ADVANCES IN ENERGY FORECASTING MODELS BASED ON ENGINEERING ECONOMICS
Ernst Worrell; Stephan Ramesohl; Gale Boyd
2004-01-01
New energy efficiency policies have been introduced around the world. Historically, most energy models were reasonably equipped to assess the impact of classical policies, such as a subsidy or change in taxation. However, these tools are often insufficient to assess the impact of alternative policy instruments. We evaluate the so-called engineering economic models used to assess future industrial energy use.
Safe-economical route model of a ship to avoid tropical cyclones using dynamic forecast environment
NASA Astrophysics Data System (ADS)
Wu, L.; Wen, Y.; Wu, D.; Zhang, J.; Xiao, C.
2014-08-01
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.
The Economic Value of Air Quality Forecasting
NASA Astrophysics Data System (ADS)
Anderson-Sumo, Tasha
Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with bad air quality warning and forecast air quality forecasts. There is a difficulty presented with evaluating the economic utility of the forecasts. All may not comply and even with a low compliance rate of 5% and 72% as the average probability of detection of poor air quality days by the air quality models, we estimate that the forecasting program saves 412 lives or 412 million dollars per year for the region. The totals we found are great or greater than other typical yearly meteorological hazard programs such as tornado or hurricane forecasting and it is clear that the economic value of air quality forecasting in the Baltimore/Washington region is vital.
Modeling for Tsunami Forecast Vasily Titov NOAA Center for Tsunami Research Pacific Marine Environmental Laboratory Seattle, WA #12;Outline Tsunami Modeling Development Toward Real- time Tsunami Forecast Challenges Modeling development in 1990 -2000 Short-term Inundation Forecast for Tsunamis Forecast system
Economic Value of Weather and Climate Forecasts
NASA Astrophysics Data System (ADS)
Katz, Richard W.; Murphy, Allan H.
1997-06-01
Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects of weather prediction. Chapters by area specialists provide a comprehensive view of this timely topic. They encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behavior. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential text for economists, statisticians, and meteorologists.
A Course in Economic Forecasting: Rationale and Content.
ERIC Educational Resources Information Center
Loomis, David G.; Cox, James E., Jr.
2000-01-01
Discusses four reasons why economic forecasting courses are important: (1) forecasting skills are in demand by businesses; (2) forecasters are in demand; (3) forecasting courses have positive externalities; (4) and forecasting provides a real-world context. Describes what should be taught in an economic forecasting course. (CMK)
A neural network model for sales forecasting
Ilona Jagielska; Ashok Jacob; Tattersal Sweep Consultation
1993-01-01
The value of neural networks in solving complex nondeterministic problems has been demonstrated across a broad range of applications. A promising area for neural network application is that of economic forecasting. This study investigates the application of the connectionist paradigm to sales forecasting. A neural network model for the prediction of sales levels was developed and the sales forecasts produced
Aggregate vehicle travel forecasting model
Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)
1995-05-01
This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729
Comparative models for electrical load forecasting
Bunn, D.; Farmer, E.D.
1985-01-01
This reference describes the methods used to forecast loads on public utility systems, featuring modern documentation and forecasting techniques for lead times of up to approximately 24 hours. Methods include automatic adaptive, univariate, and multivariate. The volume is divided into three parts: an introduction to the general economic and operational decision-making contexts and methods of short-term forecasting; six contributed insights into a wide range of model developments in load forecasting; and further insights into adjacent fields, including essays on the econometric perspective to load forecasting and the very short-term implications of state-estimation for data validation.
L. J. Williams; J. W. Boyd; R. T. Crow
1978-01-01
Forecasts of end-use consumption of electricity, petroleum, natural gas, and coal for the years 1980 to 2000 are presented. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end-use sector. The forecasts are based on a forecast of longrun economic growth coupled with three scenarios concerning energy prices and
A. M. Borges; R. T. Crow
1981-01-01
National forecasts of end use consumption of electricity, liquid hydrocarbons, gaseous hydrocarbons, and coal are presented. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end use sector. Each forecast is conditional upon a common forecast of long run economic growth, coupled with a scenario concerning energy prices and
Detecting and Forecasting Economic Regimes in Automated Exchanges
Ketter, Wolfgang
Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter # , John Collins, Maria Gini, Alok Gupta + , and Paul Schrater # Dept. of Decision and Information Sciences, RSM Erasmus
Detecting and Forecasting Economic Regimes in Automated Exchanges
Ketter, Wolfgang
Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter , John Collins , Maria Gini , Alok Gupta , and Paul Schrater Dept. of Decision and Information Sciences, Rotterdam Sch
Detecting and Forecasting Economic Regimes in Automated Exchanges
Ketter, Wolfgang
Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter , John Collins condition and to forecast market changes over a planning horizon. We forecast market changes via both techniques can be used to support rational decision making in competitive sales environments. We
Ups and downs of economics and econophysics — Facebook forecast
NASA Astrophysics Data System (ADS)
Gajic, Nenad; Budinski-Petkovic, Ljuba
2013-01-01
What is econophysics and its relationship with economics? What is the state of economics after the global economic crisis, and is there a future for the paradigm of market equilibrium, with imaginary perfect competition and rational agents? Can the next paradigm of economics adopt important assumptions derived from econophysics models: that markets are chaotic systems, striving to extremes as bubbles and crashes show, with psychologically motivated, statistically predictable individual behaviors? Is the future of econophysics, as predicted here, to disappear and become a part of economics? A good test of the current state of econophysics and its methods is the valuation of Facebook immediately after the initial public offering - this forecast indicates that Facebook is highly overvalued, and its IPO valuation of 104 billion dollars is mostly the new financial bubble based on the expectations of unlimited growth, although it’s easy to prove that Facebook is close to the upper limit of its users.
Comparison of Wind energy production forecasts, in terms of errors and economic losses
NASA Astrophysics Data System (ADS)
Mestre, O.; Texier, O.; Girard, N.; Usaola, J.; Bantegnie, P.
2009-04-01
We compare 6 forecasts productions models on two windfarms located in France. The evaluation is made in terms of root mean square errors. The power production forecasts are the products of both physical and statistical models and cover a period of 6 months. We show that the economic performances of those models can be improved using econometric approaches, where we to minimize the cost induced by the forecast error instead of minimizing the forecast error itself. This technique relies on state of the art non-parametric estimators of conditional probability distribution functions (cpdf) of energy production at a wind farm, given the wind speed forecasts of a deterministic meteorological model. In this case, no assumption is made about the shape of the underlying laws. The economical benefits of ensemble versus deterministic wind speed forecasts are also assessed.
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...
Detecting and Forecasting Economic Regimes in Automated Exchanges
Ketter, Wolfgang
Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter # , John Collins market condition and to forecast market changes over a planning horizon. We validate our methods to support rational decision making in a sales environment. We are particularly interested in environments
A. M. Borges; R. T. Crow
1981-01-01
National forecasts of end-use consumption of electricity, liquid hydrocarbons, gaseous hydrocarbons, and coal for the years 1980-2000 are presented. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end-use sector. Each forecast is conditional upon a common forecast of long-run economic growth, coupled with a scenario concerning energy prices
Potential Economic Value of Seasonal Hurricane Forecasts
Emanuel, Kerry Andrew
This paper explores the potential utility of seasonal Atlantic hurricane forecasts to a hypothetical property insurance firm whose insured properties are broadly distributed along the U.S. Gulf and East Coasts. Using a ...
Robustness of disaggregate oil and gas discovery forecasting models
Attanasi, E.D.; Schuenemeyer, J.H.
1989-01-01
The trend in forecasting oil and gas discoveries has been to develop and use models that allow forecasts of the size distribution of future discoveries. From such forecasts, exploration and development costs can more readily be computed. Two classes of these forecasting models are the Arps-Roberts type models and the 'creaming method' models. This paper examines the robustness of the forecasts made by these models when the historical data on which the models are based have been subject to economic upheavals or when historical discovery data are aggregated from areas having widely differing economic structures. Model performance is examined in the context of forecasting discoveries for offshore Texas State and Federal areas. The analysis shows how the model forecasts are limited by information contained in the historical discovery data. Because the Arps-Roberts type models require more regularity in discovery sequence than the creaming models, prior information had to be introduced into the Arps-Roberts models to accommodate the influence of economic changes. The creaming methods captured the overall decline in discovery size but did not easily allow introduction of exogenous information to compensate for incomplete historical data. Moreover, the predictive log normal distribution associated with the creaming model methods appears to understate the importance of the potential contribution of small fields. ?? 1989.
Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts
Implication of these updates Load growth is slower than draft forecast Medium forecast before conservation Energy growing at 1 MegawattsandMWa Winter Peak Summer Peak Annual Energy #12;7 Forecasted Energy is close. However equal treatment-19 RegionalAnnualEnergy(MWa) Council Utilities #12;8 Council January Peak Forecast is Lower than Utilities 19
Economic indicators selection for crime rates forecasting using cooperative feature selection
NASA Astrophysics Data System (ADS)
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina
2013-04-01
Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.
Economic benefits of improved meteorological forecasts - The construction industry
NASA Technical Reports Server (NTRS)
Bhattacharyya, R. K.; Greenberg, J. S.
1976-01-01
Estimates are made of the potential economic benefits accruing to particular industries from timely utilization of satellite-derived six-hour weather forecasts, and of economic penalties resulting from failure to utilize such forecasts in day-to-day planning. The cost estimate study is centered on the U.S. construction industry, with results simplified to yes/no 6-hr forecasts on thunderstorm activity and work/no work decisions. Effects of weather elements (thunderstorms, snow and sleet) on various construction operations are indicated. Potential dollar benefits for other industries, including air transportation and other forms of transportation, are diagrammed for comparison. Geosynchronous satellites such as STORMSAT, SEOS, and SMS/GOES are considered as sources of the forecast data.
A forecasting model of gaming revenues in Clark County, Nevada
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
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.
Interpretation of Global Forecast Model 'Flipflops'
NSDL National Science Digital Library
2014-09-14
All forecasters are familiar with occasional run-to-run changes in forecast direction that occur with medium-range (and sometimes even short-range) forecasts in the Global Forecast Model (aka AVN/MRF). This case describes two recent model "flipflops" in a pair of time-adjacent operational MRF runs, and shows how MRF ensemble forecasts shed light on what is actually going on in the operational MRF seasons.
Forecasting rates of hydrocarbon discoveries in a changing economic environment
Schuenemeyer, J.H.; Attanasi, E.D.
1984-01-01
A method is presented for the estimation of undiscovered oil and gas resources in partially explored areas where economic truncation has caused some discoveries to go unreported; therefore distorting the relationship between the observed discovery size distribution and the parent or ultimate field size distribution. The method is applied to the UK's northern and central North Sea provinces. A discovery process model is developed to estimate the number and size distribution of undiscovered fields in this area as of 1983. The model is also used to forecast the rate at which fields will be discovered in the future. The appraisal and forecasts pertain to fields in size classes as small as 24 million barrels of oil equivalent (BOE). Estimated undiscovered hydrocarbon resources of 11.79 billion BOE are expected to be contained in 170 remaining fields. Over the first 500 wildcat wells after 1 January 1983, the discovery rate in this areas is expected to decline by 60% from 15 million BOE per wildcat well to six million BOE per wildcat well. ?? 1984.
The economic value of ensemble forecasts as a tool for risk assessment: From days to decades
NASA Astrophysics Data System (ADS)
Palmer, T. N.
2002-04-01
Despite the revolutionary development of numerical weather and climate prediction (NWCP) in the second half of the last century, quantitative interaction between model developers and forecast customers has been rather limited. This is apparent in the diverse ways in which weather forecasts are assessed by these two groups: rootmean-square error of 500 hPa height on the one hand; pounds, euros or dollars saved on the other. These differences of approach are changing with the development of ensemble forecasting. Ensemble forecasts provide a qualitative tool for the assessment of weather and climate risk for a range of user applications, and on a range of time-scales, from days to decades. Examples of the commercial application of ensemble forecasting, from electricity generation, ship routeing, pollution modelling, weather-risk finance, disease prediction and crop yield modelling, are shown from all these time-scales. A generic user decision model is described that allows one to assess the potential economic value of numerical weather and climate forecasts for a range of customers. Using this, it is possible to relate analytically, potential economic value to conventional meteorological skill scores. A generalized meteorological measure of forecast skill is proposed which takes the distribution of customers into account. It is suggested that when customers' exposure to weather or climate risk can be quantified, such more generalized measures of skill should be used in assessing the performance of an operational NWCP system.
Short-Termed Integrated Forecasting System: 1993 Model documentation report
Not Available
1993-05-01
The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.
Comparison of the economic impact of different wind power forecast systems for producers
NASA Astrophysics Data System (ADS)
Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.
2014-05-01
Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.
Forecast of future aviation fuels: The model
NASA Technical Reports Server (NTRS)
Ayati, M. B.; Liu, C. Y.; English, J. M.
1981-01-01
A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.
Forecasting Economic and Financial Variables with Global VARs
Pesaran, M Hashem; Schuermann, Til; Smith, L Vanessa
forecasts by using a Newey-West type estimator of V ar(z¯i). We do not pursue this extension here since for h > 1 we do not have sufficient data to reliably carry out the panel DM tests. 6 Forecast Evaluation Results Given how many models, sample windows...
NASA Astrophysics Data System (ADS)
Yin, Yip Chee; Hock-Eam, Lim
2012-09-01
Our empirical results show that we can predict GDP growth rate more accurately in continent with fewer large economies, compared to smaller economies like Malaysia. This difficulty is very likely positively correlated with subsidy or social security policies. The stage of economic development and level of competiveness also appears to have interactive effects on this forecast stability. These results are generally independent of the forecasting procedures. Countries with high stability in their economic growth, forecasting by model selection is better than model averaging. Overall forecast weight averaging (FWA) is a better forecasting procedure in most countries. FWA also outperforms simple model averaging (SMA) and has the same forecasting ability as Bayesian model averaging (BMA) in almost all countries.
timber quality Modelling and forecasting
facilities match the more traditional requirements of timber production. As this policy evolves. · Wood products industries need to know that they will have a regular supply of consistent quality timberForest and timber quality in Europe Modelling and forecasting yield and quality in Europe Forest
Detecting and Forecasting Economic Regimes in Automated Exchanges Technical Report
Gini, Maria
Wolfgang Ketter, John Collins, Maria Gini, Alok Gupta, and Paul R. Schrater March 05, 2007 #12;#12;Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter , John Collins , Maria Gini , Alok Gupta , and Paul Schrater Dept. of Decision and Information Sciences, Rotterdam Sch
Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value
NASA Astrophysics Data System (ADS)
Courtney, Jennifer; Lynch, Peter; Sweeney, Conor
2013-04-01
The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more beneficial to the wind energy industry of Ireland.
Modeling and Forecasting Electric Daily Peak Loads
Abdel-Aal, Radwan E.
as a series of 24 hourly forecasted loads. This paper is concerned with modeling and forecasting daily peak loads with lead times of 1 to 7 days. Univariate time series techniques such as the BoxModeling and Forecasting Electric Daily Peak Loads Using Abductive Networks R. E. Abdel
L. J. Williams; J. W. Boyd; R. T. Crow
1978-01-01
This report presents forecasts of end-use consumption of electricity, petroleum, natural gas, and coal for the years 1980 to 2000. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end-use sector. The forecasts are based on a forecast of long-run economic growth coupled with three scenarios concerning energy prices
Sales forecasting using longitudinal data models
Edward W. Frees; Thomas W. Miller
2004-01-01
This paper shows how to forecast using a class of linear mixed longitudinal, or panel, data models. Forecasts are derived as special cases of best linear unbiased predictors, also known as BLUPs, and hence are optimal predictors of future realizations of the response. We show that the BLUP forecast arises from three components: (1) a predictor based on the conditional
NASA Astrophysics Data System (ADS)
Borges, A. M.; Crow, R. T.
1981-10-01
National forecasts of end use consumption of electricity, liquid hydrocarbons, gaseous hydrocarbons, and coal are presented. The forecasts are based on an econometric model whose equations represent energy consumption of each form of energy in each end use sector. Each forecast is conditional upon a common forecast of long run economic growth, coupled with a scenario concerning energy prices and conservation policy. The scenarios are composed of four alternative sets of assumptions about energy prices and three alternative sets of assumptions on conservation policy.
NASA Astrophysics Data System (ADS)
Zhao, T.; Zhao, J.; Cai, X.; Yang, D.
2011-12-01
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.
Transport models for numerical forecast
NASA Technical Reports Server (NTRS)
Burk, Stephen D.
1987-01-01
The explosive growth of computing power, coupled with scientific and technological emphasis on a national scale, has led to significant major advances in operational numerical weather prediction (NWP) during the last two decades. There are about half a dozen major centers around the world running global NWP models operationally. Many more countries have operational hemispheric or limited-area models which provide weather forecasts. The global models typically have several hundred kilometer resolution, while the limited-area models usually have horizontal spacing of 50 to 100 km. Given the pace of burgeoning growth in this area, it seems warranted to occasionally take an overview of aspects of the field common to all modelers. Here, a brief look is taken at the nature of subgrid scale turbulence transport parameterization, and some of the difficulties pertaining thereto, with particular emphasis on operational NWP models.
Total energy forecast model for rural distribution cooperatives
S. Rastogi; G. Roulet; M. Ortbals
1990-01-01
A total energy requirements model is suggested for the electric utility planning process. It requires very little data input and time, and can be developed on a personal computer with the help of an electronic spread-sheet and\\/or a statistical program. Input components used in determining the energy forecast for the suggested model include population, weather, income, and a major economic
How to Support a One-Handed Economist: The Role of Modalisation in Economic Forecasting
ERIC Educational Resources Information Center
Donohue, James P.
2006-01-01
Economic forecasting in the world of international finance confronts economists with challenging cross-cultural writing tasks. Producing forecasts in English which convey confidence and credibility entails an understanding of linguistic conventions which typify the genre. A typical linguistic feature of commercial economic forecasts produced by…
Forecasting Flash Floods with an Operational Model
P. A. Ayral; S. Sauvagnargues-Lesage; S. Gay; F. Bressand
The flash flood forecasting model ALHTAÏR (“Alarme Hydrologique Territoriale Automatisée par Indicateur de Risque”) has been\\u000a developed during the last five years by the flood-warning service of the Gard Region (SAC-30), in the South-East of France.\\u000a A spatial version for the flash flood forecasting model is described in this paper. This flash flood forecasting model is\\u000a divided in three separate
NASA Technical Reports Server (NTRS)
1977-01-01
A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.
Toward Constructing Operational Geomagnetic Activity Forecast Model
NASA Astrophysics Data System (ADS)
Nagatsuma, T.; Kunitake, M.; Murata, K. T.
2010-12-01
Prediction of geomagnetic activity is one of the fundamental issues of space weather forecast. We are developing geomagnetic activity forecasting model based on the solar wind - magnetosphere - ionosphere (SW-M-I) coupling. We are operating daily space weather forecast as Regional Warning Center of Japan in International Space Environment Service (ISES). The key point of our forecasting model is ionosphereic conductivity dependence of the coupling function. We have found that the efficiency of SW-M-I coupling is not constant but has a dependence of ionospheric conductivity within the polar cap. Therefore, operational forecasting model of geomagnetic activity should take into account these variations and dependence. Our model can explain the diurnal and semiannual and solar cycle variations of geomagnetic activity from solar wind parameter and F10.7 index. We also examine the possibility of using inner heliospheric solar wind data such as STEREO data for a few days advance of geomagnetic activity forecast. Based on the comparison between ACE and STEREO data, we have found that the solar wind velocity can be predicted from the STEREO data well, but the Bz component of interplanetary magnetic field (IMF) is difficult to predict rather than the magnitude of IMF. This suggests that the probabilistic approach is needed for the mid-term geomagnetic forecast. We will introduce the future direction of our geomagnetic activity forecasting model in our presentation.
Arora, Siddharth
Numerous time series models are available for forecasting economic output. Autoregressive models were initially applied to US gross national product (GNP), and have been extended to nonlinear structures, such as the ...
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....
NASA Technical Reports Server (NTRS)
Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh
2014-01-01
Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.
Nambe Pueblo Water Budget and Forecasting model.
Brainard, James Robert
2009-10-01
This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.
Semiparametric Model for Uncertainty in River Stage Forecasts Conditional on Point Forecast Values
NASA Astrophysics Data System (ADS)
Yan, J.; Liao, G.; Gebremichael, M.; Shedd, R.; Vallee, D.
2012-12-01
The National Weather Service (NWS) River Forecast Centers (RFCs) issue deterministic river stage forecasts for 110 locations across the Northeast USA. Nevertheless, the uncertainty information can be as important as the forecast itself for forecast users. This paper presents a conditional characterization of river stage values given each forecast value through a four-parameter skewed t distribution, with each parameter modeled as a function of the point forecast value and the 1-day-ago observed value. The model was applied to nine years of daily observed stage values in warm season and matching 6-hour-lead forecast at the Plymouth station on the Pemigewasset River in New Hampshire. For each point forecast value, the conditional distribution and resulting prediction intervals provide uncertainty information that are potentially very important to forecast users and algorithm developers in decision making and improvement of forecast quality
Weather Forecaster Understanding of Climate Models
NASA Astrophysics Data System (ADS)
Bol, A.; Kiehl, J. T.; Abshire, W. E.
2013-12-01
Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.
Weather forecasts, users' economic expenses and decision strategies
NASA Technical Reports Server (NTRS)
Carter, G. M.
1972-01-01
Differing decision models and operational characteristics affecting the economic expenses (i.e., the costs of protection and losses suffered if no protective measures have been taken) associated with the use of predictive weather information have been examined.
Pollen Forecast and Dispersion Modelling
NASA Astrophysics Data System (ADS)
Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello
2014-05-01
The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE Bologna, Italy). With pollen and meteorological dataset was created a provisional model for Poaceae. A PLSDA (Partial Least Squares Discriminant Analysis) approach was used in order to predict Poaceae pollen critical concentration (Brighetti et al. 2013) To preserve spatial correlation between pollens and PM10, we choose a Multiavariate Linear Spatial Interpolation Method to quantify pollen concentration in function of PM10, wind, rain and temperature. A test and validation procedure have been conducted to estimate the error associated to the pollen concentration. Validation for the year 2012 shows a good agreement between measured and estimated data , in each area depending of orography and of road traffic (r >0.83, 1%< RRMSE <5% ). This study aims to be a added value to agro-meteorological data in a different branch from the classic sector of defence and of crop production, emphasizing the importance of monitoring and forecast the pollen dispersal in urban areas, evaluated its effect on health and quality of life. In the health area the combined analysis between climate, pollution and dispersal of pollen allows to realize significant operational tools and to develop a reference for subsequent implementations.
Forecasting models for human resources in health care.
O'Brien-Pallas, L; Baumann, A; Donner, G; Murphy, G T; Lochhaas-Gerlach, J; Luba, M
2001-01-01
This article is a review of the approaches published between 1996 and 1999 that have been used to forecast human resource requirements for nursing. Much of the work to date generally does not consider the complex factors that influence health human resources (HHR). They also do not consider the effect of HHR decisions on population health, provider outcomes such as stress, and the cost of a decision made. Supply and demand approaches have dominated. Forecasting is limited, too, by the availability of reliable and valid data bases for examining supply and use of nursing personnel across sectors. Three models--needs based, utilization based, and effective demand based--provide substantially different estimates of future HHR need. The methods of analysis employed for forecasting range from descriptive to predictive and are borrowed from demography, epidemiology, economics, and industrial engineering. Simulation models offer the most promise for the future. The forecasting methods described have demonstrated their accuracy and usefulness for specific situations, but none has proven accurate for long-term forecasting or for estimating needs for large geographical areas or populations. PMID:11155116
Forecast-based interventions can reduce the health and economic burden of wildfires.
Rappold, Ana G; Fann, Neal L; Crooks, James; Huang, Jin; Cascio, Wayne E; Devlin, Robert B; Diaz-Sanchez, David
2014-09-16
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
Skill of regional and global model forecast over Indian region
NASA Astrophysics Data System (ADS)
Kumar, Prashant; Kishtawal, C. M.; Pal, P. K.
2015-01-01
The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).
Forecasting Turbulent Modes with Nonparametric Diffusion Models
Tyrus Berry; John Harlim
2015-01-27
This paper presents a nonparametric diffusion modeling approach for forecasting partially observed noisy turbulent modes. The proposed forecast model uses a basis of smooth functions (constructed with the diffusion maps algorithm) to represent probability densities, so that the forecast model becomes a linear map in this basis. We estimate this linear map by exploiting a previously established rigorous connection between the discrete time shift map and the semi-group solution associated to the backward Kolmogorov equation. In order to smooth the noisy data, we apply diffusion maps to a delay embedding of the noisy data, which also helps to account for the interactions between the observed and unobserved modes. We show that this delay embedding biases the geometry of the data in a way which extracts the most predictable component of the dynamics. The resulting model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and in the observation noise limit. We will show numerical examples on a wide-range of well-studied turbulent modes, including the Fourier modes of the energy conserving Truncated Burgers-Hopf (TBH) model, the Lorenz-96 model in weakly chaotic to fully turbulent regimes, and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. In these examples, forecasting skills of the nonparametric diffusion model are compared to a wide-range of stochastic parametric modeling approaches, which account for the nonlinear interactions between the observed and unobserved modes with white and colored noises.
A forecasting procedure for nonlinear autoregressive time series models
Yuzhi Cai
2005-01-01
Forecasting for nonlinear time series is an important topic in time series analysis. Existing numerical algorithms for multi-step-ahead forecasting ignore accuracy checking, alternative Monte Carlo methods are also computationally very demanding and their accuracy is difficult to control too. In this paper a numerical forecasting procedure for nonlinear autoregressive time series models is proposed. The forecasting procedure can be used
The Hanford Site New Production Reactor (NPR) economic and demographic baseline forecasts
Cluett, C.; Clark, D.C. (Battelle Human Affairs Research Center, Seattle, WA (USA)); Pittenger, D.B. (Demographics Lab., Olympia, WA (USA))
1990-08-01
The objective of this is to present baseline employment and population forecasts for Benton, Franklin, and Yakima Counties. These forecasts will be used in the socioeconomic analysis portion of the New Production Reactor Environmental Impact Statement. Aggregate population figures for the three counties in the study area were developed for high- and low-growth scenarios for the study period 1990 through 2040. Age-sex distributions for the three counties during the study period are also presented. The high and low scenarios were developed using high and low employment projections for the Hanford site. Hanford site employment figures were used as input for the HARC-REMI Economic and Demographic (HED) model to produced baseline employment forecasts for the three counties. These results, in turn, provided input to an integrated three-county demographic model. This model, a fairly standard cohort-component model, formalizes the relationship between employment and migration by using migration to equilibrate differences in labor supply and demand. In the resulting population estimates, age-sex distributions for 1981 show the relatively large work force age groups in Benton County while Yakima County reflects higher proportions of the population in the retirement ages. The 2040 forecasts for all three counties reflect the age effects of relatively constant and low fertility increased longevity, as well as the cumulative effects of the migration assumptions in the model. By 2040 the baby boom population will be 75 years and older, contributing to the higher proportion of population in the upper end age group. The low scenario age composition effects are similar. 13 refs., 5 figs., 9 tabs.
NASA Technical Reports Server (NTRS)
1975-01-01
This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.
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.
de Lijser, Peter
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
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
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
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
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
Detecting and forecasting economic regimes in multi-agent automated exchanges Wolfgang Ketter a,
Gini, Maria
Detecting and forecasting economic regimes in multi-agent automated exchanges Wolfgang Ketter a, , John Collins b , Maria Gini b , Alok Gupta c , Paul Schrater b a Department of Decision and Information
A plan for the economic assessment of the benefits of improved meteorological forecasts
NASA Technical Reports Server (NTRS)
Bhattacharyya, R.; Greenberg, J.
1975-01-01
Benefit-cost relationships for the development of meteorological satellites are outlined. The weather forecast capabilities of the various weather satellites (Tiros, SEOS, Nimbus) are discussed, and the development of additional satellite systems is examined. A rational approach is development that leads to the establishment of the economic benefits which may result from the utilization of meteorological satellite data. The economic and social impacts of improved weather forecasting for industries and resources management are discussed, and significant weather sensitive industries are listed.
Technological Forecasting---Model Selection, Model Stability, and Combining Models
Nigel Meade; Towhidul Islam
1998-01-01
The paper identifies 29 models that the literature suggests are appropriate for technological forecasting. These models are divided into three classes according to the timing of the point of inflexion in the innovation or substitution process. Faced with a given data set and such a choice, the issue of model selection needs to be addressed. Evidence used to aid model
On the dynamics of the world demographic transition and financial-economic crises forecasts
NASA Astrophysics Data System (ADS)
Akaev, A.; Sadovnichy, V.; Korotayev, A.
2012-05-01
The article considers dynamic processes involving non-linear power-law behavior in such apparently diverse spheres, as demographic dynamics and dynamics of prices of highly liquid commodities such as oil and gold. All the respective variables exhibit features of explosive growth containing precursors indicating approaching phase transitions/catastrophes/crises. The first part of the article analyzes mathematical models of demographic dynamics that describe various scenarios of demographic development in the post-phase-transition period, including a model that takes the limitedness of the Earth carrying capacity into account. This model points to a critical point in the early 2050s, when the world population, after reaching its maximum value may decrease afterward stabilizing then at a certain stationary level. The article presents an analysis of the influence of the demographic transition (directly connected with the hyperexponential growth of the world population) on the global socioeconomic and geopolitical development. The second part deals with the phenomenon of explosive growth of prices of such highly liquid commodities as oil and gold. It is demonstrated that at present the respective processes could be regarded as precursors of waves of the global financial-economic crisis that will demand the change of the current global economic and political system. It is also shown that the moments of the start of the first and second waves of the current global crisis could have been forecasted with a model of accelerating log-periodic fluctuations superimposed over a power-law trend with a finite singularity developed by Didier Sornette and collaborators. With respect to the oil prices, it is shown that it was possible to forecast the 2008 crisis with a precision up to a month already in 2007. The gold price dynamics was used to calculate the possible time of the start of the second wave of the global crisis (July-August 2011); note that this forecast has turned out to be quite correct.
Numerical weather forecasting with anelastic model
NASA Astrophysics Data System (ADS)
Wójcik, Damian; Kurowski, Marcin; Piotrowski, Zbigniew; Rosa, Bogdan; Ziemia?ski, Micha?
2013-04-01
Research conducted at Polish Institute of Meteorology and Water Management, National Research Institute, in collaboration with Consortium for Small Scale Modeling (COSMO) are aimed at developing new conservative dynamical core for next generation operational weather prediction model. Within the frames of the project a new prototype model has been developed. The dynamical core of the model is based on anelastic set of equation and numerics adopted from the EULAG model. An employment of EULAG allowed to profit from its desirable conservative properties and numerical robustness confirmed in number of benchmark tests and widely documented in scientific literature. The first stage of the project has been already successfully completed. Its main achievement is a hybrid model capable to compute weather forecast. The model consists of EULAG dynamical core implemented into the software environment of the operational COSMO model and basic COSMO physical parameterizations involving turbulence, friction, radiation, moist processes and surface fluxes (COSMO-EULAG). The presentation shows the case studies comparing results of 24-hour forecasts calculated via the hybrid model with analogous results obtained with the Runge-Kutta dynamical core standard for the COSMO operational applications. The experiments are performed with 2.2 km resolution over Alpine domain of operational MeteoSwiss numerical forecasts. The results demonstrate that the short-term forecasts employing different dynamical cores are qualitatively and quantitatively similar, especially in the middle and upper troposphere. Near the surface the COSMO-EULAG results, while similar to the Runge-Kutta ones, show more small-scale variability. It is seen that the anelastic approximation does not impose measurable adverse affects on the forecast. The presentation shows also results of another class of experiments. They involve 24-hour forecast with COSMO-EULAG over realistic Alpine domain with the horizontal resolutions of 1.1 and 0.55 km, and employing non-filtered orography calculated for every of these resolutions from the SRTM data. The results show a dependence of the forecasted flow structure on the model resolution not only for the surface features but also for the structure of upper level flow and especially structure of the jet stream over Alpine area. The results document also numerical robustness of the COSMO-EULAG dynamical core which for the horizontal resolution of 0.55 km deals with Alpine slopes reaching 56 degrees of inclination.
UNCERTAINTY, OPTIMAL USE, AND ECONOMIC VALUE OF WEATHER FORECASTS
Katz, Richard
://www.ametsoc.org/policy/enhancingwxprob_final.html -- Quotes "The American Meteorological Society endorses probability forecasts and recommends their use site: www.isse.ucar.edu/HP_rick.html Lecture: www.isse.ucar.edu/HP_rick/pdf/cometrwk.pdf #12;Quotes "If for Better Decisions Using Weather and Climate Forecasts" http://www.nap.edu/catalog/11699.html -- Quotes
NASA Technical Reports Server (NTRS)
1977-01-01
A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions so as to significantly reduce the cost for frost and freeze protection and crop losses. The design and implementation of the first phase of an economic experiment which will monitor citrus growers decisions, actions, costs and losses, and meteorological forecasts and actual weather events was carried out. The economic experiment was designed to measure the change in annual protection costs and crop losses which are the direct result of improved temperature forecasts. To estimate the benefits that may result from improved temperature forecasting capability, control and test groups were established with effective separation being accomplished temporally. The control group, utilizing current forecasting capability, was observed during the 1976-77 frost season and the results are reported. A brief overview is given of the economic experiment, the results obtained to date, and the work which still remains to be done.
New Concepts in Wind Power Forecasting Models
Kemner, Ken
New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, JoÃ£o Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. Renyi's Entropy is combined
Managerial evaluation of sales forecasting effectiveness: A MIMIC modeling approach
Heidi M. Winklhofer; Adamantios Diamantopoulos
2002-01-01
A Multiple Indicators and MultIple Causes (MIMIC) model is developed in which managerial evaluations of forecasting effectiveness are modeled as a function of different forecast performance criteria, namely, accuracy, bias, timeliness and cost. The model is estimated using data from a survey of export sales forecasting practices and several hypotheses linking the aforementioned criteria on effectiveness are tested. The findings
A supply forecasting model for Zimbabwe's corn sector: a time series and structural analysis
Makaudze, Ephias
1993-01-01
A SUPPLY FORECASTING MODEL FOR ZIMBABWE'S CORN SECTOR: A TIME SERIES AND STRUCTURAL ANALYSIS A Thesis by EPHIAS MAKAUDZE Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE December 1993 Major Subject: Agricultural Economics A SUPPLY FORECASTING MODEL FOR ZIMBABWE'S CORN SECTOR: A TIME SERIES AND STRUCTURAL ANALYSIS A Thesis by EPHIAS MAKAUDZE Submitted to Texas ARM University in partial...
Retrospective Evaluation of Preseason Forecasting Models for Pink Salmon
Steven L. Haeseker; Randall M. Peterman; Zhenming Su; Chris C. Wood
2005-01-01
Models for making preseason forecasts of adult abundance are an important component of the management of many stocks of Pacific salmon Oncorhynchus spp. Reliable forecasts could increase both the profits from fisheries and the probability of achieving conservation and other management targets. However, the predictive performance of salmon forecasting models is generally poor, in part because of the high variability
Modular learning models in forecasting natural phenomena.
Solomatine, D P; Siek, M B
2006-03-01
Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input space, and may be trained on a subset of training set. Many algorithms for allocating such regions to local models typically do this in automatic fashion. In forecasting natural processes, however, domain experts want to bring in more knowledge into such allocation, and to have certain control over the choice of models. This paper presents a number of approaches to building modular models based on various types of splits of training set and combining the models' outputs (hard splits, statistically and deterministically driven soft combinations of models, 'fuzzy committees', etc.). An issue of including a domain expert into the modeling process is also discussed, and new algorithms in the class of model trees (piece-wise linear modular regression models) are presented. Comparison of the algorithms based on modular local modeling to the more traditional 'global' learning models on a number of benchmark tests and river flow forecasting problems shows their higher accuracy and transparency of the resulting models. PMID:16531005
Garment E-Commerce Forecast Based on Grey Model
Hongqi Hui; Yidan Zu
2009-01-01
Garment e-commerce sales forecast is important for the e-business development strategy planning and the integration of garment supply chain upstream and downstream enterprises. GDP, per capita consumption expenditure of urban residents, the total retail sales of consumer goods, the number of internet users are selected as economic forecast indexes. On the basis of grey incidence degree, close correlation indexes are
Ketter, Wolfgang
Identifying and Forecasting Economic Regimes in TAC SCM Wolfgang Ketter, John Collins, Maria Gini, Alok Gupta , and Paul Schrater Department of Computer Science and Engineering Department of Information
Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?
NASA Technical Reports Server (NTRS)
Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew
2015-01-01
The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.
Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?
NASA Technical Reports Server (NTRS)
Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew
2014-01-01
The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.
Forecasting next-day electricity prices by time series models
Francisco J. Nogales; Javier Contreras; Antonio J. Conejo; Rosario Espínola
2002-01-01
In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models.
Forecasting the Air Transport Demand for Passengers with Neural Modelling
K. P. G. Alekseev; José Manoel De Seixas
2002-01-01
The air transport industry firmly relies on forecasting methods for supporting management decisions. However, optimistic forecasting has resulted in serious problems to the Brazilian industry in the past years. In this paper, models based on artificial neural networks are developed for the air transport passenger demand forecasting. It is found that neural processing can outperform the traditional econometric approach used
Forecasting of short-term rainfall using ARMA models
Paolo Burlando; Renzo Rosso; Luis G. Cadavid; Jose D. Salas
1993-01-01
Burlando, P., Rosso, R., Cadavid, L.G. and Salas, J.D., 1993. Forecasting of short-term rainfall using ARMA models. J. Hydrol., 144: 193-211. Flood forecasting depends essentially on forecasting of rainfall or snow melt. In this paper, rainfall forecasting is approached assuming that hourly rainfall follows an autoregressive moving average (ARMA) process. This assumption is based on the fact that the autocovariance
Flood forecasting for River Mekong with data-based models
NASA Astrophysics Data System (ADS)
Shahzad, Khurram M.; Plate, Erich J.
2014-09-01
In many regions of the world, the task of flood forecasting is made difficult because only a limited database is available for generating a suitable forecast model. This paper demonstrates that in such cases parsimonious data-based hydrological models for flood forecasting can be developed if the special conditions of climate and topography are used to advantage. As an example, the middle reach of River Mekong in South East Asia is considered, where a database of discharges from seven gaging stations on the river and 31 rainfall stations on the subcatchments between gaging stations is available for model calibration. Special conditions existing for River Mekong are identified and used in developing first a network connecting all discharge gages and then models for forecasting discharge increments between gaging stations. Our final forecast model (Model 3) is a linear combination of two structurally different basic models: a model (Model 1) using linear regressions for forecasting discharge increments, and a model (Model 2) using rainfall-runoff models. Although the model based on linear regressions works reasonably well for short times, better results are obtained with rainfall-runoff modeling. However, forecast accuracy of Model 2 is limited by the quality of rainfall forecasts. For best results, both models are combined by taking weighted averages to form Model 3. Model quality is assessed by means of both persistence index PI and standard deviation of forecast error.
Li, Bai
2014-01-01
Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773
2014-01-01
Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773
Modeling, Forecasting and Mitigating Extreme Earthquakes
NASA Astrophysics Data System (ADS)
Ismail-Zadeh, A.; Le Mouel, J.; Soloviev, A.
2012-12-01
Recent earthquake disasters highlighted the importance of multi- and trans-disciplinary studies of earthquake risk. A major component of earthquake disaster risk analysis is hazards research, which should cover not only a traditional assessment of ground shaking, but also studies of geodetic, paleoseismic, geomagnetic, hydrological, deep drilling and other geophysical and geological observations together with comprehensive modeling of earthquakes and forecasting extreme events. Extreme earthquakes (large magnitude and rare events) are manifestations of complex behavior of the lithosphere structured as a hierarchical system of blocks of different sizes. Understanding of physics and dynamics of the extreme events comes from observations, measurements and modeling. A quantitative approach to simulate earthquakes in models of fault dynamics will be presented. The models reproduce basic features of the observed seismicity (e.g., the frequency-magnitude relationship, clustering of earthquakes, occurrence of extreme seismic events). They provide a link between geodynamic processes and seismicity, allow studying extreme events, influence of fault network properties on seismic patterns and seismic cycles, and assist, in a broader sense, in earthquake forecast modeling. Some aspects of predictability of large earthquakes (how well can large earthquakes be predicted today?) will be also discussed along with possibilities in mitigation of earthquake disasters (e.g., on 'inverse' forensic investigations of earthquake disasters).
City Air Quality Forecasting and Impact Factors Analysis Based on Grey Model
Lin Pan; Baosheng Sun; Wei Wang
2011-01-01
As an important environmental problem, air quality influences urban population's health and economic development. To investigate air quality changing trend and main factors affecting the quality of Tianjin in China, we employed grey dynamic model group and grey relational analysis. For forecasting, we first use model group to fit the annual average air pollution concentration of Tianjin from 2001–2009, the
Electricity generation modeling and photovoltaic forecasts in China
NASA Astrophysics Data System (ADS)
Li, Shengnan
With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979--2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.
Forecasting electricity demand with end-use\\/econometric models
M. J. King; M. J. Scott
1983-01-01
The Railbelt Electricity Demand (RED) Model, reported in this paper, is a simulation model designed to forecast annual electricity consumption for the residential, commercial-industrial-government and miscellaneous end-use sectors of Alaska's Railbelt region. The model also takes into account government intervention in the energy markets via conservation programs in Alaska and produces forecasts of system annual peak demand. The forecasts of
THE MODEL EVALUATION TOOLS (MET): COMMUNITY TOOLS FOR FORECAST EVALUATION
Barbara Brown; John Halley Gotway; Randy Bullock; Eric Gilleland; Tressa Fowler; David Ahijevych; Tara Jensen
Assessments of forecast quality are a critical com- ponent of the forecast development, improvement, and application processes. While some verification capabili- ties have been used in practice for many years, modern, state-of-the-art tools are especially needed to provide meaningful evaluations of high-resolution numerical weather prediction (NWP) model forecasts. The Model Evaluation Tools (MET) verification package has been developed to provide
FORECASTING WITH PREDICTION INTERVALS FOR PERIODIC ARMA MODELS.
Anderson, Paul L; Meerschaert, Mark M; Zhang, Kai
2013-03-01
Periodic autoregressive moving average (PARMA) models are indicated for time series whose mean, variance, and covariance function vary with the season. In this paper, we develop and implement forecasting procedures for PARMA models. Forecasts are developed using the innovations algorithm, along with an idea of Ansley. A formula for the asymptotic error variance is provided, so that Gaussian prediction intervals can be computed. Finally, an application to monthly river flow forecasting is given, to illustrate the method. PMID:23956476
Quadratic Interval Innovation Diffusion Models for New Product Sales Forecasting
F.-M. Tseng
2008-01-01
An appropriate sales forecasting method is vital to the success of a business firm. The logistic model and the Gompertz model are usually adopted to forecast the growth trends and the potential market volume of innovative products. All of these models rely on statistics to explain the relationships between dependent and independent variables, and use crisp parameters. However, fuzzy relationships
Engineering\\/economic end-use energy models
Daniel M. Hamblin; Teresa A.. Vineyard
1985-01-01
A brief review is presented on engineering\\/economic end-use energy models. End-use modeling is described within the broades context of an analytical framework giving statistically sound and valid forecasts. Some aspects of the end-use modeling problem, associated with technology and technology characterization are high-lighted. The results of policy application are given. (AIP)
Engineering/economic end-use energy models
NASA Astrophysics Data System (ADS)
Hamblin, Daniel M.; Vineyard, Teresa A..
1985-11-01
A brief review is presented on engineering/economic end-use energy models. End-use modeling is described within the broades context of an analytical framework giving statistically sound and valid forecasts. Some aspects of the end-use modeling problem, associated with technology and technology characterization are high-lighted. The results of policy application are given. (AIP)
On-line economic optimization of energy systems using weather forecast information.
Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.
2009-01-01
We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.
NASA Astrophysics Data System (ADS)
Smith, P. J.; Beven, K.; Panziera, L.
2012-04-01
The issuing of timely flood alerts may be dependant upon the ability to predict future values of water level or discharge at locations where observations are available. Catchments at risk of flash flooding often have a rapid natural response time, typically less then the forecast lead time desired for issuing alerts. This work focuses on the provision of short-range (up to 6 hours lead time) predictions of discharge in small catchments based on utilising radar forecasts to drive a hydrological model. An example analysis based upon the Verzasca catchment (Ticino, Switzerland) is presented. Parsimonious time series models with a mechanistic interpretation (so called Data-Based Mechanistic model) have been shown to provide reliable accurate forecasts in many hydrological situations. In this study such a model is developed to predict the discharge at an observed location from observed precipitation data. The model is shown to capture the snow melt response at this site. Observed discharge data is assimilated to improve the forecasts, of up to two hours lead time, that can be generated from observed precipitation. To generate forecasts with greater lead time ensemble precipitation forecasts are utilised. In this study the Nowcasting ORographic precipitation in the Alps (NORA) product outlined in more detail elsewhere (Panziera et al. Q. J. R. Meteorol. Soc. 2011; DOI:10.1002/qj.878) is utilised. NORA precipitation forecasts are derived from historical analogues based on the radar field and upper atmospheric conditions. As such, they avoid the need to explicitly model the evolution of the rainfall field through for example Lagrangian diffusion. The uncertainty in the forecasts is represented by characterisation of the joint distribution of the observed discharge, the discharge forecast using the (in operational conditions unknown) future observed precipitation and that forecast utilising the NORA ensembles. Constructing the joint distribution in this way allows the full historic record of data at the site to inform the predictive distribution. It is shown that, in part due to the limited availability of forecasts, the uncertainty in the relationship between the NORA based forecasts and other variates dominated the resulting predictive uncertainty.
A B S T R A C T Nowadays, forecasting on what will happen in economic
Paris-Sud XI, Université de
. KEYWORDS: Efficient Market Hypothesis, Financial Forecasting, Chemicals, Artificial Intelligence, Genetic Programming, Decision Support System, Hybrid Neuro Fuzzy Model. 1. INTRODUCTION nnovation of Artificial of applying the new mathematics, time series and even some advanced tools, such as Artificial Intelligence
Development of Ensemble Model Based Water Demand Forecasting Model
NASA Astrophysics Data System (ADS)
Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop
2014-05-01
In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)
Modeling and forecasting industrial end-use natural gas consumption
Eugenio Fco. Sánchez-Úbeda; Ana Berzosa
2007-01-01
Forecasting industrial end-use natural gas consumption is an important prerequisite for efficient system operation and a basis for planning decisions. This paper presents a novel prediction model that provides forecasting in a medium-term horizon (1–3 years) with a very high resolution (days) based on a decomposition approach. The forecast is obtained by the combination of three different components: one that captures
Regional Model Nesting Within GFS Daily Forecasts Over West Africa
NASA Technical Reports Server (NTRS)
Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben
2010-01-01
The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.
The potential economic benefits of improvements in weather forecasting
NASA Technical Reports Server (NTRS)
Thompson, J. C.
1972-01-01
The study was initiated as a consequence of the increased use of weather satellites, electronic computers and other technological developments which have become a virtual necessity for solving the complex problems of the earth's atmosphere. Neither the economic emphasis, nor the monetary results of the study, are intended to imply their sole use as criteria for making decisions concerning the intrinsic value of technological improvements in meteorology.
Forecasts covering one month using a cut-cell model
NASA Astrophysics Data System (ADS)
Steppeler, J.; Park, S.-H.; Dobler, A.
2013-07-01
This paper investigates the impact and potential use of the cut-cell vertical discretisation for forecasts covering five days and climate simulations. A first indication of the usefulness of this new method is obtained by a set of five-day forecasts, covering January 1989 with six forecasts. The model area was chosen to include much of Asia, the Himalayas and Australia. The cut-cell model LMZ (Lokal Modell with z-coordinates) provides a much more accurate representation of mountains on model forecasts than the terrain-following coordinate used for comparison. Therefore we are in particular interested in potential forecast improvements in the target area downwind of the Himalayas, over southeastern China, Korea and Japan. The LMZ has previously been tested extensively for one-day forecasts on a European area. Following indications of a reduced temperature error for the short forecasts, this paper investigates the model error for five days in an area influenced by strong orography. The forecasts indicated a strong impact of the cut-cell discretisation on forecast quality. The cut-cell model is available only for an older (2003) version of the model LM (Lokal Modell). It was compared using a control model differing by the use of the terrain-following coordinate only. The cut-cell model improved the precipitation forecasts of this old control model everywhere by a large margin. An improved, more transferable version of the terrain-following model LM has been developed since then under the name CLM (Climate version of the Lokal Modell). The CLM has been used and tested in all climates, while the LM was used for small areas in higher latitudes. The precipitation forecasts of the cut-cell model were compared also to the CLM. As the cut-cell model LMZ did not incorporate the developments for CLM since 2003, the precipitation forecast of the CLM was not improved in all aspects. However, for the target area downstream of the Himalayas, the cut-cell model considerably improved the prediction of the monthly precipitation forecast even in comparison with the modern CLM version. The cut-cell discretisation seems to improve in particular the localisation of precipitation, while the improvements leading from LM to CLM had a positive effect mainly on amplitude.
Multi-model methods for probabilistic streamflow forecasting
NASA Astrophysics Data System (ADS)
Zhang, Lu; van Andel, Schalk Jan; Solomatine, Dimitri
2013-04-01
Streamflow forecasting is important for flood control measures and early warning. Application of a single hydrological model for probabilistic forecasting, based on parameter uncertainty, sometimes does not result in sufficiently reliable forecasts. This can be because a particular model may perform better in rising limp, or peak discharge, or low-flows, and worse in other circumstances. Therefore, this research focuses on combining several models with different characteristics with the objective to produce sharper and more reliable probabilistic streamflow forecasts. Multi-model methods are investigated (committees of models). In particular fuzzy committee method, neural network method and Bayesian model averaging method. These methods are used to combine multiple models, e.g. HBV hydrological model and a Neural Network model. Relationships between applying model-committees for increasing sharpness and for increasing reliability are being analysed. Main case study to be presented is Bagmati river, Nepal. Applicability to MOPEX catchments, USA, will be discussed.
Forecasts covering one month using a cut cell model
NASA Astrophysics Data System (ADS)
Steppeler, J.; Park, S.-H.; Dobler, A.
2013-01-01
This paper investigates the impact and potential use of the cut cell vertical discretisation for forecasts of 5 days and climate simulations. A first indication of the usefulness of this new method is obtained by a set of five-day forecasts, covering January 1989 by 6 forecasts. The model area was chosen to include much of Asia, the Himalayas and Australia. The cut cell model LMZ provides a much more accurate representation of mountains on model forecasts than the terrain following coordinate used for comparison. Therefore we are in particular interested in potential forecast improvements in the target area downwind of the Himalaya, over South East China, Korea and Japan. The LMZ has been tested so far extensively for one-day forecasts on an European area. Following indications of a reduced temperature error for the short forecasts, this paper investigates the model error for five days in an area influenced by strong orography. The forecasts indicated a strong impact of the cut cell discretisation on forecast quality. The cut cell model is available only of an older (2003) Version of the model LM. It was compared using a control model differing by the use of the terrain following coordinate only. The cut cell model improved the precipitation forecasts of this old control model everywhere by a large margin. An improved version of the terrain following model LM has been developed since then under the name CLM. The CLM has been used and tested in all climates, while the LM was used for small areas in higher latitudes. The precipitation forecasts of cut cell model were compared also to the CLM. As the cut cell model LMZ did not incorporate the developments for CLM since 2003, the precipitation forecast of the CLM was not improved in all aspects. However, for the target area downstream of the Himalaya, the cut cell model improved the prediction of the monthly precipitation forecast even in comparison with the modern model version CLM considerably. The cut cell discretisation seems to improve in particular the localisation of precipitation, while the improvements leading from LM to CLM had a positive effect mainly on amplitude.
Operational forecasting based on a modified Weather Research and Forecasting model
Lundquist, J; Glascoe, L; Obrecht, J
2010-03-18
Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.
Evaluation of the Weather Research and Forecasting Model on
Basu, Sukanta
Evaluation of the Weather Research and Forecasting Model on Forecasting Low-level Jets: Implications for Wind Energy Brandon Storm*, Wind Science and Engineering Research Center, Texas Tech (www.interscience.wiley.com) DOI: 10.1002/we.288 Research Article * Correspondence to: B. A. Storm
A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music
Jonathan Lee; Peter Boatwright; Wagner A. Kamakura
2003-01-01
n a situation where several hundred new music albums are released each month, produc- ing sales forecasts in a reliable and consistent manner is a rather difficult and cumbersome task. The purpose of this study is to obtain sales forecasts for a new album before it is intro- duced. We develop a hierarchical Bayesian model based on a logistic diffusion
2013DvorakandSailor'sEnergy Model Forecasting Accuracy Along
Firestone, Jeremy
©2013DvorakandSailor'sEnergy Model Forecasting Accuracy Along an East Coast Offshore Grid Corridor of an ideal offshore grid · Mid-Atlantic Offshore Wind Integration & Transmission (MAOWIT) region · Forecast, 2010. State electricity sales spreadsheet, 2009. 2. http://offshorewindenergy.org 3. Principle Pow er
Integration of DSM technology modeling and long-run forecasting
McMenamin, J.S. [Regional Economic Research, Inc., San Diego, CA (United States)
1995-05-01
This paper summarizes the lessons and conclusions from several projects aimed at integrating DSM into long-run forecasting models. The focus of the paper is on the technical issues that arise when attempting to incorporate DSM technology detail directly into end-use forecasting frameworks.
Wind power forecasting model using complex wavelet theory
Sukumar Mishra; Anuj Sharma; Ganpati Panda
2011-01-01
Due to growing share of wind power in world's energy consumption, forecasting of the wind power becomes essential for proper utilization. This paper proposes short term wind power forecasting model using complex wavelet transform and neural network. The past wind power values are transferred into real and complex signal; which are further transferred in Wavelet domain signal. These signals are
Time dependent Directional Profit Model for Financial Time Series Forecasting
Yao, JingTao
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
Multilayer Stock Forecasting Model Using Fuzzy Time Series
Javedani Sadaei, Hossein; Lee, Muhammad Hisyam
2014-01-01
After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS. PMID:24605058
A channel dynamics model for real-time flood forecasting
Hoos, A.B.; Koussis, A.D.; Beale, G.O.
1989-01-01
A new channel dynamics scheme ASPIRE (alternative system predictor in real time), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio. -Authors
Antonis G. Tsikalakis; Yiannis A. Katsigiannis; Pavlos S. Georgilakis; Nikos D. Hatziargyriou
2006-01-01
Many efforts have been presented in the bibliography for wind power forecasting in power systems and few of them have been used for autonomous power systems. The impact of knowing the distribution function of wind power forecasting error in the economic operation of a power system is studied in this paper. The papers proposes that the distribution of the wind
Accounting for uncertainty in distributed flood forecasting models
Steven J. Cole; Alice J. Robson; Victoria A. Bell; Robert J. Moore; Clive E. Pierce; Nigel Roberts
2010-01-01
Recent research investigating the uncertainty of distributed hydrological flood forecasting models will be presented. These findings utilise the latest advances in rainfall estimation, ensemble nowcasting and Numerical Weather Prediction (NWP). The hydrological flood model that forms the central focus of the study is the Grid-to-Grid Model or G2G: this is a distributed grid-based model that produces area-wide flood forecasts across
Monthly mean forecast experiments with the GISS model
NASA Technical Reports Server (NTRS)
Spar, J.; Atlas, R. M.; Kuo, E.
1976-01-01
The GISS general circulation model was used to compute global monthly mean forecasts for January 1973, 1974, and 1975 from initial conditions on the first day of each month and constant sea surface temperatures. Forecasts were evaluated in terms of global and hemispheric energetics, zonally averaged meridional and vertical profiles, forecast error statistics, and monthly mean synoptic fields. Although it generated a realistic mean meridional structure, the model did not adequately reproduce the observed interannual variations in the large scale monthly mean energetics and zonally averaged circulation. The monthly mean sea level pressure field was not predicted satisfactorily, but annual changes in the Icelandic low were simulated. The impact of temporal sea surface temperature variations on the forecasts was investigated by comparing two parallel forecasts for January 1974, one using climatological ocean temperatures and the other observed daily ocean temperatures. The use of daily updated sea surface temperatures produced no discernible beneficial effect.
Weather satellites and the economic value of forecasts: evidence from the electric power industry
NASA Astrophysics Data System (ADS)
Hertzfeld, Henry R.; Williamson, Ray A.; Sen, Avery
2004-08-01
Data from weather satellites have become integral to the weather forecast process in the United States and abroad. Satellite data are used to derive improved forecasts for short-term routine weather, long-term climate change, and for predicting natural disasters. The resulting forecasts have saved lives, reduced weather-related economic losses, and improved the quality of life. Weather information routinely assists in managing resources more efficiently and reducing industrial operating costs. The electric energy industry in particular makes extensive use of weather information supplied by both government and commercial suppliers. Through direct purchases of weather data and information, and through participating in the increasing market for weather derivatives, this sector provides measurable indicators of the economic importance of weather information. Space weather in the form of magnetic disturbances caused by coronal mass ejections from the sun creates geomagnetically induced currents that disturb the electric power grid, sometimes causing significant economic impacts on electric power distribution. This paper examines the use of space-derived weather information on the U.S. electric power industry. It also explores issues that may impair the most optimum use of the information and reviews the longer-term opportunities for employing weather data acquired from satellites in future commercial and government activity.
Evaluation of statistical models for forecast errors from the HBV model
NASA Astrophysics Data System (ADS)
Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur
2010-04-01
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.
Modelling Nonlinear Economic Time Series
Timo Terasvirta; Dag Tjostheim; Clive W. J. Granger
This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader
Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel
2014-01-01
The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292
Zhao, Xiuli; Yiranbon, Ethel
2014-01-01
The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Value of the GENS Forecast Ensemble as a Tool for Adaptation of Economic Activity to Climate Change
NASA Astrophysics Data System (ADS)
Hancock, L. O.; Alpert, J. C.; Kordzakhia, M.
2009-12-01
In an atmosphere of uncertainty as to the magnitude and direction of climate change in upcoming decades, one adaptation mechanism has emerged with consensus support: the upgrade and dissemination of spatially-resolved, accurate forecasts tailored to the needs of users. Forecasting can facilitate the changeover from dependence on climatology that is increasingly out of date. The best forecasters are local, but local forecasters face great constraints in some countries. Indeed, it is no coincidence that some areas subject to great weather variability and strong processes of climate change are economically vulnerable: mountainous regions, for example, where heavy and erratic flooding can destroy the value built up by households over years. It follows that those best placed to benefit from forecasting upgrades may not be those who have invested in the greatest capacity to date. More-flexible use of the global forecasts may contribute to adaptation. NOAA anticipated several years ago that their forecasts could be used in new ways in the future, and accordingly prepared sockets for easy access to their archives. These could be used to empower various national and regional capacities. Verification to identify practical lead times for the economically important variables is a needed first step. This presentation presents the verification that our team has undertaken, a pilot effort in which we considered variables of interest to economic actors in several lower income countries, cf. shepherds in a remote area of Central Asia, and verified the ensemble forecasts of those variables.
Improving statistical forecasts of seasonal streamflows using hydrological model output
NASA Astrophysics Data System (ADS)
Robertson, D. E.; Pokhrel, P.; Wang, Q. J.
2013-02-01
Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.
Forecasting natural aquifer discharge using a numerical model and convolution.
Boggs, Kevin G; Johnson, Gary S; Van Kirk, Rob; Fairley, Jerry P
2014-01-01
If the nature of groundwater sources and sinks can be determined or predicted, the data can be used to forecast natural aquifer discharge. We present a procedure to forecast the relative contribution of individual aquifer sources and sinks to natural aquifer discharge. Using these individual aquifer recharge components, along with observed aquifer heads for each January, we generate a 1-year, monthly spring discharge forecast for the upcoming year with an existing numerical model and convolution. The results indicate that a forecast of natural aquifer discharge can be developed using only the dominant aquifer recharge sources combined with the effects of aquifer heads (initial conditions) at the time the forecast is generated. We also estimate how our forecast will perform in the future using a jackknife procedure, which indicates that the future performance of the forecast is good (Nash-Sutcliffe efficiency of 0.81). We develop a forecast and demonstrate important features of the procedure by presenting an application to the Eastern Snake Plain Aquifer in southern Idaho. PMID:23914881
Systematic model forecast error in Rossby wave structure
NASA Astrophysics Data System (ADS)
Gray, S. L.; Dunning, C. M.; Methven, J.; Masato, G.; Chagnon, J. M.
2014-04-01
Diabatic processes can alter Rossby wave structure; consequently, errors arising from model processes propagate downstream. However, the chaotic spread of forecasts from initial condition uncertainty renders it difficult to trace back from root-mean-square forecast errors to model errors. Here diagnostics unaffected by phase errors are used, enabling investigation of systematic errors in Rossby waves in winter season forecasts from three operational centers. Tropopause sharpness adjacent to ridges decreases with forecast lead time. It depends strongly on model resolution, even though models are examined on a common grid. Rossby wave amplitude reduces with lead 5 days, consistent with underrepresentation of diabatic modification and transport of air from the lower troposphere into upper tropospheric ridges, and with too weak humidity gradients across the tropopause. However, amplitude also decreases when resolution is decreased. Further work is necessary to isolate the contribution from errors in the representation of diabatic processes.
Combining regional forecast and crop yield models for the USDA
NASA Astrophysics Data System (ADS)
Zuba, G.; Gibbas, M.; Lee, M.; Dailey, P.; Keller, J.
2003-04-01
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.
An Initial Study on the Forecast Model for Unemployment Rate
Mohd Nadzri Mohd Nasir; Kon Mee Hwa; Huzaifah Mohammad
The purpose of the article is to determine the most suitable technique to generate the forecast of unemployment rate using data from the series of Labour Force Surveys. The models understudied are based on Univariate Modelling Techniques i.e. Naïve with Trend Model, Average Change Model, Double Exponential Smoothing and Holt's Method Model. These models are normally used to determine the
Inaccurate forecasts of the logistic growth model for Nobel Prizes
Bruce L. Golden; Paul F. Zantek
2004-01-01
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
Spatio-temporal modeling for real-time ozone forecasting.
Paci, Lucia; Gelfand, Alan E; Holland, David M
2013-05-01
The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts. PMID:24010052
Forecasting risks of natural gas consumption in Slovenia
Alojz Poredosb
Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic
Forecasting risks of natural gas consumption in Slovenia
Primož Poto?nik; Marko Thaler; Edvard Govekar; Igor Grabec; Alojz Poredoš
2007-01-01
Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic
Andrews, L.M.; King, M.J.; Leary, N.; Perry, D.M.; Snow, C.C.
1986-12-01
The INDEPTH industrial planning methodology will enable utilities to forecast service area electricity demand. The system allows the user to develop energy forecasts for the whole industrial sector, to examine industries most important to the service area, and to study uses of electricity that are of interest in demand-side management programs. The econometric model in this volume forecasts energy use for the entire industrial sector using a set of simultaneous factor demand equations with an imposed structure derived from the economic theory of cost-minimizing behavior.
Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling
Ye, Hao; Beamish, Richard J.; Glaser, Sarah M.; Grant, Sue C. H.; Hsieh, Chih-hao; Richards, Laura J.; Schnute, Jon T.; Sugihara, George
2015-01-01
It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts. PMID:25733874
Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling.
Ye, Hao; Beamish, Richard J; Glaser, Sarah M; Grant, Sue C H; Hsieh, Chih-Hao; Richards, Laura J; Schnute, Jon T; Sugihara, George
2015-03-31
It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner-recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts. PMID:25733874
A model for Long-term Industrial Energy Forecasting (LIEF)
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
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.
Near real time wind energy forecasting incorporating wind tunnel modeling
NASA Astrophysics Data System (ADS)
Lubitz, William David
A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power production of a wind farm in the Altamont Pass, California, USA from 12 to 48 hours in advance. It is of modular construction and designed to also allow diagnostic forecasting using archived RSF data, thereby allowing different methods of completing each forecasting step to be tested and compared using the same input data. Wind-tunnel investigations of the effect of wind direction and hill geometry on wind speed-up above a hill were conducted. Field data from an Altamont Pass, California site was used to evaluate several speed-up prediction algorithms, both with and without wind direction adjustment. These algorithms were found to be of limited usefulness for the complex terrain case evaluated. Wind-tunnel and numerical simulation-based methods were developed for determining a wind farm power curve (the relation between meteorological conditions at a point in the wind farm and the power production of the wind farm). Both methods, as well as two methods based on fits to historical data, ultimately showed similar levels of accuracy: mean absolute errors predicting power production of 5 to 7 percent of the wind farm power capacity. The downscaling of RSF forecast data to the wind farm was found to be complicated by the presence of complex terrain. Poor results using the geostrophic drag law and regression methods motivated the development of a database search method that is capable of forecasting not only wind speeds but also power production with accuracy better than persistence.
Vessel Traffic Flow Forecasting Model Study Based on Support Vector Machine
NASA Astrophysics Data System (ADS)
Feng, Hongxiang; Kong, Fancun; Xiao, Yingjie
Based on vessel traffic flow data and Support Vector Machine theory, SVM regression model for short-term vessel traffic flow forecasting was presented. The forecasted vessel traffic flow and abserved ones, which by SVM regression model, coincide properly, and the forecasting results show that mean absolute percentage error of forecasting are smaller than that by SPSS regress model, which validates the feasibility of SVM regression model in the vessel traffic flow forecasting.
Distributed Hydrologic Models for Flow Forecasts - Part 2
NSDL National Science Digital Library
COMET
2010-09-28
Distributed Hydrologic Models for Flow Forecasts Part 2 is the second release in a two-part series focused on the science of distributed models and their applicability to different flow forecasting situations. Presented by Dr. Dennis Johnson, the module provides a more detailed look at the processes and mechanisms involved in distributed hydrologic models. It examines the rainfall/runoff component, snowmelt, overland flow routing, and channel response in a basin as represented in a distributed model. Calibration issues and situations in which distributed hydrologic models might be most appropriate are also addressed.
A Very Short-Term Load Forecasting Method for Economic Load Dispatching Control
NASA Astrophysics Data System (ADS)
Yamamoto, Toshiyuki; Yokoyama, Akihiko; Honda, Yuusuke; Yabuta, Hiroshi; Yoshida, Kiyoshi
This paper proposes a very short-term load forecasting technique in order to make Economic Load Dispatching Control (EDC) more effective. The controllable periodic term of EDC is made clear through power spectrum analysis of actual load and actual EDC outputs of the Kansai Electric Power Company. A real-time predictor is designed based on this result, and it can decrease the EDC error at almost the same level as the actual AR (Area Requirement: final error after EDC and LFC) through the year. In addition, it can be seen from the generation simulation by using this predictor that we can decrease the fuel cost and LFC margin.
Evaluation of the performance of DIAS ionospheric forecasting models
NASA Astrophysics Data System (ADS)
Tsagouri, Ioanna
2011-08-01
Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr). Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i) the solar wind driven autoregression model for ionospheric short-term forecast (SWIF), which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii) the geomagnetically correlated autoregression model (GCAM), which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models' predictions with actual observations obtained over almost one solar cycle (1998-2007) at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome) and on the comparison of the models' performance against two simple prediction strategies, the median- and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.
A model for Long-term Industrial Energy Forecasting (LIEF)
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
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.
Nearest neighbour models for local and regional avalanche forecasting
NASA Astrophysics Data System (ADS)
Gassner, M.; Brabec, B.
This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF). Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.
Validation of Model Forecasts of the Ambient Solar Wind
NASA Technical Reports Server (NTRS)
Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.
2009-01-01
Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.
Wind power forecasting using advanced neural networks models
G. N. Kariniotakis; G. S. Stavrakakis; E. F. Nogaret
1996-01-01
In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimised automatically by a new algorithm, that substitutes the usually applied
PROBABILISTIC FLOOD FORECASTING USING A DISTRIBUTED RAINFALL-RUNOFF MODEL
Fernandez, Thomas
PROBABILISTIC FLOOD FORECASTING USING A DISTRIBUTED RAINFALL-RUNOFF MODEL PAUL JAMES SMITH 2005 #12., for their assistance regarding rainfall- runoff modeling, and to Yoshiyuki Zushi of the Foundation of River and Basin...................................................................................................... 1 2. DISTRIBUTED RAINFALL-RUNOFF MODELING.............................................. 3 2
Development of a qualitative reasoning model for financial forecasting
Someswar Kesh; M. K. Raja
2005-01-01
Purpose – To develop a model for financial forecasting using the principles of qualitative reasoning. Design\\/methodology\\/approach – The model was developed using theories in the accounting, finance, and marketing literature. Quantitative equations were transformed into their equivalent qualitative forms. Qualitative equations, where applicable, were developed and integrated with the quantitative equations. Findings – The research demonstrated that qualitative reasoning models
Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics
NASA Astrophysics Data System (ADS)
Scheuerer, M.
2014-04-01
Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the German Meteorological Service. It yields calibrated and sharp predictive distributions and compares favourably with extended logistic regression and Bayesian model averaging which are state of the art approaches for precipitation post-processing. The incorporation of neighbourhood information further improves predictive performance and turns out to be a useful strategy to account for displacement errors of the dynamical forecasts in a probabilistic forecasting framework.
Johnson, Eric E.
://www.conference-board.org/economics/chiefEconomist.cfm The Congressional Budget Office (CBO), created by Congress to provide unbiased analysis of legislative issues://www.federalreserve.gov/monetarypolicy/mpr_default.htm The US Department of Energy, Energy Information Administration (EIA), produces both short term and long are projections of economic activity including GDP growth. These reports can be found on-line at: http
Forecasting coconut production in the Philippines with ARIMA model
NASA Astrophysics Data System (ADS)
Lim, Cristina Teresa
2015-02-01
The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.
Forecasting Diffusion of Technology by using Bass Model
NASA Astrophysics Data System (ADS)
Kim, Do-Hoi; Shin, Young-Geun; Park, Sang-Sung; Jang, Dong-Sik
2009-08-01
Generally, researching method of technology forecasting has been depended on intuition of expert until now. So there were many defects like consuming much time and money and so on. In this paper, we forecast diffusion of technology by using Bass model that is one of the quantitative analysis methods. We applied this model at technology market. And for input data of experiment, we use patent data that is representing each technology in technology market. We expect this research will be suggest new possibility that patent data can be applied in Bass model.
A New Forecasting Model for USD/CNY Exchange Rate
Cai, Zongwu; Chen, Linna; Fang, Ying
2012-09-18
in model (3.3); see Cai and Tiwari (2000) for details. Besides the above local linear estimation method, Huang and Shen (2004) proposed a global smoothing method based on a polynomial spline to estimate the functional-coefficient model. One appealing... autoregressive model (STAR), the artificial neural network model (ANN) and the functional coefficient regression model using a polynomial spline proposed by Huang and Shen (2004, denoted by HS). The forecasting performances of these models seem to be mixed. Meese...
Fuzzy temporal logic based railway passenger flow forecast model.
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
2014-01-01
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
2014-01-01
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586
NASA Astrophysics Data System (ADS)
Bao, Hongjun; Zhao, Linna
2012-02-01
A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.
A hybrid econometric—neural network modeling approach for sales forecasting
James T. Luxhøj; Jens O. Riis; Brian Stensballe
1996-01-01
Business sales forecasting is an example of management decision making in an ill-structured, uncertain problem domain. Due to the dynamic complexities of both internal and external corporate environments, many firms resort to qualitative forecasting techniques. However, these qualitative techniques lack the structure and extrapolation capability of quantitative forecasting models, and forecasting inaccuracies typically lead to dramatic disturbances in production planning.This
Combined forecast process: Combining scenario analysis with the technological substitution model
Ming-Yeu Wang; Wei-Ting Lan
2007-01-01
Forecasts can be improved by combining separate forecasts obtained by different methods. The complementary nature of the scenario analysis and technological substitution models means that combining the two can obtain improved forecasts. The former has the strength of dealing with the uncertain future, while the later offers data-based forecasts of quantifiable parameters. This study thus proposes a process for combining
River flood forecasting with a neural network model
Marina Campolo; Paolo Andreussi; Alfredo Soldati
1999-01-01
A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing
) following the method of Janjic (2001). Details are still evolving, but generally speaking, the operational in order to allow its most effective utilization as a forecast tool in the human forecast process. al., 1993) and had to account for many changes (NCEP, 2004 and COMET, 2005) to the model physics
Evaluation Of Statistical Models For Forecast Errors From The HBV-Model
NASA Astrophysics Data System (ADS)
Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.
2009-04-01
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.
China's oil reserve forecast and analysis based on peak oil models
Lianyong Feng; Junchen Li; Xiongqi Pang
2008-01-01
In order to forecast future oil production it is necessary to know the size of the reserves and use models. In this article, we use the typical Peak Oil models, the Hu–Chen–Zhang model usually called HCZ model and the Hubbert model, which have been used commonly for forecasting in China and the world, to forecast China's oil Ultimate Recovery (URR).
Bayesian Methods for Hydrometeorological Modeling and Forecasting (Invited)
NASA Astrophysics Data System (ADS)
Rajagopalan, B.; Verdin, A.; Mendoza, P. A.; Kleiber, W.; McCreight, J. L.; Wood, A. W.; Clark, M. P.; Funk, C. C.
2013-12-01
Efficient management of water resources requires skillful modeling and forecasting of hydrometeorology at the river basin scale. This includes but is not limited to basin precipitation for input into hydrologic models, downscaling precipitation from grid scale to a set of decision points and combining streamflow or precipitation from multiple model sources. Estimation of these variables in space and time, which is necessary for improved hydrometeorologic forecasting in the basins, is typically done from limited observations, thus requiring a good quantification of uncertainties. Emerging Bayesian methods offer attractive alternatives to traditional approaches to the above problems given their robust estimation of the variables and their attendant uncertainties. In this paper, we provide a sampling of Bayesian methods with three diverse applications. In the first, gridded variable such as precipitation from a climate model or satellite-based estimates need to be blended with point (station) observations to obtain improved gridded estimates of precipitation and the uncertainties, useful for driving hydrologic models and natural hazard mitigation. A Spatial Bayesian Hierarchical model is proposed for this problem and we apply this to blending precipitation over Central America and Upper Colorado River Basin. The second application consists of combining information from multiple sources - specifically, ensembles of precipitation are available at several grid points surrounding a location where a skillful estimate is desired. For this, Bayesian Model Averaging is applied to obtain a combined posterior probability distribution of precipitation. Last but not least, streamflow forecasts are generated from a number of models (dynamical and statistical) in the Colorado River Basin each with different skill and uncertainty - a robust combination method is desired that combines these forecasts to obtain an improved forecast. A Bayesian model combination applied for this problem results in a posterior distribution of streamflow forecasts that combines the different forecasts. These methods offer a sampling of applications and can be easily extended to other situations, including near term estimation of precipitation by blending model and radar observations, or combining multi-model ensembles of precipitation from the National Multi Model Ensemble (NMME) suite for applications such as hydrologic modeling.
Paris-Sud XI, Université de
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
DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS
Hickman, Mark
significant value in forecasting key economic fundamentals. A forecast that is based on an econometric modelDEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY McAleer WORKING PAPER No. 35/2010 Department of Economics and Finance College of Business
Using Bayes Model Averaging for Wind Power Forecasts
NASA Astrophysics Data System (ADS)
Preede Revheim, Pål; Beyer, Hans Georg
2014-05-01
For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data does not contain information, but it has the disadvantage of nearly doubling the number of model parameters to be estimated. Second, the BMA procedure is run with group mean wind power as the response variable instead of group mean wind speed. This also solves the problem with longer consecutive periods without information in the input data, but it leaves the power curve to also be estimated from the data. [1] Raftery, A. E., et al. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133, 1155-1174. [2]Revheim, P. P. and H. G. Beyer (2013). Using Bayesian Model Averaging for wind farm group forecasts. EWEA Wind Power Forecasting Technology Workshop,Rotterdam, 4-5 December 2013. [3]Sloughter, J. M., T. Gneiting and A. E. Raftery (2010). Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging. Journal of the American Statistical Association, Vol. 105, No. 489, 25-35
QUANTIFYING THE ECONOMIC VALUE OF WEATHER FORECASTS: REVIEW OF METHODS AND RESULTS
Katz, Richard
) Review of Case Studies (8) Discussion #12;(1) Motivation Â· Finley's Tornado Forecasts (1884) Observed Forecast Tornado No Tornado Tornado n11 = 28 n10 = 72 No Tornado n01 = 23 n00 = 2680 -- 96.6% correct #12;(1) Motivation Â· Finley's Tornado Forecasts (1884) Observed Forecast Tornado No Tornado Tornado n11 = 28 n10 = 72
Population forecasting and local economic planning: The limits on community control over uncertainty
Andrew M. Isserman; Peter S. Fisher
1984-01-01
Forecasting, planning, and controls are all attempts to cope with uncertainty about the future. The reasons that forecasts err are examined, and the limits of technical solutions are discussed. The beneficial planning uses of even error-prone forecasts are outlined, and it is argued that the concept of forecast accuracy is a basic contradiction of the essence of planning. The potential
Trend analysis model to forecast energy supply and demand
Not Available
1984-01-01
A particular approach to energy forecasting which was studied in considerable detail was trend extrapolation. This technique, termed the trend analysis model, was suggested by Dr. S. Scott Sutton, the EIA contract technical officer. While a variety of equations were explored during this part of the study, they are variations of a basic formulation. This report describes the trend analysis model, demonstrates the trend analysis model and documents the computer program used to produce the model results.
Time series modelling and forecasting of emergency department overcrowding.
Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian
2014-09-01
Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand. PMID:25053208
Networking Sensor Observations, Forecast Models & Data Analysis Tools
NASA Astrophysics Data System (ADS)
Falke, S. R.; Roberts, G.; Sullivan, D.; Dibner, P. C.; Husar, R. B.
2009-12-01
This presentation explores the interaction between sensor webs and forecast models and data analysis processes within service oriented architectures (SOA). Earth observation data from surface monitors and satellite sensors and output from earth science models are increasingly available through open interfaces that adhere to web standards, such as the OGC Web Coverage Service (WCS), OGC Sensor Observation Service (SOS), OGC Web Processing Service (WPS), SOAP-Web Services Description Language (WSDL), or RESTful web services. We examine the implementation of these standards from the perspective of forecast models and analysis tools. Interoperable interfaces for model inputs, outputs, and settings are defined with the purpose of connecting them with data access services in service oriented frameworks. We review current best practices in modular modeling, such as OpenMI and ESMF/Mapl, and examine the applicability of those practices to service oriented sensor webs. In particular, we apply sensor-model-analysis interfaces within the context of wildfire smoke analysis and forecasting scenario used in the recent GEOSS Architecture Implementation Pilot. Fire locations derived from satellites and surface observations and reconciled through a US Forest Service SOAP web service are used to initialize a CALPUFF smoke forecast model. The results of the smoke forecast model are served through an OGC WCS interface that is accessed from an analysis tool that extract areas of high particulate matter concentrations and a data comparison tool that compares the forecasted smoke with Unattended Aerial System (UAS) collected imagery and satellite-derived aerosol indices. An OGC WPS that calculates population statistics based on polygon areas is used with the extract area of high particulate matter to derive information on the population expected to be impacted by smoke from the wildfires. We described the process for enabling the fire location, smoke forecast, smoke observation, and population statistics services to be registered with the GEOSS registry and made findable through the GEOSS Clearinghouse. The fusion of data sources and different web service interfaces illustrate the agility in using standard interfaces and help define the type of input and output interfaces needed to connect models and analysis tools within sensor webs.
Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre
NASA Astrophysics Data System (ADS)
Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip
2013-04-01
Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in imperfect conditions. The forecasting model testing centre uses a repository to hold all the data and models and a catalogue to hold all the corresponding metadata. It allows to: Data transfer: Upload experimental data: We have developed FAST (Flexible Automated Streaming Transfer) tool to upload data from RP laboratories to the repository. FAST sets up data transfer requirements and selects automatically the transfer protocol. Metadata are automatically created and stored. Web data access: Create synthetic data: Users can choose a generator and supply parameters. Synthetic data are automatically stored with corresponding metadata. Select data and models: Search the metadata using criteria design for RP. The metadata of each data (synthetic or from laboratory) and models are well-described through their respective catalogues accessible by the web portal. Upload models: Upload and store a model with associated metadata. This provide an opportunity to share models. The web portal solicits and creates metadata describing each model. Run model and visualise results: Selected data and a model to be submitted to a High Performance Computational resource hiding technical details. Results are displayed in accelerated time and stored allowing retrieval, inspection and aggregation. The forecasting model testing centre proposed could be integrated into EPOS. Its expected benefits are: Improved the understanding of brittle failure prediction and its scalability to natural phenomena. Accelerated and extensive testing and rapid sharing of insights. Increased impact and visibility of RP and GeoScience research. Resources for education and training. A key challenge is to agree the framework for sharing RP data and models. Our work is provocative first step.
United States. Bonneville Power Administration.
1994-02-01
This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.
Statton, James Cody
2012-07-16
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...
CCPP-ARM Parameterization Testbed Model Forecast Data
Klein, Stephen
Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).
Adaptation of Mesoscale Weather Models to Local Forecasting
NASA Technical Reports Server (NTRS)
Manobianco, John T.; Taylor, Gregory E.; Case, Jonathan L.; Dianic, Allan V.; Wheeler, Mark W.; Zack, John W.; Nutter, Paul A.
2003-01-01
Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.
Weather load model for electric demand and energy forecasting
C. E. Asbury
1975-01-01
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.
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...
A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE
Boyer, Edmond
A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE Wensheng Zhang1,* , Hongfu Chen1 and excessive fluctuation of agricultural and livestock products price is not only harmful to residents' living, but also affects CPI (Consumer Price Index) values, and even leads to social crisis, which influences
Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting
NASA Astrophysics Data System (ADS)
Butts, M. B.; Yamagata, K.; Kobor, J.; Fontenot, E.
2008-05-01
Operational forecasting systems must provide reliable, accurate and timely flood forecasts for a range of catchments from small rapidly responding mountain catchments and urban areas to large, complex but more slowly responding fluvial systems. Flood forecasting systems have evolved from simple forecasting for flood mitigation to real-time decision support systems for real-time reservoir operations for water supply, navigation, hydropower, for managing environmental flows and habitat protection, cooling water and water quality forecasting. These different requirements lead to a number of challenges in applying distributed modelling in an operational context. These challenges include, the often short time available for forecasting that requires a trade-off between model complexity and accuracy on the one hand and on the other hand the need for efficient calculations to reduce the computation times. Limitations in the data available in real-time require modelling tools that can not only operate on a minimum of data but also take advantage of new data sources such as weather radar, satellite remote sensing, wireless sensors etc. Finally, models must not only accurately predict flood peaks but also forecast low flows and surface water-groundwater interactions, water quality, water temperature, optimal reservoir levels, and inundated areas. This paper shows how these challenges are being addressed in a number of case studies. The central strategy has been to develop a flexible modelling framework that can be adapted to different data sources, different levels of complexity and spatial distribution and different modelling objectives. The resulting framework allows amongst other things, optimal use of grid-based precipitation fields from weather radar and numerical weather models, direct integration of satellite remote sensing, a unique capability to treat a range of new forecasting problems such as flooding conditioned by surface water-groundwater interactions. Results from flood modelling on the Odra River in Poland show that this model system can perform as well as traditional models and gives good predictions in mountainous catchments. By allowing different process representations to be applied within the same framework, it is possible to develop hydrological models in a phased manner. This phased approach was used for example in the Napa Valley, California where it is important to balance water demands for urban areas, agriculture, and ecosystem preservation while maintaining flood protection and water quality. A first regional model was developed with a detailed description of the surface process and a simple linear reservoir was used to simulate the groundwater component. Then a more detailed fully-distributed finite-difference groundwater model was constructed within the same framework while maintaining the surface water components. In the DMIP case study, Blue River, Oklahoma, this flexibility has been used to evaluate the performance of different model structures, and to determine the impact of grid resolution on model accuracy. The results show clear limits to the benefit attained by increasing model complexity and resolution. In contrast, detailed flood mapping using high resolution topography carried out with this tool in South Boulder Creek, Colorado show that very detailed description of the topography and flows paths are required for accurate flood mapping and determination of the flood risk. This framework is now being used to develop a flood forecasting system for the Big Cypress Basin in Florida.
Coherent mortality forecasting: the product-ratio method with functional time series models.
Hyndman, Rob J; Booth, Heather; Yasmeen, Farah
2013-02-01
When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations. PMID:23055234
BASIN SCALE RAINFALL - RUNOFF MODELING FOR FLOOD FORECASTS
T. P. KAFLE; M. K. HAZARIKA; S. KARKI; R. M. SSHRESTHA; R. SHARMA
Flow estimation at a point in a river is vital for a number of hydrologic applications including flood forecast. This paper presents the results of a basin scale rainfall-runoff modeling on Bagmati basin in Nepal using the hydrologic model HEC-HMS in a GIS environment. The model, in combination with the GIS extension HEC-GeoHMS, was used to convert the precipitation excess
Distributed Hydrologic Models for Flow Forecasts - Part 1
NSDL National Science Digital Library
COMET
2009-07-28
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.
New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.
ERIC Educational Resources Information Center
Song, Qiang; Chissom, Brad S.
Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Granada, Universidad de
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models Jose´ Luis of Botany, University of Granada, Spain Abstract Forecasting airborne pollen concentrations is one-fuzzy models. In this work, we applied some of these models to forecast olive pollen concentrations
METRo: A New Model for Road-Condition Forecasting in Canada
Louis-Philippe Crevier; Yves Delage
2001-01-01
A numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), has been developed to run at Canadian weather centers. METRo uses roadside observations from road weather information systems stations as input, together with meteorological forecasts from the operational Global Environmental Multiscale (GEM) model of the Canadian Meteorological Centre; the meteorologist can modify this forecast
NASA Astrophysics Data System (ADS)
Hawthorne, Sandra; Wang, Q. J.; Schepen, Andrew; Robertson, David
2013-09-01
Long lead rainfall forecasts are highly valuable for planning and management of water resources and agriculture. In this study, we establish multiple statistical calibration and bridging models that use general circulation model (GCM) outputs as predictors to produce monthly rainfall forecasts for Australia with lead times up to 8 months. The statistical calibration models make use of raw forecasts of rainfall from a coupled GCM, and the statistical bridging models make use of sea surface temperature (SST) forecasts of the GCM. The forecasts from the multiple models are merged through Bayesian model averaging to take advantage of the strengths of individual models. The skill of monthly rainfall forecasts is generally low. Compared to forecasting seasonal rainfall totals, it is more challenging to forecast monthly rainfall. However, there are regions and months for which forecasts are skillful. In particular, there are months of the year for which forecasts can be skillfully made at long lead times. This is most evident for the period of November and December. Using GCM forecasts of SST through bridging clearly improves monthly rainfall forecasts. For lead time 0, the improvement is particularly evident for February to March, July and October to December. For longer lead times, the benefit of bridging is more apparent. As lead time increases, bridging is able to maintain forecast skill much better than when only calibration is applied.
NASA Astrophysics Data System (ADS)
Higgins, S. M. W.; Du, H. L.; Smith, L. A.
2012-04-01
Ensemble forecasting on a lead time of seconds over several years generates a large forecast-outcome archive, which can be used to evaluate and weight "models". Challenges which arise as the archive becomes smaller are investigated: in weather forecasting one typically has only thousands of forecasts however those launched 6 hours apart are not independent of each other, nor is it justified to mix seasons with different dynamics. Seasonal forecasts, as from ENSEMBLES and DEMETER, typically have less than 64 unique launch dates; decadal forecasts less than eight, and long range climate forecasts arguably none. It is argued that one does not weight "models" so much as entire ensemble prediction systems (EPSs), and that the marginal value of an EPS will depend on the other members in the mix. The impact of using different skill scores is examined in the limits of both very large forecast-outcome archives (thereby evaluating the efficiency of the skill score) and in very small forecast-outcome archives (illustrating fundamental limitations due to sampling fluctuations and memory in the physical system being forecast). It is shown that blending with climatology (J. Bröcker and L.A. Smith, Tellus A, 60(4), 663-678, (2008)) tends to increase the robustness of the results; also a new kernel dressing methodology (simply insuring that the expected probability mass tends to lie outside the range of the ensemble) is illustrated. Fair comparisons using seasonal forecasts from the ENSEMBLES project are used to illustrate the importance of these results with fairly small archives. The robustness of these results across the range of small, moderate and huge archives is demonstrated using imperfect models of perfectly known nonlinear (chaotic) dynamical systems. The implications these results hold for distinguishing the skill of a forecast from its value to a user of the forecast are discussed.
Reasoning-predicting Model based on Strong Relevant Logic in Road Traffic Forecasting
NASA Astrophysics Data System (ADS)
Li, Dancheng; Liu, Zhiliang; Liu, Cheng; Liu, Binsheng; Zhang, Wei
The performance of many components in intelligent transportation systems depends heavily on the quality of traffic forecasting. After analyzing the deficiency of existing algorithm and methods in traffic forecasting, we develop a new traffic forecasting model based on logic reasoning and in this paper, we describe the details of each part of this model. Finally through an example, we introduce the working order of the model in traffic forecasting.
Forecasting Lightning Threat using Cloud-resolving Model Simulations
NASA Technical Reports Server (NTRS)
McCaul, E. W., Jr.; Goodman, S. J.; LaCasse, K. M.; Cecil, D. J.
2009-01-01
As numerical forecasts capable of resolving individual convective clouds become more common, it is of interest to see if quantitative forecasts of lightning flash rate density are possible, based on fields computed by the numerical model. Previous observational research has shown robust relationships between observed lightning flash rates and inferred updraft and large precipitation ice fields in the mixed phase regions of storms, and that these relationships might allow simulated fields to serve as proxies for lightning flash rate density. It is shown in this paper that two simple proxy fields do indeed provide reasonable and cost-effective bases for creating time-evolving maps of predicted lightning flash rate density, judging from a series of diverse simulation case study events in North Alabama for which Lightning Mapping Array data provide ground truth. One method is based on the product of upward velocity and the mixing ratio of precipitating ice hydrometeors, modeled as graupel only, in the mixed phase region of storms at the -15\\dgc\\ level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domainwide statistics of the peak values of simulated flash rate proxy fields against domainwide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. A blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Weather Research and Forecast Model simulations of selected North Alabama cases show that this model can distinguish the general character and intensity of most convective events, and that the proposed methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because models tend to have more difficulty in correctly predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models, the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of cloud-allowing forecasts become available.
Forecasting wind-driven wildfires using an inverse modelling approach
NASA Astrophysics Data System (ADS)
Rios, O.; Jahn, W.; Rein, G.
2014-06-01
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.
Review of Wind Energy Forecasting Methods for Modeling Ramping Events
Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R
2011-03-28
Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.
Management Model for Economic Development.
ERIC Educational Resources Information Center
Harris, Edward
An economic development model was formulated to foster and strengthen commerce and industry retention and expansion in the state of Illinois. The main thrust of the model was on increasing productivity, decreasing business failures, encouraging entrepreneurship, and creating a favorable business climate through community support. To meet these…
Efficiency of raster-based real time flood forecasting models
NASA Astrophysics Data System (ADS)
Gerlinger, K.
2003-04-01
In 1990, the state of Baden-Wuerttemberg (Germany) established a flood forecast centre in order to be able to forecast floods on the river Rhine and other large rivers within the state boundaries. Since this time, the necessary flood forecast models have been developed and continuously improved by Ludwig Consultant Engineers. In the meantime, we also support the flood forecast centres of the states of Rhineland-Palatinate and Bavaria. The conceptual, physically based LARSIM model, created by our office, is used for a large variety of tasks associated with flood forecasting within these three states. Therefore, required adjustments and further developments of the model can be achieved in direct contact with state officials and the administration. In addition, we developed programs for pre- and postprocessing of the data as well as visualisation tools necessary for the operational use. The required input data of the model (beside the measured discharge and precipitation data) is the 48 h precipitation forecast of the German Weather Service (spatial resolution: 7 km x 7 km grid). Snow accumulation and snow melt can also be considered as well as artificial influences (e.g. storage basins, diversions or water transfer between different basins). A raster based model structure with a spatial resolution of 1 km x 1 km was chosen for the discretisation of the catchments. Length and gradients of the rivers and the river network were determined with the help of GIS tools. For each 1 km2 raster cell the runoff generation within the area as well as the flood routing in the river channels can be calculated. Up to now, these high resolution models were established covering an area of approx. 85.000 km2 (including the catchments of the Moselle (approx. 30.000 km2), the Danube (up to gauge Schwabelwies, approx. 25.000 km 2), the Neckar (approx. 14.000 km 2) and other catchments in Baden-Wuerttemberg). The use of 1 km2 raster cells offers several advantages like e.g. flexible discretisation of the catchment according to the available gauges or inclusion of high resolution radar precipitation data. In addition, LARSIM can also be applied as a water balance model for the continuous simulation of the water cycle which requires a high spatial resolution. The experiences of the operational application of the raster based 1 km2 models will be presented in terms of their practical use and in terms of their performances meeting the users' requirements.
Forecasting the duration of volcanic eruptions: an empirical probabilistic model
NASA Astrophysics Data System (ADS)
Gunn, L. S.; Blake, S.; Jones, M. C.; Rymer, H.
2014-01-01
The ability to forecast future volcanic eruption durations would greatly benefit emergency response planning prior to and during a volcanic crises. This paper introduces a probabilistic model to forecast the duration of future and on-going eruptions. The model fits theoretical distributions to observed duration data and relies on past eruptions being a good indicator of future activity. A dataset of historical Mt. Etna flank eruptions is presented and used to demonstrate the model. The data have been compiled through critical examination of existing literature along with careful consideration of uncertainties on reported eruption start and end dates between the years 1300 AD and 2010. Data following 1600 is considered to be reliable and free of reporting biases. The distribution of eruption duration between the years 1600 and 1669 is found to be statistically different from that following it and the forecasting model is run on two datasets of Mt. Etna flank eruption durations: 1600-2010 and 1670-2010. Each dataset is modelled using a log-logistic distribution with parameter values found by maximum likelihood estimation. Survivor function statistics are applied to the model distributions to forecast (a) the probability of an eruption exceeding a given duration, (b) the probability of an eruption that has already lasted a particular number of days exceeding a given total duration and (c) the duration with a given probability of being exceeded. Results show that excluding the 1600-1670 data has little effect on the forecasting model result, especially where short durations are involved. By assigning the terms `likely' and `unlikely' to probabilities of 66 % or more and 33 % or less, respectively, the forecasting model based on the 1600-2010 dataset indicates that a future flank eruption on Mt. Etna would be likely to exceed 20 days (± 7 days) but unlikely to exceed 86 days (± 29 days). This approach can easily be adapted for use on other highly active, well-documented volcanoes or for different duration data such as the duration of explosive episodes or the duration of repose periods between eruptions.
Victoria, University of
.2 Wind Energy Background Information . . . . . . . . . . . . . . . . . 2 1.2.1 Wind Power Integration . . . . . . . . . . . . . . . . 17 2.2.2 Wind Power Forecast Generation . . . . . . . . . . . . . . . . 18 2.2.3 Wind Forecast ErrorCoupled Operation of a Wind Farm and Pumped Storage Facility: Techno-Economic Modelling
Using dispersion and mesoscale meteorological models to forecast pollen concentrations
Robert Pasken; Joseph A. Pietrowicz
2005-01-01
This work describes the results of research into a source-oriented pollen concentration forecasting technique. Tests were conducted using the National Center for Atmospheric Research\\/ Penn State Fifth Generation Mesoscale Model (MM5), the National Oceanographic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT_4) Model combined with the locations of oak trees and their aerial coverage from biogenic emissions land
A Dynamic Changepoint Model for New Product Sales Forecasting
Peter S. Fader; Bruce G. S. Hardie; Chun-Yao Huang
2004-01-01
At the heart of a new product sales-forecasting model for consumer packaged goods is a multiple-event timing process. Even after controlling for the effects of time-varying marketing mix covariates, this timing process is not a stationary one, which means the standard interpurchase time models developed within the marketing literature are not suitable for new products. In this paper, we develop
Forecast model for moderate earthquakes near Parkfield, California
NASA Astrophysics Data System (ADS)
Stuart, William D.; Archuleta, Ralph J.; Lindh, Allan G.
1985-01-01
Earthquake instability models have possible application to earthquake forecasting because the models simulate both preseismic and coseismic changes of fault slip and ground deformation. In the forecast procedure proposed here, repeated measurements of preseismic fault slip and ground deformation constrain the values of model parameters. The early part of the model simulation corresponds to the available field data, and the subsequent part constitutes an estimate of future faulting and ground deformation. In particular, the time, location, and size of unstable faulting are estimates of the pending earthquake parameters. The forecast accuracy depends on the model realism and parameter resolution. The forecast procedure is applied to fault creep and trilateration data measured near Parkfield, California, where at least five magnitude 5.5 to 6 earthquakes have occurred regularly since 1881, the last in 1966. The quasi-static model consists of a flat vertical plane embedded in an elastic half space. Spacially variable fault slip of strike-slip sense is driven by an increasing regional shear stress but is impeded by a relatively strong patch of brittle, strain-softening fault. The field data are consistent with these approximate values of patch parameters: radius of 3 km, patch center 5 km deep and 8 km southeast of the 1966 epicenter, and maximum brittle strength of 26 bars. Fluctuations in the available field data prevent estimating the earthquake time with any more precision than use of the 21±8 year recurrence interval. However, the model may later give a more precise estimate of the earthquake time if the fault slip rate near the inferred patch increases before the earthquake, as predicted by the model.
Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast
............................................................................... 16 Methodology in Estimating Commercial Floor Space Requirements.................................................................................. 19 Forecasting Commercial Floor Space Requirements......................................................................... 21 Commercial Floor Space Additions
A transactions choice model for forecasting demand for alternative-fuel vehicles
David Brownstone; David S. Bunch; Thomas F. Golob; Weiping Ren
1996-01-01
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
EC-EARTH: an Earth System Model based on the ECWMF Integrated Forecasting System
F. Selten; R. Bintanja; S. Yang; C. Severijns; T. Semmler; K. Wyser; X. Wang; W. Hazeleger
2009-01-01
EC-EARTH is the name of an Earth system model that is being developed by a number of institutes in Europe. It is based on the Integrated Forecast System of the European Centre for Medium Range Weather Forecasts (ECWMF). The ECMWF model delivers the best weather forecasts in the world by an objective measure. However, when applied to climate time scales,
Long-term wind speed and power forecasting using local recurrent neural network models
Thanasis G. Barbounis; John B. Theocharis; Minas C. Alexiadis; Petros S. Dokopoulos
2006-01-01
This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to
An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008
ERIC Educational Resources Information Center
Chen, Chau-Kuang
2008-01-01
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…
ENSO informed Drought Forecasting Using Nonhomogeneous Hidden Markov Chain Model
NASA Astrophysics Data System (ADS)
Kwon, H.; Yoo, J.; Kim, T.
2013-12-01
The study aims at developing a new scheme to investigate the potential use of ENSO (El Niño/Southern Oscillation) for drought forecasting. In this regard, objective of this study is to extend a previously developed nonhomogeneous hidden Markov chain model (NHMM) to identify climate states associated with drought that can be potentially used to forecast drought conditions using climate information. As a target variable for forecasting, SPI(standardized precipitation index) is mainly utilized. This study collected monthly precipitation data over 56 stations that cover more than 30 years and K-means cluster analysis using drought properties was applied to partition regions into mutually exclusive clusters. In this study, six main clusters were distinguished through the regionalization procedure. For each cluster, the NHMM was applied to estimate the transition probability of hidden states as well as drought conditions informed by large scale climate indices (e.g. SOI, Nino1.2, Nino3, Nino3.4, MJO and PDO). The NHMM coupled with large scale climate information shows promise as a technique for forecasting drought scenarios. A more detailed explanation of large scale climate patterns associated with the identified hidden states will be provided with anomaly composites of SSTs and SLPs. Acknowledgement This research was supported by a grant(11CTIPC02) from Construction Technology Innovation Program (CTIP) funded by Ministry of Land, Transport and Maritime Affairs of Korean government.
Influence of Model Physics on NWP Forecasts - version 2
NSDL National Science Digital Library
COMET
2009-11-17
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.
Models for forecasting energy use in the US farm sector
NASA Astrophysics Data System (ADS)
Christensen, L. R.
1981-07-01
Econometric models were developed and estimated for the purpose of forecasting electricity and petroleum demand in US agriculture. A structural approach is pursued which takes account of the fact that the quantity demanded of any one input is a decision made in conjunction with other input decisions. Three different functional forms of varying degrees of complexity are specified for the structural cost function, which describes the cost of production as a function of the level of output and factor prices. Demand for materials (all purchased inputs) is derived from these models. A separate model which break this demand up into demand for the four components of materials is used to produce forecasts of electricity and petroleum is a stepwise manner.
A hybrid model for ozone forecasting
NASA Astrophysics Data System (ADS)
Weinroth, Erez; Stockwell, William; Kora?in, Darko; Kahyaoglu-Kora?in, Julide; Luria, Menachem; McCord, Travis; Podnar, Domagoj; Gertler, Alan
Significant uncertainties in the prediction of pollutant transport and dispersion limit the accuracy of air quality in areas with complex terrain, such as along the California coastline, which suffers from elevated air pollutant concentrations. Typical Lagrangian air quality models treat the dispersion of plumes better than Eulerian models but the chemical interactions induced by the mixing of intersecting plumes are ignored. In contrast, Eulerian models treat the emissions as well mixed within each grid box. To address these limitations, an air quality model with in-line chemistry and meteorology that combines the advantages of the Eulerian and Lagrangian approach to air quality modeling has been developed. In order to evaluate the model, simulation results of ozone concentrations were compared against a commonly used photochemical model (CAMx) and with airborne data from a field study made in the San Diego area of southwestern California.
The Forecasting Model of Flight Delay Based On DMT-GMT Model
NASA Astrophysics Data System (ADS)
Ding, Jianli; Li, Huafeng
In order to solve the problem of flight delay forecasting including the characteristics of airport flight operation, a new composite forecasting model based on the danger model theory and the grey model theory is proposed in this paper. The composite prediction method in this paper uses the pattern of weighted composition which is according to the occupancy proportion of the mean square errors forecasting .The model use the modified approach reflects the periodicity. The experimental result shows that the prediction results is qualified, the new model can meet the requirement of real-time prediction for the management of emergency departments.
Forecast improvement by interactive ensemble of atmospheric models
NASA Astrophysics Data System (ADS)
Basnarkov, L.; Duane, G. S.; Kocarev, L.
2013-12-01
The advances in weather forecast traditionally have been based on two lines of improvement: 1 - deepening the understanding of physical phenomena that underlies the atmospheric dynamics; and 2 - steady increase in computer power that enables use of finer grid resolution. The meteorological centers model dynamics of the atmosphere with the same basic physical laws, but sometimes take different approaches in capturing small-scale phenomena and generally use different grid sizes. As a result there are dozens operational models around the globe with various parameterizations of the unresolved processes. Newest attempts in forecast improvements are based on using ensemble prediction. Multiple outputs are taken from runs with perturbed initial conditions, or perturbed parameter values. A novel paradigm is exploiting dynamical exchange of variables between simultaneously running models. There are already simulations of models exchanging fluxes between ocean and atmospheric models, but examples with direct coupling of different atmospheric models are rather new. Within this approach the coupling schemes can be different, but as simplest appear those that combine corresponding dynamical variables or tendency components. In this work we present results with an artificial toy model-Lorenz 96 model. To make more faithful example as reality (the atmosphere) is considered one Lorenz 96 class III system, while as its imperfect models are taken three class II systems that have different forcing terms. These resemble the models used in three different meteorological centers. The interactive ensemble has tendency that is weighted combination of the individual models' tendencies. The weights are obtained with statistical techniques based on past observations that target to minimize the mismatch between the truth's and interactive ensemble's tendencies. By means of anomaly correlation it is numerically verified that this ensemble has longer range of forecast than the individual models.
NASA Astrophysics Data System (ADS)
Shukla, Shraddhanand; Funk, Christopher; Hoell, Andrew
2014-09-01
In this study we implement and evaluate a simple ‘hybrid’ forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble’s (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The ‘hybrid approach’ described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.
Application of data assimilation to solar wind forecasting models
NASA Astrophysics Data System (ADS)
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
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).
A Review of Demographic Forecasting Models for Mortality
Ewa Tabeau
The goal of Chapter 1 is to describe and comment on the methods and approaches that have been in use or have emerged in recent\\u000a years. Section 1.1 introduces the most common classifications of forecasting models for mortality. Section 1.2 is devoted\\u000a to a brief historical review of parameterisation functions. In this context, attention is paid to prediction based on
Models for forecasting the flowering of Cornicabra olive groves
NASA Astrophysics Data System (ADS)
Rojo, Jesús; Pérez-Badia, Rosa
2015-02-01
This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.
Comparison of modelling techniques for milk-production forecasting.
Murphy, M D; O'Mahony, M J; Shalloo, L; French, P; Upton, J
2014-06-01
The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ? 12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%)=8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%)=12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%)=10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions. PMID:24731634
A first large-scale flood inundation forecasting model
Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.
2013-11-04
At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode revealed that it is crucial to account for basin-wide hydrological response time when assessing lead time performances notwithstanding structural limitations in the hydrological model and possibly large inaccuracies in precipitation data.
AEP system's residential end-use forecasting model; An econometric approach
E. Villacis; M. G. Norman; K. K. Gainer
1988-01-01
The present study describes twelve monthly models which forecast the demand for electricity by American Electric Power System's (AEP) residential customers. The forecast includes projections of short- and long-term electricity consumption for each of fourteen major residential end uses as well as the penetration of these end uses in the AEP service area. In addition, the forecasts of electricity consumption
Wavelet-Based Nonlinear Multiscale Decomposition Model for Electricity Load Forecasting
Murtagh, Fionn
that is not fully utilized. On the other hand, a forecast that is too low may lead to some revenue loss from sales1 Wavelet-Based Nonlinear Multiscale Decomposition Model for Electricity Load Forecasting D Company (NEMMCO). KEYWORDS: Wavelet transform, load forecast, scale, resolution, time series
A Two-Stage Forecasting Model: Exponential Smoothing and Multiple Regression
Dwight B. Crane; James R. Crotty
1967-01-01
This paper presents a forecasting technique which attempts to combine the advantages of both time series analysis and multiple regression. In this two-stage technique, an exponentially smoothed moving average model is used to forecast values of the dependent variable and\\/or selected independent variables as desired. These forecasts, along with data for other (lagged) independent variables, are then used as inputs
Generation of ensemble streamflow forecasts using an enhanced version of the snowmelt runoff model
Technology Transfer Automated Retrieval System (TEKTRAN)
As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1-15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt-dominated ...
A statistical model to forecast short-term Atlantic hurricane intensity
Kevin T. Law
2006-01-01
The accuracy of hurricane intensity forecasts has lagged the accuracy of hurricane track forecasts thereby creating a need for improvement. Many models struggle capturing the rapid intensification period and identifying when it will occur which causes a large amount of error in the intensity forecasts. The method described in this paper uses a discriminant function analysis (DFA) to help identify
Time Dependent Directional Profit Model for Financial Time Series Forecasting
Jingtao Yao; Chew Lim Tan
2000-01-01
Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for
A monthly crude oil spot price forecasting model using relative inventories
Michael Ye; John Zyren; Joanne Shore
2005-01-01
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
A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty
Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.
2013-12-18
This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.
Determination of an optimal forecast model for ambulance demand using goal programming.
Baker, J R; Fitzpatrick, K E
1986-11-01
A multistep approach to determining the optimal parameters of an exponential smoothing model was used to forecast emergency medical service (E.M.S.) demand for four counties of South Carolina. Daily emergency and routine (non-emergency) demand data were obtained and forecast statistics generated for each county sampled, using Winters' exponential smoothing model. A goal programme was formulated to combine forecast results for emergency calls with routine call forecasts. The goal programme gave a higher priority to accurate forecasting of emergency demand. The forecast model generated implicitly weights demand by severity and provides a reliable estimate of demand overall. The optimal parameter values for the smoothing model were obtained by minimizing the objective function value of the goal programming problem. The parameter values obtained were used to forecast demand for E.M.S. in the selected counties. The results of the model were compared to those using a multiple linear regression model and a single-objective-based exponential smoothing model for 2 months of data. When compared with two single-objective forecast models, the multiple-objective approach yielded more accurate forecasts and, therefore, was more cost-effective for the planner. The model presents and demonstrates a theoretical approach to improving the accuracy of ambulance demand forecasts. The possible impact of this approach on planning efficiency is discussed. PMID:10279384
Identification and Forecasting in Mortality Models
Nielsen, Jens P.
2014-01-01
Mortality models often have inbuilt identification issues challenging the statistician. The statistician can choose to work with well-defined freely varying parameters, derived as maximal invariants in this paper, or with ad hoc identified parameters which at first glance seem more intuitive, but which can introduce a number of unnecessary challenges. In this paper we describe the methodological advantages from using the maximal invariant parameterisation and we go through the extra methodological challenges a statistician has to deal with when insisting on working with ad hoc identifications. These challenges are broadly similar in frequentist and in Bayesian setups. We also go through a number of examples from the literature where ad hoc identifications have been preferred in the statistical analyses. PMID:24987729
A Feature Fusion Based Forecasting Model for Financial Time Series
Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie
2014-01-01
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455
Forecasting volatility with neural regression: a contribution to model adequacy.
Refenes, A N; Holt, W T
2001-01-01
Neural nets' usefulness for forecasting is limited by problems of overfitting and the lack of rigorous procedures for model identification, selection and adequacy testing. This paper describes a methodology for neural model misspecification testing. We introduce a generalization of the Durbin-Watson statistic for neural regression and discuss the general issues of misspecification testing using residual analysis. We derive a generalized influence matrix for neural estimators which enables us to evaluate the distribution of the statistic. We deploy Monte Carlo simulation to compare the power of the test for neural and linear regressors. While residual testing is not a sufficient condition for model adequacy, it is nevertheless a necessary condition to demonstrate that the model is a good approximation to the data generating process, particularly as neural-network estimation procedures are susceptible to partial convergence. The work is also an important step toward developing rigorous procedures for neural model identification, selection and adequacy testing which have started to appear in the literature. We demonstrate its applicability in the nontrivial problem of forecasting implied volatility innovations using high-frequency stock index options. Each step of the model building process is validated using statistical tests to verify variable significance and model adequacy with the results confirming the presence of nonlinear relationships in implied volatility innovations. PMID:18249917
Tsunami Modeling, Forecast and Warning (Invited)
NASA Astrophysics Data System (ADS)
Satake, K.
2010-12-01
Tsunami is an infrequent natural hazard; however, once it happens, the effects are devastating and can be on global scale, as demonstrated by the 2004 Indian Ocean tsunami. Deterministic modeling of tsunami generation, propagation and coastal behavior has become popular, at least for earthquake tsunamis. Once the earthquake parameters are specified, tsunami arrival times, heights and current velocity at specific coastal points, and inland inundation area can be estimated. Such modeling has been used to make hazard maps usually by assuming largest possible earthquakes. However, smaller tsunamis than such a worst-case scenario occur more frequently. If the hazard maps are used incorrectly, it may lose reliability of coastal residents. Probabilistic tsunami hazard assessments, similar to Probabilistic Seismic Hazard Analysis, have been made for some coasts. The output is tsunami hazard curves, i.e. annual probability (or return period) for specified coastal tsunami heights. A hazard curve is obtained by integration over the aleatory uncertainties, and a large number of hazard curves are made for each branch of logic tress representing epistemic uncertainty. Probabilistic tsunami hazard analysis is used for design of critical facilities but not popularly used for disaster mitigation. Tsunami warning systems, which have been significantly developed since 2004, rely on seismic and sea-level monitoring and pre-made numerical simulation. Real-time data assimilation of offshore sea level measurements can be used to update the warning levels. Tsunami from the February 2010 Chilean earthquake was recorded on many tide gauges and ocean bottom pressure gauges in the Pacific, before it arrived on the Japanese coast about 22 hours after the earthquake. The tsunami height was up to 2 m on the Japanese coast, causing fishery damage amounting 60 million US dollars, but did not cause any human damage.
Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models
Efthymia V Tsitsika; Christos D Maravelias; John Haralabous
2007-01-01
Univariate and multivariate autoregressive integrated moving average (ARIMA) models were used to model and forecast the monthly\\u000a pelagic production of fish species in the Mediterranean Sea during 1990–2005. Autocorrelation (AC) and partial autocorrelation\\u000a (PAC) functions were estimated, which led to the identification and construction of seasonal ARIMA models, suitable in explaining\\u000a the time series and forecasting the future catch per
Traffic congestion forecasting model for the INFORM System. Final report
Azarm, A.; Mughabghab, S.; Stock, D.
1995-05-01
This report describes a computerized traffic forecasting model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM system roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information system. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical forecasting of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy forecasted (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.
Forecasting Lightning Threat using Cloud-Resolving Model Simulations
NASA Technical Reports Server (NTRS)
McCaul, Eugene W., Jr.; Goodman, Steven J.; LaCasse, Katherine M.; Cecil, Daniel J.
2008-01-01
Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models,the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of forecasts become available.
NASA Astrophysics Data System (ADS)
Kucera, P. A.; Brown, B.; Nance, L. B.; Crosby, K. M.; Williams, C.; Jensen, T.
2010-12-01
In 2009, the National Center for Atmospheric Science (NCAR)/Research Applications Laboratory’s (RALs) Joint Numerical Testbed (JNT) Program formed a new entity called the Tropical Cyclone Modeling Team (TCMT). The focus of this team is testing and evaluation of experimental models with the goal of improving tropical cyclone forecasts. Much of this effort is sponsored by NOAA's Hurricane Forecast Improvement Project (HFIP). For HFIP, the TCMT designs model evaluation experiments and provides general testing and evaluation of the various forecast models included in the HFIP annual forecasting demonstrations and retrospective experiments. The TCMT is developing statistical approaches that are appropriate for evaluating a variety of tropical cyclone forecast attributes. These methods include new diagnostic tools to aid, for example, in the evaluation of track and intensity errors, precipitation and tropical cyclone structure forecasts. Currently, the TCMT is conducting an evaluation of a suite of experimental models that are candidates for future inclusion into the operational forecasting system at the National Hurricane Center (NHC). This 2010 retrospective analysis is being conducted using storms observed during the 2008 and 2009 hurricane seasons in the Eastern Pacific and Atlantic basins. The goals of the 2010 retrospective testing are to (1) provide adequate statistics for assessing the skill of the model candidates, (2) help identify modeling systems to could be included in future operational forecast guidance, and (3) provide information that may help to calibrate the multi-model ensemble forecasts. The retrospective testing focused on a representative sample of 27 storms from the 2008 and 2009 hurricane seasons. Four modeling groups participated in the retrospective testing. The models included two configurations of the Weather Research and Forecasting (WRF) model, a new version of the NOAA Geophysical Fluid Dynamic Laboratory’s (GFDL) model, and the Navy’s tropical cyclone model. This presentation will provide an overview of the 2010 retrospective testing, a summary of the differences in track and intensity errors for the comparisons of the individual participating models, and an evaluation of the impact of including the experimental model in the NHC conventional consensus forecast. The presentation will also provide an overview of future plans for the evaluation of tropical cyclone forecasts.
Short Range Solar Forecasting Using Geostationary Satellite and High Resolution Model Data
NASA Astrophysics Data System (ADS)
Rogers, M. A.; Miller, S. D.
2011-12-01
A blended technique utilizing observations and retrievals of cloud properties from geostationary satellite platforms combined with high-resolution mesoscale model data is applied to short-term (0-6hr) forecasts of surface insolation for solar power generation. GOES observations of the continental United States (CONUS), combined with cloud property retrievals including cloud type and cloud top height are used to create a database of cloud cover by cloud steering height. Combined with forecast model wind guidance, satellite-observed clouds can be advected forward in time by model winds to give a short-term forecast of cloud location. This information, when combined with a radiative transfer model, and accounting for variables such as solar geometry, can be used to provide an improved surface insolation forecast over current empirical methods. Additionally, model output from the High-Resolution Rapid Refresh (HRRR) model is being evaluated for potential use as an insolation forecast model. Mesoscale models utilizing real-time platforms such as the WSR-88D network for initialization show promise in forecasting cloud position and therefore insolation. Blending these model forecasts with satellite-derived cloud advection forecasts promises to greatly improve the accuracy and timeliness of insolation forecasts for the growing solar power industry.
River water temperature and fish growth forecasting models
NASA Astrophysics Data System (ADS)
Danner, E.; Pike, A.; Lindley, S.; Mendelssohn, R.; Dewitt, L.; Melton, F. S.; Nemani, R. R.; Hashimoto, H.
2010-12-01
Water is a valuable, limited, and highly regulated resource throughout the United States. When making decisions about water allocations, state and federal water project managers must consider the short-term and long-term needs of agriculture, urban users, hydroelectric production, flood control, and the ecosystems downstream. In the Central Valley of California, river water temperature is a critical indicator of habitat quality for endangered salmonid species and affects re-licensing of major water projects and dam operations worth billions of dollars. There is consequently strong interest in modeling water temperature dynamics and the subsequent impacts on fish growth in such regulated rivers. However, the accuracy of current stream temperature models is limited by the lack of spatially detailed meteorological forecasts. To address these issues, we developed a high-resolution deterministic 1-dimensional stream temperature model (sub-hourly time step, sub-kilometer spatial resolution) in a state-space framework, and applied this model to Upper Sacramento River. We then adapted salmon bioenergetics models to incorporate the temperature data at sub-hourly time steps to provide more realistic estimates of salmon growth. The temperature model uses physically-based heat budgets to calculate the rate of heat transfer to/from the river. We use variables provided by the TOPS-WRF (Terrestrial Observation and Prediction System - Weather Research and Forecasting) 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.
NASA Astrophysics Data System (ADS)
Masbou, M.; Müller, M. D.; Steeneveld, G. J.; Cermak, J.; Bott, A.
2010-07-01
The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. An improvement of localisation, duration and variations in visibility therefore holds an immense operational value for the field of transportation in conditions of low visibility. However, fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Therefore, a detailed description of the microphysical processes within the three-dimensional dynamical core of the forecast model is necessary. For this purpose, three-dimensional fog forecast models with a high vertical resolution with different microphysical complexity have been developed. COSMO-FOG and NMMFOG include a new microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Also, we compare WRF mesoscale model results using different boundary-layer schemes that ignore or account for specific fog microphysics. In some realistic fog situations (radiative fog) the potential of these three-dimensional fog models will be presented. The fog spatial extension will be compared with MSG satellite products for fog and low cloud. It will be shown that the initialisation and the interaction between the earth’s surface and the atmosphere is one of the most important issues for reliable fog forecasts.
MJO empirical modeling and improved prediction by "Past Noise Forecasting"
NASA Astrophysics Data System (ADS)
Kondrashov, D. A.; Chekroun, M.; Robertson, A. W.; Ghil, M.
2011-12-01
The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropics and plays an important role in global climate. Here we presents modeling and prediction study of MJO by using Empirical Model Reduction (EMR). EMR is a methodology for constructing stochastic models based on the observed evolution of selected climate fields; these models represent unresolved processes as multivariate, spatially correlated stochastic forcing. In EMR, multiple polynomial regression is used to estimate the nonlinear, deterministic propagator of the dynamics, as well as multi-level additive stochastic forcing -"noise", directly from the observational dataset. The EMR approach has been successfully applied on the seasonal-to-interannual time scale for real-time ENSO prediction (Kondrashov et al. 2005), as well as atmospheric midlatitude intraseasonal variability (Kondrashov et al. 2006,2010). In this study nonlinear (quadratic) with annual cycle, three-level EMR model was developed to model and predict leading pair of real-time multivariate Madden-Julian oscillation (RMM1,2) daily indices (June 1974- January 2009, http://cawcr.gov.au/staff/mwheeler/maproom/RMM/). The EMR model captures essential MJO statistical features, such as seasonal dependence, RMM1,2 autocorrelations and spectra. By using the "Past Noise Forecasting" (PNF) approach developed and successfully applied to improve long-term ENSO prediction in Chekroun et al. (2011), we are able to notably improve the cross-validated prediction skill of RMM indices- especially at lead times of 15-to-30 days. The EMR/PNF method has two steps: (i) select noise samples - or "snippets" - from the past noise, which have forced the EMR model to yield the MJO phase resembling the one at the the currently observed state; and (ii) use these "noise" snippets to create ensemble forecast of EMR model. The MJO phase identification is based on Singular Spectrum Analysis reconstruction of 30-60 day MJO cycle.
Lightning forecasting in southeastern Brazil using the WRF model
NASA Astrophysics Data System (ADS)
Zepka, G. S.; Pinto, O.; Saraiva, A. C. V.
2014-01-01
This paper introduces a lightning forecasting method called Potential Lightning Region (PLR), which is the probability of the occurrence of lightning over a region of interest. The PLR was calculated using a combination of meteorological variables obtained from high-resolution Weather Research and Forecasting (WRF) model simulations during the summer season in southeastern Brazil. The model parameters used in the PLR definition were: surface-based Convective Available Potential Energy (SBCAPE), Lifted Index (LI), K-Index (KI), average vertical velocity between 850 and 700 hPa (w), and integrated ice-mixing ratio from 700 to 500 hPa (QICE). Short-range runs of twelve non-severe thunderstorm cases were performed with the WRF model, using different convective and microphysical schemes. Through statistical evaluations, the WRF cloud parameterizations that best described the convective thunderstorms with lightning in southeastern Brazil were the combination of Grell-Devenyi and Thompson schemes. Two calculation methods were proposed: the Linear PLR and Normalized PLR. The difference between them is basically how they deal with the influence of lightning flashes over the WRF domain's grid points for the twelve thunderstorms analyzed. Three case studies were used to test both methods. A statistical evaluation lowering the spatial resolution of the WRF grid into larger areas was performed to study the behavior and accuracy of the PLR methods. The Normalized PLR presented the most suitable one, predicting flash occurrence appropriately.
NASA Astrophysics Data System (ADS)
Leedal, D.; Weerts, A. H.; Smith, P. J.; Beven, K. J.
2013-01-01
The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new flood forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).
Sangmun Shin; Yonghee Lee; Yongsun Choi; Charles Kim
2007-01-01
For the efficient management of radio resources, a scientific\\/systematic management system using an engineering approach based on cost-benefit analysis needs to be prepared. In forecasting market competition and demand for various services provided by frequency band, it should be considered necessary to enhance the reliability of forecasting data using the most rational forecast techniques. Many researchers have investigated models of
Forecasting gaming revenues in Clark County, Nevada: Issues and methods
Edwards, B.K.; Bando, A.
1992-07-01
This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.
Forecasting gaming revenues in Clark County, Nevada: Issues and methods
Edwards, B.K.; Bando, A.
1992-01-01
This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.
A two-stage dynamic sales forecasting model for the fashion retail
Yanrong Ni; Feiya Fan
2011-01-01
The difficulty with fashion retail forecasting is due to a number of factors such as the season, region and fashion effect and causes a nonlinear change in the original sales rules. To improve the accuracy of fashion retail forecasting, a two-stage dynamic forecasting model is proposed, which is combined with both long-term and short-term predictions. The model introduces the improved
Ronald Bewley; William E. Griffiths
2003-01-01
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
Collaborative Forecasting Models for the Machine Tools Industry via the Internet
Jeng-teng Tsai; Jung-hua Lee; Chung-chieh Hsu; Shui-shun Lin; Chyung Perng; Wen-chih Chiou
2006-01-01
The purpose of this research is to investigate appropriate collaborative forecasting models, both for acceptable accuracy and effectiveness for information sharing via the Internet to partners. Several forecasting models were investigated in this research. For comparing the models, five years historical data are collected from a lathe machine manufacturer in Taiwan, and one of its partners - a ball screw
A Capacity Forecast Model for Volatile Data in Maintenance Logistics
NASA Astrophysics Data System (ADS)
Berkholz, Daniel
2009-05-01
Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.
Model Refinement for Economic Assessments of
12.3 First Deliverable Economic Analysis - Scenario Selection Prepared by Hawai`i Natural EnergyModel Refinement for Economic Assessments of Hawai`i Clean Energy Policies: Scenario Selection agency thereof. #12;Model Refinement for Economic Assessments of Hawaii Clean Energy Policies Selection
Teaching Economics: A Cooperative Learning Model.
ERIC Educational Resources Information Center
Caropreso, Edward J.; Haggerty, Mark
2000-01-01
Describes an alternative approach to introductory economics based on a cooperative learning model, "Learning Together." Discussion of issues in economics education and cooperative learning in higher education leads to explanation of how to adapt the Learning Together Model to lesson planning in economics. A flow chart illustrates the process for a…
Bayesian model selection for dark energy using weak lensing forecasts
NASA Astrophysics Data System (ADS)
Debono, Ivan
2014-01-01
The next generation of weak lensing probes can place strong constraints on cosmological parameters by measuring the mass distribution and geometry of the low-redshift Universe. We show that a future all-sky tomographic cosmic shear survey with design properties similar to Euclid can provide the statistical accuracy required to distinguish between different dark energy models. Using a fiducial cosmological model which includes cold dark matter, baryons, massive neutrinos (hot dark matter), a running primordial spectral index and possible spatial curvature as well as dark energy perturbations, we calculate Fisher matrix forecasts for different dynamical dark energy models. Using a Bayesian evidence calculation, we show how well a future weak lensing survey could do in distinguishing between a cosmological constant and dynamical dark energy.
A multiscale statistical model for time series forecasting
NASA Astrophysics Data System (ADS)
Wang, W.; Pollak, I.
2007-02-01
We propose a stochastic grammar model for random-walk-like time series that has features at several temporal scales. We use a tree structure to model these multiscale features. The inside-outside algorithm is used to estimate the model parameters. We develop an algorithm to forecast the sign of the first difference of a time series. We illustrate the algorithm using log-price series of several stocks and compare with linear prediction and a neural network approach. We furthermore illustrate our algorithm using synthetic data and show that it significantly outperforms both the linear predictor and the neural network. The construction of our synthetic data indicates what types of signals our algorithm is well suited for.
A high resolution WRF model for wind energy forecasting
NASA Astrophysics Data System (ADS)
Vincent, Claire Louise; Liu, Yubao
2010-05-01
The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the diffusion constant caused damping of the unrealistic fluctuations, but did not completely solve the problem. Using two-way nesting also mitigated the unrealistic fluctuations significantly. It can be concluded that for real case LES modelling of wind farm circulations, care should be taken to ensure the consistency between the mesoscale weather forcing and LES models to avoid exciting spurious noise along the forcing boundary. The development of algorithms that adequately model the sub-grid-scale mixing that cannot be resolved by LES models is an important area for further research. References Liu, Y. Y._W. Liu, W. Y.Y. Cheng, W. Wu, T. T. Warner and K. Parks, 2009: Simulating intra-farm wind variations with the WRF-RTFDDA-LES modeling system. 10th WRF Users' Workshop, Boulder, C, USA. June 23 - 26, 2009. Skamarock, W., J. Dudhia, D.O. Gill, D.M. Barker, M.G.Duda, X-Y. Huang, W. Wang and J.G. Powers, A Description of the Advanced Research WRF version 3, NCAR Technical Note TN-475+STR, NCAR, Boulder, Colorado, 2008.
Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls.
NASA Astrophysics Data System (ADS)
El-Fandy, M. G.; Taiel, S. M. M.; Ashour, Z. H.
1994-01-01
Long-term rainfall forecasting is used in making economic and agricultural decisions in many countries. It may also be a tool in minimizing the devastation resulting from recurrent droughts. To be able to forecast the total annual rainfall or the levels of seasonal floods, a class of models has first been chosen. The model parameters have then been estimated with an appropriate parameter estimation algorithm. Finally, diagnostic tests have been performed to verify the adequacy of the model. These are the general principles of system identification, which is the most crucial part of the forecasting procedure. In this paper several sets of data have been studied using different statistical procedures. The examined data include a historical 835-year record representing the levels of the seasonal Nile floods in Cairo, Egypt, during the period A.D. 622-1457. These readings were originally carried out by the Arabsto a great degree of accuracy in order to be used in estimating yearly taxes or Zacat (islamic duties). The observations also comprise recent total annual rainfall data over Addis Ababa (Ethiopia) (1907-1984), the total annual discharges of Ethiopian rivers (including the river Sobat discharges at Hillet Doleib, Blue Nile discharge at Roseris, river Dinder, river Rahar, and river Atbara), equatorial lake plateau supply as contributed at Aswan during the period 1912-1982, and the total annual discharges at Aswan during the period 1871-1982. Periodograms have been used to uncover possible peridodicities. Trends of rainfall and discharges of some rivers of east and central Africa have been also estimated.Using the first half of the available record, two autoregressive integrated moving average (ARIMA) time series models have been identified, one for the levels of the seasonal Nile floods in Cairo, the second to model the annual rainfall over Ethiopia. The time series models have been applied in 1-year-ahead forecasting to the other hall of the available record and give fairly promising results, thus indicating the adequacy of the fitted models.
L. Sraibman; G. J. Berri
2009-01-01
A mesoscale boundary-layer model (BLM) is used for running 12-h low-level wind forecasts for the La Plata River region. Several\\u000a experiments are performed with different boundary conditions that include operational forecasts of the Eta\\/CPTEC model, local\\u000a observations, as well as a combination of both. The BLM wind forecasts are compared to the surface wind observations of five\\u000a weather stations during
Comparison of Dst Forecast Models for Intense Geomagnetic Storms
NASA Technical Reports Server (NTRS)
Ji, Eun-Young; Moon, Y.-J.; Gopalswamy, N.; Lee, D.-H.
2012-01-01
We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.
NASA Astrophysics Data System (ADS)
Sulaiman, M.; El-Shafie, A.; Karim, O.; Basri, H.
2011-10-01
Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.
A Wind Forecasting System for Energy Application
NASA Astrophysics Data System (ADS)
Courtney, Jennifer; Lynch, Peter; Sweeney, Conor
2010-05-01
Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.
A Model For Rapid Estimation of Economic Loss
NASA Astrophysics Data System (ADS)
Holliday, J. R.; Rundle, J. B.
2012-12-01
One of the loftier goals in seismic hazard analysis is the creation of an end-to-end earthquake prediction system: a "rupture to rafters" work flow that takes a prediction of fault rupture, propagates it with a ground shaking model, and outputs a damage or loss profile at a given location. So far, the initial prediction of an earthquake rupture (either as a point source or a fault system) has proven to be the most difficult and least solved step in this chain. However, this may soon change. The Collaboratory for the Study of Earthquake Predictability (CSEP) has amassed a suite of earthquake source models for assorted testing regions worldwide. These models are capable of providing rate-based forecasts for earthquake (point) sources over a range of time horizons. Furthermore, these rate forecasts can be easily refined into probabilistic source forecasts. While it's still difficult to fully assess the "goodness" of each of these models, progress is being made: new evaluation procedures are being devised and earthquake statistics continue to accumulate. The scientific community appears to be heading towards a better understanding of rupture predictability. Ground shaking mechanics are better understood, and many different sophisticated models exists. While these models tend to be computationally expensive and often regionally specific, they do a good job at matching empirical data. It is perhaps time to start addressing the third step in the seismic hazard prediction system. We present a model for rapid economic loss estimation using ground motion (PGA or PGV) and socioeconomic measures as its input. We show that the model can be calibrated on a global scale and applied worldwide. We also suggest how the model can be improved and generalized to non-seismic natural disasters such as hurricane and severe wind storms.
A 30-day forecast experiment with the GISS model and updated sea surface temperatures
NASA Technical Reports Server (NTRS)
Spar, J.; Atlas, R.; Kuo, E.
1975-01-01
The GISS model was used to compute two parallel global 30-day forecasts for the month January 1974. In one forecast, climatological January sea surface temperatures were used, while in the other observed sea temperatures were inserted and updated daily. A comparison of the two forecasts indicated no clear-cut beneficial effect of daily updating of sea surface temperatures. Despite the rapid decay of daily predictability, the model produced a 30-day mean forecast for January 1974 that was generally superior to persistence and climatology when evaluated over either the globe or the Northern Hemisphere, but not over smaller regions.
Characteristics of Operational Space Weather Forecasting: Observations and Models
NASA Astrophysics Data System (ADS)
Berger, Thomas; Viereck, Rodney; Singer, Howard; Onsager, Terry; Biesecker, Doug; Rutledge, Robert; Hill, Steven; Akmaev, Rashid; Milward, George; Fuller-Rowell, Tim
2015-04-01
In contrast to research observations, models and ground support systems, operational systems are characterized by real-time data streams and run schedules, with redundant backup systems for most elements of the system. We review the characteristics of operational space weather forecasting, concentrating on the key aspects of ground- and space-based observations that feed models of the coupled Sun-Earth system at the NOAA/Space Weather Prediction Center (SWPC). Building on the infrastructure of the National Weather Service, SWPC is working toward a fully operational system based on the GOES weather satellite system (constant real-time operation with back-up satellites), the newly launched DSCOVR satellite at L1 (constant real-time data network with AFSCN backup), and operational models of the heliosphere, magnetosphere, and ionosphere/thermosphere/mesophere systems run on the Weather and Climate Operational Super-computing System (WCOSS), one of the worlds largest and fastest operational computer systems that will be upgraded to a dual 2.5 Pflop system in 2016. We review plans for further operational space weather observing platforms being developed in the context of the Space Weather Operations Research and Mitigation (SWORM) task force in the Office of Science and Technology Policy (OSTP) at the White House. We also review the current operational model developments at SWPC, concentrating on the differences between the research codes and the modified real-time versions that must run with zero fault tolerance on the WCOSS systems. Understanding the characteristics and needs of the operational forecasting community is key to producing research into the coupled Sun-Earth system with maximal societal benefit.
Log-likelihood of earthquake models: evaluation of models and forecasts
NASA Astrophysics Data System (ADS)
Harte, D. S.
2015-05-01
There has been debate in the Collaboratory for the Study of Earthquake Predictability project about the most appropriate form of the likelihood function to use to evaluate earthquake forecasts in specified discrete space-time intervals, and also to evaluate the validity of the model itself. The debate includes whether the likelihood function should be discrete in nature, given that the forecasts are in discrete space-time bins, or continuous. If discrete, can different bins be assumed to be statistically independent, and is it satisfactory to assume that the forecasted count in each bin will have a Poisson distribution? In order to discuss these questions, we start with the most simple models (homogeneous Poisson), and progressively develop the model complexity to include self exciting point process models. For each, we compare the discrete and continuous time likelihoods. Examples are given where it is proven that the counts in discrete space-time bins are not Poisson. We argue that the form of the likelihood function is intrinsic to the given model, and the required forecast for some specified space-time region simply determines where the likelihood function should be evaluated. We show that continuous time point process models where the likelihood function is also defined in continuous space and time can easily produce forecasts over discrete space-time intervals.
Energy Forecast Model Based on Combination of GM(1,1) and Neural Network
Ren-yuan Liu; Jue Zhang; Qiang Huang; Bao-dong Lei
2009-01-01
Energy consumption forecast is an essential component in making energy plan. In the light of the complexity and nonlinearity of energy consumption system, the gray forecast model and neural network model are respectively established by using the energy consumption historical data of certain province. Then their advantages and disadvantages are analyzed. Lastly, the method of optimal combination is applied in
Critique of the mid-range energy forecasting, system oil and gas supply models
Patton
1980-01-01
The Mid-Range Energy Forecasting System (MEFS) is a model used by the Department of Energy to forecast domestic production, consumption and price for conventional energy sources on a regional basis over a period of 5 to 15 years. Among the energy sources included in the model are oil, gas and other petroleum fuels, coal, uranium, and electricity. Final consumption of
A model of export sales forecasting behavior and performance: development and testing
Heidi Winklhofer; Adamantios Diamantopoulos
2003-01-01
This paper presents and tests a path model of export sales forecasting behavior and performance, incorporating organizational and export-specific characteristics. The proposed links are based on insights provided by the literature on forecasting practices as well as the export literature. The model is tested on a sample of UK exporters and is shown to have a good fit. The results
Web-based hydrological modeling system for flood forecasting and risk mapping
Lei Wang; Qiuming Cheng
2008-01-01
Mechanism of flood forecasting is a complex system, which involves precipitation, drainage characterizes, land use\\/cover types, ground water and runoff discharge. The application of flood forecasting model require the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, pre-processing and manipulation, analysis and display of model results. The extensive datasets usually involve multiple organizations, but no
NASA Astrophysics Data System (ADS)
Engeland, Kolbjorn; Steinsland, Ingelin
2014-05-01
This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.
NASA Astrophysics Data System (ADS)
Skaugen, Thomas; Haddeland, Ingjerd
2014-05-01
A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal resolution. Running DDD at a 3h resolution will give a better prediction of flood peaks in small catchments, where the averaging over 24 hrs will lead to a underestimation of high events, and we can better describe the progress floods in larger catchments. Also, at a 3h temporal resolution we make better use of the meteorological forecasts that for long have been provided at a very detailed temporal resolution.
Fast Kalman Filter for Random Walk Forecast model
NASA Astrophysics Data System (ADS)
Saibaba, A.; Kitanidis, P. K.
2013-12-01
Kalman filtering is a fundamental tool in statistical time series analysis to understand the dynamics of large systems for which limited, noisy observations are available. However, standard implementations of the Kalman filter are prohibitive because they require O(N^2) in memory and O(N^3) in computational cost, where N is the dimension of the state variable. In this work, we focus our attention on the Random walk forecast model which assumes the state transition matrix to be the identity matrix. This model is frequently adopted when the data is acquired at a timescale that is faster than the dynamics of the state variables and there is considerable uncertainty as to the physics governing the state evolution. We derive an efficient representation for the a priori and a posteriori estimate covariance matrices as a weighted sum of two contributions - the process noise covariance matrix and a low rank term which contains eigenvectors from a generalized eigenvalue problem, which combines information from the noise covariance matrix and the data. We describe an efficient algorithm to update the weights of the above terms and the computation of eigenmodes of the generalized eigenvalue problem (GEP). The resulting algorithm for the Kalman filter with Random walk forecast model scales as O(N) or O(N log N), both in memory and computational cost. This opens up the possibility of real-time adaptive experimental design and optimal control in systems of much larger dimension than was previously feasible. For a small number of measurements (~ 300 - 400), this procedure can be made numerically exact. However, as the number of measurements increase, for several choices of measurement operators and noise covariance matrices, the spectrum of the (GEP) decays rapidly and we are justified in only retaining the dominant eigenmodes. We discuss tradeoffs between accuracy and computational cost. The resulting algorithms are applied to an example application from ray-based travel time tomography.
Kako, Shin'ichiro; Isobe, Atsuhiko; Magome, Shinya; Hinata, Hirofumi; Seino, Satoquo; Kojima, Azusa
2011-02-01
This study attempts to establish a system for hindcasting/forecasting the quantity of litter reaching a beach using an ocean circulation model, a two-way particle tracking model (PTM) to find litter sources, and an inverse method to compute litter outflows at each source. Twelve actual beach survey results, and satellite and forecasted wind data were also used. The quantity of beach litter was hindcasted/forecasted using a forward in-time PTM with the surface currents computed in the ocean circulation model driven by satellite-derived/forecasted wind data. Outflows obtained using the inverse method was given for each source in the model. The time series of the hindcasted/forecasted quantity of beach litter were found consistent with the quantity of beach litter determined from sequential webcam images of the actual beach. The accuracy of the model, however, is reduced drastically by intense winds such as typhoons which disturb drifting litter motion. PMID:21093000
A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty
Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.
2013-07-25
This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.
NWP Models Wind Forecast Evaluation Over Complex Terrain
NASA Astrophysics Data System (ADS)
Tomé, R.; Miranda, P.; Dutra, E.
2009-04-01
A full five years (2001-2005) of numerical simulations with MM5 mesoscale model are used to evaluate the performance of this model in wind forecast in the Island of Madeira. Simulations test the sensitivity of the model to horizontal resolution, vertical resolution and options in the parameterization of the boundary layer. In the later year (2005) the results are compared against simulations of the WRF and MesoNH mesoscale models. The simulations use ECMWF reanalysis data as initial and boundary. Results are compared with field observations in 6 masts at the mountain plateau of Paul da Serra, 1.5 km above sea level. The quality of the simulated fields is evaluated to assess wind energy distribution in a very complex terrain. Results don't reveal a positive response to the increased horizontal resolution for the MM5 model, but, in general, all models overestimate wind speeds and MesoNH shows a slightly better performance than the American models.
Estimating Demand for Industrial and Commercial Land Use Given Economic Forecasts
Batista e Silva, Filipe; Koomen, Eric; Diogo, Vasco; Lavalle, Carlo
2014-01-01
Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on ‘land use intensity’ measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models’ results were compared with estimations generated using the ‘null model’ (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the ‘null model’, but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models’ performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions. PMID:24647587
NASA Astrophysics Data System (ADS)
Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.
2013-10-01
Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.
An application of ensemble/multi model approach for wind power production forecast.
NASA Astrophysics Data System (ADS)
Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.
2010-09-01
The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.
Forecasting and Modeling Trans-Pacific Transport of Asian Pollution in ITCT2K2
NASA Astrophysics Data System (ADS)
Li, Q.; Jacob, D.; Yantosca, R.; Hudman, R.; Jaegle, L.
2002-12-01
We used the GEOS-CHEM global 3-D model of tropospheric chemistry driven by forecast meteorological fields from the Goddard Earth Observing System(GEOS) to forecast transpacific transport events during the ITCT2K2 aircraft mission over the west coast in April-May 2001. ITCT2K2 focused on characterizing Asian inflow to North America. The forecast simulations transported five tagged CO tracers from different source regions. The model successfully predicted several transpacific transport events that were confirmed by in situ observations. We further investigate the performance of the model forecasts by comparing forecast results to the observations and to the post-mission full-chemistry simulations driven by reanalysis meteorological fields. The model is evaluated with the in situ aircraft and ground measurements from both ITCT2K2 and the PEACE-B aircraft mission. The chemical evolution of Asian pollution during transport and the seasonal variations of transpacific transport are investigated.
A Hierarchical Bayesian Model to Quantify Uncertainty of Stream Water Temperature Forecasts
Bal, Guillaume; Rivot, Etienne; Baglinière, Jean-Luc; White, Jonathan; Prévost, Etienne
2014-01-01
Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i) an emotive simulated example, ii) application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife. PMID:25541732
NASA Astrophysics Data System (ADS)
Pattantyus, A.; Businger, S.
2013-12-01
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.
A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.
Bal, Guillaume; Rivot, Etienne; Baglinière, Jean-Luc; White, Jonathan; Prévost, Etienne
2014-01-01
Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i) an emotive simulated example, ii) application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife. PMID:25541732
NASA Astrophysics Data System (ADS)
Bao, H.-J.; Zhao, L.-N.; He, Y.; Li, Z.-J.; Wetterhall, F.; Cloke, H. L.; Pappenberger, F.; Manful, D.
2011-02-01
The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.
NASA Astrophysics Data System (ADS)
Tiwari, Mukesh K.; Adamowski, Jan
2013-10-01
A new hybrid wavelet-bootstrap-neural network (WBNN) model is proposed in this study for short term (1, 3, and 5 day; 1 and 2 week; and 1 and 2 month) urban water demand forecasting. The new method was tested using data from the city of Montreal in Canada. The performance of the WBNN method was compared with the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average model with exogenous input variables (ARIMAX), traditional NNs, wavelet analysis-based NNs (WNN), bootstrap-based NNs (BNN), and a simple naïve persistence index model. The WBNN model was developed as an ensemble of several NNs built using bootstrap resamples of wavelet subtime series instead of raw data sets. The results demonstrated that the hybrid WBNN and WNN models produced significantly more accurate forecasting results than the traditional NN, BNN, ARIMA, and ARIMAX models. It was also found that the WBNN model reduces the uncertainty associated with the forecasts, and the performance of WBNN forecasted confidence bands was found to be more accurate and reliable than BNN forecasted confidence bands. It was found in this study that maximum temperature and total precipitation improved the accuracy of water demand forecasts using wavelet analysis. The performance of WBNN models was also compared for different numbers of bootstrap resamples (i.e., 25, 50, 100, 200, and 500) and it was found that WBNN models produced optimum results with different numbers of bootstrap resamples for different lead time forecasts with considerable variability.
NASA Astrophysics Data System (ADS)
Sapunov, Valentin; Dikinis, Alexandr; Voronov, Nikolai
2014-05-01
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.
A Global Aerosol Model Forecast for the ACE-Asia Field Experiment
NASA Technical Reports Server (NTRS)
Chin, Mian; Ginoux, Paul; Lucchesi, Robert; Huebert, Barry; Weber, Rodney; Anderson, Tad; Masonis, Sarah; Blomquist, Byron; Bandy, Alan; Thornton, Donald
2003-01-01
We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.
A stochastic post-processing method for solar irradiance forecasts derived from NWPs models
NASA Astrophysics Data System (ADS)
Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.
2010-09-01
Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.
T GREAT ALASKA WEATHER MODELING SYMPOSIUM WHAT: About 50 scientists, forecasters, decision
Moelders, Nicole
T GREAT ALASKA WEATHER MODELING SYMPOSIUM WHAT: About 50 scientists, forecasters, decision makers, and others interested in Alaska weather prediction met to present recent research, introduce new application tools, and identify difficulties in Alaska and polar weather forecasting that need to be addressed
Northwest Energy Policy Project: energy demand modeling and forecasting final report
McHugh
1977-01-01
The Northwest Energy Policy Project was undertaken to develop the necessary tools for energy policy development in the Pacific Northwest states individually and as a region. Mathematical Sciences Northwest, Inc. (MSNW) prepared the demand forecasting model for this project. This volume is the final report and incorporates a discussion of alternative methods of demand forecasting, the detailed formulation of MSNW's
A comparative study of linear and nonlinear models for aggregate retail sales forecasting
Ching-Wu Chu; Guoqiang Peter Zhang
2003-01-01
The purpose of this paper is to compare the accuracy of various linear and nonlinear models for forecasting aggregate retail sales. Because of the strong seasonal fluctuations observed in the retail sales, several traditional seasonal forecasting methods such as the time series approach and the regression approach with seasonal dummy variables and trigonometric functions are employed. The nonlinear versions of
Webster, Peter J.
Extendedrange seasonal hurricane forecasts for the North Atlantic with a hybrid 20 September 2010; published 9 November 2010. [1] A hybrid forecast model for seasonal hurricane between the number of seasonal hurricane and the large scale variables from ECMWF hindcasts. The increase
Threat Level Forecast for Ship's Oil Spill - Based on BP Neural Network Model
Cai Wenxue; Zheng Yanwu; Shi Yongqiang; Zhong Huiling
2009-01-01
It's very important to assess the threat level in time when the ship's oil spill occurred, because the threat level forecast will help to come to a decision when dealing with the accident. BP neural network model is proposed in this paper to build a thread level forecast method for ship's oil spill accident. Train the BP neural network first
Thirty Years of Flood Forecasting with John Schaake: Latest Advances in Distributed Modeling
R. L. Bras; E. R. Vivoni; V. Y. Ivanov
2001-01-01
John Schaake must be one of the most versatile hydrologists anywhere. Over his long career John has dealt with everything from urban hydrology to climate change. Throughout that trajectory he has always maintained an avid interest in the very real and pragmatic problem of flood forecasting. This paper briefly discusses modern distributed models for flood forecasting in the context of
Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon?
Castro, Christopher L.
Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early to represent the North American monsoon, in terms of its cli- matology and interannual variability
Model climate as a function of forecast lead time in an imperfect model scenario
NASA Astrophysics Data System (ADS)
Martinez-Alvarado, Oscar
2014-05-01
Numerical weather and climate models constitute the best available tools to tackle the problems of weather prediction and climate projection. These models have played a key role in the attribution of the observed climate change to anthropogenic causes. However, a better understanding of the current models and the development of improved models are still required to address issues such as the interpretation of climate projections and the large uncertainties still present in regional climate change studies. Two assumptions lie at the heart the climate model suitability: (1) a climate attractor exists, and (2) the model attractor lies on the climate attractor, or at least on the projection of the climate attractor onto model space. In this contribution, two versions of the Lorenz '96 system are used, one as a prototype system and another as an imperfect model, to investigate the implications of assumption (2). In particular, the dependence of model-generated climate on forecast lead time is examined. It is shown that forecasts produced by the imperfect model rapidly diverge from the system's orbit and that this divergence is mainly due to model error. As a result, climatologies produced from these divergent forecasts show a dependence on forecast lead time. This dependence is characterised by an initial rapid bias growth with respect to the system's climatology. The initial bias growth ends at a saturation level which is reached as the transient period in individual forecasts dies out (spin-up period). Furthermore, it is shown that, once the spin-up period is over, climatologies generated with long-term integrations of both the prototype system and the imperfect model are essentially the same as climatologies generated from short-term forecasts from a perfect and an imperfect model, respectively. Despite its simplicity with respect to the actual climate system, this study about the Lorenz '96 system shows features that are relevant for climate studies and the understanding of climate models. In order to show this, two examples using real-world data from operational forecasting systems and climate experiments are also discussed.
Lang, K.
1982-01-01
in conjunction with an overall forecasting methodology to study the potential reductions in electricity purchases by the industrial sector in the Pacific Northwest. The study was supported by the Bonneville Power Administration (BPA) which... is the electric power marketing agency of the U.S. Department of Energy in that region. Traditionally a hydroelectric power developer, BPA is now also involved in thermal power genera tion. Because the cost of the thermally generated power...
NASA Astrophysics Data System (ADS)
Weng, F.; Zou, X.; Shi, Q.; Zhang, B.
2012-12-01
In hurricane and severe storm conditions, data obtained from satellite microwave temperature and water vapor sounders provide three dimensional warm core features that could be indirectly and directly used for vortex initialization and hurricane data assimilation. The Advanced Technology of Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS) on board the recently launched Suomi National Polar-Orbiting Partnership (NPP) satellite launched on October 28, 2011, as well as Advanced Microwave Scanning Radiometer (AMSR-2) on board the Global Change Observation Mission 1st - Water (GCOM-W1) satellite launched on May 18, 2012, are providing data for atmospheric temperature profiles, moisture profiles, sea surface temperature, and sea surface wind within and around tropical cyclones. Over 90% of the satellite data ingested by NWP models is only a small fraction of available satellite data. Many satellite data are excluded by either a data thinning process for timely processing and for avoiding horizontal correlation of observation errors, or a quality control process to eliminate cloud- or rain-affected radiances and surface-sensitive channels that would otherwise render assimilation results more prone to error. However, we will show that high-resolution observations, cloud-sensitive and surface-sensitive channels contain useful and rich information about hurricane structures, and satellite data assimilation is extremely sensitive to model top height and vertical and horizontal resolutions. As an example, sensitivity of hurricane track forecasts to both model top heights and vertical resolutions for 2012 hurricane season will be presented. The control experiment (EXP1_L42T50) uses a model configuration same as the 2011 NCEP trunk version, with 42 vertical levels from surface to 50hPa. The second experiment (EXP2_L42T1) is the same as EXP1_L42T50 but with model top raised to 1hPa, and the third experiment (EXP3_L64T1) is the same as EXP1_L42T50 except for having 64 vertical levels from surface to 1hPa. So far numerical experiments for a total of 10 tropical storms, hurricanes, and typhoons over Atlantic and Pacific are carried out. On average, a 10% improvement is obtained for hurricane track forecasts by EXP2_L42T1 compared with EXP1_L42T50 but only during the first 48 hours model forecast period. The track forecast skills of EXP3_L64T1 are consistently improved for all forecast leading hours compared with EXP1_L42T50. It is worth mentioning that our control experiment EXP2_L42T1 outperforms HWRF operational (HOPS) forecasts for both track and intensity forecasts. The forecast error statistics for different ocean basins and more cases will be presented at the conference when more 2012 tropical storms, hurricanes, and typhoons become available.
Modeling the wind-fields of accidental releases by mesoscale forecasting
Albritton, J.R.; Lee, R.L.; Mobley, R.L.; Pace, J.C. [Lawrence Livermore National Lab., CA (United States); Hodur, R.A.; Lion, C.S. [Navel Research Lab, Monterey, CA (United States)
1997-07-01
Modeling atmospheric releases even during fair weather can present a sever challenge to diagnostic, observed-data-driven, models. Such schemes are often handicapped by sparse input data from meteorological surface stations and soundings. Forecasting by persistence is only acceptable for a few hours and cannot predict important changes in the diurnal cycle or from synoptic evolution. Many accident scenarios are data-sparse in space and/or time. Here we describe the potential value of limited-area, mesoscale, forecast models for real-time emergency response. Simulated wind-fields will be passed to ARAC`s operational models to produce improved forecasts of dispersion following accidents.
NASA Technical Reports Server (NTRS)
Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco
2012-01-01
Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.
Qi, Cheng; Chang, Ni-Bin
2011-06-01
Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments. PMID:21324581
Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework
NASA Astrophysics Data System (ADS)
Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.
2014-10-01
Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.
How Useful Are Regional Climate Models For Downscaling Seasonal Forecasts?
NASA Astrophysics Data System (ADS)
Robertson, A. W.; Qian, J.; Moron, V.; Tippett, M.; Lucero, A.
2010-12-01
A longstanding yet very important question concerns the additional value derived from labor intensive regional climate models (RCMs) nested within GCM seasonal forecast models, over and above simple statistical methods of downscaling. This paper compares the two types of downscaling of precipitation "hindcasts" over the data-rich region of the Philippines, using observed data from 77 raingauges for the April-June monsoon onset season. Spatial interpolation of RCM and GCM grid box values to station locations is compared with cross-validated regression-based techniques such as canonical correlation analysis. The GCM "hindcasts" are formed from an ensemble of simulations from the ECHAM4.5 model at T42 resolution made with observed SSTs prescribed, over the 1977-2004 period. The RegCM3 with 25km resolution is nested within each of a 10-member GCM ensemble over the Philippines. To first order, we find that anomaly correlation skill at the station scale for simulations of seasonal total rainfall and monsoon onset date is quite similar using all the techniques considered, including simple spatial interpolation of the GCM values. The RCM has significantly smaller RMS error than the "raw" interpolated GCM, although statistical correction can greatly improve the latter. We examine the role of the availability of sufficiently long records of observed data as a deciding factor, which enters as a means to validate both types of the hindcasts, while being needed in addition to train the more "data hungry" statistical downscaling methods.
Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A
2014-08-01
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
A gene-wavelet model for long lead time drought forecasting
NASA Astrophysics Data System (ADS)
Danandeh Mehr, Ali; Kahya, Ercan; Özger, Mehmet
2014-09-01
Drought forecasting is an essential ingredient for drought risk and sustainable water resources management. Due to increasing water demand and looming climate change, precise drought forecasting models have recently been receiving much attention. Beginning with a brief discussion of different drought forecasting models, this study presents a new hybrid gene-wavelet model, namely wavelet-linear genetic programing (WLGP), for long lead-time drought forecasting. The idea of WLGP is to detect and optimize the number of significant spectral bands of predictors in order to forecast the original predictand (drought index) directly. Using the observed El 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.
Endogenicity of economic growth models
Abdul Qayum
2005-01-01
In his celebrated 1956 article, “A Contribution to the Theory of Economic Growth,” Solow calibrated the stylized facts of economic growth observed in the Western developed countries and summed up by Kaldor. Solow reconciled steady-state rate of growth of per capita output with constant capital\\/output and capital\\/labor ratios by introducing labor augmenting technological progress and measuring physical labor time in
Modeling and forecasting of KLCI weekly return using WT-ANN integrated model
NASA Astrophysics Data System (ADS)
Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi
2013-04-01
The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.
NASA Astrophysics Data System (ADS)
Subramanian, A. C.; Zhang, G. J.
2013-12-01
This study investigates the MJO forecast biases, especially at the initiation stage, of the Community Atmosphere Model CAM3. We diagnose the dynamic and thermodynamic forecast misfits in a version of CAM3 for the MJO event observed during the DYNAMO field campaign during October 2011. The CAM3 forecasts were initialized from ECMWF Reanalyses fields and the MJO forecast skill was analyzed for daily and climatological SST boundary conditions. Further, CAM forecasts were nudged towards ECMWF Reanalyses fields to obtain nudging increments during the forecast period. These nudging increments were then analyzed to diagnose deficiencies in model physics and thermodynamic structures in the model as observed in the MJO. The forecast experiments show that the initiation of the MJO event is captured well in the runs forced with daily SST. The nudging experiments reveal that the model has too strong a zonal shear during the evolution of the MJO and the heating structure in the model does not emulate the observed top heavy baroclinic heating structure of the MJO. We conclude with a discussion of suggested improvements to the deep convection scheme in the context of tropical convection and the MJO prediction.
Forecasting exposure to volcanic ash based on ash dispersion modeling
NASA Astrophysics Data System (ADS)
Peterson, Rorik A.; Dean, Ken G.
2008-03-01
A technique has been developed that uses Puff, a volcanic ash transport and dispersion (VATD) model, to forecast the relative exposure of aircraft and ground facilities to ash from a volcanic eruption. VATD models couple numerical weather prediction (NWP) data with physical descriptions of the initial eruptive plume, atmospheric dispersion, and settling of ash particles. Three distinct examples of variations on the technique are given using ERA-40 archived reanalysis NWP data. The Feb. 2000 NASA DC-8 event involving an eruption of Hekla volcano, Iceland is first used for analyzing a single flight. Results corroborate previous analyses that conclude the aircraft did encounter a diffuse cloud of volcanic origin, and indicate exposure within a factor of 10 compared to measurements made on the flight. The sensitivity of the technique to dispersion physics is demonstrated. The Feb. 2001 eruption of Mt. Cleveland, Alaska is used as a second example to demonstrate how this technique can be utilized to quickly assess the potential exposure of a multitude of aircraft during and soon after an event. Using flight tracking data from over 40,000 routes over three days, several flights that may have encountered low concentrations of ash were identified, and the exposure calculated. Relative changes in the quantity of exposure when the eruption duration is varied are discussed, and no clear trend is evident as the exposure increased for some flights and decreased for others. A third application of this technique is demonstrated by forecasting the near-surface airborne concentrations of ash that the cities of Yakima Washington, Boise Idaho, and Kelowna British Columbia might have experienced from an eruption of Mt. St. Helens anytime during the year 2000. Results indicate that proximity to the source does not accurately determine the potential hazard. Although an eruption did not occur during this time, the results serve as a demonstration of how existing cities or potential locations of research facilities or military bases can be assessed for susceptibility to hazardous and unhealthy concentrations of ash and other volcanic gases.
Training the next generation of scientists in Weather Forecasting: new approaches with real models
NASA Astrophysics Data System (ADS)
Carver, Glenn; Vá?a, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah
2014-05-01
The European Centre for Medium Range Weather Forecasts operationally produce medium range forecasts using what is internationally acknowledged as the world leading global weather forecast model. Future development of this scientifically advanced model relies on a continued availability of experts in the field of meteorological science and with high-level software skills. ECMWF therefore has a vested interest in young scientists and University graduates developing the necessary skills in numerical weather prediction including both scientific and technical aspects. The OpenIFS project at ECMWF maintains a portable version of the ECMWF forecast model (known as IFS) for use in education and research at Universities, National Meteorological Services and other research and education organisations. OpenIFS models can be run on desktop or high performance computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. ECMWF also provide the Metview desktop application, a modern, graphical, and easy to use tool for analysing and visualising forecasts that is routinely used by scientists and forecasters at ECMWF and other institutions. The combination of Metview with the OpenIFS models has the potential to deliver classroom-friendly tools allowing students to apply their theoretical knowledge to real-world examples using a world-leading weather forecasting model. In this paper we will describe how the OpenIFS model has been used for teaching. We describe the use of Linux based 'virtual machines' pre-packaged on USB sticks that support a technically easy and safe way of providing 'classroom-on-a-stick' learning environments for advanced training in numerical weather prediction. We welcome discussions with interested parties.
NASA Astrophysics Data System (ADS)
Chen, Jie; Brissette, François; Arsenault, Richard; Gatien, Philippe; Roy, Pierre-Olivier; Li, Zhi; Turcotte, Richard
2013-04-01
Probabilistic streamflow prediction based on past climate records or meteorological forecasts have drawn much attention in recent years. It is usually incorporated into operational forecasting systems by government agencies and industries to deal with water resources management and regulation problems. This work presents an operational prototype for short to medium term ensemble streamflow predictions over Quebec, Canada. The system uses ensemble meteorological forecasts for short term (up to 7 days) forecasting, transitioning to a stochastic weather generator conditioned on historical data for the period exceeding 7 days. The precipitation and temperature series are then fed into a combination of 32 hydrology models to account for both the meteorological and hydrology modelling uncertainties. A novel post-processing approach was implemented to correct the biases and the under-dispersion of ensemble meteorological forecasts. This post-processing approach links the mean of the ensemble meteorological forecast to parameters of a stochastic weather generator (absolute probability of precipitation and observed precipitation mean in the case of precipitation). The stochastic weather generator is then used to generated unbiased times series with accurate spread. Results show that the post-processed meteorological forecasts displayed skill for a period up to 7 days for both precipitation and temperature. The ensemble streamflow prediction displayed more skill than when using the deterministic forecast or the stochastic weather generator not conditioned on the ensemble meteorological forecasts. To tackle the uncertainty linked to the hydrology model, 4 different models calibrated with up to 9 different efficiency metrics (for a combination of 32 models/calibrations). Nine different averaging schemes were compared to attribute weights to the 32 combinations. The best averaging method (Granger-Ramanathan) produced estimates with a much better efficiency than the best performing model, while removing all biases linked to the hydrology modelling.
Integrated water and sediment flow simulation and forecasting models for river reaches
NASA Astrophysics Data System (ADS)
Choudhury, Parthasarathi; Sil, Briti Sundar
2010-05-01
SummaryIn the present study integrated water and sediment flow simulation and forecasting models for a river reach have been developed. The new models combine Muskingum model and the sediment rating model leading to integrated water discharge-sediment concentration model ( WSCM) and water discharge-sediment discharge model ( WSDM) for a reach. The models depict coherence in water discharge and sediment load variations at a site; incorporate two hydrologic variables, water discharge and sediment load for the gauge sites and represent revised forms of the basic Muskingum model. The models can be recast into forecasting form useful for obtaining downstream water and sediment flow forecasts ?t'=2kx time unit ahead. During calibration the models can select a commensurate inflow-outflow set depending on upstream and the downstream relative sediment discharge characteristics for a reach. The models can be used for developing Muskingum model for river reaches having no water discharge records. With forecasting capabilities the present models are useful in the real time management of sediment related pollution hazards in water courses. The study indicates that a single model could be used to describe both water and sediment flow in river reaches. The proposed model formulations are demonstrated for simulating and forecasting sediment concentration, sediment discharge and water discharge in the Mississippi River Basin, USA. Model parameters are estimated using non-dominated sorting Genetic Algorithm II (NSGA-II). Comparison of models performances with reported works show better performances by the present models.
Residential Saudi load forecasting using analytical model and Artificial Neural Networks
NASA Astrophysics Data System (ADS)
Al-Harbi, Ahmad Abdulaziz
In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.
Generation of Three-Dimensional Lake Model Forecasts for Lake Erie
John G. W. Kelley; Jay S. Hobgood; Keith W. Bedford; David J. Schwab
1998-01-01
A one-way coupled atmospheric-lake modeling system was developed to generate short-term, mesoscale lake circulation, water level, and temperature forecasts for Lake Erie. The coupled system consisted of the semi- operational versions of the Pennsylvania State University-National Center for Atmospheric Research three- dimensional, mesoscale meteorological model (MM4), and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS). The
NASA Technical Reports Server (NTRS)
Hoffman, Ross N.; Nehrkorn, Thomas; Grassotti, Christopher
1997-01-01
We proposed a novel characterization of errors for numerical weather predictions. In its simplest form we decompose the error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and is required to vary slowly and smoothly with position. A general distortion representation allows for the displacement and amplification or bias correction of forecast anomalies. Characterizing and decomposing forecast error in this way has two important applications, which we term the assessment application and the objective analysis application. For the assessment application, our approach results in new objective measures of forecast skill which are more in line with subjective measures of forecast skill and which are useful in validating models and diagnosing their shortcomings. With regard to the objective analysis application, meteorological analysis schemes balance forecast error and observational error to obtain an optimal analysis. Presently, representations of the error covariance matrix used to measure the forecast error are severely limited. For the objective analysis application our approach will improve analyses by providing a more realistic measure of the forecast error. We expect, a priori, that our approach should greatly improve the utility of remotely sensed data which have relatively high horizontal resolution, but which are indirectly related to the conventional atmospheric variables. In this project, we are initially focusing on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically, we study the forecast errors of the sea level pressure (SLP) and 500 hPa geopotential height fields for forecasts of the short and medium range. Since the forecasts are generated by the GEOS (Goddard Earth Observing System) data assimilation system with and without ERS 1 scatterometer data, these preliminary studies serve several purposes. They (1) provide a testbed for the use of the distortion representation of forecast errors, (2) act as one means of validating the GEOS data assimilation system and (3) help to describe the impact of the ERS 1 scatterometer data.
A. P. Weigel; R. Knutti; M. A. Liniger; C. Appenzeller
2010-01-01
During the past decade, seasonal forecasting has become a well-established technique, and dynamical seasonal prediction systems are now in operational use at a range of climate prediction centers. In the wake of these developments, an enormous data-base of climate model simulations has been created, which has not only advanced our knowledge about seasonal predictability per se. Given that these data
Andreas Weigel; Mark Liniger; Christof Appenzeller; Andreas Fischer
2010-01-01
During the past decade, seasonal forecasting has become a well-established technique, and dynamical seasonal prediction systems are now in operational use at a range of climate prediction centers. In the wake of these developments, an enormous data-base of climate model simulations has been created, which has not only advanced our knowledge about seasonal predictability per se. Given that these data
Multidimensional approaches to performance evaluation of competing forecasting models
Xu, Bing
2009-01-01
The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions ...
Tracking signal test to monitor an intelligent time series forecasting model
NASA Astrophysics Data System (ADS)
Deng, Yan; Jaraiedi, Majid; Iskander, Wafik H.
2004-03-01
Extensive research has been conducted on the subject of Intelligent Time Series forecasting, including many variations on the use of neural networks. However, investigation of model adequacy over time, after the training processes is completed, remains to be fully explored. In this paper we demonstrate a how a smoothed error tracking signals test can be incorporated into a neuro-fuzzy model to monitor the forecasting process and as a statistical measure for keeping the forecasting model up-to-date. The proposed monitoring procedure is effective in the detection of nonrandom changes, due to model inadequacy or lack of unbiasedness in the estimation of model parameters and deviations from the existing patterns. This powerful detection device will result in improved forecast accuracy in the long run. An example data set has been used to demonstrate the application of the proposed method.
Sensitivity of hurricane forecasts to cumulus parameterizations in the HWRF model
NASA Astrophysics Data System (ADS)
Biswas, Mrinal K.; Bernardet, Ligia; Dudhia, Jimy
2014-12-01
The 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.
NASA Astrophysics Data System (ADS)
Schepen, Andrew; Wang, Q. J.
2014-11-01
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.
A spatial model to forecast raccoon rabies emergence.
Recuenco, Sergio; Blanton, Jesse D; Rupprecht, Charles E
2012-02-01
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
High-resolution geomagnetic field modeling and forecasting
NASA Astrophysics Data System (ADS)
Soukhovitskaya, Veronika
2010-12-01
We use geomagnetic observatory data, survey data and satellite data from the CHAMP, Oersted, MAGSAT, DE-2 and POGO missions to derive two time-dependent spherical harmonic models of Earth's magnetic field at the core-mantle boundary: one for the years 1957-2009 and the other for the years 2001-2009 (in order to investigate the limits of core field resolution with the most recent, highly accurate data). We pay particular attention to observatory and satellite data analysis and to spatial and temporal data distributions in order to separate external and internal fields. Our approach is to produce models with varying spatial roughness and to examine them with respect to correlations with known structures of core and crustal fields. The final models are consistent with other main field models in their general structure, but show differences predominantly in places where main field features are known to be complex (e.g. the South Atlantic Anomaly). Thus, the models reveal a more detailed spatial and temporal structure of the magnetic field at the core-mantle boundary. Such high-resolution models can be used to infer small-scale core surface flows and core dynamics. We use the 1957-2009 geomagnetic field model to derive time-dependent core flow models and produce hindcasts of the Earth's main magnetic field. The goal of this study is to explore whether we can accurately forecast changes in geomagnetic secular variation by advecting core-surface flows forward in time and accounting for torsional oscillations. We compare hindcasts produced over different time intervals and computed from steady and time-varying core flow models, and also consider differently parametrized core flows (such as steady flow, steadily accelerated flow and steadily accelerated flow with torsional oscillations). We find that the steadily accelerated flow plus torsional oscillations is able to accurately reproduce changes in the Earth's magnetic field for short-term (5 years) and medium-term (13 years) hindcasts and over time intervals characterized by both slower and faster secular variation. We also find that hindcasts are strongly dependent on the accuracy of the core flow models, and that hindcasts can be improved by properly accounting for non-steady flow acceleration in addition to torsional oscillations.
Snowmelt runoff modeling in simulation and forecasting modes with the Martinec-Mango model
NASA Technical Reports Server (NTRS)
Shafer, B.; Jones, E. B.; Frick, D. M. (principal investigators)
1982-01-01
The Martinec-Rango snowmelt runoff model was applied to two watersheds in the Rio Grande basin, Colorado-the South Fork Rio Grande, a drainage encompassing 216 sq mi without reservoirs or diversions and the Rio Grande above Del Norte, a drainage encompassing 1,320 sq mi without major reservoirs. The model was successfully applied to both watersheds when run in a simulation mode for the period 1973-79. This period included both high and low runoff seasons. Central to the adaptation of the model to run in a forecast mode was the need to develop a technique to forecast the shape of the snow cover depletion curves between satellite data points. Four separate approaches were investigated-simple linear estimation, multiple regression, parabolic exponential, and type curve. Only the parabolic exponential and type curve methods were run on the South Fork and Rio Grande watersheds for the 1980 runoff season using satellite snow cover updates when available. Although reasonable forecasts were obtained in certain situations, neither method seemed ready for truly operational forecasts, possibly due to a large amount of estimated climatic data for one or two primary base stations during the 1980 season.
A crop loss-related forecasting model for sclerotinia stem rot in winter oilseed rape.
Koch, S; Dunker, S; Kleinhenz, B; Röhrig, M; Tiedemann, A von
2007-09-01
Sclerotinia stem rot (SSR) is an increasing threat to winter oilseed rape (OSR) in Germany and other European countries due to the growing area of OSR cultivation. A forecasting model was developed to provide decision support for the fungicide spray against SSR at flowering. Four weather variables-air temperature, relative humidity, rainfall, and sunshine duration-were used to calculate the microclimate in the plant canopy. From data reinvestigated in a climate chamber study, 7 to 11 degrees C and 80 to 86% relative humidity (RH) were established as minimum conditions for stem infection with ascospores and expressed as an index to discriminate infection hours (Inh). Disease incidence (DI) significantly correlated with Inh occurring post-growth stage (GS) 58 (late bud stage) (r(2) = 0.42, P model calculates the developmental stages of OSR based on temperature in the canopy and starts the model calculation at GS 58. The novel forecasting system, SkleroPro, consists of a two-tiered approach, the first providing a regional assessment of the disease risk, which is assumed when 23 Inh have accumulated after the crop has passed GS 58. The second tier provides a field-site-specific, economy-based recommendation. Based on costs of spray, expected yield, and price of rapeseed, the number of Inh corresponding to DI at the economic damage threshold (Inh(i)) is calculated. A decision to spray is proposed when Inh >/= Inh(i). Historical field data (1994 to 2004) were used to assess the impact of agronomic factors on SSR incidence. A 2-year crop rotation enhanced disease risk and, therefore, lowered the infection threshold in the model by a factor of 0.8, whereas in 4-year rotations, the threshold was elevated by a factor 1.3. Number of plants per square meter, nitrogen fertilization, and soil management did not have significant effects on DI. In an evaluation of SkleroPro with 76 historical (1994 to 2004) and 32 actual field experiments conducted in 2005, the percentage of economically correct decisions was 70 and 81%, respectively. Compared with the common practice of routine sprays, this corresponded to savings in fungicides of 39 and 81% and to increases in net return for the grower of 23 and 45 euro/ha, respectively. This study demonstrates that, particularly in areas with abundant inoculum, the level of SSR in OSR can be predicted from conditions of stem infection during late bud or flowering with sufficient accuracy, and does not require simulation of apothecial development and ascospore dispersal. SkleroPro is the first crop-loss-related forecasting model for a Sclerotinia disease, with the potential of being widely used in agricultural practice, accessible through the Internet. Its concept, components, and implementation may be useful in developing forecasting systems for Sclerotinia diseases in other crops or climates. PMID:18944183
HTGR Application Economic Model Users' Manual
A.M. Gandrik
2012-01-01
The High Temperature Gas-Cooled Reactor (HTGR) Application Economic Model was developed at the Idaho National Laboratory for the Next Generation Nuclear Plant Project. The HTGR Application Economic Model calculates either the required selling price of power and/or heat for a given internal rate of return (IRR) or the IRR for power and/or heat being sold at the market price. The user can generate these economic results for a range of reactor outlet temperatures; with and without power cycles, including either a Brayton or Rankine cycle; for the demonstration plant, first of a kind, or nth of a kind project phases; for up to 16 reactor modules; and for module ratings of 200, 350, or 600 MWt. This users manual contains the mathematical models and operating instructions for the HTGR Application Economic Model. Instructions, screenshots, and examples are provided to guide the user through the HTGR Application Economic Model. This model was designed for users who are familiar with the HTGR design and Excel and engineering economics. Modification of the HTGR Application Economic Model should only be performed by users familiar with the HTGR and its applications, Excel, and Visual Basic.
THE GLOBAL IMPACT OF SATELLITE-DERIVED POLAR WINDS ON MODEL FORECASTS
Wisconsin at Madison, University of
THE GLOBAL IMPACT OF SATELLITE-DERIVED POLAR WINDS ON MODEL FORECASTS by David A. Santek........................................................................................................... 1 2. Satellite-derived winds algorithm........................................................................... 6 2.1 Geostationary satellite winds algorithm
Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model.
An, Yan; Zou, Zhihong; Zhao, Yanfei
2015-03-01
An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. PMID:25766025
Evaluation of Monthly Precipitation Forecasting Skill of the National Multi-Model Ensemble
NASA Astrophysics Data System (ADS)
Wang, H.
2012-12-01
National Multi-Model Ensemble (NMME) comprises of seven climate models from different sources, including NOAA, NASA, NCAR and the International Research Institute for Climate and Society (IRI). It provides 89 ensemble members for precipitation forecasts at different lead time. Precipitation forecasting from climate models have been applied to streamflow forecasts and its utility in water resources system operation has been demonstrated in the literature. In this study, one-month-ahead precipitation forecasts from NMME is evaluated for 180 grids of 2.5 degree by 2.5 degree over the continental United States using Mean square error (MSE) and Rank probability score (RPS). The forecasting skill over different months and its spatial variability are discussed. Preliminary results show that the variability of the forecasting skill reveals the correlation between precipitation observation and large scale oceanic-atmospheric indexes, e.g., NINO 3.4. Such analyses have implications for monthly/seasonal streamflow forecasts and water resources management at the watershed scale.
Liuxihe Model and its application in flood forecasting in Southern China: results and challenges
NASA Astrophysics Data System (ADS)
Chen, Y.
2011-12-01
Liuxihe Model is a physically-based distributed hydrological model mainly proposed for watershed flood forecasting. This paper first discusses the results of Liuxihe Model's applications in several river basins' flood forecast modeling in southern China with the basin areas ranging from several hundred to ten thousand square kilometers. The results are satisfactory and suggest its maturity for real-time flood forecasting. Then several issues related to the model application to real-time operation, such as the parameter sensitivity, parameter adjusting methods and results, data assimilation for modeling in data-spare basins with remotely sensed data, spatial and temporal scaling, soil moisture estimation and its effect to model performance, model coupling with radar-estimated precipitation, and parallel computing code for lager river basin modeling. Future works to be done is to build a test bed in southern China for model validation, parallel high performance computer system for model developing and simulation that will be open to public worldwide.
Ahmad, Sajjad
The Match That Can Ignite the Economy Economic forecasters have stared into their crystal balls for most of this young century, hoping to see clearly the near-term future of the economy. The crystal ball into the economy over the past three years, little evidence yet exists that the stimuli moved the economy off its
Do Artificial Neural Networks Provide Better Forecasts? Evidence from Latin American Stock Indexes
André Carvalhal; Tulio Ribeiro
2008-01-01
Forecasting is a key activity for academics and investors in the fields of finance and economics. This paper explores the usefulness of the non-linear artificial neural network (ANN) for forecasting Latin American stock indexes. Our goal is to estimate and compare the forecast accuracy of the ANN with three traditional models: random walk, ARMA, and GARCH. Our results provide strong
Forecast-skill-based simulation of streamflow forecasts
NASA Astrophysics Data System (ADS)
Zhao, Tongtiegang; Zhao, Jianshi
2014-09-01
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.
DEVELOPING A GEOGRAPHIC INFORMATION SYSTEM (GIS) TRAVEL DEMAND FORECASTING MODEL FOR LAS VEGAS
P J Shinbein
1999-01-01
A geographic information system (GIS)-based regional travel demand forecasting model was developed for Las Vegas, Nevada. The urban transportation planning system (UTPS) four-step planning procedure of trip generation, trip distribution, mode split, and traffic assignment is performed in an integrated GIS environment thereby improving detail and amount of data available to the analyst. A GIS-based forecasting model provides a wide
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
A. El-Shafie; A. Noureldin; M. R. Taha; A. Hussain
2011-01-01
Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series
Khait, Maria
2012-01-01
The broad literature documents the empirical regularity that slope of the term structure of interest rates is a reliable predictor of future real economic activity. Steeper slopes presage increasing growth, and downward ...
L. M. Andrews; M. J. King; N. Leary; D. M. Perry; C. C. Snow
1986-01-01
The INDEPTH industrial planning methodology will enable utilities to forecast service area electricity demand. The system allows the user to develop energy forecasts for the whole industrial sector, to examine industries most important to the service area, and to study uses of electricity that are of interest in demand-side management programs. The econometric model in this volume forecasts energy use
Combining forecast weights: Why and how?
NASA Astrophysics Data System (ADS)
Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim
2012-09-01
This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.
PROBABILISTIC QUANTITATIVE PRECIPITATION FIELD FORECASTING USING A TWO-STAGE SPATIAL MODEL 1
J. Berrocal; Adrian E. Raftery; Tilmann Gneiting
2008-01-01
Short-range forecasts of precipitation fields are needed in a wealth of agricultural, hydrological, ecological and other applications. Forecasts from numerical weather prediction models are often biased and do not provide uncertainty information. Here we present a postprocessing technique for such numerical forecasts that produces correlated probabilistic forecasts of precipitation accumulation at multiple sites simultaneously. The statistical model is a spatial version of a two-stage model that represents the distribution of precipitation by a mixture of a point mass at zero and a Gamma density for the continuous distribution of precipitation accumulation. Spatial correlation is captured by assuming that two Gaussian processes drive precipitation occurrence and precipitation amount, respectively. The first process is latent and drives precipitation occurrence via a threshold. The second process explains the spatial correlation in precipitation accumulation. It is related to precipitation via a site-specific transformation function, so as to retain the marginal right-skewed distribution of precipitation while modeling spatial dependence. Both processes take into account the information contained in the numerical weather forecast and are modeled as stationary isotropic spatial processes with an exponential correlation function. The two-stage spatial model was applied to 48-hour-ahead forecasts of daily precipitation accumulation over the Pacific Northwest
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...
Heterogeneous Agent Models in Economics and Finance
Cars H. Hommes
2005-01-01
This paper surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect,
Serving Collections of Forecast Model Runs with the THREDDS Data Server
NASA Astrophysics Data System (ADS)
Caron, J.
2006-12-01
The THREDDS Data Server (TDS) is a web server that provides metadata and data access for scientific datasets. It provides OPeNDAP, WCS, HTTP and netCDF subsetting services for a number of data formats, including netCDF, HDF5, GRIB, BUFR, etc. The TDS is 100% Java, and runs within the Tomcat web server. We have added a new way to serve model data, which takes a collection of Forecast Model Run datasets, and constructs a single dataset with a 2D time coordinate (run time, forecast time). In the case of Unidata's server, these are collections of GRIB files, and we deal correctly with missing data records by using the forecast and run dates, rather than array indices. The TDS also creates various other "synthetic" datasets from the collection: 1) all data from one analysis run; 2) data with the same forecast offset hour (eg all the 3 hour forecasts, from different runs); 3) data with a constant forecast date (eg all the data with forecast/valid time of 2006-08-08T12:00:00Z, from different runs); and 4) the "best" time series, taking the data from the most recent run available. We are currently working with a number of data partners to test and extend this functionality.
A novel hybrid forecasting model for PM?? and SO? daily concentrations.
Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng
2015-02-01
Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM?? (particles with a diameter of 10 ?m or less) concentrations and SO? (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. PMID:25461118
The Influence of Seasonal Forecast Accuracy on Farmer Behavior: An Agent-Based Modeling Approach
NASA Astrophysics Data System (ADS)
Jacobi, J. H.; Nay, J.; Gilligan, J. M.
2013-12-01
Seasonal climates dictate the livelihoods of farmers in developing countries. While farmers in developed countries often have seasonal forecasts on which to base their cropping decisions, developing world farmers usually make plans for the season without such information. Climate change increases the seasonal uncertainty, making things more difficult for farmers. Providing seasonal forecasts to these farmers is seen as a way to help buffer these typically marginal groups from the effects of climate change, though how to do so and the efficacy of such an effort is still uncertain. In Sri Lanka, an effort is underway to provide such forecasts to farmers. The accuracy of these forecasts is likely to have large impacts on how farmers accept and respond to the information they receive. We present an agent-based model to explore how the accuracy of seasonal rainfall forecasts affects the growing decisions and behavior of farmers in Sri Lanka. Using a decision function based on prospect theory, this model simulates farmers' behavior in the face of a wet, dry, or normal forecast. Farmers can either choose to grow paddy rice or plant a cash crop. Prospect theory is used to evaluate outcomes of the growing season; the farmer's memory of the level of success under a certain set of conditions affects next season's decision. Results from this study have implications for policy makers and seasonal forecasters.
Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model
Zhu, Qing; Zou, Yingchao; Lai, Kin Keung
2014-01-01
As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations. PMID:25061614
A spatial-temporal projection model for 10-30 day rainfall forecast in South China
NASA Astrophysics Data System (ADS)
Hsu, Pang-Chi; Li, Tim; You, Lijun; Gao, Jianyun; Ren, Hong-Li
2015-03-01
Extended-range (10-30 days) forecast, lying between well-developed short-range weather and long-range (monthly and seasonal) climate predictions, is one of the most challenging forecast currently faced by operational meteorological centers around the world. In this study, a set of spatial-temporal projection (STP) models was developed to predict low-frequency rainfall events at lead times of 5-30 days. We focused on early monsoon rainy season (mid April-mid July) in South China. To ensure that the model developed can be used for real-time forecast, a non-filtering method was developed to extract the low-frequency atmospheric signals of 10-60 days without using a band-pass filter. The empirical models were built based on 12-year (1996-2007) data, and independent forecast was then conducted for a 5 year (2008-2012) period. The assessment of the 5-year forecast of rainfall over South China indicates that the ensemble prediction of the STP models achieved a useful skill (with a temporal correlation coefficient exceeding 95 % confidence level) at a lead time of 20 days. The amplitude error was generally less than one standard deviation at all lead times of 5-30 days. Furthermore, the STP models provided useful probabilistic forecasts with the ranked probability skill score between 0.3-0.5 up to 30-day forecast in advance. The evaluation demonstrated that the STP models exhibited useful 10-30 days forecast skills for real-time extended-range rainfall prediction in South China.
NASA Astrophysics Data System (ADS)
Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.
2011-12-01
The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.
NASA Astrophysics Data System (ADS)
Noh, S.; Rakovec, O.; Weerts, A.; Tachikawa, Y.
2013-12-01
While important advances have been achieved in flood forecasting, due to various uncertainties that originate from simulation models, observations, and forcing data, they are still insufficient to obtain accurate prediction results with the required lead times. To increase the certainty of the hydrological forecast, data assimilation (DA) may be utilized to consider or propagate all of these sources of uncertainty through the hydrological modelling chain embedded in a flood forecasting system. Although numerous sophisticated DA algorithms have been proposed to mitigate uncertainty, DA methods dealing with the correction of model inputs, states, and initial conditions are conducted in a rather empirical and subjective way, which may reduce credibility and transparency to operational forecasts. In this study, we investigate the effect of noise specification on the quality of hydrological forecasts via an advanced DA procedure using a distributed hydrological model driven by numerical weather predictions. The sequential DA procedure is based on (1) a multivariate rainfall ensemble generator, which provides spatial and temporal correlation error structures of input forcing and (2) lagged particle filtering to update past and current state variables simultaneously in a lag-time window to consider the response times of internal hydrologic processes. The strength of the proposed procedure is that it requires less subjectivity to implement DA compared to conventional methods using consistent and objectively-induced error models. The procedure is evaluated for streamflow forecasting of three flood events in two Japanese medium-sized catchments. The rainfall ensembles are derived from ground based rain gauge observations for the analysis step and numerical weather predictions for the forecast step. Sensitivity analysis is performed to assess the impacts of uncertainties coming from DA such as random walk state noise and different DA methods with/without objectively-induced rainfall uncertainty conditions. The results show that multivariate rainfall ensembles provide sound input perturbations and model states updated by lagged particle filtering produce improved streamflow forecasts in conjunction with fine-resolution numerical weather predictions.
NASA Astrophysics Data System (ADS)
Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.
2009-12-01
With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric system implies a strong requirement for accurate wind forecasts, that can be used to forecast this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a system for 72h energy forecast in mainland Portugal was setup, using 6km resolution meteorological forecasts, forced by global GFS forecasts by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed system, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological forecast, and converts the forecasted wind fields into forecasted energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind forecasts, requiring fully developed meteorological models. In general, results from 2 full years of forecast indicate solid performance, in particular in what concerns the impact of synoptic scale systems going through the domain. However, there remain significant problems coming both from phasing errors in the evolution of synoptic systems and, more importantly, from limitations of the representation of surface and boundary layer processes in atmospheric models. Improvements in (quantitative) high-resolution meteorological forecasts may be a critical issue to support a sustained growth of the share on wind energy. The present paper presents a description of the developed system, results from the model evaluation exercise, and an analysis of the operational performance of the wind energy forecasts.
Forecasting sales of new vehicle with limited data using Bass diffusion model and Grey theory
NASA Astrophysics Data System (ADS)
Abu, Noratikah; Ismail, Zuhaimy
2015-02-01
New product forecasting is a process that determines a reasonable estimate of sales attainable under a given set of conditions. There are several new products forecasting method in practices and Bass Diffusion Model (BDM) is one of the most common new product diffusion model used in many industries to forecast new product and technology. Hence, this paper proposed a combining BDM with Grey theory to forecast sales of new vehicle in Malaysia that certainly have limited data to build a model on. The aims of this paper is to examine the accuracy of different new product forecasting models and thus identify which is the best among the basic BDM and combining BDM with Grey theory. The results show that combining BDM with Grey theory performs better than the basic BDM based on in-sample and out-sample mean absolute percentage error (MAPE). Results also reveals combining model forecast more effectively and accurately even with insufficient previous data on the new vehicle in Malaysia.
Validation and Implementation of Neutral Density Models for Space Weather Forecast Laboratory
NASA Astrophysics Data System (ADS)
Wise, J. O.; Lin, C. S.; Tanyi, K. L.; Marcos, F. A.; Huang, C. Y.; Delay, S. H.
2009-12-01
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.
NASA Astrophysics Data System (ADS)
Silva, J. M.; Saad, S. I.; Palma, G.; Rocha, H.; Palmeira, R. M.; Silva, B. L.; Pessoa, A. A.; Ramos, C. G.; Cecchini, M. A.
2013-05-01
Electrical energy in Brazil depends essentially on the streamflow, as hydropowers accounts for up to 79% of the total electrical energy installed capacity. Therefore, streamflow forecasts are very important tools to assist in the planning and operation of Brazilian hydroelectric reservoirs. This study evaluated the performance of a distributed hydrological model, Soil and Water Assessment Tool (SWAT) daily streamflow forecasts into four Reservoirs sited in the Alto do Rio Doce Watershed, in Southeast of Brazil. SWAT model was used with precipitation forecast from the regional meteorological model MM5. The calibration and validation processes of SWAT were accomplished using data from four monitoring stations. The model has been run for the 2010-2012 period, and while the apr/2010-set/2011 period has been used for calibration conducted manually, the validation reached the rest of the period. The manual calibration was conducted by the means of sensibility tests of parameters that control surface runoff and groundwater flow, specially the surlag and alpha_bf, respectively the surface runoff lag coefficient and the baseflow recession constant. The daily and monthly Nash-Sutcliffe, R2 and the mean relative error performance indicators were used to assess the relative performance of the model. Results showed that streamflow forecast was very similar toobservations, except in reservoirs with lower drainage areas, where the model did not simulated the beginning of the flood (Dec-Feb). The streamflow forecasts was strongly dependent on the quality of precipitation forecasts used. Given that no correction in the simulated rainfall by the MM5 model in the Alto do Rio Doce watershed has been conducted and no automated calibration method was applied to the parameters of the hydrologic model, we can conclude that the application of the SWAT hydrologic model employing the output data from the MM5 atmospheric model for the streamflow forecast was shown to be a tool of great potential for real-time operation of reservoirs.
GARCH modelling in association with FFT-ARIMA to forecast ozone episodes
NASA Astrophysics Data System (ADS)
Kumar, Ujjwal; De Ridder, Koen
2010-11-01
In operational forecasting of the surface O 3 by statistical modelling, it is customary to assume the O 3 time series to be generated through a homoskedastic process. In the present work, we've taken heteroskedasticity of the O 3 time series explicitly into account and have shown how it resulted in O 3 forecasts with improved forecast confidence intervals. Moreover, it also enabled us to make more accurate probability forecasts of ozone episodes in the urban areas. The study has been conducted on daily maximum O 3 time series for four urban sites of two major European cities, Brussels and London. The sites are: Brussels (Molenbeek) (B1), Brussels (PARL.EUROPE) (B2), London (Brent) (L1) and London (Bloomsbury) (L2). Fast Fourier Transform (FFT) has been used to model the periodicities (annual periodicity is especially distinct) exhibited by the time series. The residuals of "actual data subtracted with their corresponding FFT component" exhibited stationarity and have been modelled using ARIMA (Autoregressive Integrated Moving Average) process. The MAPEs (Mean absolute percentage errors) using FFT-ARIMA for one day ahead 100 out of sample forecasts, were obtained as follows: 20%, 17.8%, 19.7% and 23.6% at the sites B1, B2, L1 and L2. The residuals obtained through FFT-ARIMA have been modelled using GARCH (Generalized Autoregressive Conditional Heteroskedastic) process. The conditional standard deviations obtained using GARCH have been used to estimate the improved forecast confidence intervals and to make probability forecasts of ozone episodes. At the sites B1, B2, L1 and L2, 91.3%, 90%, 70.6% and 53.8% of the times probability forecasts of ozone episodes (for one day ahead 30 out of sample) have correctly been made using GARCH as against 82.6%, 80%, 58.8% and 38.4% without GARCH. The incorporation of GARCH also significantly reduced the no. of false alarms raised by the models.
New statistical models for long-range forecasting of southwest monsoon rainfall over India
NASA Astrophysics Data System (ADS)
Rajeevan, M.; Pai, D. S.; Anil Kumar, R.; Lal, B.
2007-06-01
The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981 2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.
Low-order stochastic model and "past-noise forecasting" of the Madden-Julian Oscillation
NASA Astrophysics Data System (ADS)
Kondrashov, D.; Chekroun, M. D.; Robertson, A. W.; Ghil, M.
2013-10-01
This paper presents a predictability study of the Madden-Julian Oscillation (MJO) that relies on combining empirical model reduction (EMR) with the "past-noise forecasting" (PNF) method. EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity, seasonality and serial correlation in the estimated noise, while PNF constructs an ensemble of forecasts that accounts for interactions between (i) high-frequency variability (noise), estimated here by EMR, and (ii) the low-frequency mode of MJO, as captured by singular spectrum analysis (SSA). A key result is that—compared to an EMR ensemble driven by generic white noise—PNF is able to considerably improve prediction of MJO phase. When forecasts are initiated from weak MJO conditions, the useful skill is of up to 30 days. PNF also significantly improves MJO prediction skill for forecasts that start over the Indian Ocean.
NASA Technical Reports Server (NTRS)
MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan
2011-01-01
The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.
NASA Astrophysics Data System (ADS)
Chen, X.; Hao, Z.; Devineni, N.; Lall, U.
2014-04-01
A Hierarchal Bayesian model is presented for one season-ahead forecasts of summer rainfall and streamflow using exogenous climate variables for east central China. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multi-level structure with regression coefficients modeled from a common multi-variate normal distribution resulting in partial pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include receiver operating characteristic, reduction of error, coefficient of efficiency, rank probability skill scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast season-ahead regional summer rainfall and streamflow offers potential for developing adaptive water risk management strategies.
Comparison of short-term rainfall prediction models for real-time flood forecasting
E. Toth; A. Brath; A. Montanari
2000-01-01
This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive moving-average (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the considered methods are then routed through a lumped, conceptual, rainfall–runoff
A combined wavelet - ARFIMA model for daily streamflow forecasting considering long range dependence
NASA Astrophysics Data System (ADS)
Szolgayová, Elena; Arlt, Josef; Blöschl, Günter; Szolgay, Ján
2013-04-01
Short term streamflow forecasting is of importance in water resources management, especially from the point of view of operational flow control and risk management. Beside deterministic rainfall runoff and flow routing models, stochastic time series models are also in operational use for this purpose. The fitting of such stochastic models is preceded, when suitable, by removing the systematic components in the time series (such as trends, seasonality). Usually the interest of practitioners lies in the fitting of the stochastic part of the time series model and removing the systematic components is considered rather a routine task. However, each deseasonalization method has an effect on time series analyzed, affecting the autocorrelation structure and thus influencing the following model choice and the fitted model parameters. When choosing an appropriate stochastic model the practitioners often neglect the presence of long range dependence when considering short term forecasting. This, however, might have an effect on the forecasts even in short term horizon. The autoregressive integrated moving average models (ARFIMA) are often used for modelling of time series displaying long range dependence in hydrology. In hydrology, wavelets are mostly applied for feature extraction and process description rather then modelling and forecasting. In this work we attempted to improve the deseasonalization step of the modelling process by using wavelet analysis. We proposed to combine an ARFIMA model with a wavelet transform used for deseasonalization. The quality of the model is tested on one to ten days ahead forecasts of mean daily runoffs from the Danube River measured at Kienstock in Lower Austria. A comparison with two other models - an ARFIMA model combined with moving average deseasonalization and a linear wavelet based model was performed. The results of the model comparison showed that use of wavelets provides a suitable alternative to the moving average deseasonalization. For one and two days forecasting horizon the new approach did not show improvement in the forecasting performance over the other tested models. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models, thus offering improvement compared to the usual moving average deseasonalization. Since none of the three models was able to remove autocorrelation from the squared residuals, usually indicating heteroscedasticity in the time series, the concept of the wavelet deseasonalization may be explored further in combination of other possibly suitable model, such as a fractionally integrated generalized autoregressive conditional heteroscedasticity model type.
Serpen, Gursel
An Exploratory Study for Neural Network Forecasting of Retail Sales Trends Using Industry networks (feed forward multi-layer perceptron and Elman recurrent networks) in forecasting sales trends and Elman recurrent neural networks show potential in being able to forecast sales trends with reasonable
Satellite data input to Windy Gap computerized streamflow forecasting model
JOHN R. ECKHARDT
1986-01-01
The basis for timely residual streamflow forecasts for the recently completed Windy Gap Project in Colorado is a remote hydrologie data collection network which utilizes the GOES Satellite for data transmission to project headquarters in Loveland, Colorado. Snowpack, soil moisture, streamflow, precipitation, temperature, and wind data collected at 15 remote sites in the Fraser River Basin are used to update
An Annual Midterm Energy Forecasting Model Using Fuzzy Logic
Charalambos N. Elias; Nikos D. Hatziargyriou
2009-01-01
The objective of this paper is to present a new fuzzy logic method for midterm energy forecasting. The proposed method properly transforms the input variables to differences or relative differences, in order to predict energy values not included in the training set and to use a minimal number of patterns. The input variables, the number of the triangular membership functions
Wind speed and power forecasting based on spatial correlation models
M. C. Alexiadis; P. S. Dokopoulos; H. S. Sahsamanoglou
1999-01-01
Wind energy conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for power system schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates
Radiation fog forecasting using a 1-dimensional model
Peyraud, Lionel
2001-01-01
The importance of fog forecasting to the aviation community, to road transportation and to the public at large is irrefutable. The deadliest aviation accident in history was in fact partly a result of fog back on 27 March 1977. This has, along...
Alaska North Slope regional gas hydrate production modeling forecasts
Wilson, S.J.; Hunter, R.B.; Collett, T.S.; Hancock, S.; Boswell, R.; Anderson, B.J.
2011-01-01
A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside" case captures the full potential of unconstrained development with widely spaced wells. The results of this study indicate that recoverable gas hydrate resources may exist in the Eileen accumulation and that it represents a good opportunity for continued research. ?? 2010 Elsevier Ltd.
NASA Astrophysics Data System (ADS)
Kuznetsova, Maria
The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) was established at the dawn of the new millennium as a long-term flexible solution to the problem of transition of progress in space environment modeling to operational space weather forecasting. CCMC hosts an expanding collection of state-of-the-art space weather models developed by the international space science community. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment and developing and maintaining custom displays and powerful web-based systems and tools ready to be used by researchers, space weather service providers and decision makers. In support of space weather needs of NASA users CCMC is developing highly-tailored applications and services that target specific orbits or locations in space and partnering with NASA mission specialists on linking CCMC space environment modeling with impacts on biological and technological systems in space. Confidence assessment of model predictions is an essential element of space environment modeling. CCMC facilitates interaction between model owners and users in defining physical parameters and metrics formats relevant to specific applications and leads community efforts to quantify models ability to simulate and predict space environment events. Interactive on-line model validation systems developed at CCMC make validation a seamless part of model development circle. The talk will showcase innovative solutions for space weather research, validation, anomaly analysis and forecasting and review on-going community-wide model validation initiatives enabled by CCMC applications.
A multiplicative seasonal growth model for multivariate time series analysis and forecasting
Emanuel Pimentel Barbosa; Regina Sadownik
1999-01-01
This paper is devoted to a model for analysis and forecasting of vector time series and the corresponding procedure of Bayesian sequential estimation. This model can also be viewed as a multivariate extension of the (univariate) seasonal growth multiplicative model(Harrison, 1965; Migon, 1984). The basic structure of this multivariate model consists of a locally linear trend component for each individual
Forecasting scheme for swan coastal river streamflow using combined model of IOHLN and Niño4
NASA Astrophysics Data System (ADS)
Rehman, Saqib Ur; Saleem, Kashif
2014-02-01
The study aims to investigate the possible relationship between Niño 4 and Indian Ocean high longitude (IOHLN) with the Swan coastal river flow by constructing a regression model which predict streamflow patterns and which enables to obtain long time lead to forecasting, in a period when there was not much rainfall. Many streamflow forecast models use rainfall and runoff relationship, which is dependent on basin response time and hence cannot provide large forecasting lead time. For water resource management, this lead time of predictability is not capable for a long period of drying trend. Significant findings of this study suggest that Niño 4 and Indian Ocean high pressure longitude (IOHLN) can be used for forecasting of flow in Swan river. In this study not only qualitative forecast of Swan coastal river is presented based on the conditional probability, but also a quantitative forecast is done by combining Niño.4 and IOHLN indices using multiple regression, which shows enhancement over other climate indicators when used alone. The Conditional probability model correctly predict 7 years category of flow out of 8 years flow.
One-Way Coupled Atmospheric-Lake Model Forecasts for Lake Erie.
NASA Astrophysics Data System (ADS)
Kelley, John Gormley Walsh
1995-01-01
A one-way coupled atmospheric-lake modeling system was developed to generate short-term, mesoscale lake circulation, water level and temperature forecasts for Lake Erie. The coupled system consisted of the semi-operational version of the Penn State University/National Center for Atmospheric Research three-dimensional, mesoscale meteorological model (MM4) and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS). The coupled system was tested using archived 36 -h MM4 forecast output for four cases during 1992 and 1993. The cases were chosen to demonstrate and evaluate the forecasts produced by the coupled system during severe lake conditions and at different stages in the lake's annual thermal cycle. For each case, the lake model was run for 36 hours using surface heat and momentum fluxes estimated from MM4's hourly meteorological forecasts and surface water temperatures from the lake model. Evaluations of the lake forecasts were conducted by comparing forecasts to observations and lake hindcasts. Lake temperatures were generally well forecasted by the coupled system with an average bias of +0.3 ^circC. This bias was related to the overestimation of the surface heat flux. Below the surface, the forecasts indicated correctly the rapid evolution of lake's thermal structure. The greatest shortcomings were in the predictions of peak water levels and their times of occurrence. The average algebraic bias in the maximum water level was -0.75m and +0.62m for the minimum levels. Average biases in the timing of the maximum and minimum levels were 6.2 hours too late and 1.8 hours too early, respectively. These differences were related to an underestimation of surface wind speeds by MM4. The coupled modeling system discussed here represent the first known attempt to generate forecasts of the three -dimensional physical structure of an inland water body using a coupled atmospheric-lake model system. The operational implementation of such a system is possible using existing numerical models and data networks.
... regular feature of the annual flu season. Adapting Weather Models Flu forecasting adapts approaches used by meteorologists ... when meteorologists seem to get it wrong, but weather prediction is actually very good," says Jeffrey Shaman, ...
NASA Astrophysics Data System (ADS)
Li, Weihua; Sankarasubramanian, A.; Ranjithan, R. S.; Brill, E. D.
2014-08-01
Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.
A multi-model approach to tephra dispersal forecast: The Mt. Etna’s case
NASA Astrophysics Data System (ADS)
Neri, A.; Barsotti, S.; Coltelli, M.; Costa, A.; Folch, A.; Macedonio, G.; Nannipieri, L.; Prestifilippo, M.; Scollo, S.; Spata, G.
2009-12-01
Since 1979, Mt. Etna has produced several explosive events that are of concern to civil aviation, especially since it is located close to the Catania International Airport. During the 2006 crisis, there was persistent explosive activity for several months. This disrupted airport operations several times, causing discomfort to the population and resulting in severe economic losses. These and many other examples worldwide highlight the importance to know in advance the volcanic cloud movements and its dispersion in the atmosphere. However, atmospheric transport dynamics are complex as they depend on: the nature of air-borne particles; the type of explosive activity, and the transient, 3D structure of the atmosphere. Numerical modelling is a powerful tool to quantitatively describe such phenomena and today several numerical codes exist to simulate an explosive eruption and its associated tephra dispersal. The fundamental aim of this work is to analyze, and possibly improve, the tephra dispersal forecasts by using a multi-model approach. In fact the use of different codes, based on different physical and mathematical formulations, allows to gain crucial insight on the strengths and weaknesses of different models as well as produce quantitative comparisons on key model outputs. In detail, each day an automatic web-based procedure produces ash concentration maps of FALL3D, PUFF, and VOL-CALPUFF models and ground deposition maps of TEPHRA, PUFF, FALL3D, VOL-CALPUFF, and HAZMAP models for two eruptive scenarios. These maps are then synthesised to establish the spatial regions that have air and mass loadings that are higher than fixed thresholds. Results of different models are compared allowing to produce a first estimate of the model-dependent uncertainty also as a function of eruptive and atmospheric conditions.
NASA Astrophysics Data System (ADS)
Demirel, M. C.; Booij, M. J.; Hoekstra, A. Y.
2015-01-01
This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA
NASA Technical Reports Server (NTRS)
Watson, Leela R.; Bauman, William H., III
2008-01-01
NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.
Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?
NASA Astrophysics Data System (ADS)
Wei, Yu
2012-11-01
In most previous works on forecasting oil market volatility, squared daily returns were taken as the proxy of unobserved actual volatility. However, as demonstrated by Andersen and Bollerslev (1998) [22], this proxy with too high measurement noise could be perfectly outperformed by a so-called realized volatility (RV) measure calculated by the cumulative sum of squared intraday returns. With this motivation, we further extend earlier works by employing intraday high-frequency data to compare the performance of three typical volatility models in the daily out-of-sample volatility forecasting of fuel oil futures on the Shanghai Futures Exchange (SHFE): the GARCH-type, stochastic volatility (SV) and realized volatility models. By taking RV as the proxy of actual daily volatility and then computing forecasting errors, we find that the realized volatility model based on intraday high-frequency data produces significantly more accurate volatility forecasts than the GARCH-type and SV models based on daily returns. Furthermore, the SV model outperforms many linear and nonlinear GARCH-type models that capture long-memory volatility and/or the asymmetric leverage effect in volatility. These results also prove that abundant volatility information is available in intraday high-frequency data, and can be used to construct more accurate oil volatility forecasting models.
A Four-Stage Hybrid Model for Hydrological Time Series Forecasting
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782
Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets
Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex
2005-06-30
The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.
NSDL National Science Digital Library
John Nielsen-Gammon
1996-09-01
Weather Forecasting is a set of computer-based learning modules that teach students about meteorology from the point of view of learning how to forecast the weather. The modules were designed as the primary teaching resource for a seminar course on weather forecasting at the introductory college level (originally METR 151, later ATMO 151) and can also be used in the laboratory component of an introductory atmospheric science course. The modules assume no prior meteorological knowledge. In addition to text and graphics, the modules include interactive questions and answers designed to reinforce student learning. The module topics are: 1. How to Access Weather Data, 2. How to Read Hourly Weather Observations, 3. The National Collegiate Weather Forecasting Contest, 4. Radiation and the Diurnal Heating Cycle, 5. Factors Affecting Temperature: Clouds and Moisture, 6. Factors Affecting Temperature: Wind and Mixing, 7. Air Masses and Fronts, 8. Forces in the Atmosphere, 9. Air Pressure, Temperature, and Height, 10. Winds and Pressure, 11. The Forecasting Process, 12. Sounding Diagrams, 13. Upper Air Maps, 14. Satellite Imagery, 15. Radar Imagery, 16. Numerical Weather Prediction, 17. NWS Forecast Models, 18. Sources of Model Error, 19. Sea Breezes, Land Breezes, and Coastal Fronts, 20. Soundings, Clouds, and Convection, 21. Snow Forecasting.
NASA Astrophysics Data System (ADS)
Osthus, D.; Caragea, P. C.; Higdon, D.; Morley, S. K.; Reeves, G. D.; Weaver, B. P.
2014-06-01
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.
Forecasting asthma-related hospital admissions in London using negative binomial models.
Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe
2013-05-01
Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy. PMID:23620439
Mohaghegh, Shahab
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
Modelling and forecasting the diffusion of innovation – A 25-year review
Nigel Meade; Towhidul Islam
2006-01-01
The wealth of research into modelling and forecasting the diffusion of innovations is impressive and confirms its continuing importance as a research topic. The main models of innovation diffusion were established by 1970. (Although the title implies that 1980 is the starting point of the review, we allowed ourselves to relax this constraint when necessary.) Modelling developments in the period
LOGISTIC SUBSTITUTION MODEL AND TECHNOLOGICAL FORECASTING Dmitry Kucharavy, Roland De Guio
Paris-Sud XI, UniversitÃ© de
LOGISTIC SUBSTITUTION MODEL AND TECHNOLOGICAL FORECASTING Dmitry Kucharavy, Roland De Guio INSA Abstract In this paper the application of several models, based on the logistic growth function (simple logistic, component logistic and logistic substitution models) in the context of technology change
An Enhancement to the Linear Dynamic System Model for Air Traffic Forecasting
Sun, Dengfeng
functionalities of air traffic controls. The Linear Dynamic System Model that predicts the traffic demand within the Air Route Traffic Control Centers serves well for this purpose. This model formulates inflowsAn Enhancement to the Linear Dynamic System Model for Air Traffic Forecasting Yi Cao School
Wilkin, John
Modeling the Hudson River PlumeModeling the Hudson River Plume Forecast plume variability L CE A N B S E R V A T IO N AB R U U T G E R S N I V E R S I T Y The Hudson River plume and adjacent shelf circulation in theThe Hudson River plume and adjacent shelf circulation in the New York Bight were
Modeling and Forecasting Livestock Feed Resources in India Using Climate Variables
Suresh, K. P.; Kiran, G. Ravi; Giridhar, K.; Sampath, K. T.
2012-01-01
The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030. PMID:25049586
FOGCAST: Probabilistic fog forecasting based on operational (high-resolution) NWP models
NASA Astrophysics Data System (ADS)
Masbou, M.; Hacker, M.; Bentzien, S.
2013-12-01
The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. The improvement of localization, duration and variations in visibility therefore holds an immense operational value. Fog is generally a small scale phenomenon and mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Sophisticated three-dimensional fog models, based on advanced microphysical parameterization schemes and high vertical resolution, have been already developed and give promising results. Nevertheless, the computational time is beyond the range of an operational setup. Therefore, mesoscale numerical weather prediction models are generally used for forecasting all kinds of weather situations. In spite of numerous improvements, a large uncertainty of small scale weather events inherent in deterministic prediction cannot be evaluated adequately. Probabilistic guidance is necessary to assess these uncertainties and give reliable forecasts. In this study, fog forecasts are obtained by a diagnosis scheme similar to Fog Stability Index (FSI) based on COSMO-DE model outputs. COSMO-DE I the German-focused high-resolution operational weather prediction model of the German Meteorological Service. The FSI and the respective fog occurrence probability is optimized and calibrated with statistical postprocessing in terms of logistic regression. In a second step, the predictor number of the FOGCAST model has been optimized by use of the LASSO-method (Least Absolute Shrinkage and Selection Operator). The results will present objective out-of-sample verification based on the Brier score and is performed for station data over Germany. Furthermore, the probabilistic fog forecast approach, FOGCAST, serves as a benchmark for the evaluation of more sophisticated 3D fog models. Several versions have been set up based on different numerical weather prediction systems: 1- COSMO-DE operational forecasts (50 vertical layers, dz_min=20m), 2- COSMO-DE forecasts with different vertical grid setups, 3- COSMO-DE forecasts with fog microphysics of the one dimensional fog forecast model, PAFOG 4- COSMO-FOG forecasts with a very high vertical resolution (60 layers, dz_min=4m) and an one-moment fog microphysics based on the PAFOG model. The results will quantify the impact of vertical grid resolution, and the importance of detailed cloud microphysics, considering explicitly cloud droplet distribution and sedimentation processes.
2011-01-01
Modeling and Sales Forecasting Using EViews John T. Cuddington Colorado School of Mines Irina Khindanova forecasting using the EViews software package. The integration enables us to use the forecast of future sales (especially sales forecasting).2 However, there is seldom an attempt to integrate them within a unified
Lee, Ya-Ting; Turcotte, Donald L.; Holliday, James R.; Sachs, Michael K.; Rundle, John B.; Chen, Chien-Chih; Tiampo, Kristy F.
2011-01-01
The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M?4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M?4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor–Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most “successful” in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts. PMID:21949355
Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis
NASA Technical Reports Server (NTRS)
Hoffman, Ross N.; Nehrkorn, Thomas; Grassotti, Christopher
1998-01-01
We proposed a novel characterization of errors for numerical weather predictions. A general distortion representation allows for the displacement and amplification or bias correction of forecast anomalies. Characterizing and decomposing forecast error in this way has several important applications, including the model assessment application and the objective analysis application. In this project, we have focused on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically, we study the forecast errors of the sea level pressure (SLP), the 500 hPa geopotential height, and the 315 K potential vorticity fields for forecasts of the short and medium range. The forecasts are generated by the Goddard Earth Observing System (GEOS) data assimilation system with and without ERS-1 scatterometer data. A great deal of novel work has been accomplished under the current contract. In broad terms, we have developed and tested an efficient algorithm for determining distortions. The algorithm and constraints are now ready for application to larger data sets to be used to determine the statistics of the distortion as outlined above, and to be applied in data analysis by using GEOS water vapor imagery to correct short-term forecast errors.
Post-processing of multi-model ensemble river discharge forecasts using censored EMOS
NASA Astrophysics Data System (ADS)
Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian
2014-05-01
When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a daily basis over a period of three years. For the two catchments considered, this resulted in well calibrated and sharp forecast distributions over all lead-times from 1 to 114 h. Training observations tended to be better indicators for the dependence structure than the raw ensemble.
An economic-demographic model of the United States labor market.
Anderson, J M
1982-01-01
An econometric model that has been developed to investigate the effects of demographic change on the US economy is described. The specific demographic features examined are the sizes of age sex groups in the US working age population. The size of these groups from now through the end of the 20th century will be determined primarily by past and current levels of fertility so they can be forecast with some degree of confidence. The model expands both the domain and accuracy of longterm economic forecasting by making use of the considerable quantity of demographic information that can be forecast, at least through this century, with a fairly great degree of confidence. In addition to economic forecasting, this study of the impact of demographic changes on the US labor market contributes to the investigation of the interrelationships among economic and demographic changes. The task of the model is as follows: given an exogenous projection of fertility and mortality rates and net immigration and given exogenous forecasts of variables such as rates of technical change, government demand for goods and services, and tax rates, the model forecasts variables characterizing the labor market and the macroeconomy. The model uses the fundamental principles of supply and demand, the economic theory of production, and the theory of household allocation of time and income to draw the implications of changes in demographic variables for the labor market and the economy. The crux of the model is a set of relationships depicting the behavior of the US labor market. In the labor market submodel, the input of labor of each of 16 age sex groups and its piece in each period is determined by the interaction of supply and demand. The 16 demographic groups are males and females, respectively, of ages 14-15, 16-17, 18-24, 25-34, 35-44, 45-54, 55-64, and 65 and over. Equations depicting the supply of and demand for labor of various demographic groups are estimated and provide the behavioral relationships of the labor market submodel. The following description of the model is in 3 parts: the demand for labor; the labor supply equations; and the intergration of the 2 and the complete growth model. Some illustrative forecasts are included. In all 3 forecasts, the proportion of the labor force accounted for by workers in the middle age groups, 25-54, increases, reaching the highest levels in the post World War 2 period in the 1990-2000 decade. The proportion accounted for by males in that age group does not rise notably and remains lower than it was in the 1950s and 1960s. The proportion accounted for by women age 25-54 rises markedly. This trend is possible the most salient feature of the forecasts. PMID:12264899
Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model
NASA Technical Reports Server (NTRS)
Goodman, H. Michael; Conover, Helen; Zavodsky, Bradley; Maskey, Manil; Jedlovec, Gary; Regner, Kathryn; Li, Xiang; Lu, Jessica; Botts, Mike; Berthiau, Gregoire
2008-01-01
The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.
Forecasting quantitative rainfall over India using multi-model ensemble technique
NASA Astrophysics Data System (ADS)
Durai, V. R.; Bhardwaj, Rashmi
2014-10-01
A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.
Stratospheric wind errors, initial states and forecast skill in the GLAS general circulation model
NASA Technical Reports Server (NTRS)
Tenenbaum, J.
1983-01-01
Relations between stratospheric wind errors, initial states and 500 mb skill are investigated using the GLAS general circulation model initialized with FGGE data. Erroneous stratospheric winds are seen in all current general circulation models, appearing also as weak shear above the subtropical jet and as cold polar stratospheres. In this study it is shown that the more anticyclonic large-scale flows are correlated with large forecast stratospheric winds. In addition, it is found that for North America the resulting errors are correlated with initial state jet stream accelerations while for East Asia the forecast winds are correlated with initial state jet strength. Using 500 mb skill scores over Europe at day 5 to measure forecast performance, it is found that both poor forecast skill and excessive stratospheric winds are correlated with more anticyclonic large-scale flows over North America. It is hypothesized that the resulting erroneous kinetic energy contributes to the poor forecast skill, and that the problem is caused by a failure in the modeling of the stratospheric energy cycle in current general circulation models independent of vertical resolution.
NASA Astrophysics Data System (ADS)
Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.
2013-12-01
One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.
Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation
NASA Technical Reports Server (NTRS)
Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.
2007-01-01
Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model- the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.
Fenton, Norman
) using a standard profitability measure with discrepancy levels at 5%, the model generates profit under1 Working Paper, draft 5, February 2012 pi-football: A Bayesian network model for forecasting be both objective and subjective. We present a Bayesian network model for forecasting Association Football
NASA Astrophysics Data System (ADS)
Gan, Chuen-Meei
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.
Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies
Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.; Diao, Ruisheng; Lu, Ning
2014-04-14
To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.
Evaluation of DNI forecast based on the WRF mesoscale atmospheric model for CPV applications
NASA Astrophysics Data System (ADS)
Lara-Fanego, V.; Ruiz-Arias, J. A.; Pozo-Vázquez, A. D.; Gueymard, C. A.; Tovar-Pescador, J.
2012-10-01
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.
NASA Technical Reports Server (NTRS)
Mehra, R. K.; Rouhani, R.; Jones, S.; Schick, I.
1980-01-01
A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function.
AtmoSwing, an Analogue Technique model for statistical weather forecasting
NASA Astrophysics Data System (ADS)
Horton, Pascal; Obled, Charles; Jaboyedoff, Michel; García Hernández, Javier
2013-04-01
The analogue method has been implemented for real-time precipitation forecasting in the framework of flood management in the upper Rhône catchment in Switzerland. It identifies analogue days, in terms of atmospheric circulation and humidity variables, in a long archive of past situations and then uses the corresponding measured precipitation to establish an empirical conditional distribution considered as the probabilistic forecast for the target day. This method is used in different institutions for hydro-meteorological forecasting in the framework of real-time flood management or electricity production. The developed software is called AtmoSwing for "Analog Technique MOdel for Statistical Weather forecastING" and is constituted of two tools: the Forecaster that automatically processes the forecast and the Viewer that displays the resulting files in a GIS environment. AtmoSwing is written in C++ and uses open source libraries. It is fully cross-platform and has the native look of the corresponding operating system thanks to wxWidgets. The model is standalone and automatically handles the download of the GFS global numerical weather prediction forecasts on which the analogy is processed. The development aimed at creating a very modular object oriented tool that can be used to parameterize any known version of the analogue method. There is no limitation on the number of analogy steps, neither on the amount of atmospheric variables used as input. The Viewer has a GIS engine that allows changing the map layers in order to be adapted to any new region. It offers different levels of detail, from an overview of all lead times and all parameterizations, which provides a quick identification of potential critical situations, to local time series and details of analogues distributions. AtmoSwing is running operationally since October 2011 in the Swiss Alps. The implemented parameterizations are the most common reference methods developed by Bontron (2004). These presented globally good results on the whole period, and forecasted in a very satisfying way some significant events. Last improvements to the parameterization by means of a global optimization technique should now even improve the reliability of the forecast.
Sanford, Ward E.; Pope, Jason P.
2010-01-01
A three-dimensional model of the aquifer system of the Eastern Shore of Virginia, USA was calibrated to reproduce historical water levels and forecast the potential for saltwater intrusion. Future scenarios were simulated with two pumping schemes to predict potential areas of saltwater intrusion. Simulations suggest that only a few wells would be threatened with detectable salinity increases before 2050. The objective was to examine whether salinity increases can be accurately forecast for individual wells with such a model, and to address what the challenges are in making such model forecasts given current (2009) simulation capabilities. The analysis suggests that even with current computer capabilities, accurate simulations of concentrations within a regional-scale (many km) transition zone are computationally prohibitive. The relative paucity of data that is typical for such regions relative to what is needed for accurate transport simulations suggests that even with an infinitely powerful computer, accurate forecasting for a single well would still be elusive. Useful approaches may include local-grid refinement near wells and geophysical surveys, but it is important to keep expectations for simulated forecasts at wells in line with chloride concentration and other data that can be obtained at that local scale.
A physical and economic model of the nuclear fuel cycle
NASA Astrophysics Data System (ADS)
Schneider, Erich Alfred
A model of the nuclear fuel cycle that is suitable for use in strategic planning and economic forecasting is presented. The model, to be made available as a stand-alone software package, requires only a small set of fuel cycle and reactor specific input parameters. Critical design criteria include ease of use by nonspecialists, suppression of errors to within a range dictated by unit cost uncertainties, and limitation of runtime to under one minute on a typical desktop computer. Collision probability approximations to the neutron transport equation that lead to a computationally efficient decoupling of the spatial and energy variables are presented and implemented. The energy dependent flux, governed by coupled integral equations, is treated by multigroup or continuous thermalization methods. The model's output includes a comprehensive nuclear materials flowchart that begins with ore requirements, calculates the buildup of 24 actinides as well as fission products, and concludes with spent fuel or reprocessed material composition. The costs, direct and hidden, of the fuel cycle under study are also computed. In addition to direct disposal and plutonium recycling strategies in current use, the model addresses hypothetical cycles. These include cycles chosen for minor actinide burning and for their low weapons-usable content.
NASA Astrophysics Data System (ADS)
Gica, Edison; Titov, Vasily V.; Moore, Christopher; Wei, Yong
2014-11-01
Model forecast applications use various models of tsunami sources inferred from different measurement data. Even the same type of observation data can produce substantially different tsunami source models during a real-time forecast when more data are obtained during the real-time analysis. Improved tsunami observations enable investigation of the influence of such model source variability on the final forecast using different source data sets of several events. The 2010 Maule, Chile and 2011 Tohoku, Japan tsunamis were two recent events that provide ample observations throughout the Pacific and were, thus, used here to study the sensitivity of different model inputs for forecasting. The sources for these events were derived using the following three different methods: (1) real time or post event inversion of tsunameter water level data; (2) prediction of sea floor deformations via analysis of seismic wave forms and application of a finite fault model; and (3) prediction of sea floor deformation using real-time GPS data. For the March 11, 2011 Tohoku tsunami, two examples of each method are used, while for the February 27, 2010 Maule event, only one tsunameter inversion and one finite fault model method were used due to a much more limited data set. Observed data from the Deep-ocean Assessment and Reporting for Tsunamis (DART) network, Japan GPS buoys, and select tide gauges across the Pacific were compared with forecasts to assess the sensitivity of these three methods using root-mean-square error analysis. We divided the analysis by the type of data and the distance from the source. This sensitivity analysis showed that increasing the resolution of a tsunami source model does not necessarily improve tsunami forecast quality, even in the near-field. Instead, the findings suggest that when forecasting coastal impact, defining the overall energy characteristic of a tsunami source may be more important than refining small source details. Source models based on direct tsunami observations are better at reproducing a tsunami signal: this finding is not very surprising but has implications for tsunami forecasting and warning operations.
NASA Astrophysics Data System (ADS)
Gica, Edison; Titov, Vasily V.; Moore, Christopher; Wei, Yong
2015-03-01
Model forecast applications use various models of tsunami sources inferred from different measurement data. Even the same type of observation data can produce substantially different tsunami source models during a real-time forecast when more data are obtained during the real-time analysis. Improved tsunami observations enable investigation of the influence of such model source variability on the final forecast using different source data sets of several events. The 2010 Maule, Chile and 2011 Tohoku, Japan tsunamis were two recent events that provide ample observations throughout the Pacific and were, thus, used here to study the sensitivity of different model inputs for forecasting. The sources for these events were derived using the following three different methods: (1) real time or post event inversion of tsunameter water level data; (2) prediction of sea floor deformations via analysis of seismic wave forms and application of a finite fault model; and (3) prediction of sea floor deformation using real-time GPS data. For the March 11, 2011 Tohoku tsunami, two examples of each method are used, while for the February 27, 2010 Maule event, only one tsunameter inversion and one finite fault model method were used due to a much more limited data set. Observed data from the Deep-ocean Assessment and Reporting for Tsunamis (DART) network, Japan GPS buoys, and select tide gauges across the Pacific were compared with forecasts to assess the sensitivity of these three methods using root-mean-square error analysis. We divided the analysis by the type of data and the distance from the source. This sensitivity analysis showed that increasing the resolution of a tsunami source model does not necessarily improve tsunami forecast quality, even in the near-field. Instead, the findings suggest that when forecasting coastal impact, defining the overall energy characteristic of a tsunami source may be more important than refining small source details. Source models based on direct tsunami observations are better at reproducing a tsunami signal: this finding is not very surprising but has implications for tsunami forecasting and warning operations.
A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures
Mohanbir S. Sawhney; Jehoshua Eliashberg
1996-01-01
The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to provide insights into the impact of distribution policies on the adoption of new products. The model is intended to assist motion picture exhibitor chains (retailers) in managing
MÃ¼ller, Hans-Georg
Modeling Hazard Rates as Functional Data for the Analysis of Cohort Lifetables and Mortality). #12;Modeling Hazard Rates as Functional Data for the Analysis of Cohort Lifetables and Mortality Forecasting Abstract As world populations age, the analysis of demographic mortality data and demographic
Global and multi-scale features of solar wind-magnetosphere coupling: From modeling to forecasting
Sitnov, Mikhail I.
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
Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan
Lalarukh Kamal; Yasmin Zahra Jafri
1997-01-01
Stochastic simulation and forecast models of hourly average wind speeds are presented. Time series models take into account several basic features of wind speed data including autocorrelation, non-Gaussian distribution and diurnal nonstationarity. The positive correlation between consecutive wind speed observations is taken into account by flitting an ARMA (p,q) process to wind speed data transformed to make their distribution approximately
Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge
Paris-Sud XI, UniversitÃ© de
Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge D applications of logistic S-curve and component logistics are considered in a framework of long- term. First, the features of a simple logistic model are presented and diverse types of competition
Evaluation of Advanced Wind Power Forecasting Models Results of the Anemos Project
Paris-Sud XI, UniversitÃ© de
1 Evaluation of Advanced Wind Power Forecasting Models Â Results of the Anemos Project I. MartÃ1.kariniotakis@ensmp.fr Abstract An outstanding question posed today by end-users like power system operators, wind power producers or traders is what performance can be expected by state-of-the-art wind power prediction models. This paper
Classical Mathematical Models for Description and Forecast of Preclinical Tumor Growth
Boyer, Edmond
! 1! Classical Mathematical Models for Description and Forecast of Preclinical Tumor Growth. hal-00922553,version2-30Dec2013 #12;! 3! These results could be of value for preclinical cancer research by suggesting what model is best adapted when assessing anti-cancer drugs efficacies. They also
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer
Paris-Sud XI, UniversitÃ© de
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We show that this methodology can improve the accuracy of meteorological data estimation compared
Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation
NASA Technical Reports Server (NTRS)
Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.
2007-01-01
Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.
NASA Astrophysics Data System (ADS)
Xu, Wei; Zhang, Chi; Peng, Yong; Fu, Guangtao; Zhou, Huicheng
2014-12-01
This paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process.
Economic Models for Industrial Waiting Line Problems
Frederick S. Hillier
1963-01-01
Studies of industrial waiting line problems typically involve determining the proper balance between the amount of service and the amount of waiting for that service. This paper presents some basic results for the case where the study is to be based upon fundamental cost considerations and the assumption of an infinite calling population. Included are a number of economic models
Application of a new phenomenological coronal mass ejection model to space weather forecasting
NASA Astrophysics Data System (ADS)
Howard, T. A.; Tappin, S. J.
2010-07-01
Recent work by the authors has produced a new phenomenological model for coronal mass ejections (CMEs). This model, called the Tappin-Howard (TH) Model, takes advantage of the breakdown of geometrical linearity when CMEs are observed by white-light imagers at large distances from the Sun. The model extracts 3-D structure and kinematic information on the CME using heliospheric image data. This can estimate arrival times of the CME at 1 AU and impact likelihood with the Earth. Hence the model can be used for space weather forecasting. We present a preliminary evaluation of this potential with three mock trial forecasts performed using the TH Model. These are already-studied events from 2003, 2004 and 2007 but we performed the trials assuming that they were observed for the first time. The earliest prediction was made 17 hours before impact and predicted arrival times reached differences within one hour for at least one forecast for all three events. The most accurate predicted arrival time was 15 min from the actual, and all three events reach accuracies of the order of 30 min. Arrival speeds were predicted to be very similar to the bulk plasma speed within the CME near 1 AU for each event, with the largest difference around 300 km/s and the least 40 km/s. The model showed great potential and we aspire to fully validate it for integration with existing tools for space weather forecasting.
Al-Idrisi, M.; Hamad, G.
1987-04-01
This study was conducted in cooperation with the Department of Industrial Engineering of King Abdulaziz University. The main objective of this study is to meet some of the goals of the Solar Energy Water Desalination Plant (SEWDP) plan in the area of economic evaluation. The first part of this project focused on describing the existing trend in the operation and maintenance (OandM) cost for the SOLERAS Solar Energy Water Desalination Plant in Yanbu. The second part used the information obtained on existing trends to find suitable forecasting models. These models, which are found here, are sensitive to changes in costs trends. Nevertheless, the study presented here has established the foundation for (OandM) costs estimating in the plant. The methodologies used in this study should continue as more data on operation and maintenance costs become available, because, in the long run, the trend in costs will help determine where cost effectiveness might be improved. 7 refs., 24 figs., 15 tabs.
Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao
2011-09-29
This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.
Kinetic Exchange Models in Economics and Sociology
Goswami, Sanchari
2014-01-01
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.
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
NASA Astrophysics Data System (ADS)
El-Shafie, A.; Noureldin, A.; Taha, M. R.; Hussain, A.
2011-07-01
Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network namely; Multi-Layer Peceptron Neural network (MLP-NN), Radial Basis Function Neural Network (RBFNN) and Input Delay Neural Network (IDNN), respectively, have been examined in this study. Those models had been developed for two time horizon in monthly and weekly rainfall basis forecasting at Klang River, Malaysia. Data collected over 12 yr (1997-2008) on weekly basis and 22 yr (1987-2008) for monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural network. Results showed that MLP-NN neural network model able to follow the similar trend of the actual rainfall, yet it still relatively poor. RBFNN model achieved better accuracy over the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model outperformed during training and testing stage which prove a consistent level of accuracy with seen and unseen data. Furthermore, the IDNN significantly enhance the forecasting accuracy if compared with the other static neural network model as they could memorize the sequential or time varying patterns.
A deterministic storm surge forecast model focused on the Adriatic Italian coast
NASA Astrophysics Data System (ADS)
Bajo, Marco; Coraci, Elisa; Cordella, Marco; Umgiesser, Georg; Ferla, Maurizio
2013-04-01
A new storm surge forecast system for the Mediterranean Sea is running operationally, from mid 2011, at the Italian Institute for Environmental Protection and Research, ISPRA, in Venice. The system is based on a finite element hydrodynamic model, named SHYFEM, developed at the Institute of Marine Sciences, ISMAR, in Venice. Simulations are forced with wind and MSL pressure forecast fields, provided by ECMWF Centre. Open boundary conditions are prescribed in the Atlantic Ocean, about three hundred kilometres West of the Gibraltar Strait, with the water level set to a zero and free normal fluxes. As the model is focused on the Adriatic Sea, results are validated over nine stations along the Italian Adriatic coast. In these stations the astronomical tide, computed using the harmonic components, is added to the modelled surge and the mean sea level is corrected with levels observed one day before the simulation. The final forecast is a good estimate of the total sea level in the selected stations. In order to give an accurate forecast even for the Venice lagoon, a second simulation is run inside the lagoon, using, as boundary conditions, the results of the first one. Results are extracted for the whole year 2012, and show good forecast skills along all the Adriatic coast. Moreover an exceptional storm surge event, happened in Venice on November 11, 2012, was very well predicted, obtaining a higher accuracy than statistical models, more commonly used for tide forecasts in Venice. Finally a data assimilation system, based on the 4D-PSAS technique, has been recently developed and the results of this version will be available soon.
NASA Astrophysics Data System (ADS)
Stravs, L.; Brilly, M.
2009-04-01
Good and accurate long-term low flow forecasting is important in the fields of sustainable water management, water rights, water supply management, industrial use of freshwater, optimization of the reservoir operations for the production of electric energy and other water-related disciplines. Today, low flow forecasting is usually performed as an integrated part of calibrated rainfall-runoff models, but in our research we developed two types of simple empirical 7-day ahead low flow forecasting models by using the M5 machine learning method for the generation of regression and model trees. Development of the first type of models was based solely on the application of the M5 machine learning method (1-, 2-, 3-, 4-, 5-, 6-and 7-day lead time low flow forecasting model trees were developed from using only past flow data and then combined to produce 7-day ahead forecast curve), while the development of the other type of models included the conceptual knowledge of linear reservoir recession functions AND application of the M5 machine learning method (we modelled the streamflow recession coefficient k as a function of the flow rate at which the 7-day low flow forecast is made and the decrease in the flow rate from the previous day). Both types of 7-day ahead low flow forecasting models were developed by using the same type and amount of data and were built for the Podhom gauging station on the Radovna River and the Medvode gauging station on the Sora River (both are Slovenian tributaries of the Sava River, which itself is a Danube River tributary). The results were compared and tested both visually and numerically.
NASA Astrophysics Data System (ADS)
Lima, Carlos H. R.; Lall, Upmanu
2010-01-01
SummaryStreamflow simulation and forecasts have been widely used in water resources management, particularly for flood and drought analysis and for the determination of optimal operational rules for reservoir systems used for water supply and energy production. Here we include climate information in a periodic-auto-regressive model in order to provide monthly streamflow forecasts for 54 hydropower sites in Brazil. Large scale climate information is included in the model through the use of climate indices obtained from the sea surface temperature field of the tropical Pacific and sub-tropical Atlantic oceans and the low-level zonal wind field over southeast Brazil. Correlation analysis of climate predictors and streamflow data show that the dependence of the latter on climate variability is seasonal and also a function of the lead time of the forecasts. A ridge regression framework is adopted in order to shrink parameter estimates and improve model outputs. The proposed model is compared with an ordinary linear regression based model with predictors selected by the BIC criterion and with the classical linear periodic-auto-regressive model (PAR), where no climate information is used. Cross-validated results show that the inclusion of climate indexes is able to improve forecast skills up to 3 months lead time. Higher skills are observed for reservoirs with large catchment areas.
Model-based approach to seasonal ensemble forecast of snowmelt water inflow into a reservoir
NASA Astrophysics Data System (ADS)
Gelfan, Alexander; Motovilov, Yuri; Moreido, Vsevolod
2014-05-01
An approach to seasonal ensemble forecast of snowmelt runoff has been developed and applied for forecasting lateral water inflow into the Cheboksary Reservoir (the watershed area is 374,000 km2) located in the middle Volga River basin. The approach combines a physically-based semi-distributed hydrological ECOMAG model with ensembles of future weather scenarios for a specified lead-time of the forecast, which are then used as inputs for a hydrological model. The ECOMAG model describes processes of snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface and channel flow. The hydrological model is forced using daily meteorological variables (precipitation, air temperature, and air humidity) taken from the available observation data prior to the forecast date. Using these datasets, the initial watershed state (primarily, areal distribution of snow water equivalent, soil moisture content and soil freezing depth) as well as the initial river channel state are simulated by the model. Results from these spin-up simulations are routinely controlled by comparing them with observations from snow and agricultural surveys and streamflow observations. To assign ensemble of weather scenarios for the specified lead-time of the forecast (3 months ahead in this study), two approaches are applied: (1) the historical, observed daily weather patterns are utilized which assumed to be representative of possible future weather conditions; and (2) the artificial daily weather patterns Monte-Carlo are simulated by a stochastic weather generator. Being forced by the assigned ensembles of weather patterns for the forecast lead time, the ECOMAG model produces ensembles of hydrographs of inflow into the Cheboksary Reservoir. Using the developed approach, hindcasts have been produced for 30 spring seasons beginning from the filling of the reservoir in 1982 and the statistical properties of the obtained ensembles of runoff characteristics (volume and peak discharge) have been evaluated. The median forecast traces have been analyzed using the traditional Nash-and-Sutcliffe criterion as well as the distribution-oriented verification measures have been utilized to assess the probabilistic information contained in both forecast ensembles.
NASA Astrophysics Data System (ADS)
Kogan, Felix; Kussul, Nataliia; Adamenko, Tatiana; Skakun, Sergii; Kravchenko, Oleksii; Kryvobok, Oleksii; Shelestov, Andrii; Kolotii, Andrii; Kussul, Olga; Lavrenyuk, Alla
2013-08-01
Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April-May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2-3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April-May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha-1 in June and 0.4 t ha-1 in April, while performance of three approaches for 2011 was almost the same (0.5-0.6 t ha-1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2-3 months prior to harvest, while providing minimum requirements to input datasets.
Dyson, Brian [Department of Environmental Engineering, Texas A and M University-Kingsville, MSC 213, Kingsville, TX 78363 (United States); Chang, N.-B. [Department of Environmental Engineering, Texas A and M University-Kingsville, MSC 213, Kingsville, TX 78363 (United States)]. E-mail: nchang@even.tamuk.edu
2005-07-01
Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach - system dynamics modeling - for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool - Stella[reg]. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.
Hendon, Harry
Forecast Model MEI ZHAO, HARRY H. HENDON, OSCAR ALVES, YONGHONG YIN, AND DAVID ANDERSON Centre available ocean observations into the forecast model (e.g., Alves et al. 2004; Balmaseda and Anderson 2009 stratification is also important, for instance at the onset of El Nin~o (Maes and Picaut 2002; Maes et al. 2005
SOM-based Hybrid Neural Network Model for Flood Inundation Extent Forecasting
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John
2014-05-01
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.
Mohaghegh, Shahab
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
Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations
Tilman Ehrbeck; Robert Waldmann
1996-01-01
Professional forecasters may not simply aim to minimize expected squared forecast errors. In models with repeated forecasts, the pattern of forecasts reveals valuable information about the forecasters even before the outcome is realized. Rational forecasters will compromise between minimizing errors and mimicking prediction patterns typical of able forecasters. Simple models based on this argument imply that forecasts are biased in
Municipal water consumption forecast accuracy
NASA Astrophysics Data System (ADS)
Fullerton, Thomas M.; Molina, Angel L.
2010-06-01
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.
Boyer, Edmond
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
Evaluation of Different Model-Error Schemes in Mesoscale Ensemble Forecasts (Invited)
NASA Astrophysics Data System (ADS)
Berner, J.; Smith, K. R.; Ha, S.; Hacker, J.; Snyder, C.
2013-12-01
The performance of several different model-error schemes and selected combinations is verified for probabilistic forecasts with the WRF-ARW mesoscale ensemble system over the Contiguous United States. Including a model-error representation leads to more spread and small, but significant increases in forecast skill. In the free atmosphere, a stochastic kinetic-energy backscatter scheme performs best, while multiple-physics schemes tend to be superior near the surface. Combing multiple stochastic and deterministic parameterizations results in the biggest improvement throughout. To investigate if the model-error schemes are able to represent structural uncertainty or if the improved skill is solely the result of an increase in ensemble spread, two additional computations were performed: First, the Brier score is decomposed into reliability, resolution and uncertainty, which have different sensitivities to spread. Secondly, all forecasts are calibrated to have the same variance as the observations, which results in similar ensemble spreads. In the raw and re-calibrated ensemble systems, the decomposition of the Brier score improves both, the resolution and reliability component, indicating that the benefits of including a model-error scheme goes beyond increasing the ensemble spread. The improvements are quantified for biased and de-biased forecast. We find that the relative performance of the different model-error schemes remains similar in the raw and postprocessed ensemble experiments.
NASA Astrophysics Data System (ADS)
Suparta, Wayan; Gusrizal
2014-04-01
We employed the Hierarchical Bayesian spatio temporal (HBST) Gaussian Process (GP) model for forecasting the distribution of the Earth's trapped particle. The model was applied in the South Atlantic Anomaly (SAA) region. Data from 1-30 January 2000 of >30 keV electron flux acquired by National Oceanic and Atmospheric (NOAA) 15 satellite was carried to model. The purpose was to forecast the flux value on 31 January 2000. Gridding process of 10x10 lot-lan was performed after cleaning and log transforming data. The HBST GP model was undertaken by implementing the Monte Carlo Markov Chain (MCMC) method. The forecasting result was interpolated by using Kriging technique to draw the distribution map of particle flux. Statistical validation represented by mean square error, root mean square error, mean absolute error, mean absolute percentage error, bias, relative bias, and mean relative separation shows good indicators. The visual validation also figured a quite similarity with NOAA's map that the model capable to forecast the particle flux.
Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic
2014-01-01
The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region. PMID:24842026
Inner heliosphere MHD modeling system applicable to space weather forecasting for the other planets
NASA Astrophysics Data System (ADS)
Shiota, D.; Kataoka, R.; Miyoshi, Y.; Hara, T.; Tao, C.; Masunaga, K.; Futaana, Y.; Terada, N.
2014-04-01
We developed a magnetohydrodynamic (MHD) solar wind model which can be used for practical use in real-time space weather forecasting at Earth's orbit and those of other planets. The MHD simulation covering 3 years (2007-2009) was performed to test the accuracy, and the numerical results show reasonable agreement with in situ measurements of the solar wind at Earth's orbit and with measurements at Venus and Mars by Venus Express and Mars Express, respectively. The comparison also shows that the numerical results can be used to detect stream interfaces, which is useful for space weather forecast of killer electrons in the outer Van Allen belt.
Yang, Wan; Karspeck, Alicia; Shaman, Jeffrey
2014-01-01
A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters—a basic particle filter (PF) with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)—and three ensemble filters—the ensemble Kalman filter (EnKF), the ensemble adjustment Kalman filter (EAKF), and the rank histogram filter (RHF)—were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS) model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003–2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1–5 weeks in the future; the ensemble filters are more accurate predicting peaks in the past. PMID:24762780
Yang, Wan; Karspeck, Alicia; Shaman, Jeffrey
2014-04-01
A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters--a basic particle filter (PF) with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)--and three ensemble filters--the ensemble Kalman filter (EnKF), the ensemble adjustment Kalman filter (EAKF), and the rank histogram filter (RHF)--were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS) model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003-2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1-5 weeks in the future; the ensemble filters are more accurate predicting peaks in the past. PMID:24762780
Likelihood- and residual-based evaluation of medium-term earthquake forecast models for California
NASA Astrophysics Data System (ADS)
Schneider, Max; Clements, Robert; Rhoades, David; Schorlemmer, Danijel
2014-09-01
Seven competing models for forecasting medium-term earthquake rates in California are quantitatively evaluated using the framework of the Collaboratory for the Study of Earthquake Predictability (CSEP). The model class consists of contrasting versions of the Every Earthquake a Precursor According to Size (EEPAS) and Proximity to Past Earthquakes (PPE) modelling approaches. Models are ranked by their performance on likelihood-based tests, which measure the consistency between a model forecast and observed earthquakes. To directly compare one model against another, we run a classical paired t-test and its non-parametric alternative on an information gain score based on the forecasts. These test scores are complemented by several residual-based methods, which offer detailed spatial information. The experiment period covers 2009 June-2012 September, when California experienced 23 earthquakes above the magnitude threshold. Though all models fail to capture seismicity during an earthquake sequence, spatio-temporal differences between models also emerge. The overall best-performing model has strong time- and magnitude-dependence, weights all earthquakes equally as medium-term precursors of larger events and has a full set of fitted parameters. Models with this time- and magnitude-dependence offer a statistically significant advantage over simpler baseline models. In addition, models that down-weight aftershocks when forecasting larger events have a desirable feature in that they do not overpredict following an observed earthquake sequence. This tendency towards overprediction differs between the simpler model, which is based on fewer parameters, and more complex models that include more parameters.
Retrospective Evaluation of Preseason Forecasting Models for Sockeye and Chum Salmon
Steven L. Haeseker; Randall M. Peterman; Zhenming Su; Chris C. Wood
2008-01-01
Using comprehensive data sets for chum salmon Oncorhynchus keta (40 stocks) and sockeye salmon O. nerka (37 stocks) throughout their North American ranges, we compared the retrospective performance of 11 models in preseason forecasting of adult abundance. Chum and sockeye salmon have more complicated age structures than pink salmon O. gorbuscha, which we investigated previously (Haeseker et al. 2005), and
J. D. Mirocha; J. K. Lundquist; B. Kosovic; F. K. Chow
2008-01-01
Future expansion of wind power production requires resolution of several outstanding research issues, many of which involve the complicated and highly variable near-surface atmospheric flow field. To these ends we have made several improvements to the Weather Research and Forecasting model (WRF) to improve its Large Eddy Simulation (LES) capability. These improvements enable exploration of how terrain heterogeneity, turbulence and
Every cloud has a silver lining: Weather forecasting models could predict brain tumor
Kuang, Yang
, and combine them with incoming data streams from weather stations and satellites. Now, an innovative new study little progress has been made in this area, GBM is an important area to study, and is a particularly goodEvery cloud has a silver lining: Weather forecasting models could predict brain tumor growth Ever
A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models
NASA Astrophysics Data System (ADS)
Haworth, J.; Cheng, T.
2014-11-01
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.
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) ...
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...
Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models
West, Mike
Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models and hierarchically across several multivariate series. A key example, the main focus here, is that of time series analysis of time series of flows of students in the Italian secondary education system as an illustration
Volatility Forecasts in Financial Time Series with HMM-GARCH Models
Chen, Yiling
Volatility Forecasts in Financial Time Series with HMM-GARCH Models Xiong-Fei Zhuang and Lai. 1 Introduction Volatility analysis of financial time series is an important aspect of many financial. For example, Lamoureux [1] demonstrated that any shift in the structure of financial time series
Rainfall forecasting using an artificial neural network model to prevent flash floods
Izyan'Izzati Abdul Rahman; Nik Mohd Asrol Alias
2011-01-01
Flash floods are a dangerous natural disaster as they have killed more people than any other natural disaster and caused millions of ringgit in property damage. This paper presents a new approach for modeling rainfall forecasting using the artificial neural network technique (ANN). Daily actual data from the years 2007 to 2010, collected from 3 main stations in Selangor, were
Building and Evaluating an Operational SST-based Fire Season Forecast Model in the Southern Amazon
NASA Astrophysics Data System (ADS)
Chen, Y.; Randerson, J. T.; Morton, D. C.
2013-12-01
Base on time-lagged correlation analyses between satellite-observed active fire counts and two ocean climate indices (OCIs) that represent mean sea surface temperature anomalies over the tropical Pacific and Atlantic, we developed an operational model to forecast fire season severities (FSSs) in different regions of southern Amazon. The forecasts for FSS in each fire year were performed and reported at every month between November in previous year and the beginning of the fire season (June). Each prediction was derived from an optimized regression model that uses historical fire observations and OCI data at or before the prediction month only. Although the model performance generally decreased as the lead time for prediction increased, the rate of decline varied in different regions of the southern Amazon. In some regions such as Rondonia and Para, high-quality forecasts can be made as early as in November and December of previous year. We predicted that the 2012 fire season severity was below average across the southern Amazon because of strong La Nina conditions in the Pacific and below average sea surface temperatures in the North Atlantic. This prediction was validated by comparing with observed fire season severity. In several months before the 2013 fire season, we also predicted that the FSS in this year will be considerably higher than in 2011 or 2012 and average or above average relative to the long term mean in all regions. Specific points are recommended for future improvement of the forecast model.
Brad Seely; Clive Welham; Hamish Kimmins
2002-01-01
The effect of alternative harvesting practices on long-term ecosystem productivity and carbon sequestration was investigated with the ecosystem simulation model, FORECAST. Three tree species, white spruce (Picea glauca), trembling aspen (Populus tremuloides), and lodgepole pine (Pinus contorta var. latifolia), were each used in combination with different rotation lengths. An additional run was conducted to investigate the effect of nitrogen addition
Identification of seasonal short-term load forecasting models using statistical decision functions
Hubele, N.F.; Cheng, C.S. (Arizona State Univ., Tempe, AZ (USA). Dept. of Industrial Engineering)
1990-02-01
After the seasonal variation of the daily electric load has been identified with statistical decision functions, accurate short-term forecasts may be produced using rather simple models. A hierarchical classification algorithm is applied to hourly temperature readings to divide the historical database into seasonal subsets. These subsets are used to statistically identify and fit a response function for each season. These functional models constitute a library of models useful to the power scheduler. For a particular day, the appropriate model is selected by performing discriminant analysis. This approach is illustrated using data from a summer peaking utility. This application demonstrates that an entire procedure for specifying forecasting models may be formed with currently available statistical software. Furthermore, the models may be implemented on a microcomputer spreadhseet.
Fusion of Hurricane Models and Observations: Developing the Technology to Improve the Forecasts
NASA Astrophysics Data System (ADS)
Hristova-Veleva, S. M.; Li, P.; Knosp, B.; Turk, F. J.; Vu, Q. A.; Lambrigtsen, B.; Tanelli, S.; Niamsuwan, N.; Haddad, Z.; Poulsen, W. L.
2012-12-01
Recognizing an urgent need for more accurate hurricane forecasts, the National Oceanic and Atmospheric Administration (NOAA) recently established the multi-agency 10-year Hurricane Forecast Improvement Project (HFIP). The two critical pathways to hurricane forecast improvement are: validation and improvement of hurricane models through the use of satellite data; development and implementation of advanced techniques for assimilation of satellite observations inside the hurricane precipitating core. Despite the significant amount of satellite observations today, they are still underutilized in hurricane research and operations. This talk will describe our efforts in developing new technology to bring models and observations into a common information system. We will begin by briefly describing two previous very successful NASA-funded projects, the JPL Tropical Cyclone Information System -TCIS - (http://tropicalcyclone.jpl.nasa.gov and http://grip.jpl.nasa.gov) and the NASA Earth Observing System Simulator Suite (NEOS3). These two efforts resulted in building the critical components for our current work, aimed at providing fusion of hurricane models and observations with the goal to improve hurricane forecast. The talk will outline the three areas of on-going research: - the coupling of the instrument simulator with operational hurricane forecast models and incorporation of simulated satellite observables into the existing database of satellite and air-borne observations (TCIS). As part of this integration we will develop tools for model-observations fusion (e.g. data mining to determine when and what satellite observations are available inside the model domain; model sub-sampling in accordance with the time and space coverage of the satellite/airborne overpasses) - the development of a set of analysis tools that will enable users to calculate joint statistics, produce composites, compare modeled and observed quantities, and apply advanced strategies to assimilate remote sensing observations into meso-scale models. - the development of data immersion techniques to enable real-time interaction with the models and visualization of highly complex systems. We will build upon the approach we have developed to visualize a comprehensive set of satellite observations (see http://grip.jpl.nasa.gov). Under this effort we will develop new approaches to include the visualization of the time-series 3D model data. Finally, we will provide examples of model validation and improvement studies that will be facilitated by the new tools that we are now developing. These new tools will provide the missing components that are needed to fully realize the potential of NASA's satellite and airborne observations to validate and improve hurricane forecasts, demonstrating to the public the high value of NASA's satellite data in monitoring and accurately predicting extreme weather events with high societal impact.
Downey, P.C.; Klontz, G.W.
1983-03-01
Computer implementation of the mathematical models of quantitative relationships in aquaculture systems is a dynamic process which provides a conceptual framework for understanding systems behavior. These models can provide useful information on variable significance to systems functioning. This computer-implemented mathematical model addresses one of the significant limitations of aquaculture systems management, namely, production forecasting, by providing a method of using current technology to predict Allowable Growth Rate (AGR).
NASA Astrophysics Data System (ADS)
Loos, Sibren; Sumihar, Julius; Min, Joong-Hyuk; El Serafy, Ghada; Kim, Kyunghyun; Weerts, Albrecht
2013-04-01
Data assimilation in operational systems is a promising method to enhance the lead-time and reduce the uncertainty of water quality forecasts and provides a good base for the setup of monitoring schemes in large catchments (locations and frequency of sampling). In the River Han (Korea) three weirs have been constructed to prevent flooding and improve the water quality in the main stream. With real-time automated data imports and two water quality models, HSPF and EFDC, embedded in the FEWS-NIER forecasting platform, information about the current water quality status and daily water quality forecasts seven days ahead is provided to -water management agencies in the basin. To improve both the quality and the lead time of the water quality forecasts the EFDC hydrodynamics and water quality model has been implemented in OpenDA, an open interface standard for data assimilation (DA) in numerical models. The setup of this real-time water quality data assimilation system to enhance the algal dynamics modelling and the forecasts in the Han River basin (20,960 km² in size) was performed by a number of steps using Ensemble Kalman Filtering (EnKF). Using a twin experiment the correct working of the algorithm was tested. Noise was applied to several water quality variables in the main tributaries with a sequential simulation algorithm, to obtain correct noise settings that result in a realistic spread between the individual ensemble members. As the next step, the inclusion of observations in the main stream for data assimilation was tested using the EnKF algorithm to define their effect on the model results. Noise was applied to global solar radiation to improve water temperature forecasts, as well as to phosphate, nitrate and chlorophyll-? concentrations in the large tributaries to improve the prediction of algal level upstream of the weirs. Different combinations of noise and observation settings (standard deviation and time correlation) to find the best model update of algae concentrations have been tested. The first results indicate that an improvement occurs every time when weekly observations are available. The tests show that the data assimilation has a clear effect on the water quality in the whole river downstream of the assimilation location leading to adjustments that persist for at least five days. Keywords: data assimilation, water quality predictions, real-time monitoring, operational forecasting system.
NASA Astrophysics Data System (ADS)
Wang, H.; Akmaev, R. A.; Fang, T.-W.; Fuller-Rowell, T. J.; Wu, F.; Maruyama, N.; Iredell, M. D.
2014-03-01
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.
Coupling Climate Models and Forward-Looking Economic Models
NASA Astrophysics Data System (ADS)
Judd, K.; Brock, W. A.
2010-12-01
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.
NASA Technical Reports Server (NTRS)
Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt
2009-01-01
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.
MAFALDA: An early warning modeling tool to forecast volcanic ash dispersal and deposition
NASA Astrophysics Data System (ADS)
Barsotti, S.; Nannipieri, L.; Neri, A.
2008-12-01
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.
Kenneth D. West
This chapter summarizes recent literature on asymptotic inference about forecasts. Both analytical and simulation based methods are discussed. The emphasis is on techniques applicable when the number of competing models is small. Techniques applicable when a large number of models is compared to a benchmark are also briefly discussed.
W. M. McHugh; J. M. Storie; J. W. Lockett; S. G. Scott; E. A. Holt
1977-01-01
Operating instructions and system documentation for a computerized energy demand forecasting model are presented. The model has the capability to forecast energy demand for four fuel types for the three Northwest states, in five-year steps, from 1980 through the year 2000. The forecasts were further broken down into the residential, commercial, industrial, transportation, and other sectors. The model written in
NASA Astrophysics Data System (ADS)
Mount, N. J.; Dawson, C. W.; Abrahart, R. J.
2013-01-01
In this paper we address the difficult problem of gaining an internal, mechanistic understanding of a neural network river forecasting (NNRF) model. Neural network models in hydrology have long been criticised for their black-box character, which prohibits adequate understanding of their modelling mechanisms and has limited their broad acceptance by hydrologists. In response, we here present a new, data-driven mechanistic modelling (DDMM) framework that incorporates an evaluation of the legitimacy of a neural network's internal modelling mechanism as a core element in the model development process. The framework is exemplified for two NNRF modelling scenarios, and uses a novel adaptation of first order, partial derivate, relative sensitivity analysis methods as the means by which each model's mechanistic legitimacy is explored. The results demonstrate the limitations of standard, goodness-of-fit validation procedures applied by NNRF modellers, by highlighting how the internal mechanisms of complex models that produce the best fit scores can have much lower legitimacy than simpler counterparts whose scores are only slightly inferior. The study emphasises the urgent need for better mechanistic understanding of neural network-based hydrological models and the further development of methods for elucidating their mechanisms.
8:00 Tsunami Overview Eddie Bernard 8:30 Tsunami Forecast Modeling and Discussion Vasily Titov
8:00 Tsunami Overview Eddie Bernard 8:30 Tsunami Forecast Modeling and Discussion Vasily Titov 9:15 Tsunami Hazard Assessment and Discussion Diego Arcas 10:00 Break 10:15 Tsunami Measurements: Tour and Discussion Chris Meinig 11:30 Tsunami Forecast System Demonstration Don Denbo, Chris Moore 12:15 Tsunami Wrap
Bretherton, Chris
depolarization measurements at SHEBA indicate that both liquid and ice phase clouds occurred over a wide range at Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp J. A. Beesley,1,2 C. S. Bretherton,3 C. Jakob,4 E the European Centre for Medium- Range Weather Forecasts (ECMWF) forecast model were compared with measurements
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...
Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters
NASA Technical Reports Server (NTRS)
Hesse, Michael
2009-01-01
The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.
A Hidden Markov Model for avalanche forecasting on Chowkibal-Tangdhar road axis in Indian Himalayas
NASA Astrophysics Data System (ADS)
Joshi, Jagdish Chandra; Srivastava, Sunita
2014-12-01
A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal-Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992-2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008-2009, 2009-2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.
Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models
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
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.
An economic model of the manufacturers' aircraft production and airline earnings potential, volume 3
NASA Technical Reports Server (NTRS)
Kneafsey, J. T.; Hill, R. M.
1978-01-01
A behavioral explanation of the process of technological change in the U. S. aircraft manufacturing and airline industries is presented. The model indicates the principal factors which influence the aircraft (airframe) manufacturers in researching, developing, constructing and promoting new aircraft technology; and the financial requirements which determine the delivery of new aircraft to the domestic trunk airlines. Following specification and calibration of the model, the types and numbers of new aircraft were estimated historically for each airline's fleet. Examples of possible applications of the model to forecasting an individual airline's future fleet also are provided. The functional form of the model is a composite which was derived from several preceding econometric models developed on the foundations of the economics of innovation, acquisition, and technological change and represents an important contribution to the improved understanding of the economic and financial requirements for aircraft selection and production. The model's primary application will be to forecast the future types and numbers of new aircraft required for each domestic airline's fleet.
2013-01-01
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
Spatial Analytic Hierarchy Process Model for Flood Forecasting: An Integrated Approach
NASA Astrophysics Data System (ADS)
Nasir Matori, Abd; Umar Lawal, Dano; Yusof, Khamaruzaman Wan; Hashim, Mustafa Ahmad; Balogun, Abdul-Lateef
2014-06-01
Various flood influencing factors such as rainfall, geology, slope gradient, land use, soil type, drainage density, temperature etc. are generally considered for flood hazard assessment. However, lack of appropriate handling/integration of data from different sources is a challenge that can make any spatial forecasting difficult and inaccurate. Availability of accurate flood maps and thorough understanding of the subsurface conditions can adequately enhance flood disasters management. This study presents an approach that attempts to provide a solution to this drawback by combining Geographic Information System (GIS)-based Analytic Hierarchy Process (AHP) model as spatial forecasting tools. In achieving the set objectives, spatial forecasting of flood susceptible zones in the study area was made. A total number of five set of criteria/factors believed to be influencing flood generation in the study area were selected. Priority weights were assigned to each criterion/factor based on Saaty's nine point scale of preference and weights were further normalized through the AHP. The model was integrated into a GIS system in order to produce a flood forecasting map.
NASA Astrophysics Data System (ADS)
Yang, Jiachuan; Wang, Zhi-Hua; Chen, Fei; Miao, Shiguang; Tewari, Mukul; Voogt, James A.; Myint, Soe
2015-04-01
Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimate. In recent decades, a number of urban canopy models have been developed and implemented into the Weather Research and Forecasting (WRF) model to capture urban land-surface processes. Most of these models are inadequate due to the lack of realistic representation of urban hydrological processes. Here, we implement physically-based parametrizations of urban hydrological processes into the single layer urban canopy model in the WRF model. The new single-layer urban canopy model features the integration of, (1) anthropogenic latent heat, (2) urban irrigation, (3) evaporation from paved surfaces, and (4) the urban oasis effect. The new WRF-urban modelling system is evaluated against field measurements for four different cities; results show that the model performance is substantially improved as compared to the current schemes, especially for latent heat flux. In particular, to evaluate the performance of green roofs as an urban heat island mitigation strategy, we integrate in the urban canopy model a multilayer green roof system, enabled by the physical urban hydrological schemes. Simulations show that green roofs are capable of reducing surface temperature and sensible heat flux as well as enhancing building energy efficiency.
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
NASA Astrophysics Data System (ADS)
El-Shafie, A.; Noureldin, A.; Taha, M.; Hussain, A.; Mukhlisin, M.
2012-04-01
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.
Testing for ontological errors in probabilistic forecasting models of natural systems
Marzocchi, Warner; Jordan, Thomas H.
2014-01-01
Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265
Forecasting, Structural Time Series Models and the Kalman Filter
Andrew C. Harvey
1989-01-01
In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology.
Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation
NASA Technical Reports Server (NTRS)
Watson, Leela R.
2007-01-01
This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.
Why Models Don%3CU%2B2019%3Et Forecast.
McNamara, Laura A.
2010-08-01
The title of this paper, Why Models Don't Forecast, has a deceptively simple answer: models don't forecast because people forecast. Yet this statement has significant implications for computational social modeling and simulation in national security decision making. Specifically, it points to the need for robust approaches to the problem of how people and organizations develop, deploy, and use computational modeling and simulation technologies. In the next twenty or so pages, I argue that the challenge of evaluating computational social modeling and simulation technologies extends far beyond verification and validation, and should include the relationship between a simulation technology and the people and organizations using it. This challenge of evaluation is not just one of usability and usefulness for technologies, but extends to the assessment of how new modeling and simulation technologies shape human and organizational judgment. The robust and systematic evaluation of organizational decision making processes, and the role of computational modeling and simulation technologies therein, is a critical problem for the organizations who promote, fund, develop, and seek to use computational social science tools, methods, and techniques in high-consequence decision making.
Corzo, Gerald; Solomatine, Dimitri
2007-05-01
Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased. PMID:17532609
Economic tour package model using heuristic
NASA Astrophysics Data System (ADS)
Rahman, Syariza Abdul; Benjamin, Aida Mauziah; Bakar, Engku Muhammad Nazri Engku Abu
2014-07-01
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.
Impact assessment and forecasting of soot from Kuwaiti oil fires using a modeling approach
NASA Astrophysics Data System (ADS)
Husain, Tahir; Khan, Suhail M.
In order to assess the impact on air quality due to the burning oil wells and to develop an early warning system by forecasting the movement and dispersion of the plumes, the Air Resources Laboratories Atmospheric Transport and Dispersion (ARL-ATAD), a regional transport model, was used. Based on the information on the number of wells on fire and their emission factors, an emission inventory of soot particles was developed and their values were periodically adjusted. It was observed that the region between south Kuwait and Safaniyah was most affected by the smoke trajectories during March May 1991. Monthly average simulated pollutant concentration and deposition contours showed that the concentration pattern for the region changed with the changes in climatic conditions and the source emissions. Using real and forecasted synoptic and upper air meteorological data, trajectories were produced at 980, 850 and 700 mb pressure levels. Heavy and light smoke concentration zones were identified and air pollution forecast maps were prepared. In order to compare the results of the air pollution forecasting system with the real concentration pattern of the smoke, satellite images of the smoke were obtained. By comparing the concentration contours with the satellite images, it was observed that the model results follow more or less the same pattern as the real smoke plume movement. The daily average observed PM10 values were also compared with the simulated smoke concentration pattern. The forecast turned out to be reliable about 80° o of the time and the performance of the model proved to be quite satisfactory.
NASA Astrophysics Data System (ADS)
Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.
2012-04-01
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.
Home Economics Education Career Path Guide and Model Curriculum Standards.
ERIC Educational Resources Information Center
California State Univ., Northridge.
This curriculum guide developed in California and organized in 10 chapters, provides a home economics education career path guide and model curriculum standards for high school home economics programs. The first chapter contains information on the following: home economics education in California, home economics careers for the future, home…
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Pei-chann Chang; Yen-wen Wang
2006-01-01
Abstract Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments,in enhancing the model’s performance. Parameters chosen as inputs to the
NASA Astrophysics Data System (ADS)
Pattanaik, D. R.; Kumar, Arun
2014-10-01
The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19-25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5-11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8-10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.
NASA Technical Reports Server (NTRS)
Kalnay, Eugenia; Dalcher, Amnon
1987-01-01
It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.
A spatial-temporal projection model for extended-range forecast in the tropics
NASA Astrophysics Data System (ADS)
Zhu, Zhiwei; Li, Tim; Hsu, Pang-chi; He, Jinhai
2014-10-01
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.
An optimal energy forecasting model for the economy environment match
L. Suganthi; A. A. Samuel
1999-01-01
Econometric demand models which are used in developed countries consider only price and national income. The modified model developed earlier by the authors links energy consumption with the economy, technology and the environment. The modified model is validated by comparing with an econometric and time series regression model based on least squared error, square of correlation coefficient and Durbin Watson
Real-time weather forecasting in the Western Mediterranean Basin: An application of the RAMS model
NASA Astrophysics Data System (ADS)
Gómez, I.; Caselles, V.; Estrela, M. J.
2014-03-01
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.
Regional forecasting with global atmospheric models; Fourth year report
Crowley, T.J.; North, G.R.; Smith, N.R. [Applied Research Corp., College Station, TX (United States)
1994-05-01
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.
North American Ensemble Forecasting System (NAEFS): Bias Removal and Multi-Model Ensemble
NASA Astrophysics Data System (ADS)
Beauregard, S.; Candille, G.; Gagnon, N.
2009-05-01
The North American Forecasting System (NAEFS) currently combines the U.S. National Weather Service (NWS) and Meteorological Service of Canada (MSC) ensemble forecast systems. Previous studies show clear benefits to merging the two ensembles. A running mean bias removal scheme is applied on the NAEFS ensemble for a number of variables and pressure levels. Here we present verification results against upper air observations comparing the NAEFS, NWS and MSC raw ensemble forecasts with the respective bias corrected version of each. The bias, dispersion and Continuous Ranked Probability Scores (CRPS) of both raw and bias corrected systems are compared. A bootstrap method is used to compute 90% confidence intervals on the differences in scores for both systems. It is found that the bias removal scheme improves the bias and the CRPS of both the NWS and MSC ensemble systems when compared to upper air observations. However the effect on the combined NAEFS system is relatively small in CRPS term. In fact, it is found that the raw NAEFS combined forecasts (40 members) have similar scores than the bias corrected ones. This may highlight the limits of the current simple bias removal framework and/or the efficiency of the multi-model approach.
Air pollution forecasting by coupled atmosphere-fire model WRF and SFIRE with WRF-Chem
Kochanski, Adam K; Mandel, Jan; Clements, Craig B
2013-01-01
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.
Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price
NASA Astrophysics Data System (ADS)
Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John
2015-02-01
Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.
Implementation of the Immersed Boundary Method in the Weather Research and Forecasting model
Lundquist, K A
2006-12-07
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.
NASA Astrophysics Data System (ADS)
Pattnaik, S.; Abhilash, S.; De, S.; Sahai, A. K.; Phani, R.; Goswami, B. N.
2013-07-01
This study investigates the influence of Simplified Arakawa Schubert (SAS) and Relax Arakawa Schubert (RAS) cumulus parameterization schemes on coupled Climate Forecast System version.1 (CFS-1, T62L64) retrospective forecasts over Indian monsoon region from an extended range forecast perspective. The forecast data sets comprise 45 days of model integrations based on 31 different initial conditions at pentad intervals starting from 1 May to 28 September for the years 2001 to 2007. It is found that mean climatological features of Indian summer monsoon months (JJAS) are reasonably simulated by both the versions (i.e. SAS and RAS) of the model; however strong cross equatorial flow and excess stratiform rainfall are noted in RAS compared to SAS. Both the versions of the model overestimated apparent heat source and moisture sink compared to NCEP/NCAR reanalysis. The prognosis evaluation of daily forecast climatology reveals robust systematic warming (moistening) in RAS and cooling (drying) biases in SAS particularly at the middle and upper troposphere of the model respectively. Using error energy/variance and root mean square error methodology it is also established that major contribution to the model total error is coming from the systematic component of the model error. It is also found that the forecast error growth of temperature in RAS is less than that of SAS; however, the scenario is reversed for moisture errors, although the difference of moisture errors between these two forecasts is not very large compared to that of temperature errors. Broadly, it is found that both the versions of the model are underestimating (overestimating) the rainfall area and amount over the Indian land region (and neighborhood oceanic region). The rainfall forecast results at pentad interval exhibited that, SAS and RAS have good prediction skills over the Indian monsoon core zone and Arabian Sea. There is less excess rainfall particularly over oceanic region in RAS up to 30 days of forecast duration compared to SAS. It is also evident that systematic errors in the coverage area of excess rainfall over the eastern foothills of the Himalayas remains unchanged irrespective of cumulus parameterization and initial conditions. It is revealed that due to stronger moisture transport in RAS there is a robust amplification of moist static energy facilitating intense convective instability within the model and boosting the moisture supply from surface to the upper levels through convergence. Concurrently, moisture detrainment from cloud to environment at multiple levels from the spectrum of clouds in the RAS, leads to a large accumulation of moisture in the middle and upper troposphere of the model. This abundant moisture leads to large scale condensational heating through a simple cloud microphysics scheme. This intense upper level heating contributes to the warm bias and considerably increases in stratiform rainfall in RAS compared to SAS. In a nutshell, concerted and sustained support of moisture supply from the bottom as well as from the top in RAS is the crucial factor for having a warm temperature bias in RAS.
Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.
2007-12-01
This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model paramet