research shows that weak interannual and decadal oscillations in the climate signal are strengthened where groundwater discharge dominates streamflow. The second emphasis is focused on developing a long-range physically-based precipitation-runoff model. A low- dimensional integral-balance model is developed for a hydrologic system where multiple time scales of basin storage play the dominant role on a precipitation-runoff process. The genetic algorithm (GA) technique is implemented for parameter identification with the observed data. The model is developed for the Upper West Branch of the Susquehanna River in Pennsylvania, within the Appalachian Plateaus. Model performance was assessed for runoff over calibration and verification. The ``optimal'' conceptual model has two nonlinear modes: fast and slow responses. The accuracy of the model suggests the utility of low-dimensional models for probabilistic flood and drought forecasting, as well as quantifying the impacts of changing land use and climate.

Shun, Tongying

1999-10-01

166

An important determinant of our energy future is the rate at which energy conservation technologies, once developed, are put into use. At Synergic Resources Corporation, we have adapted and applied a methodology to forecast the use of conservation...

Lang, K.

1982-01-01

167

River water temperature and fish growth forecasting models

NASA Astrophysics Data System (ADS)

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

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

2010-12-01

168

NASA Technical Reports Server (NTRS)

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

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

1990-01-01

169

Performance of a Southern Ocean sea ice forecast model

NASA Astrophysics Data System (ADS)

The presentation examines the forecast peformance of an oriented fracture sea ice model applied to the Southern Ocean to predict sea ice state up to five days in advance. The model includes a modified Coulombic elastic-viscous-plastic rheology, enthalpy conserving thermodynamics and a new method of parameterising thickness distribution mechanics. 15 ice thickness classes are employed within each grid cell with a horizontal resolution of 50km. The model provides considerable insight into the thickness evolution and climatology of Antarctic sea ice. To date, thickness evolution of the Southern Ocean sea ice zone has mostly been assessed using course two-category models in climate simulations and results presented in this talk provide much greater detail over some existing model output. Simulations are presented from the model driven with NCEP-2 atmospheric analyses, NOAA sea surface temperatures, and mean climatogological currents generated using an eddy resolving ocean model. Analyses are generated by nudging ice concentrations with daily satellite derived open water fractions, and simulations using this method are compared to those without. There are important considerations in assimilating passive microwave ice concentration data into thickness distribution models, and particular attention is given to the treatment of lead ice and the impact this has on estimated total Southern Ocean sea ice volume. It is shown that nudging the model with satellite derived concentrations has an impact on ice mechanics as judged from simulated buoy tracks. A comparison with sonar soundings of sea ice draft is also favourable but shows variation with location. Whilst 5 day forecasts are reasonably skilled, predictive performance changes with season. Application of this research to operational ocean data assimilation systems is discussed in the final stages of the talk.

Heil, P.; Roberts, A.; Budd, W.

2003-12-01

170

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

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

171

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

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

172

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

173

Annual data on the market penetration of music CDs in 12 countries are used to consider two issues in a forecasting comparison of 12 model specifications and three sample sizes. Firstly, particular attention is paid to the impact of stochastic specification of the models on the estimation of the saturation levels and forecasts. Secondly, the issue of model complexity and

Ronald Bewley; William E. Griffiths

2003-01-01

174

Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts

A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models. While most of these studies have documented highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence, traditional ex post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts. Contrary to this contention, the authors show that

Torben G. Andersen; Tim Bollerslev

1998-01-01

175

Nowcasting of precipitation - Advective statistical forecast model (SAM) for the Czech Republic

NASA Astrophysics Data System (ADS)

This article describes a model SAM for nowcasting precipitation in the Czech Republic. The model is based on an advective-statistical algorithm and utilises radar, satellite, lightning and numerical weather prediction (NWP) model prognostic data. The statistical part of the model is complemented by a distribution correction of forecasted values. The model provides forecasts for mean 1-h, 2-h and 3-h precipitation totals in 9 km by 9 km grid cells. The formulation of the forecast serves the needs of operational hydrologic prediction by the Czech Hydrometeorological Institute (CHMI). The accuracy of the forecasts is evaluated with independent data by the root-mean-square error, absolute error, bias, and the critical success index (CSI) categorical skill score after transforming the quantitative forecasts into categorical forecasts. Results are also compared with the existing operational forecasts by the COTREC model calculated at the CHMI. The results of this study confirmed that the most important predictors are derived from radar data and that, in addition to rain rates, the predictors include information about the top of the radar echo. For the 2-h and 3-h precipitation totals, satellite and NWP model predictors were selected, whereas predictors derived from lightning data were not selected. Evaluation of the forecasts showed that the SAM model yielded more accurate predictions than the COTREC model for almost all forecast accuracy statistics considered. The SAM model does not change the horizontal structure of the COTREC forecasts, because both models use almost identical advection fields. However, the SAM is able to model precipitation development and, thus, improve the forecast accuracy.

Sokol, Zbynek; Pesice, Petr

2012-01-01

176

A stochastic HMM-based forecasting model for fuzzy time series.

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast. PMID:20028637

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

177

Comparison of Dst Forecast Models for Intense Geomagnetic Storms

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

178

THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION

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

179

Assessment of point process models for earthquake forecasting Andrew Bray1

Assessment of point process models for earthquake forecasting Andrew Bray1 and Frederic Paik Schoenberg1 1 UCLA Department of Statistics, 8125 Math Sciences Building, Los Angeles, CA 90095-1554 Abstract Models for forecasting earthquakes are currently tested prospectively in well- organized testing centers

Schoenberg, Frederic Paik (Rick)

180

Model bias correction for dust storm forecast using ensemble Kalman filter

Model bias correction for dust storm forecast using ensemble Kalman filter Caiyan Lin,1,2 Jiang Zhu Kalman filter (EnKF) assimilation targeting heavy dust episodes during the period of 15Â24 March 2002. Wang (2008), Model bias correction for dust storm forecast using ensemble Kalman filter, J. Geophys

181

Forecast Model of Civil Aviation Passenger Transportation Volume Based on System Dynamics

In order to resolve the traditional forecasting methods which do not consider the factors affecting the civil aviation passenger transportation volume, this paper sets up system dynamic model to forecast civil aviation passenger transportation volume. This model can consider the main influence factors and set up scientific linking between these factors. It will help us make reasonable and scientific decision.

Jun gai Tian; Hong jun Xu

2011-01-01

182

Estimating Demand for Industrial and Commercial Land Use Given Economic Forecasts

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

183

NASA Astrophysics Data System (ADS)

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

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

184

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

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

185

Computer models used by AFGWC and NMC for weather analysis and forecasting. Technical note

Describes the numerical analysis and forecast models most widely used by U.S. Air Force meteorologists. These models are: the Air Force Global Weather Central (AFGWC) Global Spectral Model (GSM) the AFGWC Real-Time Nephanalysis (RTNEPH); the AFGWC High Resolution Analysis (HIRAS) models; the AFGWC Five-Layer cloud forecast model (5-LAYER); the National Meteorological Center (NMC) Nested Grid Model (NGM); and the NMC Aviation/Medium Range Forecast (AVN/MRF) model. Report also describes model grids and tells how the grids are built. Strengths and weaknesses of the various models are discussed, along with AFGWC and NMC production cycles. Meteorology, Weather, Forecasting, Computers, Supercomputers, Computer programs, Models, Analysis, Computer analysis, Numerical analysis, Cray, Grids, Resolution, Topography, Map projections, More.

Conklin, R.J.

1992-08-01

186

Improving solar radiation forecasts from Eta/CPTEC model using statistical post-processing

NASA Astrophysics Data System (ADS)

Solar radiation forecasts are mainly demanded by the energy sector besides other applications Accurate short-term forecasts of solar energy resources are required for management of co-generation systems and energy dispatch in transmission lines Mesoscale weather forecast models usually have radiation parameterization codes since solar radiation is the main energy source for atmospheric processes The Eta model running operationally in the Brazilian Center of Weather Forecast and Climate Studies CPTEC INPE is a mesoscale model with 40 km horizontal resolution This model has outputs for many meteorological variables including solar radiation incidence on ground These radiation forecasts are nevertheless greatly overestimated As an attempt to improve the forecasts of solar energy resources using Eta model statistical post-processing models or refining models were used Multiple linear regression MLR models were adjusted and artificial neural networks ANN were trained using a statistically selected group of 7 variables predicted by the Eta model not including the Eta solar radiation forecast itself This group of variables expresses the future weather and surface conditions Theoretical solar radiation amount on the top of atmosphere TOA was calculated and used as another input Solar radiation measurements from piranometers Kipp Zonen CM-21 installed on two ground-stations of the SONDA Project were used as the targets to be simulated throughout the adjustment training of the models These measurements were also used

Guarnieri, R. A.; Pereira, E. B.; Chou, S. C.

187

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

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

Bal, Guillaume; Rivot, Etienne; Baglinière, Jean-Luc; White, Jonathan; Prévost, Etienne

2014-01-01

188

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

189

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

190

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

191

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

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.

192

An econometric modeling approach to short-term crude oil price forecasting

In the competitive petroleum markets, oil price forecasting is becoming increasingly relevant to producers and consumers. This paper develops a structural econometric model of the Brent crude spot price using the explanatory variable of defined relative inventory and OPEC production to analyze and forecast short-run oil price. A Hodrick-Prescott filter method presented obtains the relative inventory variables caused by the

Weiqi Li; Linwei Ma; Yaping Dai; Pei Liu

2011-01-01

193

Forecasting solar wind structures and shock arrival times using an ensemble of models

Forecasting the time of arrival at Earth of interplanetary shocks following solar metric type II activity is an important first step in the establishment of an operational space weather prediction system. The quality of the forecasts is of utmost importance. The performances of the shock time of arrival (STOA) and interplanetary shock propagation models (ISPM) were previously evaluated by Smith

C. D. Fry; M. Dryer; Z. Smith; W. Sun; C. S. Deehr; S.-I. Akasofu

2003-01-01

194

Game Theory and Economic Modelling

Over the past two decades, academic economics has undergone a mild revolution in methodology. The language, concepts and techniques of noncooperative game theory have become central to the discipline. This book provides the reader with some basic concepts from noncooperative theory, and then goes on to explore the strengths, weaknesses, and future of the theory as a tool of economic

David M. Kreps

1990-01-01

195

Cone Model for Halo CMEs: Application to Space Weather Forecasting

NASA Technical Reports Server (NTRS)

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

Xie, Hong; Ofman, Leon; Lawrence, Gareth

2004-01-01

196

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

197

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

198

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

199

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

200

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.

201

Fuzzy computing based rainfall-runoff model for real time flood forecasting

NASA Astrophysics Data System (ADS)

This paper analyses the skills of fuzzy computing based rainfall-runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time-steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one-step-ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one-step-ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times.

Nayak, P. C.; Sudheer, K. P.; Ramasastri, K. S.

2005-03-01

202

Predictability limits in an ionospheric model for space weather forecasting

NASA Astrophysics Data System (ADS)

The Electra model for the high-latitude ionosphere is parametrized with polar cap activity and solar wind key parameters. An early version of it, driven with real-time solar wind input from the ACE spacecraft produces regular real-time forecasts of the large-scale activity (see website address below). Prediction outputs include qualitative maps of activity, geomagnetic indices, and local ground magnetic and ionospheric electric fields. Prediction accuracy is determined primarily by the input, and type of model, and additionally by the initial conditions. We present three test cases: a) a Northward BZ interval characterized by low magnetic activity and reverse convection patterns; b) time-dependent enhanced convection, and c) two small-scale (-600 nT) substorm intervals. The model reproduces the large-scale spatial development of convection and magnetospheric substorms as well as the regional indices of geomagnetic activity. For those cases, the choice of the input sequence is much more important than the initial conditions. The local fields, however, are predicted less accurately. In that case the prediction error is additionally a function of local time and type of activity. >http://lep694.gsfc.nasa.gov/RTSM/People/vassi/rt/spatio.html

Klimas, A. J.; Vassiliadis, D.; Weigel, R. S.; Uritsky, V. M.

2001-12-01

203

Forecast of ionospheric disturbances using a high-resolution atmosphere-ionosphere coupled model

NASA Astrophysics Data System (ADS)

Space weather forecasts are about to enter a stage incorporating numerical forecasts based on realistic numerical simulation, in addition to conventional methods used by forecasters to make predictions based on observational data and experience. At the National Institute of Information and Communications Technology (NICT) of Japan, we have developed an atmosphere-ionosphere coupled model, which includes the whole neutral atmosphere and the ionosphere. The model is called GAIA (Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy). The present version has spatial resolution of about 1 degree in horizontal direction. In addition, we are also developing a high-resolution regional ionospheric model, which has a horizontal resolution of about 10 km.We plan to combine GAIA and the regional model to reproduce mesoscale ionospheric phenomena, such as plasma bubbles and SED (storm enhanced density). The model will be a useful tool for space weather forecast. We will report previous results, and a plan for the new model.

Shinagawa, Hiroyuki; Miyoshi, Yasunobu; Fujiwara, Hitoshi; Yokoyama, Tatsuhiro; Jin, Hidekatsu

204

Fuzzy short-term electric load forecasting

Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling, and for system security such as peak load shaving by power interchange with interconnected utilities. In this paper a fuzzy linear regression model for summer and winter seasons is developed. The estimation fuzzy problem for the model is turned out to linear

A. M. Al-Kandari; S. A. Soliman; M. E. El-Hawary

2004-01-01

205

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

206

Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging J. MCLEAN not apply in its original form to precipitation, because the predictive PDF of precipitation is nonnormal BMA is extended to probabilistic quantitative precipitation forecasting. The predictive PDF

Raftery, Adrian

207

A gene-wavelet model for long lead time drought forecasting

NASA Astrophysics Data System (ADS)

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

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

2014-09-01

208

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

209

NASA Astrophysics Data System (ADS)

The Model Output Statistics (MOS) approach is used to develop a procedure for forecasting the occurrence of a local wind regime at Rota, Spain known as the levante. Variables derived solely from surface pressure and 500 mb height forecast fields of the Fleet Numerical Oceanography Center operational model are used as possible predictors of levante occurrence. These variables are screened by a stepwise discriminant analysis program to determine those which best discriminate between the occurrence and non-occurrence of a levante. The variables which are selected are used by the program as predictors in a levante forecast procedure.The forecast procedure is developed on a four-year data set and is then tested on a separate one-year data set. In addition to an independent data test, a ten is conducted to compare the levante forecasts produced by this procedure with the forecasts produced by the procedure used by forecasters at Rota. The MOS procedure shows skill and compares favorably with the Rota forecast procedure.

Godfrey, Robert A.

1982-12-01

210

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

211

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

212

Short-range forecasts with the GISS model of the global atmosphere

NASA Technical Reports Server (NTRS)

Results of tests carried out during the period from December 1972 through January 1973 to determine the short-term forecasting accuracy of a multilevel numerical primitive-equation (PE) model of the global atmosphere developed at the Goddard Institute for Space Studies (GISS). Six 48-hr forecasts were carried out with the aid of this model, using nine vertical levels and a horizontal grid spacing of 4 deg in latitude and 5 deg in longitude for an effective grid point separation averaging slightly more than 400 km. Verification of forecast sea-level pressures, 1000-mb heights, and 500-mb heights, as well as 1000-mb and 500-mb vector geostrophic winds, shows that the model has forecast skill comparable to that of operational PE models. Based on the 36-hr evolution of 18 extratropical cyclones, the model forecasts exhibit a tendency toward underestimating their propagation speeds and overestimating their central pressures. Both deficiencies are attributed to inadequate horizontal grid resolution. Quantitative verification of forecast surface temperatures over the eastern United States shows a forecast skill equal to that achieved by combined dynamical-statistical procedures.

Druyan, L. M.

1974-01-01

213

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

214

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

215

Radiation fog forecasting using a 1-dimensional model

and for troubleshooting purposes was much appreciated. An individual thank you goes to James Lott, John Distefano, and John Center from the National Weather Service Forecast Office, Wilmington, Ohio for their precious aid in converting the sounding data into the proper... measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing...

Peyraud, Lionel

2012-06-07

216

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

217

Forecasting Daytime Seeing Conditions Using a Mesoscale Numerical Weather Predcition Model

NASA Astrophysics Data System (ADS)

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

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

2002-12-01

218

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

219

Sensitivity of hurricane forecasts to cumulus parameterizations in the HWRF model

NASA Astrophysics Data System (ADS)

Developmental Testbed Center used the Hurricane Weather Research and Forecasting (HWRF) system to test the sensitivity of tropical cyclone track and intensity forecasts to different convective schemes. A control configuration that employed the HWRF Simplified Arakawa Scheme (SAS) was compared with the Kain-Fritsch and Tiedtke schemes, as well as with a newer implementation of the SAS. A comprehensive test for Atlantic and Eastern North Pacific storms shows that the SAS scheme produces the best track forecasts. Even though the convective parameterization was absent in the inner 3 km nest, the intensity forecasts are sensitive to the choice of cumulus scheme on the outer grids. The impact of convective-scale heating on the environmental flow accumulates in time since the hurricane vortex is cycled in the HWRF model initialization. This study shows that, for a given forecast, the sensitivity to cumulus parameterization combines the influence of physics and initial conditions.

Biswas, Mrinal K.; Bernardet, Ligia; Dudhia, Jimy

2014-12-01

220

A spatial model to forecast raccoon rabies emergence.

Although raccoons are widely distributed throughout North America, the raccoon rabies virus variant is enzootic only in the eastern United States, based on current surveillance data. This variant of rabies virus is now responsible for >60% of all cases of animal rabies reported in the United States each year. Ongoing national efforts via an oral rabies vaccination (ORV) program are aimed at preventing the spread of raccoon rabies. However, from an epidemiologic perspective, the relative susceptibility of naïve geographic localities, adjacent to defined enzootic areas, to support an outbreak, is unknown. In the current study, we tested the ability of a spatial risk model to forecast raccoon rabies spread in presumably rabies-free and enzootic areas. Demographic, environmental, and geographical features of three adjacent states (Ohio, West Virginia, and Pennsylvania), which include distinct raccoon rabies free, as well as enzootic areas, were modeled by using a Poisson Regression Model, which had been developed from previous studies of enzootic raccoon rabies in New York State. We estimated susceptibility to raccoon rabies emergence at the census tract level and compared the results with historical surveillance data. Approximately 70% of the disease-free region had moderate to very high susceptibility, compared with 23% in the enzootic region. Areas of high susceptibility for raccoon rabies lie west of current ORV intervention areas, especially in southern Ohio and western West Virginia. Predicted high susceptibility areas matched historical surveillance data. We discuss model implications to the spatial dynamics and spread of raccoon rabies, and its application for designing more efficient disease control interventions. PMID:21995266

Recuenco, Sergio; Blanton, Jesse D; Rupprecht, Charles E

2012-02-01

221

NASA Astrophysics Data System (ADS)

Monthly streamflow forecasts with long lead time are being sought by water managers in Australia. In this study, we take a first step towards a monthly streamflow modelling approach by harnessing a coupled ocean-atmosphere general circulation model (CGCM) to produce monthly rainfall forecasts for three catchments across Australia. Bayesian methodologies are employed to produce forecasts based on CGCM raw rainfall forecasts and also CGCM sea surface temperature forecasts. The Schaake Shuffle is used to connect forecast ensemble members of individual months to form ensemble monthly time series forecasts. Monthly forecasts and three-monthly forecasts of rainfall are assessed for lead times of 0-6 months, based on leave-one-year-out cross-validation for 1980-2010. The approach is shown to produce well-calibrated ensemble forecasts that source skill from both the atmospheric and ocean modules of the CGCM. Although skill is generally low, moderate skill scores are observed in some catchments for lead times of up to 6 months. In months and catchments where there is limited skill, the forecasts revert to climatology. Thus the forecasts developed can be considered suitable for continuously forecasting time series of streamflow to long lead times, when coupled with a suitable monthly hydrological model.

Schepen, Andrew; Wang, Q. J.

2014-11-01

222

HTGR Application Economic Model Users' Manual

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

223

The Match That Can Ignite the Economy Economic forecasters have stared into their crystal balls technology (IT), spending. Consumer spending kept the recession on the positive side of zero growth, making and the University of Michigan Survey Research Center recently announced improvements in consumer confidence. But

Ahmad, Sajjad

224

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

225

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

NASA Astrophysics Data System (ADS)

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

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

2013-07-01

226

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

NASA Astrophysics Data System (ADS)

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

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

2013-07-01

227

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

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

228

Using multi-layer models to forecast gas flow rates in tight gas reservoirs

The petroleum industry commonly uses single-layer models to characterize and forecast long-term production in tight gas reservoir systems. However, most tight gas reservoirs are layered systems where the permeability and porosity of each layer can...

Jerez Vera, Sergio Armando

2007-04-25

229

Reverse supply chain forecasting and decision modeling for improved inventory management

This thesis details research performed during a six-month engagement with Verizon Wireless (VzW) in the latter half of 2012. The key outcomes are a forecasting model and decision-support framework to improve management of ...

Petersen, Brian J. (Brian Jude)

2013-01-01

230

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

231

Forecast-skill-based simulation of streamflow forecasts

NASA Astrophysics Data System (ADS)

Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.

Zhao, Tongtiegang; Zhao, Jianshi

2014-09-01

232

The impact of forecasting model selection on the value of information sharing in a supply chain

Abstract This paper presents a study on the impact,of forecasting model,selection on the value of information,sharing in a supply chain with one capacitated supplier and multiple retailers. Using a computer simulation model, this study ex- amines demand forecasting and inventory replenishment decisions by the retailers, and production decisions by the supplier under,different demand,patterns and,capacity tightness. Analyses of the simulation,output,indicate that

Xiande Zhao; Jinxing Xie; Janny Leung

2002-01-01

233

Combining forecast weights: Why and how?

NASA Astrophysics Data System (ADS)

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

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

2012-09-01

234

In November 2007, the Federal Open Market Committee (FOMC) announced a change in the way it communicates its view of the economic outlook: It increased the frequency of its forecasts from two to four times per year, and it increased the length of the forecasting horizon from two to three years. The FOMC does not release the individual members' forecasts

William T. Gavin; Geetanjali Pande

2008-01-01

235

a river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month

C. A. S. Coelho; D. B. Stephenson; F. J. Doblas-Reyes; M. Balmaseda; A. Guetter; G. J. van Oldenborgh

2006-01-01

236

Heterogeneous Agent Models in Economics and Finance

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

237

ECONOMIC MODELING OF ELECTRIC POWER SECTOR

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

238

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

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

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

2014-01-01

239

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

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

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

2014-01-01

240

HTGR Application Economic Model Users' Manual

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

A. M. Gandrik

2012-01-01

241

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

242

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

NASA Astrophysics Data System (ADS)

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

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

2014-06-01

243

The Use of Generalized Additive Models for Forecasting the Abundance of Queets River Coho Salmon

We examined three types of models for preseason forecasting of the abundance of Queets River coho salmon Oncorhynchus kisutch: (1) a simple model in which estimates of smolt production are multiplied by projected marine survival rates, (2) a Ricker spawner–recruitment model, and (3) a regression model relating log-transformed adult recruitment to smolt production. Each type of model was formulated with

Shizhen Wang; Gary Morishima; Rishi Sharma; Larry Gilbertson

2009-01-01

244

A Forecasting Model Incorporating Replacement Purchase: Mobile Handsets in South Korea's Market

The paper introduces a replacement forecasting model that operates at the brand level and overcomes limitations of existing models. The model (1) consists of a diffusion model and a time series model; (2) separately identifies the diffusion of first-time purchases and that of replacement purchases; (3) reflects brands¡¯ competitive factors affecting product diffusion; and (4) characterizes consumers¡¯ different replacement cycles.The

Jongsu Lee; Chul-Yong Lee

2009-01-01

245

Empirical research to date on the relative effectiveness of Economic Value Added (EVA) and earnings per share (EPS) as measures of firm performance for stock valuation has been mixed. In contrast to prior research, which primarily focuses on the correspondence of these measures with shareholder value and changes therein, we examine their relative effectiveness in predicting future earnings and their

Susan M. Machuga; Ray J Pfeiffer Jr; Kiran Verma

2002-01-01

246

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

247

Validation and Implementation of Neutral Density Models for Space Weather Forecast Laboratory

NASA Astrophysics Data System (ADS)

Efforts are underway to assess and validate key empirical and physics-based models for neutral density specification for the thermosphere. One empirical model will be incorporated into the Space Weather Forecast Laboratory (SWFL) which is a test bed for a future operational capability at Air Force Weather Agency. An important step is to validate available empirical models to assess their nowcasting and forecasting potential. The baseline or reference model against which other models are compared is the Jacchia-Bowman 2008 (JB2008) model. We examine this model as well as the JB2006, NRL MSIS-2000 and J70 models against available neutral density data over the latest solar cycle, including daily drag, HASDM, and CHAMP/GRACE accelerometer measurements. A one-day and two-day forecast for JB08 using predicted solar proxies is compared to subsequent global HASDM neutral densities.

Wise, J. O.; Lin, C. S.; Tanyi, K. L.; Marcos, F. A.; Huang, C. Y.; Delay, S. H.

2009-12-01

248

Modelling wildlife rabies: Transmission, economics, and conservation

Rabies is a fatal zoonotic disease of mammals; it exacerbates the uncertainty of conserving populations of some threatened mammals (TM). Modelling affords an inexpensive, a priori way of studying key parameters of wildlife rabies transmission, rabies management economics, and TM conservation. Numerous models of rabies transmission have been published. Linear density dependent models predicted that a threshold density (KT?1.0), possibly

Ray T. Sterner; Graham C. Smith

2006-01-01

249

NASA Astrophysics Data System (ADS)

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

250

Forecast of Hourly Average Wind Speed Using ARMA Model with Discrete Probability Transformation

\\u000a In this paper the methodology for wind speed forecasting with ARMA model is revised. The transformation, standardization,\\u000a estimation and diagnostic checking processes are analyzed and a discrete probability transformation is introduced. Using time\\u000a series historical data of three weather stations of the Royal Netherlands Meteorological Institute, the forecasting accuracy\\u000a is evaluated for prediction intervals between 1 and 10 hours ahead

Juan M. Lujano-Rojas; José L. Bernal-Agustín; Rodolfo Dufo-López; José A. Domínguez-Navarro

251

Forecasting the monthly volume of orders for southern pine lumber - an econometric model

forecasting equations of the de- mand for Douglas-fir and ponderosa pine lumber were also derived using the regression analysis. Holland carried out two other related studies. The first was concerned with analyzing changes in lumber price and consump...FORECASTING THE MONTHLY VOLUME OF ORDERS FOR SOUTHERN PINE LUMBER - AH ECONOMETRIC MODEL A Thesis by BEN DOUGLAS JACKSON Submitted to the Graduate College of Texas ASM University in Partial fulfillment of the requirement for the degree...

Jackson, Ben Douglas

2012-06-07

252

NASA Astrophysics Data System (ADS)

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

MacNeice, P. J.; Taktakishvili, A.; Jackson, B. V.; Clover, J. M.; Bisi, M. M.; Odstrcil, D.

2011-12-01

253

Alaska North Slope regional gas hydrate production modeling forecasts

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

254

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

255

The Value of a Probability Forecast from Portfolio Theory

A probability forecast scored ex post using a probability scoring rule (e.g. Brier) is analogous to a risky financial security. With only superficial adaptation,\\u000a the same economic logic by which securities are valued ex ante – in particular, portfolio theory and the capital asset pricing model (CAPM) – applies to the valuation of probability forecasts.\\u000a Each available forecast of a

D. J. Johnstone

2007-01-01

256

NASA Astrophysics Data System (ADS)

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

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

2015-01-01

257

A Multiproduct Network Economic Model of Cybercrime Financial Services

A Multiproduct Network Economic Model of Cybercrime in Financial Services Anna Nagurney Department, Massachusetts 01003 September 2014 Abstract: In this paper, we propose a network economic model of cybercrime sensitive data. Keywords: cybercrime, network economics, financial services, cybersecurity 1 #12

Nagurney, Anna

258

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

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

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

259

Probabilistic Quantitative Precipitation Forecasting over East China using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

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

Yang, Ai; Yuan, Huiling

2014-05-01

260

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

261

An empirical model to forecast solar wind velocity through statistical modeling

NASA Astrophysics Data System (ADS)

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

Gao, Y.; Ridley, A. J.

2013-12-01

262

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

263

Summary In this study, the Lagrangian tracer transport model FLEXPART is shown to be a useful forecasting tool for the flight planning during aircraft measurement campaigns. The advantages of this model are that it requires only a short computation time, has a finer spatial resolution and does not suffer numerical diffusion compared to chemistry transport models (CTMs). It is a

Caroline Forster; A. Stohl; H. Huntrieser; O. Cooper; S. Eckhardt; P. James; J. Heland; H. Schlager; F. Arnold; H. Aufmhoff; N. Spichtinger; E. Dunlea; D. K. Nicks; J. S. Holloway; G. Hübler; D. D. Parrish; T. Ryerson

264

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

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

265

Using Artificial Intelligence Shahab D. Mohaghegh, Intelligent Solutions, Inc. & West Virginia of artificial intelligence and data mining as a workflow to build a full field reservoir model for forecasting-Down, Intelligent Reservoir Modeling (Top-Down Modeling - TDM - or short) as it is applied to shale formations

Mohaghegh, Shahab

266

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

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

267

NASA Astrophysics Data System (ADS)

Relationships exist between radiation belt electron flux intensities and solar drivers such as solar wind speed, ion density, and magnetic fields. The particulars of these relationships, however, are not well understood. Many forecasting models have been developed in the last 25 years, attempting to make sense of these relationships and produce accurate forecasts for electron flux intensities. We discuss some of the inherent limitations that many forecasting models (e.g., static models) possess when trying to untangle the intricate and dynamic relationships between electron flux levels and solar wind drivers. Dynamics related to the solar cycle limit physical interpretations for static forecasting models to customized and narrow time windows. Furthermore, the interrelatedness of solar drivers severely limit the ability to uniquely partition and describe the relationship between any one solar driver with electron flux levels. We suggest an alternate approach using dynamic linear models (DLMs). DLMs avoid some of the inherent limitations of physical understanding static models possess. We compare the 1 day ahead forecast accuracy of a relatively simple DLM to the current NOAA relativistic electron forecast model (REFM). The REFM does produce a more favorable prediction efficiency averaged across years when compared to the relatively simple DLM (0.749 to 0.721). However, the competitiveness of the DLM suggests that further development may lead to more accurate and interpretable models in the future.

Osthus, D.; Caragea, P. C.; Higdon, D.; Morley, S. K.; Reeves, G. D.; Weaver, B. P.

2014-06-01

268

Modeling and Forecasting Livestock Feed Resources in India Using Climate Variables

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

269

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

270

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

271

NASA Astrophysics Data System (ADS)

Recent versions of the Polar Weather Research and Forecasting model are evaluated over the Antarctic to assess the impact of model improvements, resolution, large-scale circulation variability, and uncertainty in initial and lateral boundary conditions. The model skill differs more between forecasts using different sources of lateral boundary data than between forecasts from different model versions or simulated years. Using the ERA-Interim reanalysis for initial and lateral boundary conditions produces the best skill. The forecasts have a cold summer and a warm winter bias in 2 m air temperatures, with similar but smaller bias in dew point temperatures. Upper air temperature biases are small and remain less than 1 °C except at the tropopause in summer. Geopotential height biases increase with height in both seasons. Deficient downward longwave radiation in all seasons and an under representation of clouds enhance radiative loss, leading to the cold summer bias. Excess summer surface incident shortwave radiation plays a secondary role, because 80% of it is reflected, leading to greater skill for clear compared with cloudy skies. The positive wind speed bias produces a warm surface bias in winter resulting from anomalously large downward flux of sensible heat toward the surface. Low temperatures on the continent limit sublimation and hence the precipitable water amounts over the ice sheet. ERA-Interim experiments with higher precipitable water showed reduced biases in downwelling shortwave and longwave radiation. Increasing horizontal resolution from 60 to 15 km improves the skill of surface wind forecasts.

Bromwich, David H.; Otieno, Francis O.; Hines, Keith M.; Manning, Kevin W.; Shilo, Elad

2013-01-01

272

NASA Astrophysics Data System (ADS)

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

Cobourn, W. Geoffrey

2010-08-01

273

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

274

Atmospheric and seeing forecast: WRF model validation with in situ measurements at ORM

NASA Astrophysics Data System (ADS)

We present a comparison between in situ measurements and forecasted data at the Observatorio del Roque de Los Muchachos. Forecasting is obtained with the Weather Research and Forecasting (WRF) model associated with a turbulence parametrization which follows Trinquet-Vernin model. The purpose of this study is to validate the capability of the WRF model to forecast the atmospheric and optical conditions (seeing and related adaptive optics parameters). The final aim is to provide a tool to optimize the observing time in the observatories, the so-called flexible scheduling. More than 4500 h of simulations above Observatorio del Roque de Los Muchachos (ORM) site with WRF in 2009 were calculated, and compared with data acquired during 2009 with Automatic Weather Station, Differential Image Motion Monitor and Multiple Aperture Scintillation Sensor. Each simulation corresponds to a 24h in advance forecasting with one predicted value each hour. Comparison shows that WRF forecasting agrees well with the effective meteorological parameters at ground level, such as pressure (within a scatter ?P = 1.1 hPa), temperature (?T = 2 K), wind speed (?|V| = 3.9 m s-1) and relative humidity (? _{R_h}=18.9 per cent). Median precipitable water vapour content above the ORM predicted by WRF in 2009 is 3 mm, close to 3.8 mm reported in the literature over the period 2001-2008. For what concern optical parameters (seeing, coherence time, isoplanatic angle), WRF forecasting are in good agreement on nightly or monthly basis, better than random or carbon-copy tries. We hope to improve these results with a better vertical and horizontal grid resolution. Our method is robust enough to be applied to potential astronomical sites, where no instruments are available.

Giordano, C.; Vernin, J.; Vázquez Ramió, H.; Muñoz-Tuñón, C.; Varela, A. M.; Trinquet, H.

2013-04-01

275

NASA Astrophysics Data System (ADS)

Ozone forecast models using nonlinear regression (NLR) have been successfully applied to daily ozone forecast for seven metro areas in Kentucky, including Ashland, Bowling Green, Covington, Lexington, Louisville, Owensboro, and Paducah. In this study, the updated 2005 NLR ozone forecast models for these metro areas were evaluated on both the calibration data sets and independent data sets. These NLR ozone forecast models explained at least 72% of the variance of the daily peak ozone. Using the models to predict the ozone concentrations during the 2005 ozone season, the metro area mean absolute errors (MAEs) of the model hindcasts ranged from 5.90 ppb to 7.20 ppb. For the model raw forecasts, the metro area MAEs ranged from 7.90 ppb to 9.80 ppb. Based on previously developed NLR ozone forecast models for those areas, Takagi-Sugeno fuzzy system models were developed for the seven metro areas. The fuzzy "c-means" clustering technique coupled with an optimal output predefuzzification approach (least square method) was used to train the Takagi-Sugeno fuzzy system. Two types of fuzzy models, basic fuzzy and NLR-fuzzy system models, were developed. The basic fuzzy and NLR-fuzzy models exhibited essentially equivalent performance to the existing NLR models on 2004 ozone season hindcasts and forecasts. Both types of fuzzy models had, on average, slightly lower metro area averaged MAEs than the NLR models. Among the seven Kentucky metro areas Ashland, Covington, and Louisville are currently designated nonattainment areas for both ground level O 3 and PM2.5. In this study, summer PM2.5 forecast models were developed for providing daily average PM2.5 forecasts for the seven metro areas. The performance of the PM2.5 forecast models was generally not as good as that of the ozone forecast models. For the summer 2004 model hindcasts, the metro-area average MAE was 5.33 mug/m 3. Exploratory research was conducted to find the relationship between the winter PM2.5 concentrations and the meteorological parameters and other derived prediction parameters. Winter PM2.5 forecast models were developed for seven selected metro areas in Kentucky. For the model fits, the MAE for the seven forecast models ranged from 3.23 mug/m3 to 4.61 mug/m3 (˜26--28% NMAE). The fuzzy technique was also applied on PM2.5 forecast models to seek more accurate PM2.5 prediction. The NLR-fuzzy PM2.5 had slightly better performance than the NLR models.

Lin, Yiqiu

2007-12-01

276

From recent past, the computational Grid based flood assessment and forecasting models is getting emerged as an interdisciplinary integrated `near real time\\/real time model'. Many such, Grid based flood assessment and forecasting model supports in logically integrating various components of flood related scientific simulations such as Metrological, Hydrological, Hydraulic, RADAR, LIDAR Remote Sensing, GIS, Satellite Communication and other technologies and

M. Manavalan; S. Chattopadhyay; M. Mangala; Y. S. Rao

2010-01-01

277

NASA Astrophysics Data System (ADS)

Skill in model-based hydrologic forecasting depends on the ability to estimate a watershed's initial moisture and energy conditions, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We investigate these influences in a series of predictability experiments using calibrated hydrologic simulation models for a 630-watershed dataset that spans the continental US (CONUS), and using the current major simulation models of National Weather Service streamflow forecasting operations. Earlier work in this area (Wood and Lettenmaier, GRL 2008) outlined an ensemble-based strategy for attributing streamflow forecast uncertainty between two endpoints representing zero information about future forcings (ie, the NWS ensemble streamflow prediction, or ESP approach) versus zero information about initial conditions (termed ';reverse-ESP'). This study adopts a more comprehensive approach to characterize the effects of varying levels of uncertainty, from zero knowledge to perfect information in the model world, on streamflow prediction uncertainty. Ensemble hindcasts reflecting varying levels of uncertainty are initialized on a monthly basis for the basins' periods of record, creating background sensitivity information that helps to decompose total hydrologic prediction error into the three components identified above. Observed streamflow prediction errors are then coupled with estimates of realistic uncertainties in future forcing and with model simulation error to infer initial condition errors. This presentation reports findings from the predictability experiments, summarizing the relative importance of uncertainties in basin initial conditions and weather and climate forecasts, and their dependence on forecast lead time, initiation date and regional hydroclimate characteristics.

Wood, A. W.; Hopson, T. M.; Newman, A. J.; Sampson, K. M.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Clark, M. P.

2013-12-01

278

NASA Astrophysics Data System (ADS)

Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.

Gan, Chuen-Meei

279

FLEXIBLE PROCESS-BASED HYDROLOGICAL MODELLING FRAMEWORK FOR FLOOD FORECASTING – MIKE SHE

Abstract New developments ,of grid-based hydrological modelling ,have been spurred by increasing access to meteorological modelling, radar and satellite remote sensing. However, state of the art operational hydrological forecasting models ,are usually sub-catchment-based conceptual orempirical models, using to a greater or lesser degree the physics of rainfall-runoff processes. By contrast, state-of-the-art hydrological modelling is represented by fully distributed physically-based modelling

W. Szalinska

280

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.

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

2014-04-14

281

Evaluation of DNI forecast based on the WRF mesoscale atmospheric model for CPV applications

NASA Astrophysics Data System (ADS)

The integration of large-scale solar electricity production into the energy supply structures depends es-sentially on the precise advance knowledge of the available resource. Numerical weather prediction (NWP) models provide a reliable and comprehensive tool for short-and medium-range solar radiation forecasts. The methodology followed here is based on the WRF model. For CPV systems the primary energy source is the direct normal irradi-ance (DNI), which is dramatically affected by the presence of clouds. Therefore, the reliability of DNI forecasts is directly related to the accuracy of cloud information. Two aspects of this issue are discussed here: (i) the effect of the model's horizontal spatial resolution; and (ii) the effect of the spatial aggregation of the predicted irradiance. Results show that there is no improvement in DNI forecast skill at high spatial resolutions, except under clear-sky conditions. Furthermore, the spatial averaging of the predicted irradiance noticeably reduces their initial error.

Lara-Fanego, V.; Ruiz-Arias, J. A.; Pozo-Vázquez, A. D.; Gueymard, C. A.; Tovar-Pescador, J.

2012-10-01

282

Assimilation of AIRS radiances for short term regional forecasts using community models

NASA Astrophysics Data System (ADS)

With the hyperspectral sounder's capability of providing information about temperature and humidity of the atmosphere at increased vertical resolution, the assimilation of these radiances has proven to improve numerical weather prediction in global models. The current two hyperspectral infrared sounders in orbit, AIRS and IASI, each contributed to a 12% error reduction in the ECMWF global forecasts, emerging as the single space-borne sensor to contribute the largest forecast improvement in global models (Cardinali, 2009). In this study, regional assimilation of clear sky AIRS radiances was carried out using a community available data assimilation system GSI coupled with the WRF forecast model. As the systems used were not optimized, tuning was necessary prior to carrying out the assimilation. Components of the assimilation system that required tuning included the background error covariance matrix, the satellite radiance bias correction and quality control procedures for AIRS radiances. In addition, the forecast model vertical resolution had been increased with more levels included in the stratosphere. Adopting procedures used by NCEP's operational regional data assimilation, experiments with and without AIRS radiances were carried out for a period of 16 days to access the impact of including AIRS radiances. Diagnostics from the assimilation system showed that analyses had larger temperature biases for experiments ending at 06 and 18 UTC. In addition, biases were still significant after assimilation for satellite channels that were sensitive to surface properties and water vapor. Forecasts were verified with a wide range of datasets ranging from model analyses, radiosondes, observed satellite radiances and 24 hour accumulated precipitation. With assimilation of clear sky AIRS radiances, largest improvement in bias was observed when forecasts were verified with radiosondes and satellite observations. The 00 and 12 UTC forecast were typically of better quality than the 06 and 18 UTC forecasts possibly due to the amount of AIRS data available for each assimilation cycle. Precipitation skill scores varied little with AIRS radiance assimilation except 18 UTC, due to biased analyses. Overall, the impact on forecast was neutral with the assimilation of AIRS clear sky radiances.

Lim, Agnes Huei Ni

283

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

284

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

285

NASA Astrophysics Data System (ADS)

Model forecast applications use various models of tsunami sources inferred from different measurement data. Even the same type of observation data can produce substantially different tsunami source models during a real-time forecast when more data are obtained during the real-time analysis. Improved tsunami observations enable investigation of the influence of such model source variability on the final forecast using different source data sets of several events. The 2010 Maule, Chile and 2011 Tohoku, Japan tsunamis were two recent events that provide ample observations throughout the Pacific and were, thus, used here to study the sensitivity of different model inputs for forecasting. The sources for these events were derived using the following three different methods: (1) real time or post event inversion of tsunameter water level data; (2) prediction of sea floor deformations via analysis of seismic wave forms and application of a finite fault model; and (3) prediction of sea floor deformation using real-time GPS data. For the March 11, 2011 Tohoku tsunami, two examples of each method are used, while for the February 27, 2010 Maule event, only one tsunameter inversion and one finite fault model method were used due to a much more limited data set. Observed data from the Deep-ocean Assessment and Reporting for Tsunamis (DART) network, Japan GPS buoys, and select tide gauges across the Pacific were compared with forecasts to assess the sensitivity of these three methods using root-mean-square error analysis. We divided the analysis by the type of data and the distance from the source. This sensitivity analysis showed that increasing the resolution of a tsunami source model does not necessarily improve tsunami forecast quality, even in the near-field. Instead, the findings suggest that when forecasting coastal impact, defining the overall energy characteristic of a tsunami source may be more important than refining small source details. Source models based on direct tsunami observations are better at reproducing a tsunami signal: this finding is not very surprising but has implications for tsunami forecasting and warning operations.

Gica, Edison; Titov, Vasily V.; Moore, Christopher; Wei, Yong

2014-11-01

286

Global and multi-scale features of solar wind-magnetosphere coupling: From modeling to forecasting

Global and multi-scale features of solar wind-magnetosphere coupling: From modeling to forecasting is a spatially extended nonlinear system driven far from equilibrium by the turbulent solar wind. During issue. This paper presents a data-derived model of the solar wind-magnetosphere coupling that combines

Sitnov, Mikhail I.

287

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

J Syst Sci Complex (2012) 25: 641Â674 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

288

AN EVALUATION OF HIGH-RESOLUTION MODELING AND STATISTICAL FORECAST TECHNIQUES OVER COMPLEX TERRAIN

ABSTRACT The accuracy ,of weather ,forecasts produced ,during the 2002 Olympic ,and Paralympic Games (23 Jan – 25 Mar 2002) by a multiply ,nested version of the ,PSU- NCAR Mesoscale Model (MM5) and associated model output statistics (MOS) system is evaluated using observations collected by the MesoWest cooperative network. Using traditional verification measures, the accuracy of MM5 wind and precipitation

Kenneth Alan Hart

289

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

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

290

Application of wavelet-based multiple linear regression model to rainfall forecasting in Australia

NASA Astrophysics Data System (ADS)

In this study, a wavelet-based multiple linear regression model is applied to forecast monthly rainfall in Australia by using monthly historical rainfall data and climate indices as inputs. The wavelet-based model is constructed by incorporating the multi-resolution analysis (MRA) with the discrete wavelet transform and multiple linear regression (MLR) model. The standardized monthly rainfall anomaly and large-scale climate index time series are decomposed using MRA into a certain number of component subseries at different temporal scales. The hierarchical lag relationship between the rainfall anomaly and each potential predictor is identified by cross correlation analysis with a lag time of at least one month at different temporal scales. The components of predictor variables with known lag times are then screened with a stepwise linear regression algorithm to be selectively included into the final forecast model. The MRA-based rainfall forecasting method is examined with 255 stations over Australia, and compared to the traditional multiple linear regression model based on the original time series. The models are trained with data from the 1959-1995 period and then tested in the 1996-2008 period for each station. The performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and correlation coefficient. The results show that the wavelet-based regression model provides considerably more accurate monthly rainfall forecasts for all of the selected stations over Australia than the traditional regression model.

He, X.; Guan, H.; Zhang, X.; Simmons, C.

2013-12-01

291

An Aggregate Air Traffic Forecasting Model subject to Stochastic Christabelle S. Bosson

. There have also been several algorithms developed for air traffic control. In 1993, Delahaye and OdoniAn Aggregate Air Traffic Forecasting Model subject to Stochastic Inputs Christabelle S. Bosson-2045 This paper introduces an aggregate air traffic model that calculates the number of aircraft in each Air Route

Sun, Dengfeng

292

Meteorological time series forecasting based on MLP modelling using heterogeneous transfer

Meteorological time series forecasting based on MLP modelling using heterogeneous transfer to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We show that this methodology can improve the accuracy of meteorological data estimation compared

Paris-Sud XI, UniversitÃ© de

293

On the models of peak load forecast uncertainty in probabilistic production costing algorithms

This paper presents a model and related methodology for the problem of reliability calculation and production costing simulation with peak load forecast uncertainty. The model uses conditional load duration curves, each of them represents a realization of the random peak load. The conditional load duration curves are arbitrary but identical over the interval of the base load domain, and linear

J. Hoffer; M. Prill

1996-01-01

294

A Student-Tracking and Near-Future Student Enrollment-Forecasting Model.

ERIC Educational Resources Information Center

Describes the development of two enrollment management tools: (1) a student-tracking model designed to monitor and report on student transitions through the educational experience; and (2) a near-term student enrollment-forecasting model. Follows a fictional class for several years to illustrate the tools. (EV)

Glynn, Joseph G.; Miller, Thomas E.

2003-01-01

295

Compumetric forecasting of crude oil prices

This paper contains short term monthly forecasts of crude oil prices using compumetric methods. Compumetric forecasting methods are ones that use computers to identify the underlying model that produces the forecast. Typically, forecasting models are designed or specified by humans rather than machines. Compumetric methods are applied to determine whether models they provide produce reliable forecasts. Forecasts produced by two

M. A. Kaboudan

2001-01-01

296

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

297

Plays Using Artificial Intelligence & Data Mining S. Esmaili, A. Kalantari-Dahaghi, SPE,West Virginia University, S.D. Mohaghegh, SPE, Intelligent Solution, Inc. & West Virginia University Copyright 2012 the modeling efforts more challenging. In this paper, the application of a recently developed AI (Artificial

Mohaghegh, Shahab

298

Kinetic Exchange Models in Economics and Sociology

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

299

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

300

Characteristics of surface cyclone forecasts in the Aviation Run of the Global Spectral Model

Results are presented of an evaluation of the performance of the Aviation Run (AVN) of the NMC Global Spectral Model (GSM) in predicting surface cyclones, which was conducted during the autumn of 1990 through the winter of 1992. The results indicated that the finer-resolution T126 GSM produces stronger and deeper cyclones than the old T80 GSM. The errors in AVN position forecasts of surface cyclones were smaller than those found in the NMC Nested Grid Model (NGM). The geographical distribution of the pressure errors were similar to those found in the NGM over eastern North America and the adjacent western Atlantic Ocean. The AVN tended to underpredict the 1000-500-mb thickness over surface cyclones, especially during the first 36 h of the forecast cycle. The T126 AVN forecasts are accurate enough to provide guidance for basic weather forecasts to three days, as has been done for the two-day forecasts for the past 25-30 yr. 19 refs.

Grumm, R.H. (NOAA, Meteorological Operations Div., Camp Springs, MD (United States))

1993-03-01

301

Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

NASA Astrophysics Data System (ADS)

Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network namely; Multi-Layer Peceptron Neural network (MLP-NN), Radial Basis Function Neural Network (RBFNN) and Input Delay Neural Network (IDNN), respectively, have been examined in this study. Those models had been developed for two time horizon in monthly and weekly rainfall basis forecasting at Klang River, Malaysia. Data collected over 12 yr (1997-2008) on weekly basis and 22 yr (1987-2008) for monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural network. Results showed that MLP-NN neural network model able to follow the similar trend of the actual rainfall, yet it still relatively poor. RBFNN model achieved better accuracy over the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model outperformed during training and testing stage which prove a consistent level of accuracy with seen and unseen data. Furthermore, the IDNN significantly enhance the forecasting accuracy if compared with the other static neural network model as they could memorize the sequential or time varying patterns.

El-Shafie, A.; Noureldin, A.; Taha, M. R.; Hussain, A.

2011-07-01

302

NASA Astrophysics Data System (ADS)

The lead time dependent climates of the ECMWF weather prediction model, initialized with ERA-40 reanalysis, are analysed using 44 years of day-1 to day-10 forecasts of the northern hemispheric 500-hPa geopotential height fields. The study addresses the question whether short-term tendencies have an impact on long-term trends. Comparing climate trends of ERA-40 with those of the forecasts, it seems that the forecast model rapidly loses the memory of initial conditions creating its own climate. All forecast trends show a high degree of consistency. Comparison results suggest that: (i) Only centers characterized by an upward trend are statistical significant when increasing the lead time. (ii) In midilatitudes an upward trend larger than the one observed in the reanalysis characterizes the forecasts, while in the tropics there is a good agreement. (iii) The downward trend in reanalysis at high latitudes characterizes also the day-1 forecast which, however, increasing lead time approaches zero.

Bordi, I.; Fraedrich, K.; Sutera, A.

2010-06-01

303

The implementation of new flood forecasting systems for the Ukrainian part of the Tisza basin has started last years by the customisation of Mike-11 model for the Uzh River and Latoritsa River (part of the Bodrog Catchment) in the frame of the joint project with the 'DHI Water&Environment'. The calibration and testing of the lumped parameter model NAM was provided

S. Belov; G. Donchytz; S. Kivva; A. Kuschan; M. Zheleznyak

2003-01-01

304

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

305

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

306

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

307

This paper presents a study on the impact of forecasting model selection on the value of information sharing in a supply chain with one capacitated supplier and multiple retailers. Using a computer simulation model, this study ex- amines demand forecasting and inventory replenishment decisions by the retailers, and production decisions by the supplier under different demand patterns and capacity tightness.

Xiande Zhao; Jinxing Xie; Janny Leung

308

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

309

Intercomparison of mesoscale meteorological models for precipitation forecasting 799 Hydrology and Earth System Sciences, 7(6), 799811 (2003) Â© EGU Intercomparison of mesoscale meteorological models, a series of past heavy precipitation events has been simulated with different meteorological models

Boyer, Edmond

310

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models. PMID:23924415

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

311

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

This paper develops a methodology for integrating a land-use forecasting model with an event scale, rainfall-runoff model in support of improving land-use policy formulation at the watershed scale. The models selected for integration are loosely coupled, structured upon a common GIS platform that facilitates data exchange. The hydrologic model HEC-HMS is calibrated for a specific storm event that occurred within

Chris McColl; Graeme Aggett

2007-01-01

312

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...

313

be classified into two types: dynamical downscaling and statistical downscaling. The method of dynamical down, the statistical downscaling method tends to be more straightforward than dynamical downscaling. And it can alsoA Statistical Downscaling Model for Forecasting Summer Rainfall in China from DEMETER Hindcast

314

to assess the impact of model improvements, resolution, large-scale circulation variability, and uncertainty-equipped LC-130 Hercules aircraft flown by the New York Air National Guard completed 359 missions while the 62 [Hannon, 2012]. In addition to transport logistics, accurate forecasts are also needed for safety

Howat, Ian M.

315

Assimilation and Modeling of the Atmospheric Hydrological Cycle in the ECMWF Forecasting System

Several new types of satellite instrument will provide improved measurements of Earth's hydrological cycle and the humidity of the atmosphere. In an effort to make the best possible use of these data, the modeling and assimilation of humidity, clouds, and precipitation are currently the subjects of a comprehensive research program at the European Centre for Medium-Range Weather Forecasts (ECMWF). Impacts

Erik Andersson; Peter Bauer; Anton Beljaars; Frederic Chevallier; Elías Hólm; Marta Janisková; Per Kållberg; Graeme Kelly; Philippe Lopez; Anthony McNally; Emmanuel Moreau; Adrian J. Simmons; Jean-Noël Thépaut; Adrian M. Tompkins

2005-01-01

316

A distributed hydrological model for drought and flood forecast in the upper Yangtze River basin

The Yangzte River (also called Changjiang in Chinese) is the largest river basin in China, which has frequent flood and drought. Building on the physically-based description of hydrological processes, a distributed model has been established in the upper Yangtze River for drought and flood forecast have been addressed in this study. For assessing water resources and drought, a large scale

X. Jijun; Y. Dawen; L. Zhidong; H. Wei

2007-01-01

317

Forecasting model for crude oil prices based on artificial neural networks

This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that

Imad Haidar; Siddhivinayak Kulkarni; Heping Pan

2008-01-01

318

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

Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting SAMUEL RE significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new by using an ensemble Kalman filter (EnKF; Evensen 1994, 2003). Theoreti- cally, ensemble filters

Ribes, AurÃ©lien

319

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

320

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

NASA Astrophysics Data System (ADS)

Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London's road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on L1 penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.

Haworth, J.; Cheng, T.

2014-11-01

321

The effect of alternative harvesting practices on long-term ecosystem productivity and carbon sequestration was investigated with the ecosystem simulation model, FORECAST. Three tree species, white spruce (Picea glauca), trembling aspen (Populus tremuloides), and lodgepole pine (Pinus contorta var. latifolia), were each used in combination with different rotation lengths. An additional run was conducted to investigate the effect of nitrogen addition

Brad Seely; Clive Welham; Hamish Kimmins

2002-01-01

322

Wavelet-Based Nonlinear Multiscale Decomposition Model for Electricity Load Forecasting

1 Wavelet-Based Nonlinear Multiscale Decomposition Model for Electricity Load Forecasting D autoregressive approach for the prediction of one-hour ahead ahead load based on historical electricity load data updated. We assess results produced by this multiscale autoregressive (MAR) method, in both linear and non-linear

Murtagh, Fionn

323

Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge

In this paper applications of logistic S-curve and component logistics are considered in a framework of long-term forecasting of emerging technologies. Several questions and issues are discussed in connection with the presented ways of studying the transition from invention to innovation and further evolution of technologies. First, the features of a simple logistic model are presented and diverse types of

Dmitry Kucharavy; Eric Schenk; Roland De Guio

2009-01-01

324

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) ...

325

NASA Technical Reports Server (NTRS)

The multi-scale organization of tropical convection and scale interaction are grand challenges in the prediction of weather and climate. As part of a international effort UN Year of Planet Earth, this proposed effort to observe, model and forecast the effects of organized tropical convection is reviewed. This viewgraph presentation reviews the proposal.

Waliser, Duane E.

2006-01-01

326

LOGISTIC SUBSTITUTION MODEL AND TECHNOLOGICAL FORECASTING Dmitry Kucharavy, Roland De Guio

. In the present paper we develop the applications for reliable and reproducible long-term technologicalLOGISTIC SUBSTITUTION MODEL AND TECHNOLOGICAL FORECASTING Dmitry Kucharavy, Roland De Guio INSA Strasbourg - Graduate School of Science and Technology, LGECO - Design Engineering Laboratory, France

Paris-Sud XI, UniversitÃ© de

327

The systematic study of the stability of forecasts in the rate- and state-dependent model.

NASA Astrophysics Data System (ADS)

Numerous observations have shown a general spatial correlation between positive Coulomb failure stress changes due to an earthquake and the locations of aftershocks. However this correlation does not give any indication of the rate from which we can infer the magnitude using the Gutenberg-Richter law. Dieterich's rate- and state-dependent model can be used to obtain a forecast of the observed aftershock rate for the space and time evolution of seismicity caused by stress changes applied to an infinite population of nucleating patches. The seismicity rate changes on this model depend on eight parameters: the stressing rate, the amplitude of the stress perturbation, the physical constitutive properties of faults, the spatial parameters (location and radii of the cells), the start and duration of each of the temporal windows as well as the background seismicity rate. The background seismicity is obtained from the epidemic type aftershock sequence model. We use the 1992 Landers earthquake as a case study, using the Southern California Earthquake Data Centre (SCEDC) catalogue, to examine if Dieterich's rate- and state-dependent model can forecast the aftershock seismicity rate. A systematic study is performed on a range of values on all the parameters to test the forecasting ability of this model. The results obtained suggest variable success in forecasting, when varying the values for the parameters, with the spatial and temporal parameters being the most sensitive. Dieterich's rate- and state-dependent model is compared with a well studied null hypothesis, the Omori-Utsu law. This law describes the aftershock rate as a power law in time following the main shock and depends on only three parameters: the aftershock productivity, the elapsed time since the main shock and the constant time shift, all of which can be estimated in the early part of the aftershock sequence and then extrapolated to give a long term rate forecast. All parameters are estimated using maximum likelihood methods. We compare the Dieterich and the Omori-Utsu forecasts using the Akaike information criterion which appropriately penalises each model for the number of free parameters used in the fit and explore the full spatial distribution of parameters, forecasts and forecast skill. We find that the Omori-Utsu law consistently out-performs the Dieterich model. The method described is then applied to other earthquake sequences and we assess its usefulness as a real time aftershock forecasting protocol. Finally, we produce a synthetic catalogue. The spatial seismicity of this catalogue is governed by the structural fractal complexity of the region, while the rate of the earthquakes (the temporal seismicity) follows the rate- and state-dependent model. This enables us to examine if we are able to recover the rate- and state-dependent model parameters and help us understand if the variable success in forecasting is due to the physics in the model differing from the real world or else due to the lack of enough data in the catalogue.

De Gaetano, D.; McCloskey, J.; Nalbant, S.

2012-04-01

328

Forecasting of Software Development Work Effort: Evidence on Expert Judgment and Formal Models

programming, economic production models, soft computing, fuzzy logic modeling, statistical bootstrapping regression-based. Soon, however, more sophisticated effort estimation models appeared, for example models founded on case-based reasoning, classification and regression trees, simulation, neural networks

329

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

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

1983-03-01

330

An AI-Agent-Based Trapezoidal Fuzzy Ensemble Forecasting Model for Crude Oil Price Prediction

In this study, a Al-agent-based trapezoidal fuzzy ensemble forecasting model is proposed for crude oil price prediction. In the proposed ensemble model, some single AI models are first used as predictors for crude oil price prediction. Then these single prediction results produced by the single Al-based predictors are fuzzified into some fuzzy prediction representations. Subsequently, these fuzzified representations are fused

Lean Yu; Shouyang Wang; Bo Wen; Kin Keung Lai

2008-01-01

331

An EMD-Based Neural Network Ensemble Learning Model for World Crude Oil Spot Price Forecasting

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning model is proposed for world crude\\u000a oil spot price modeling and forecasting. For this purpose, the original crude oil spot price series were first decomposed\\u000a into a finite and often small number of intrinsic mode functions (IMFs). Then the three-layer feed-forward neural network\\u000a (FNN) model was used

Lean Yu; Shouyang Wang; Kin Keung Lai

2008-01-01

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

NASA Astrophysics Data System (ADS)

We present the first "weather forecast" with a coupled whole-atmosphere/ionosphere model of Integrated Dynamics in Earth's Atmosphere (IDEA) for the January 2009 Sudden Stratospheric Warming (SSW). IDEA consists of the Whole Atmosphere Model and Global Ionosphere-Plasmasphere model. A 30 day forecast is performed using the IDEA model initialized at 0000 UT on 13 January 2009, 10 days prior to the peak of the SSW. IDEA successfully predicts both the time and amplitude of the peak warming in the polar cap. This is about 2 days earlier than the National Centers for Environmental Prediction operational Global Forecast System terrestrial weather model forecast. The forecast of the semidiurnal, westward propagating, zonal wave number 2 (SW2) tide in zonal wind also shows an increase in the amplitude and a phase shift to earlier hours in the equatorial dynamo region during and after the peak warming, before recovering to their prior values about 15 days later. The SW2 amplitude and phase changes are shown to be likely due to the stratospheric ozone and/or circulation changes. The daytime upward plasma drift and total electron content in the equatorial American sector show a clear shift to earlier hours and enhancement during and after the peak warming, before returning to their prior conditions. These ionospheric responses compare well with other observational studies. Therefore, the predicted ionospheric response to the January 2009 SSW can be largely explained in simple terms of the amplitude and phase changes of the SW2 zonal wind in the equatorial E region.

Wang, H.; Akmaev, R. A.; Fang, T.-W.; Fuller-Rowell, T. J.; Wu, F.; Maruyama, N.; Iredell, M. D.

2014-03-01

334

MAFALDA: An early warning modeling tool to forecast volcanic ash dispersal and deposition

NASA Astrophysics Data System (ADS)

Forecasting the dispersal of ash from explosive volcanoes is a scientific challenge to modern volcanology. It also represents a fundamental step in mitigating the potential impact of volcanic ash on urban areas and transport routes near explosive volcanoes. To this end we developed a Web-based early warning modeling tool named MAFALDA (Modeling and Forecasting Ash Loading and Dispersal in the Atmosphere) able to quantitatively forecast ash concentrations in the air and on the ground. The main features of MAFALDA are the usage of (1) a dispersal model, named VOL-CALPUFF, that couples the column ascent phase with the ash cloud transport and (2) high-resolution weather forecasting data, the capability to run and merge multiple scenarios, and the Web-based structure of the procedure that makes it suitable as an early warning tool. MAFALDA produces plots for a detailed analysis of ash cloud dynamics and ground deposition, as well as synthetic 2-D maps of areas potentially affected by dangerous concentrations of ash. A first application of MAFALDA to the long-lasting weak plumes produced at Mt. Etna (Italy) is presented. A similar tool can be useful to civil protection authorities and volcanic observatories in reducing the impact of the eruptive events. MAFALDA can be accessed at http://mafalda.pi.ingv.it.

Barsotti, S.; Nannipieri, L.; Neri, A.

2008-12-01

335

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

336

Modeling and forecasting river flow rate from the Melen Watershed, Turkey

NASA Astrophysics Data System (ADS)

SummaryThe Melen Watershed is located in Western Black Sea region of Turkey. Buyuk Melen and Kucuk Melen Rivers are located in this watershed. By 2010 more than 50% of Istanbul's water demand is supplied from the Buyuk Melen River. This paper presents a new approach using an artificial neural network (ANN) technique to improve precipitation forecast performance. Missing value predictions and the future precipitation value estimations were researched throughout this study. A case study was performed in Bolu and Duzce provinces of Turkey that are located in Black Sea Region. The most crucial objective of this study was to estimate missing values and to generate quantitative forecasts for future precipitation data of Duzce. Monthly average daily precipitation data of Bolu and limited number of Duzce precipitation data were used for this purpose. Ultimately, monthly river flow rate from the Melen Watershed of Turkey was modeled and forecasted through the SWAT Model using the generated precipitation data and other required spatial and temporal data. Results show that there is a considerable relation between the simulated model and observed results. This study also shows that water supply for Istanbul can be managed based on precipitation forecast.

Akiner, Muhammed Ernur; Akkoyunlu, Atilla

2012-08-01

337

A whole environmental impact prediction system has been gradually developed during the last years in order to prevent SO2 pollution. The system uses meteorological forecasts to initialise a dispersion model and then predicts the pollutant concentration in the vicinity of the industrial site. It is quite successful as more than 80% of the pollution peaks are well forecasted and thus

D. Caro; D. Leroy; E. Buisson; F. Brocheton; L. Donnat; O. Duclaux; C. Puel

2009-01-01

338

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...

339

Controlling chaos in an economic model

NASA Astrophysics Data System (ADS)

A Cournot duopoly, with a bounded inverse demand function and different constant marginal production costs, can be modeled as a discrete-time dynamical system, which exhibits complex bifurcating and chaotic behaviors. Based on some essential features of the model, we show how bifurcation and chaos can be controlled via the delayed feedback control method. We then propose and evaluate an adaptive parameter-tuning algorithm for control. In addition, we discuss possible economic implications of the chaos control strategies described in the paper.

Chen, Liang; Chen, Guanrong

2007-01-01

340

ABSTRACT The movements in oil prices are very complex and therefore, seem to be unpredictable. One of the main challenges of the econometric models is to forecast such a seemingly unpredictable economic ,series. The traditional linear structural models ,have not been promising when used for forecasting, particularly in the case of complex series such as oil prices. Although linear and

Saeed Moshiri; Faezeh Foroutan

341

A Hidden Markov Model for avalanche forecasting on Chowkibal-Tangdhar road axis in Indian Himalayas

NASA Astrophysics Data System (ADS)

A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal-Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992-2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008-2009, 2009-2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.

Joshi, Jagdish Chandra; Srivastava, Sunita

2014-12-01

342

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

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

343

Groundwater Forecasting Optimization Pertain to Dam Removal

NASA Astrophysics Data System (ADS)

There is increasing interest in removing dams due to changing ecological and societal values. Groundwater recharge rate is closely connected to reservoir presence or absence. With the removal of dams and their associated reservoirs, reductions in groundwater levels are likely to impact water supplies for domestic, industrial and agricultural use. Therefore accessible economic and time effective tools to forecast groundwater level declines with acceptable uncertainty following dam removals are critical for public welfare and healthy regional economies. These tools are also vital to project planning and provide beneficial information for restoration and remediation managements. The standard tool for groundwater forecasting is 3D Numerical modeling. Artificial Neural Networks (ANNs) may be an alternative tool for groundwater forecasting pertain to dam removal. This project compared these two tools throughout the Milltown Dam removal in Western Montana over a five year period. It was determined that ANN modeling had equal or greater accuracy for groundwater forecasting with far less effort and cost involved.

Brown, L.; Berthelote, A. R.

2011-12-01

344

Background In Japan, a shortage of physicians, who serve a key role in healthcare provision, has been pointed out as a major medical issue. The healthcare workforce policy planner should consider future dynamic changes in physician numbers. The purpose of this study was to propose a physician supply forecasting methodology by applying system dynamics modeling to estimate future absolute and relative numbers of physicians. Method We constructed a forecasting model using a system dynamics approach. Forecasting the number of physician was performed for all clinical physician and OB/GYN specialists. Moreover, we conducted evaluation of sufficiency for the number of physicians and sensitivity analysis. Result & conclusion As a result, it was forecast that the number of physicians would increase during 2008–2030 and the shortage would resolve at 2026 for all clinical physicians. However, the shortage would not resolve for the period covered. This suggests a need for measures for reconsidering the allocation system of new entry physicians to resolve maldistribution between medical departments, in addition, for increasing the overall number of clinical physicians. PMID:23981198

2013-01-01

345

A Ricardo model with economics of scale

The simple n-good two-country Ricardo model of international trade has been extremely influential in economics. In this classical model the outcome from trade is always beneficial to both trading countries, and always better than autarky, i.e. no trade. However, in the classical Ricardo models the production functions are always assumed to be either linear in their inputs or to have diseconomies, that is, decreasing returns to scale. This paper extends the analysis of the classical n-good two-country Ricardo Model of international trade to the case where the production functions have economies of scale. It addresses the technical difficulties inherent in economies of scale by integer programming and linear programming methods. The analysis reveals the existence of a well defined region which fills in solidly with equilibrium points as the number of goods becomes large. New economic conclusions follow from the ability to analyze these large models, among them that, in the presence of economies of scale, considerable conflict exists between the interests of the two trading partners and outcomes worse than autarky occur.

Gomory, R.

1994-12-31

346

Economic tour package model using heuristic

NASA Astrophysics Data System (ADS)

A tour-package is a prearranged tour that includes products and services such as food, activities, accommodation, and transportation, which are sold at a single price. Since the competitiveness within tourism industry is very high, many of the tour agents try to provide attractive tour-packages in order to meet tourist satisfaction as much as possible. Some of the criteria that are considered by the tourist are the number of places to be visited and the cost of the tour-packages. Previous studies indicate that tourists tend to choose economical tour-packages and aiming to visit as many places as they can cover. Thus, this study proposed tour-package model using heuristic approach. The aim is to find economical tour-packages and at the same time to propose as many places as possible to be visited by tourist in a given geographical area particularly in Langkawi Island. The proposed model considers only one starting point where the tour starts and ends at an identified hotel. This study covers 31 most attractive places in Langkawi Island from various categories of tourist attractions. Besides, the allocation of period for lunch and dinner are included in the proposed itineraries where it covers 11 popular restaurants around Langkawi Island. In developing the itinerary, the proposed heuristic approach considers time window for each site (hotel/restaurant/place) so that it represents real world implementation. We present three itineraries with different time constraints (1-day, 2-day and 3-day tour-package). The aim of economic model is to minimize the tour-package cost as much as possible by considering entrance fee of each visited place. We compare the proposed model with our uneconomic model from our previous study. The uneconomic model has no limitation to the cost with the aim to maximize the number of places to be visited. Comparison between the uneconomic and economic itinerary has shown that the proposed model have successfully achieved the objective that minimize the tour cost and cover maximum number of places to be visited.

Rahman, Syariza Abdul; Benjamin, Aida Mauziah; Bakar, Engku Muhammad Nazri Engku Abu

2014-07-01

347

Economic valuation of cultural heritage sites: A choice modeling approach

Despite growing attention by researchers and policy makers on the economic value of cultural heritage sites, debate surrounds the use of adequate methods. Although choice modeling techniques have been applied widely in the environmental economics field, their application in tourism and cultural economics has been much more limited. This paper contributes to the knowledge on the economic valuation of cultural

Andy S. Choi; Brent W. Ritchie; Franco Papandrea; Jeff Bennett

2010-01-01

348

Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

NASA Astrophysics Data System (ADS)

Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997-2008) on a weekly basis and 22 yr (1987-2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

El-Shafie, A.; Noureldin, A.; Taha, M.; Hussain, A.; Mukhlisin, M.

2012-04-01

349

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

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

350

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

351

Forecasting, Structural Time Series Models and the Kalman Filter

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology.

Andrew C. Harvey

1989-01-01

352

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

353

Economic viability of rangeland based ranching enterprises

the profitability of their enterprises by becoming more proactive in their management practices. An annual economic model is used to analyze the effects of using seasonal climate forecasts in cattle ranching enterprises in Sutton County, Texas. Unique...

Jochec, Kristi Gayle

2012-06-07

354

Forecasting coastal optical properties using ocean color and coastal circulation models

Coupling the 3-d ocean optical imagery with 3-d circulation models provides a new capability to understand coastal processes. Particle distribution derived from ocean color optical properties were coupled with numerical circulation models to determine a 24 hour forecast of particle concentrations. A 3-d particle concentration field for the coastal ocean was created by extending the surface satellite bio-optical properties vertically

R. A. Arnone; B. Casey; D. Ko; P. Flynn; L. Carrolo; S. Ladner

2007-01-01

355

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

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

356

This paper establishes a cointegration and vector error correction model to forecast the crude oil demand in China after analyzing main factors affecting crude oil demand. The model proves that GDP, population, the share of industrial sector in GDP and the oil price are the main factors influencing crude oil demand. Especially population and the share of industrial sector make

Xiong Jiping; Wu Ping

2008-01-01

357

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

358

Real-time weather forecasting in the Western Mediterranean Basin: An application of the RAMS model

NASA Astrophysics Data System (ADS)

A regional forecasting system based on the Regional Atmospheric Modeling System (RAMS) is being run at the CEAM Foundation. The model is started twice daily with a forecast range of 72 h. For the period June 2007 to August 2010 the verification of the model has been done using a series of automatic meteorological stations from the CEAM network and located within the Valencia Region (Western Mediterranean Basin). Air temperature, relative humidity and wind speed and direction of the output of the model have been compared with observations. For these variables, an operational verification has been performed by computing different statistical scores for 18 weather stations. This verification process has been carried out for each season of the year separately. As a result, it has been revealed that the model presents significant differences in the forecast of the meteorological variables analysed throughout the year. Moreover, due to the physical complexity of the area of study, the model presents different degree of accuracy between coastal and inland stations. Precipitation has also been verified by means of yes/no contingency tables as well as scatter plots. These tables have been built using 4 specific thresholds that have permitted to compute some categorical statistics. From the results found, it is shown that the precipitation forecast in the area of study is in general over-predicted, but with marked differences between the seasons of the year. Finally, dividing the available data by season of the year, has permitted us to analyze differences in the observed patterns for the magnitudes mentioned above. These results have been used to better understand the behavior of the RAMS model within the Valencia Region.

Gómez, I.; Caselles, V.; Estrela, M. J.

2014-03-01

359

Regional forecasting with global atmospheric models; Fourth year report

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

360

European models reliability over West Africa: from seasonal forecasting to climate scenarios

NASA Astrophysics Data System (ADS)

The severe drought that stroke the Sahel during the 1970's and the 1980's had dramatic consequences regarding to impacts in terms of food security and health. Improving the prediction of the West African Monsoon (WAM) system and its impacts on health, water resources and food security became a priority at all time scales, namely from seasonal forecasting to longer climate change perspectives. However, the actual state of the art General Circulation Model (GCM) mainly fail in reproducing key features of the WAM when a full ocean-atmosphere coupled approach is considered. This leads to strong uncertainties in simulated future rainfall changes over Africa at the end of the 21st century. This work proposes to highlight the differences and similarities of the GCM biases in both forecasting (seasonal to decadal) and climatic approaches. This is carried out using seasonal and decadal forecasting outputs from the ENSEMBLES project and climate historical runs from the CMIP3 dataset, used within the IPCC fourth report assessment. Preliminary results highlight consistent warm biases over the Gulf of Guinea, weak predictability of rainfall over the Sahel and problems in reproducing precipitation / orography feedbacks. The major biases highlighted in forecasting mode are generally similar to the ones depicted in climate simulations. This leads to the intermediate conclusion that the GCM biases are mainly related to their intrinsic parameterization whatever the approach considered.

Caminade, C.; Morse, A.; Jones, A.

2009-04-01

361

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

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

362

Implementation of the Immersed Boundary Method in the Weather Research and Forecasting model

Accurate simulations of atmospheric boundary layer flow are vital for predicting dispersion of contaminant releases, particularly in densely populated urban regions where first responders must react within minutes and the consequences of forecast errors are potentially disastrous. Current mesoscale models do not account for urban effects, and conversely urban scale models do not account for mesoscale weather features or atmospheric physics. The ultimate goal of this research is to develop and implement an immersed boundary method (IBM) along with a surface roughness parameterization into the mesoscale Weather Research and Forecasting (WRF) model. IBM will be used in WRF to represent the complex boundary conditions imposed by urban landscapes, while still including forcing from regional weather patterns and atmospheric physics. This document details preliminary results of this research, including the details of three distinct implementations of the immersed boundary method. Results for the three methods are presented for the case of a rotation influenced neutral atmospheric boundary layer over flat terrain.

Lundquist, K A

2006-12-07

363

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

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

2013-01-01

364

This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

2007-12-01

365

Over the last two decades a number of heatwaves have brought the need for heatwave early warning systems (HEWS) to the attention of many European governments. The HEWS in Europe are operating under the assumption that there is a high correlation between observed and forecasted temperatures. We investigated the sensitivity of different temperature mortality relationships when using forecast temperatures. We modelled mortality in Stockholm using observed temperatures and made predictions using forecast temperatures from the European Centre for Medium-range Weather Forecasts to assess the sensitivity. We found that the forecast will alter the expected future risk differently for different temperature mortality relationships. The more complex models seemed more sensitive to inaccurate forecasts. Despite the difference between models, there was a high agreement between models when identifying risk-days. We find that considerations of the accuracy in temperature forecasts should be part of the design of a HEWS. Currently operating HEWS do evaluate their predictive performance; this information should also be part of the evaluation of the epidemiological models that are the foundation in the HEWS. The most accurate description of the relationship between high temperature and mortality might not be the most suitable or practical when incorporated into a HEWS. PMID:25546283

Åström, Christofer; Ebi, Kristie L.; Langner, Joakim; Forsberg, Bertil

2014-01-01

366

Over the last two decades a number of heatwaves have brought the need for heatwave early warning systems (HEWS) to the attention of many European governments. The HEWS in Europe are operating under the assumption that there is a high correlation between observed and forecasted temperatures. We investigated the sensitivity of different temperature mortality relationships when using forecast temperatures. We modelled mortality in Stockholm using observed temperatures and made predictions using forecast temperatures from the European Centre for Medium-range Weather Forecasts to assess the sensitivity. We found that the forecast will alter the expected future risk differently for different temperature mortality relationships. The more complex models seemed more sensitive to inaccurate forecasts. Despite the difference between models, there was a high agreement between models when identifying risk-days. We find that considerations of the accuracy in temperature forecasts should be part of the design of a HEWS. Currently operating HEWS do evaluate their predictive performance; this information should also be part of the evaluation of the epidemiological models that are the foundation in the HEWS. The most accurate description of the relationship between high temperature and mortality might not be the most suitable or practical when incorporated into a HEWS. PMID:25546283

Åström, Christofer; Ebi, Kristie L; Langner, Joakim; Forsberg, Bertil

2014-01-01

367

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

368

"Developing a multi hazard air quality forecasting model for Santiago, Chile"

NASA Astrophysics Data System (ADS)

Santiago, Chile has reduced annual particulate matter from 69ug/m3 (in 1989) to 25ug/m3 (in 2012), mostly by forcing industry, the transport sector, and the residential heating sector to adopt stringent emission standards to be able to operate under bad air days. Statistical forecasting has been used to predict bad air days, and pollution control measures in Santiago, Chile, for almost two decades. Recently an operational PM2.5 deterministic model has been implemented using WRF-Chem. The model was developed by the University of Iowa and is run at the Chilean Meteorological Office. Model configuration includes high resolution emissions gridding (2km) and updated population distribution using 2008 data from LANDSCAN. The model is run using a 2 day spinup with a 5 day forecast. This model has allowed a preventive approach in pollution control measures, as episodes are the results of multiple days of bad dispersion. Decreeing air pollution control measures in advance of bad air days resulted in a reduction of 40% of alert days (80ug/m3 mean 24h PM2.5) and 66% of "preemergency days" (110ug/m3 mean 24h PM2.5) from 2011 to 2012, despite similar meteorological conditions. This model will be deployed under a recently funded Center for Natural Disaster Management, and include other meteorological hazards such as flooding, high temperature, storm waves, landslides, UV radiation, among other parameters. This paper will present the results of operational air quality forecasting, and the methodology that will be used to transform WRF-Chem into a multi hazard forecasting system.

Mena, M. A.; Delgado, R.; Hernandez, R.; Saide, P. E.; Cienfuegos, R.; Pinochet, J. I.; Molina, L. T.; Carmichael, G. R.

2013-05-01

369

Ensemble Model of Intelligent Paradigms for Stock Market Forecasting

The use of intelligent systems for stock market predictions has been widely established. This paper introduces a ensemble model of SVM and ANNs for the prediction of three stock indices. The performance of this model is then compared with support vector machine model and an artificial neural network respectively. Empirical results reveal that the ensemble result obtain the best results.

Qiang Wu; Yuehui Chen; Zhen Liu

2008-01-01

370

NASA Astrophysics Data System (ADS)

Two previous studies have evaluated eight multi-model forecasting strategies that combined hydrological forecasts for contrasting catchments: the River Ouse in Northern England and the Upper River Wye in Central Wales. The level and discharge inputs that were combined comprised a mixed set of independent forecasts produced using different modelling methodologies. Earlier multi-model combination approaches comprised: arithmetic-averaging, a probabilistic method in which the best model from the last time step is used to generate the current forecast, two different neural network operations, two different soft computing methodologies, a regression tree solution and instance-based learning. The nature and properties of past combination functions was not however explored and no theoretical outcome to support subsequent improvements resulted. This paper presents a pair of counterpart mathematical equations that were evolved in GeneXproTools 4.0: a powerful software package that is used to perform symbolic regression operations using gene expression programming. The results suggest that simple mathematical equations can be used to perform efficacious multi-model combinations; that similar mathematical solutions can be developed to fulfil different hydrological modelling requirements; and that the procedure involved produces mathematical outcomes that can be explained in terms of minimalist problem-solving strategies.

Abrahart, R. J.; Shamseldin, A. Y.; Fernando, D. A. K.

2009-04-01

371

Stochastic Modeling of Economic Injury Levels with Respect to Yearly Trends in Price Commodity

The economic injury level (EIL) concept integrates economics and biology and uses chemical applications in crop protection only when economic loss by pests is anticipated. The EIL is defined by five primary variables: the cost of management tactic per production unit, the price of commodity, the injury units per pest, the damage per unit injury, and the proportionate reduction of injury averted by the application of a tactic. The above variables are related according to the formula EIL = C/VIDK. The observable dynamic alteration of the EIL due to its different parameters is a major characteristic of its concept. In this study, the yearly effect of the economic variables is assessed, and in particular the influence of the parameter commodity value on the shape of the EIL function. In addition, to predict the effects of the economic variables on the EIL level, yearly commodity values were incorporated in the EIL formula and the generated outcomes were further modelled with stochastic linear autoregressive models having different orders. According to the AR(1) model, forecasts for the five-year period of 2010–2015 ranged from 2.33 to 2.41 specimens per sampling unit. These values represent a threshold that is in reasonable limits to justify future control actions. Management actions as related to productivity and price commodity significantly affect costs of crop production and thus define the adoption of IPM and sustainable crop production systems at local and international levels. PMID:25373206

2014-01-01

372

Computable General Equilibrium (CGE) Modelling for Regional Economic Development Analysis

Partridge M. D. and Rickman D. S. Computable general equilibrium (CGE) modelling for regional economic development analysis, Regional Studies. Despite their long-standing use in economic policy analysis generally, and increasing popularity in regional policy analysis, computable general equilibrium (CGE) models have yet to become the dominant approach for analysis of regional economic development policies. This review discusses the likely reasons

Mark D. Partridge; Dan S. Rickman

2010-01-01

373

NASA Astrophysics Data System (ADS)

Stream flow forecasts are required at different time scales for different applications, and play a vital role in water resources management. Over the last five years, wavelet transform based models have begun to be explored for hydrologic forecasting applications. In general, a particular wavelet transform (and a particular set of levels of decomposition) is selected as the ';optimal' wavelet transform to be used for forecasting purposes. However, different wavelets have different strengths in capturing the different characteristics of particular hydrological processes. Therefore, relying on a single model based on a single wavelet often leads to predictions that capture some phenomena at the expenses of others. Ensemble approaches based on the use of multiple different wavelets, in conjunction with a multi model setup, could potentially improve model performances and also allow for uncertainty estimation. In this study, a new multi-wavelet based ensemble method was developed for the wavelet Volterra coupled model. Different wavelets, levels of decomposition, and model setups are used in this new approach to generate an ensemble of forecasts. These ensembles are combined using Bayesian Model Averaging (BMA) to develop more skilful and reliable forecasts. The new BMA based ensemble multi-wavelet Volterra approach was applied for forecasting stream flow at different scales (daily, weekly and monthly) observed at two stations in the USA (that were selected since their flow regimes are highly varied and thus helped in rigorously testing the capability of the proposed approach). It was found that simply averaging the model results by assigning equal weights to the model does not improve the results in comparison with the best single model. In some cases, averaging decreases the performance of the model. However, the weighted averaging based on the BMA technique improved model performance significantly. In the case of monthly forecasting, the BMA based WVC model improved the forecasting performance by 9% and 20% with respect to the best single WVC model for stations I and II, respectively. Similarly, in the case of the weekly forecasts, the BMA based WVC model improved the efficiency by 3.85% and 14% for stations I and II, respectively. In the case of daily forecasts, the BMA based WVC model produced slightly better results than the best single WVC model. The results of this study reveal that the proposed BMA based ensemble multi-wavelet Volterra nonlinear model outperforms the single best wavelet Volterra model, as well as the mean averaged ensemble wavelet Volterra model.

Adamowski, J. F.; Khosa, R.; Rathinasamy, M.

2013-12-01

374

Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification

Rapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make future forecasts. It is often important to also quantify inherent risks...

Olalotiti-Lawal, Feyisayo

2013-05-17

375

NASA Astrophysics Data System (ADS)

In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in which the dynamic framework of hydrostatic equilibrium is taken. The main contributions are: the mixing ratios of all species of hydrometeors are added as the prognostic variables of model, the prognostic equations of these hydrometeors are introduced, the cloud physical framework is specially designed, some technical measures are used to resolve a series of physical, mathematical and computational problems arising from water-bearing; and so on. The various problems (in such aspects as the designs of physical and calculating schemes and the composition of computational programme) which are exposed in feasibility test, in sensibility test, and especially in operational forecasting experiments are successfully resolved using a lot of technical measures having been developed from researches and tests. Finally, the operational forecasting running of the water-bearing numerical model and its forecasting system is realized stably and reliably, and the fine forecasts are obtained. All of these mentioned above will be described in second paper.

Xia, Daqing; Xu, Youping

1998-06-01

376

In November 2007, the Federal Open Market Committee (FOMC) announced a change in the way it communicates its view of the economic outlook: It increased the frequency of its forecasts from two to four times per year, and it increased the length of the forecasting horizon from two to three years. The FOMC does not release the individual members ’ forecasts or standard measures of consensus such as the mean or median. Rather, it continues to release the forecast information as a range of forecasts, both the full range between the high and the low and a central tendency that omits the extreme values. This paper uses individual forecaster data from the Survey of Professional Forecasters (SPF) to mimic the FOMC’s method for creating their central tendency. The authors show that the midpoint of the central tendency of the SPF is a reliable measure of the consensus, suggesting that the FOMC reporting method is also a reliable measure of consensus. For the dates when both are available, the authors also compare the relative forecast accuracy of the FOMC and SPF consensus forecasts for output growth and inflation. Overall, the differences in forecast accuracy are too small to be statistically significant. (JEL C42, E17, E37, E52)

William T. Gavin; Geetanjali P

377

A RETROSPECTIVE ANALYSIS OF MODEL UNCERTAINTY FOR FORECASTING HYDROLOGIC CHANGE

GIS-based hydrologic modeling offers a convenient means of assessing the impacts associated with land-cover/use change for environmental planning efforts. Alternative future scenarios can be used as input to hydrologic models and compared with existing conditions to evaluate pot...

378

Modeling and Forecast of Operating Capability of Chinese Logistics Industry

The sustainable and rapid development of Chinese economy objectively brings forward new requirements for operating capability and development potential of Chinese logistics industry. Under the background, the paper used advance neural network method to establish a decision-making model of operating capability of Chinese logistics industry, and made efficient examining through real logistics data. The result proved that the model could

Wang Li-ping

2009-01-01

379

A new model for time-series forecasting using radial basis functions and exogenous data

In this paper, we present a new model for time-series forecasting using radial basis functions (RBFs) as a unit of artificial neural networks (ANNs), which allows the inclusion of exogenous information (EI) without additional pre-processing. We begin by summarizing the most well-known EI techniques used ad hoc, i.e., principal component analysis (PCA) and independent component analysis (ICA). We analyze the

Juan Manuel Górriz Sáez; Carlos García Puntonet; Moisés Salmerón; Juan José González De La Rosa

2004-01-01

380

Regional forecasting with global atmospheric models; Third year report

This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third 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 several 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

381

Regional forecasting with global atmospheric models; Final report

The purpose of the project was to conduct model simulations for past and future climate change with respect to the proposed Yucca Mtn. repository. The authors report on three main topics, one of which is boundary conditions for paleo-hindcast studies. These conditions are necessary for the conduction of three to four model simulations. The boundary conditions have been prepared for future runs. The second topic is (a) comparing the atmospheric general circulation model (GCM) with observations and other GCMs; and (b) development of a better precipitation data base for the Yucca Mtn. region for comparisons with models. These tasks have been completed. The third topic is preliminary assessments of future climate change. Energy balance model (EBM) simulations suggest that the greenhouse effect will likely dominate climate change at Yucca Mtn. for the next 10,000 years. The EBM study should improve rational choice of GCM CO{sub 2} scenarios for future climate change.

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

1994-05-01

382

NASA Astrophysics Data System (ADS)

In this paper, a multi-layer perceptron (MLP) model and the Finnish variant of the numerical weather prediction model HIRLAM (High Resolution Limited Area Model) were integrated and evaluated for the forecasting in time of urban pollutant concentrations. The forecasts of the combination of the MLP and HIRLAM models are compared with the corresponding forecasts of the MLP models that utilise meteorologically pre-processed input data. A novel input selection method based on the use of a multi-objective genetic algorithm (MOGA) is applied in conjunction with the sensitivity analysis to reduce the excessively large number of potential meteorological input variables; its use improves the performance of the MLP model. The computed air quality forecasts contain the sequential hourly time series of the concentrations of nitrogen dioxide (NO 2) and fine particulate matter (PM 2.5) from May 2000 to April 2003; the corresponding concentrations have also been measured at two urban air quality stations in Helsinki. The results obtained with the MLP models that use HIRLAM forecasts show fairly good overall agreement for both pollutants. The model performance is substantially better, when the HIRLAM forecasts are used, compared with those obtained both using either HIRLAM analysis data or meteorological pre-processor, for both pollutants. The performance of the currently widely used statistical forecasting methods (such as those based on neural networks) could therefore be significantly improved by using the forecasts of NWP models, instead of the conventionally utilised directly measured or meteorological pre-processed input data. However, the performance of all operational models considered is relatively worse in the course of air pollution episodes.

Niska, Harri; Rantamäki, Minna; Hiltunen, Teri; Karppinen, Ari; Kukkonen, Jaakko; Ruuskanen, Juhani; Kolehmainen, Mikko

383

Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the cr...

Kulkarni, Siddhivinayak

2009-01-01

384

NASA Astrophysics Data System (ADS)

The accuracy of hurricane intensity forecasts produced by coupled forecast models is influenced by errors and biases in SST forecasts produced by the ocean model component and the resulting impact on the enthalpy flux from ocean to atmosphere that powers the storm. Errors and biases in fields used to initialize the ocean model seriously degrade SST forecast accuracy. One strategy for improving ocean model initialization is to design a targeted observing program using airplanes and in-situ devices such as floats and drifters so that assimilation of the additional data substantially reduces errors in the ocean analysis system that provides the initial fields. Given the complexity and expense of obtaining these additional observations, observing system design methods such as OSSEs are attractive for designing efficient observing strategies. A new fraternal-twin ocean OSSE system based on the HYbrid Coordinate Ocean Model (HYCOM) is used to assess the impact of targeted ocean profiles observed by hurricane research aircraft, and also by in-situ float and drifter deployments, on reducing errors in initial ocean fields. A 0.04-degree HYCOM simulation of the Gulf of Mexico is evaluated as the nature run by determining that important ocean circulation features such as the Loop Current and synoptic cyclones and anticyclones are realistically simulated. The data-assimilation system is run on a 0.08-degree HYCOM mesh with substantially different model configuration than the nature run, and it uses a new ENsemble Kalman Filter (ENKF) algorithm optimized for the ocean model's hybrid vertical coordinates. The OSSE system is evaluated and calibrated by first running Observing System Experiments (OSEs) to evaluate existing observing systems, specifically quantifying the impact of assimilating more than one satellite altimeter, and also the impact of assimilating targeted ocean profiles taken by the NOAA WP-3D hurricane research aircraft in the Gulf of Mexico during the Deepwater Horizon oil spill. OSSE evaluation and calibration is then performed by repeating these two OSEs with synthetic observations and comparing the resulting observing system impact to determine if it differs from the OSE results. OSSEs are first run to evaluate different airborne sampling strategies with respect to temporal frequency of flights and the horizontal separation of upper-ocean profiles during each flight. They are then run to assess the impact of releasing multiple floats and gliders. Evaluation strategy focuses on error reduction in fields important for hurricane forecasting such as the structure of ocean currents and eddies, upper ocean heat content distribution, and upper-ocean stratification.

Halliwell, G. R.; Srinivasan, A.; Kourafalou, V. H.; Yang, H.; Le Henaff, M.; Atlas, R. M.

2012-12-01

385

Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir

................................................................................................ xi 1. INTRODUCTION AND LITERATURE REVIEW ...................................... 1 1.1 Introduction ......................................................................................... 1 1.2 Background... ......................................................................................... 3 1.3 Research Objectives .......................................................................... 21 2. METHODOLOGY AND MODEL DESCRIPTION ................................... 22 2.1 Introduction...

Almohammadi, Hisham

2013-07-26

386

JEDI: Jobs and Economic Development Impacts Model The Jobs and Economic Development Impact (JEDI America program to model wind energy jobs and impacts, JEDI has been expanded to biofuels, concentrating from industry norms), JEDI estimates the number of jobs and economic impacts to a local area (usually

387

NASA Astrophysics Data System (ADS)

The first implementation of an automatic operational ocean modeling system of the brazilian oceanic region was created and is under continuous development. The operational system is a joint effort between a group of institutions in a research and development consortium called Oceanographic Modelling and Research Network (with Portuguese acronym REMO). Among the objectives of this network is the contribution for a better understanding of the ocean, including mesoscale, shelf and tidal circulation, and to provide oceanographic forecasts for the Brazilian shelf/slope as support of the activities of the oil industry. The model underwent through a 9.5 years spinup being forced at the boundaries with climatological data from global simulations of the model OCCAM1-4, and at surface with data from NCEP (first 9 years) and GFS 1°. The operational stage started at the 1st of July 2009 and is producing daily analysis and 5 days forecasts. Currently the model uses OCCAM1-12 boundary climatologies and GFS 0.5° surface forcings. The ocean model being used is the Regional Ocean Modeling System, ROMS, an advanced and robust rapidly evolving comunity-code model. ROMS has been applied in deterministic simulations in a wide range of space and time scales and oceanic systems types. In terms of technical operations, the task needed for the operational ocean model to run, like the creation of inputs files, extraction of atmospheric data, as well as the control of the successfulness of the simulations and all the operational flow, is done with OOF? (Operational Ocean Forecast Engine), a collection of Python modules prepared to perform all the work required for the operational modeling system, including data visualisation. Due to its design, OOF? requires almost no human intervention, and except for some initial refinements and performance issues, OOF? is now working in a totally automatic manner.

Marta-Almeida, Martinho; Cirano, Mauro; Pereira, Janini; Ruiz-Villarreal, Manuel

2010-05-01

388

NASA Astrophysics Data System (ADS)

Floods are the most common and widespread climate-related hazards throughout the globe. Most human losses due to floods occur in the tropical regions of Africa, Asia, and Central America. The use of flood forecasting can reduce the death toll associated with floods. Recent research suggests that the frequency and severity of extreme rainfall events will increase; therefore, there is an urgent need for timely flood forecasting. In those tropical regions, a paucity of the ground-based precipitation data collection networks and the lack of data sharing across international borders for trans-boundary basins have made it impractical to use traditional flood forecasting that relies on station-measured precipitation data. Precipitation estimated from satellite data offers an effective means for calculating areal precipitation estimates in sparsely gauged regions. Because of the apparent uncertainty associated with satellite-based precipitation estimates, the use of such data in hydrologic modeling has been limited in the past. We will present results from our research on the utility of precipitation estimates from satellite data for flood forecasting and snowpack monitoring purposes. We found that remotely sensed precipitation data in combination with distributed hydrologic models can play an important role in early warning and monitoring of floods. For large basins the results of hydrologic models forced with satellite-based precipitation were comparable those the stream flow simulated stream using precipitation measured with ground-based networks. Snowpack simulated with precipitation estimates from satellite data underestimated the snow water content compared with snow water recorded by the SNOTEL network or simulated by SNODAS system; nevertheless, the estimates were found to be useful in mapping the snowpack.

Artan, G. A.; Shrestha, M.; Tokar, S.; Rowland, J.; Verdin, J. P.; Amer, S.

2012-12-01

389

Forecasting Tuberculosis Incidence in Iran Using Box-Jenkins Models

Background: Predicting the incidence of tuberculosis (TB) plays an important role in planning health control strategies for the future, developing intervention programs and allocating resources. Objectives: The present longitudinal study estimated the incidence of tuberculosis in 2014 using Box-Jenkins methods. Materials and Methods: Monthly data of tuberculosis cases recorded in the surveillance system of Iran tuberculosis control program from 2005 till 2011 was used. Data was reviewed regarding normality, variance equality and stationary conditions. The parameters p, d and q and P, D and Q were determined, and different models were examined. Based on the lowest levels of AIC and BIC, the most suitable model was selected among the models whose overall adequacy was confirmed. Results: During 84 months, 63568 TB patients were recorded. The average was 756.8 (SD = 11.9) TB cases a month. SARIMA (0,1,1)(0,1,1)12 with the lowest level of AIC (12.78) was selected as the most adequate model for prediction. It was predicted that the total nationwide TB cases for 2014 will be about 16.75 per 100,000 people. Conclusions: Regarding the cyclic pattern of TB recorded cases, Box-Jenkins and SARIMA models are suitable for predicting its prevalence in future. Moreover, prediction results show an increasing trend of TB cases in Iran. PMID:25031852

Moosazadeh, Mahmood; Nasehi, Mahshid; Bahrampour, Abbas; Khanjani, Narges; Sharafi, Saeed; Ahmadi, Shanaz

2014-01-01

390

On model selection forecasting, Dark Energy and modified gravity

The Fisher matrix approach (Fisher 1935) allows one to calculate in advance how well a given experiment will be able to estimate model parameters, and has been an invaluable tool in experimental design. In the same spirit, we present here a method to predict how well a given experiment can distinguish between different models, regardless of their parameters. From a Bayesian viewpoint, this involves computation of the Bayesian evidence. In this paper, we generalise the Fisher matrix approach from the context of parameter fitting to that of model testing, and show how the expected evidence can be computed under the same simplifying assumption of a gaussian likelihood as the Fisher matrix approach for parameter estimation. With this `Laplace approximation' all that is needed to compute the expected evidence is the Fisher matrix itself. We illustrate the method with a study of how well upcoming and planned experiments should perform at distinguishing between Dark Energy models and modified gravity theories. In particular we consider the combination of 3D weak lensing, for which planned and proposed wide-field multi-band imaging surveys will provide suitable data, and probes of the expansion history of the Universe, such as proposed supernova and baryonic acoustic oscillations surveys. We find that proposed large-scale weak lensing surveys from space should be able readily to distinguish General Relativity from modified gravity models.

A. F. Heavens; T. D. Kitching; L. Verde

2007-06-23

391

NASA Astrophysics Data System (ADS)

Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.

Ismail, A.; Hassan, Noor I.

2013-09-01

392

This paper investigates forecasts of US inflation at the 12-month horizon. The starting point is the conventional unemployment rate Phillips curve, which is examined in a simulated out-of-sample forecasting framework. Inflation forecasts produced by the Phillips curve generally have been more accurate than forecasts based on other macroeconomic variables, including interest rates, money and commodity prices. These forecasts can however

James H. Stock; Mark W. Watson

1999-01-01

393

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

NASA Astrophysics Data System (ADS)

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.

Owens, M. J.; Horbury, T. S.; Wicks, R. T.; McGregor, S. L.; Savani, N. P.; Xiong, M.

2014-06-01

394

Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece

NASA Astrophysics Data System (ADS)

Airborne pollen have been associated with allergic symptoms in sensitized individuals, having a direct impact on the overall quality of life of a considerable fraction of the population. Therefore, forecasting elevated airborne pollen concentrations and communicating this piece of information to the public are key issues in prophylaxis and safeguarding the quality of life of the overall population. In this study, we adopt a data-oriented approach in order to develop operational forecasting models (1-7 days ahead) of daily average airborne pollen concentrations of the highly allergenic taxa: Poaceae, Oleaceae and Urticaceae. The models are developed using a representative dataset consisting of pollen and meteorological time-series recorded during the years 1987-2002, in the city of Thessaloniki, Greece. The input variables (features) of the models have been optimized by making use of genetic algorithms, whereas we evaluate the performance of three algorithms: i) multi-Layer Perceptron, ii) support vector regression and iii) regression trees originating from distinct domains of Computational Intelligence (CI), and compare the resulting models with traditional multiple linear regression models. Results show the superiority of CI methods, especially when forecasting several days ahead, compared to traditional multiple linear regression models. Furthermore, the CI models complement each other, resulting to a combined model that performs better than each one separately. The overall performance ranges, in terms of the index of agreement, from 0.85 to 0.93 clearly suggesting the potential operational use of the models. The latter ones can be utilized in provision of personalized and on-time information services, which can improve quality of life of sensitized citizens.

Voukantsis, Dimitris; Niska, Harri; Karatzas, Kostas; Riga, Marina; Damialis, Athanasios; Vokou, Despoina

2010-12-01

395

Growth Diagnostics for Dark Energy models and EUCLID forecast

In this work we introduce a new set of parameters $(r_{g}, s_{g})$ involving the linear growth of matter perturbation that can distinguish and constrain different dark energy models very efficiently. Interestingly, for $\\Lambda$CDM model these parameters take exact value $(1,1)$ at all red shifts whereas for models different from $\\Lambda$CDM, they follow different trajectories in the $(r_{g}, s_{g})$ phase plane. By considering the parametrization for the dark energy equation of state ($w$) and for the linear growth rate ($f_{g}$), we show that different dark energy behaviours with similar evolution of the linear density contrast, can produce distinguishable trajectories in the $(r_{g}, s_{g})$ phase plane. Moreover, one can put stringent constraint on these phase plane using future measurements like EUCLID ruling out some of the dark energy behaviours.

Sampurnanand; Anjan A. Sen

2013-01-06

396

Climate-Based Models for Understanding and Forecasting Dengue Epidemics

Background Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system. Methodology/Principal Findings Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively. Conclusions/Significance The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries. PMID:22348154

Descloux, Elodie; Mangeas, Morgan; Menkes, Christophe Eugène; Lengaigne, Matthieu; Leroy, Anne; Tehei, Temaui; Guillaumot, Laurent; Teurlai, Magali; Gourinat, Ann-Claire; Benzler, Justus; Pfannstiel, Anne; Grangeon, Jean-Paul; Degallier, Nicolas; De Lamballerie, Xavier

2012-01-01

397

NASA Astrophysics Data System (ADS)

over the central United States is responsible for large socioeconomic losses. Atmospheric rivers (ARs), narrow regions of intense moisture transport within the warm conveyor belt of extratropical cyclones, can give rise to high rainfall amounts leading to flooding. Short-term forecasting of AR activity can provide basic information toward improving preparedness for these events. This study focuses on the verification of the skill of five numerical weather prediction models in forecasting AR activity over the central United States. We find that these models generally forecast AR occurrences well at short lead times, with location errors increasing from one to three decimal degrees as the lead time increases to about 1 week. The skill (both in terms of occurrence and location errors) decreases with increasing lead time. Overall, these models are not skillful in forecasting AR activity over the central United States beyond a lead time of about 7 days.

Nayak, Munir A.; Villarini, Gabriele; Lavers, David A.

2014-06-01

398

Hybrid forecasting model research on stock data mining

The synergy effect's benefit is widely accepted. The object of this paper is to investigate whether a hybrid approach combining different stock prediction approaches together can dramatically outperform the single approach and compare the performance of different hybrid approaches. The hybrid model includes three well-researched algorithms: back propagation neural network (BPNN), adaptive network-based fuzzy neural inference system (ANFIS) and support

Fangwen Zhai; Qinghua Wen; Zehong Yang; Yixu Song

2010-01-01

399

Integrated Modeling for Watershed Ecosystem Services Assessment and Forecasting

Regional scale watershed management decisions must be informed by the science-based relationship between anthropogenic activities on the landscape and the change in ecosystem structure, function, and services that occur as a result. We applied process-based models that represent...

400

Making the Best Use of Cybersecurity Economic Models

This article describes an analysis of several representative cybersecurity economic models, where the authors seek to determine whether each model's underlying assumptions are realistic and useful. They find that many of the assumptions are the same across disparate models, and most assumptions are far from realistic. They recommend several changes so that the predictions from economic models can be more

Rachel Rue; Shari Lawrence Pfleeger

2009-01-01

401

NASA Astrophysics Data System (ADS)

The Hawaiian Islands consist of dramatic terrain changes over short distances, resulting in a variety of microclimates in close proximity. To handle these challenging conditions, weather models must be run at very fine vertical and horizontal resolutions to produce accurate forecasts. Computational demands require WRF to be executed in parallel on the Maui High Performance Computing Center’s Mana system, a PowerEdge M610 Linux cluster. This machine has 1,152 compute nodes, each with two 2.8 GHz quad-core Intel® Nehalem processors and 24 GB RAM. Realizing maximum performance on Mana relied on the determination of an optimal number of cores to use per socket, the efficiency of an MPI only implementation, an optimal set of parameters for adaptive time stepping, a way to meet the strict stability requirements necessary for Hawaii, effective choices for processor and memory affinity, and parallel automation techniques for producing forecast imagery.

Roe, K.; Stevens, D.

2010-09-01