Added value of dynamical downscaling of winter seasonal forecasts over North America

NASA Astrophysics Data System (ADS)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

Integrating predictive information into an agro-economic model to guide agricultural planning

NASA Astrophysics Data System (ADS)

Block, Paul; Zhang, Ying; You, Liangzhi

2017-04-01

Seasonal climate forecasts can inform long-range planning, including water resources utilization and allocation, however quantifying the value of this information on the economy is often challenging. For rain-fed farmers, skillful season-ahead predictions may lead to superior planning, as compared to business as usual strategies, resulting in additional benefits or reduced losses. In this study, regional-level probabilistic precipitation forecasts of the major rainy season in Ethiopia are fed into an agro-economic model, adapted from the International Food Policy Research Institute, to evaluate economic outcomes (GDP, poverty rates, etc.) as compared with a no-forecast approach. Based on forecasted conditions, farmers can select various actions: adjusting crop area and crop type, purchasing drought resistant seed, or applying additional fertilizer. Preliminary results favor the forecast-based approach, particularly through crop area reallocation.

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

Medium-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

2018-06-01

Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

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.

Robustness of disaggregate oil and gas discovery forecasting models

USGS Publications Warehouse

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.

Value versus Accuracy: application of seasonal forecasts to a hydro-economic optimization model for the Sudanese Blue Nile

NASA Astrophysics Data System (ADS)

Satti, S.; Zaitchik, B. F.; Siddiqui, S.; Badr, H. S.; Shukla, S.; Peters-Lidard, C. D.

2015-12-01

The unpredictable nature of precipitation within the East African (EA) region makes it one of the most vulnerable, food insecure regions in the world. There is a vital need for forecasts to inform decision makers, both local and regional, and to help formulate the region's climate change adaptation strategies. Here, we present a suite of different seasonal forecast models, both statistical and dynamical, for the EA region. Objective regionalization is performed for EA on the basis of interannual variability in precipitation in both observations and models. This regionalization is applied as the basis for calculating a number of standard skill scores to evaluate each model's forecast accuracy. A dynamically linked Land Surface Model (LSM) is then applied to determine forecasted flows, which drive the Sudanese Hydroeconomic Optimization Model (SHOM). SHOM combines hydrologic, agronomic and economic inputs to determine the optimal decisions that maximize economic benefits along the Sudanese Blue Nile. This modeling sequence is designed to derive the potential added value of information of each forecasting model to agriculture and hydropower management. A rank of each model's forecasting skill score along with its added value of information is analyzed in order compare the performance of each forecast. This research aims to improve understanding of how characteristics of accuracy, lead time, and uncertainty of seasonal forecasts influence their utility to water resources decision makers who utilize them.

A Hybrid Approach on Tourism Demand Forecasting

NASA Astrophysics Data System (ADS)

Nor, M. E.; Nurul, A. I. M.; Rusiman, M. S.

2018-04-01

Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run.

PubMed

Armeanu, Daniel; Andrei, Jean Vasile; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run

PubMed Central

Armeanu, Daniel; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets. PMID:28742100

Socioeconomic Forecasting

DOT National Transportation Integrated Search

2012-05-01

The role of the REMI Policy Insight+ model in socioeconomic forecasting and economic impact analysis of transportation projects was assessed. The REMI : PI+ model is consistent with the state of the practice in forecasting and impact analysis. REMI P...

Socioeconomic forecasting.

DOT National Transportation Integrated Search

2012-05-01

The role of the REMI Policy Insight+ model in socioeconomic forecasting and economic impact analysis of transportation projects was assessed. The REMI : PI+ model is consistent with the state of the practice in forecasting and impact analysis. REMI P...

Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city.

PubMed

Rimaityte, Ingrida; Ruzgas, Tomas; Denafas, Gintaras; Racys, Viktoras; Martuzevicius, Dainius

2012-01-01

Forecasting of generation of municipal solid waste (MSW) in developing countries is often a challenging task due to the lack of data and selection of suitable forecasting method. This article aimed to select and evaluate several methods for MSW forecasting in a medium-scaled Eastern European city (Kaunas, Lithuania) with rapidly developing economics, with respect to affluence-related and seasonal impacts. The MSW generation was forecast with respect to the economic activity of the city (regression modelling) and using time series analysis. The modelling based on social-economic indicators (regression implemented in LCA-IWM model) showed particular sensitivity (deviation from actual data in the range from 2.2 to 20.6%) to external factors, such as the synergetic effects of affluence parameters or changes in MSW collection system. For the time series analysis, the combination of autoregressive integrated moving average (ARIMA) and seasonal exponential smoothing (SES) techniques were found to be the most accurate (mean absolute percentage error equalled to 6.5). Time series analysis method was very valuable for forecasting the weekly variation of waste generation data (r (2) > 0.87), but the forecast yearly increase should be verified against the data obtained by regression modelling. The methods and findings of this study may assist the experts, decision-makers and scientists performing forecasts of MSW generation, especially in developing countries.

A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods

NASA Astrophysics Data System (ADS)

Courdent, Vianney; Grum, Morten; Munk-Nielsen, Thomas; Mikkelsen, Peter S.

2017-05-01

Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.

Economic Perspectives of Technological Progress: New Dimensions for Forecasting Technology

ERIC Educational Resources Information Center

Twiss, Brian

1976-01-01

Discusses the causal relationship between the allocation of financial resources and technological growth. Argues that economic constraints are becoming an important determinant of technological progress that must be incorporated into technology forecasting techniques. (Available from IPC (America) Inc., 205 East 42 Street, New York, NY 10017;…

Defining conservation priorities using fragmentation forecasts

Treesearch

David Wear; John Pye; Kurt H. Riitters

2004-01-01

Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...

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.

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

Better Forecasting for Better Planning: A Systems Approach.

ERIC Educational Resources Information Center

Austin, W. Burnet

Predictions and forecasts are the most critical features of rational planning as well as the most vulnerable to inaccuracy. Because plans are only as good as their forecasts, current planning procedures could be improved by greater forecasting accuracy. Economic factors explain and predict more than any other set of factors, making economic…

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

ERIC Educational Resources Information Center

Lefberg, Irv; And Others

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

From Negative to Positive Stability: How the Syrian Refugee Crisis Can Improve Jordan’s Outlook

DTIC Science & Technology

2015-01-01

Syrian Refugees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Forecast: The Impact of Syrian Refugees on Jordan’s Economic Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22...Syrians find economic stability , they will be less vulnerable to the loss of aid, and therefore less likely to present a drain on Jordanian society...with Syrian refugees, and presents a forecast of Syrian refugee impacts on Jordan’s economy and such economic stability factors as unem- ployment and

Projecting county pulpwood production with historical production and macro-economic variables

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

We explored forecasting of county roundwood pulpwood produc-tion with county-vector autoregressive (CVAR) and spatial panelvector autoregressive (SPVAR) methods. The analysis used timberproducts output data for the state of Florida, together with a set ofmacro-economic variables. Overall, we found the SPVAR specifica-tion produced forecasts with lower error rates...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

Analysis and forecast of railway coal transportation volume based on BP neural network combined forecasting model

NASA Astrophysics Data System (ADS)

Xu, Yongbin; Xie, Haihong; Wu, Liuyi

2018-05-01

The share of coal transportation in the total railway freight volume is about 50%. As is widely acknowledged, coal industry is vulnerable to the economic situation and national policies. Coal transportation volume fluctuates significantly under the new economic normal. Grasp the overall development trend of railway coal transportation market, have important reference and guidance significance to the railway and coal industry decision-making. By analyzing the economic indicators and policy implications, this paper expounds the trend of the coal transportation volume, and further combines the economic indicators with the high correlation with the coal transportation volume with the traditional traffic prediction model to establish a combined forecasting model based on the back propagation neural network. The error of the prediction results is tested, which proves that the method has higher accuracy and has practical application.

FAA Aviation Forecast Conference Proceedings (16th)

DTIC Science & Technology

1991-02-01

FORECASTS The FAA forecasting process is a continuous one which involves FAA Forecast Branch’s interaction with various FAA Offices and Services... process uses various economic and aviation data bases, the outputs of several econometric models and equations, and other analytical techniques. The FAA...workload measures, summarized numerically in the table on page 8, are the resultant forecasts of this process and are used annually by the agency for

Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

NASA Astrophysics Data System (ADS)

Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

Coping with Changes in International Classifications of Sectors and Occupations: Application in Skills Forecasting. Research Paper No 43

ERIC Educational Resources Information Center

Kvetan, Vladimir, Ed.

2014-01-01

Reliable and consistent time series are essential to any kind of economic forecasting. Skills forecasting needs to combine data from national accounts and labour force surveys, with the pan-European dimension of Cedefop's skills supply and demand forecasts, relying on different international classification standards. Sectoral classification (NACE)…

Methods and Techniques of Revenue Forecasting.

ERIC Educational Resources Information Center

Caruthers, J. Kent; Wentworth, Cathi L.

1997-01-01

Revenue forecasting is the critical first step in most college and university budget-planning processes. While it seems a straightforward exercise, effective forecasting requires consideration of a number of interacting internal and external variables, including demographic trends, economic conditions, and broad social priorities. The challenge…

Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach.

PubMed

Boonen, Tim J; Li, Hong

2017-10-01

Research on mortality modeling of multiple populations focuses mainly on extrapolating past mortality trends and summarizing these trends by one or more common latent factors. This article proposes a multipopulation stochastic mortality model that uses the explanatory power of economic growth. In particular, we extend the Li and Lee model (Li and Lee 2005) by including economic growth, represented by the real gross domestic product (GDP) per capita, to capture the common mortality trend for a group of populations with similar socioeconomic conditions. We find that our proposed model provides a better in-sample fit and an out-of-sample forecast performance. Moreover, it generates lower (higher) forecasted period life expectancy for countries with high (low) GDP per capita than the Li and Lee model.

Some economic benefits of a synchronous earth observatory satellite

NASA Technical Reports Server (NTRS)

Battacharyya, R. K.; Greenberg, J. S.; Lowe, D. S.; Sattinger, I. J.

1974-01-01

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution.

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

NASA Technical Reports Server (NTRS)

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

2014-01-01

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

Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator

NASA Astrophysics Data System (ADS)

Fernández-Vázquez, Esteban; Moreno, Blanca

2017-10-01

Forecast combination has been studied in econometrics for a long time, and the literature has shown the superior performance of forecast combination over individual predictions. However, there is still controversy on which is the best procedure to specify the forecast weights. This paper explores the possibility of using a procedure based on Entropy Econometrics, which allows setting the weights for the individual forecasts as a mixture of different alternatives. In particular, we examine the ability of the Data-Weighted Prior Estimator proposed by Golan (J Econom 101(1):165-193, 2001) to combine forecasting models in a context of small sample sizes, a relative common scenario when dealing with time series for regional economies. We test the validity of the proposed approach using a simulation exercise and a real-world example that aims at predicting gross regional product growth rates for a regional economy. The forecasting performance of the Data-Weighted Prior Estimator proposed is compared with other combining methods. The simulation results indicate that in scenarios of heavily ill-conditioned datasets the approach suggested dominates other forecast combination strategies. The empirical results are consistent with the conclusions found in the numerical experiment.

Leverage effect, economic policy uncertainty and realized volatility with regime switching

NASA Astrophysics Data System (ADS)

Duan, Yinying; Chen, Wang; Zeng, Qing; Liu, Zhicao

2018-03-01

In this study, we first investigate the impacts of leverage effect and economic policy uncertainty (EPU) on future volatility in the framework of regime switching. Out-of-sample results show that the HAR-RV including the leverage effect and economic policy uncertainty with regimes can achieve higher forecast accuracy than RV-type and GARCH-class models. Our robustness results further imply that these factors in the framework of regime switching can substantially improve the HAR-RV's forecast performance.

Calibration of decadal ensemble predictions

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Rust, Henning W.; Bhend, Jonas; Liniger, Mark; Grieger, Jens; Müller, Wolfgang; Ulbrich, Uwe

2017-04-01

Decadal climate predictions are of great socio-economic interest due to the corresponding planning horizons of several political and economic decisions. Due to uncertainties of weather and climate, forecasts (e.g. due to initial condition uncertainty), they are issued in a probabilistic way. One issue frequently observed for probabilistic forecasts is that they tend to be not reliable, i.e. the forecasted probabilities are not consistent with the relative frequency of the associated observed events. Thus, these kind of forecasts need to be re-calibrated. While re-calibration methods for seasonal time scales are available and frequently applied, these methods still have to be adapted for decadal time scales and its characteristic problems like climate trend and lead time dependent bias. Regarding this, we propose a method to re-calibrate decadal ensemble predictions that takes the above mentioned characteristics into account. Finally, this method will be applied and validated to decadal forecasts from the MiKlip system (Germany's initiative for decadal prediction).

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

NASA Technical Reports Server (NTRS)

1977-01-01

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

A study of the economic benefits of meteorological satellite data

NASA Technical Reports Server (NTRS)

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

1980-01-01

Satellite data, while most useful in data poor areas, serves to fine tune forecasts in data rich areas. It consequently has a resulting significant economic benefit because, as previously stated, even one improved forecast per client per year can save each client thousands of dollars. Multiply this by several hundred clients and the dollar savings are sizeable. The great educational value which experience with satellite data gives undoubtedly leads to improved forecasts. Any type of future satellite data delivery system should take into account the needs and facilities of the user community to make it most useful.

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.

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

ERIC Educational Resources Information Center

Leigh, J. Paul; Du, Juan

2015-01-01

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

Econometric Models for Forecasting of Macroeconomic Indices

ERIC Educational Resources Information Center

Sukhanova, Elena I.; Shirnaeva, Svetlana Y.; Mokronosov, Aleksandr G.

2016-01-01

The urgency of the research topic was stipulated by the necessity to carry out an effective controlled process by the economic system which can hardly be imagined without indices forecasting characteristic of this system. An econometric model is a safe tool of forecasting which makes it possible to take into consideration the trend of indices…

Effects of Forecasts on the Revisions of Concurrent Seasonally Adjusted Data Using the X-11 Seasonal Adjustment Procedure.

ERIC Educational Resources Information Center

Bobbitt, Larry; Otto, Mark

Three Autoregressive Integrated Moving Averages (ARIMA) forecast procedures for Census Bureau X-11 concurrent seasonal adjustment were empirically tested. Forty time series from three Census Bureau economic divisions (business, construction, and industry) were analyzed. Forecasts were obtained from fitted seasonal ARIMA models augmented with…

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels

EIA Publications

2003-01-01

This paper presents a short-term monthly forecasting model of West Texas Intermediate crude oil spot price using Organization for Economic Cooperation and Development (OECD) petroleum inventory levels.

Assessing the Value of Frost Forecasts to Orchardists: A Dynamic Decision-Making Approach.

NASA Astrophysics Data System (ADS)

Katz, Richard W.; Murphy, Allan H.; Winkler, Robert L.

1982-04-01

The methodology of decision analysis is used to investigate the economic value of frost (i.e., minimum temperature) forecasts to orchardists. First, the fruit-frost situation and previous studies of the value of minimum temperature forecasts in this context are described. Then, after a brief overview of decision analysis, a decision-making model for the fruit-frost problem is presented. The model involves identifying the relevant actions and events (or outcomes), specifying the effect of taking protective action, and describing the relationships among temperature, bud loss, and yield loss. A bivariate normal distribution is used to model the relationship between forecast and observed temperatures, thereby characterizing the quality of different types of information. Since the orchardist wants to minimize expenses (or maximize payoffs) over the entire frost-protection season and since current actions and outcomes at any point in the season are related to both previous and future actions and outcomes, the decision-making problem is inherently dynamic in nature. As a result, a class of dynamic models known as Markov decision processes is considered. A computational technique called dynamic programming is used in conjunction with these models to determine the optimal actions and to estimate the value of meteorological information.Some results concerning the value of frost forecasts to orchardists in the Yakima Valley of central Washington are presented for the cases of red delicious apples, bartlett pears, and elberta peaches. Estimates of the parameter values in the Markov decision process are obtained from relevant physical and economic data. Twenty years of National Weather Service forecast and observed temperatures for the Yakima key station are used to estimate the quality of different types of information, including perfect forecasts, current forecasts, and climatological information. The orchardist's optimal actions over the frost-protection season and the expected expenses associated with the use of such information are determined using a dynamic programming algorithm. The value of meteorological information is defined as the difference between the expected expense for the information of interest and the expected expense for climatological information. Over the entire frost-protection season, the value estimates (in 1977 dollars) for current forecasts were $808 per acre for red delicious apples, $492 per acre for bartlett pears, and $270 per acre for elberta peaches. These amounts account for 66, 63, and 47%, respectively, of the economic value associated with decisions based on perfect forecasts. Varying the quality of the minimum temperature forecasts reveals that the relationship between the accuracy and value of such forecasts is nonlinear and that improvements in current forecasts would not be as significant in terms of economic value as were comparable improvements in the past.Several possible extensions of this study of the value of frost forecasts to orchardists are briefly described. Finally, the application of the dynamic model formulated in this paper to other decision-making problems involving the use of meteorological information is mentioned.

SEASAT economic assessment. Volume 8: Ocean fishing case study. [economic benefits of SEASAT satellites to ocean fishing industries in the United States and Canada

NASA Technical Reports Server (NTRS)

1975-01-01

The potential application of SEASAT data with regard to ocean fisheries is discussed. Tracking fish populations, indirect assistance in forecasting expected populations and assistance to fishing fleets in avoiding costs incurred due to adverse weather through improved ocean conditions forecasts were investigated. Case studies on fisheries in the United States and Canada are cited.

The Practice of Manpower Forecasting: A Collection of Case Studies. Studies on Education; Vol. 1.

ERIC Educational Resources Information Center

Ahamad, Bashir; And Others

In the 1960's academics, politicians, administrators and industrialists became convinced of the importance of education for economic development. The forecasting of qualified manpower needs was able to turn this new idea into practice. During the decade hundreds of manpower forecasts were made, and innumerable international conferenceses were held…

The Global Economic Prospect: New Sources of Economic Stress. Worldwatch Paper 20.

ERIC Educational Resources Information Center

Brown, Lester R.

American economic analysts will better understand current economic trends if they investigate economic problems in light of the expanding global economy. Reasons for the failure of economists to explain the simultaneous existence of rapid inflation and high unemployment include preoccupation with economic indicators, short-term forecasts, and…

The case for probabilistic forecasting in hydrology

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman

2001-08-01

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the 'best' estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

Evaluation of trade influence on economic growth rate by computational intelligence approach

NASA Astrophysics Data System (ADS)

Sokolov-Mladenović, Svetlana; Milovančević, Milos; Mladenović, Igor

2017-01-01

In this study was analyzed the influence of trade parameters on the economic growth forecasting accuracy. Computational intelligence method was used for the analyzing since the method can handle highly nonlinear data. It is known that the economic growth could be modeled based on the different trade parameters. In this study five input parameters were considered. These input parameters were: trade in services, exports of goods and services, imports of goods and services, trade and merchandise trade. All these parameters were calculated as added percentages in gross domestic product (GDP). The main goal was to select which parameters are the most impactful on the economic growth percentage. GDP was used as economic growth indicator. Results show that the imports of goods and services has the highest influence on the economic growth forecasting accuracy.

The Impact of Weather Forecasts of Various Lead Times on Snowmaking Decisions Made for the 2010 Vancouver Olympic Winter Games

NASA Astrophysics Data System (ADS)

Doyle, Chris

2014-01-01

The Vancouver 2010 Winter Olympics were held from 12 to 28 February 2010, and the Paralympic events followed 2 weeks later. During the Games, the weather posed a grave threat to the viability of one venue and created significant complications for the event schedule at others. Forecasts of weather with lead times ranging from minutes to days helped organizers minimize disruptions to sporting events and helped ensure all medal events were successfully completed. Of comparable importance, however, were the scenarios and forecasts of probable weather for the winter in advance of the Games. Forecasts of mild conditions at the time of the Games helped the Games' organizers mitigate what would have been very serious potential consequences for at least one venue. Snowmaking was one strategy employed well in advance of the Games to prepare for the expected conditions. This short study will focus on how operational decisions were made by the Games' organizers on the basis of both climatological and snowmaking forecasts during the pre-Games winter. An attempt will be made to quantify, economically, the value of some of the snowmaking forecasts made for the Games' operators. The results obtained indicate that although the economic value of the snowmaking forecast was difficult to determine, the Games' organizers valued the forecast information greatly. This suggests that further development of probabilistic forecasts for applications like pre-Games snowmaking would be worthwhile.

Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.

PubMed

Zhao, Xin; Han, Meng; Ding, Lili; Calin, Adrian Cantemir

2018-01-01

The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neural network (MIDAS-BP model) to forecast carbon dioxide emissions. Such analysis uses mixed frequency data to study the effects of quarterly economic growth on annual carbon dioxide emissions. The forecasting ability of MIDAS-BP is remarkably better than MIDAS, ordinary least square (OLS), polynomial distributed lags (PDL), autoregressive distributed lags (ADL), and auto-regressive moving average (ARMA) models. The MIDAS-BP model is suitable for forecasting carbon dioxide emissions for both the short and longer term. This research is expected to influence the methodology for forecasting carbon dioxide emissions by improving the forecast accuracy. Empirical results show that economic growth has both negative and positive effects on carbon dioxide emissions that last 15 quarters. Carbon dioxide emissions are also affected by their own change within 3 years. Therefore, there is a need for policy makers to explore an alternative way to develop the economy, especially applying new energy policies to establish a low carbon society.

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

Metric optimisation for analogue forecasting by simulated annealing

NASA Astrophysics Data System (ADS)

Bliefernicht, J.; Bárdossy, A.

2009-04-01

It is well known that weather patterns tend to recur from time to time. This property of the atmosphere is used by analogue forecasting techniques. They have a long history in weather forecasting and there are many applications predicting hydrological variables at the local scale for different lead times. The basic idea of the technique is to identify past weather situations which are similar (analogue) to the predicted one and to take the local conditions of the analogues as forecast. But the forecast performance of the analogue method depends on user-defined criteria like the choice of the distance function and the size of the predictor domain. In this study we propose a new methodology of optimising both criteria by minimising the forecast error with simulated annealing. The performance of the methodology is demonstrated for the probability forecast of daily areal precipitation. It is compared with a traditional analogue forecasting algorithm, which is used operational as an element of a hydrological forecasting system. The study is performed for several meso-scale catchments located in the Rhine basin in Germany. The methodology is validated by a jack-knife method in a perfect prognosis framework for a period of 48 years (1958-2005). The predictor variables are derived from the NCEP/NCAR reanalysis data set. The Brier skill score and the economic value are determined to evaluate the forecast skill and value of the technique. In this presentation we will present the concept of the optimisation algorithm and the outcome of the comparison. It will be also demonstrated how a decision maker should apply a probability forecast to maximise the economic benefit from it.

Air Traffic Forecasting at the Port Authority of New York and New Jersey

NASA Technical Reports Server (NTRS)

Augustine, J. G.

1972-01-01

Procedures for conducting air traffic forecasts with specific application to the Port Authority of New York and New Jersey are discussed. The procedure relates air travel growth to detailed socio-economic and demographic characteristics of the U.S. population rather than to aggregate economic data such as Gross National Product, personal income, and industrial production. Charts are presented to show the relationship between various selected characteristics and the use of air transportation facilities.

Bayesian analyses of seasonal runoff forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Reese, S.

1991-12-01

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

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.

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

PubMed Central

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

2015-01-01

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

Neural networks and traditional time series methods: a synergistic combination in state economic forecasts.

PubMed

Hansen, J V; Nelson, R D

1997-01-01

Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.

Forecasting the demand for privatized transport : what economic regulators should know and why

DOT National Transportation Integrated Search

2001-09-01

While public-private partnerships in the delivery of transport infrastructures and services is expanding, there is also growing evidence of the lack of appreciation of the importance of demand forecasting in preparing and monitoring these partnership...

Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

NASA Astrophysics Data System (ADS)

Fabianová, Jana; Kačmáry, Peter; Molnár, Vieroslav; Michalik, Peter

2016-10-01

Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.

The Research of Regression Method for Forecasting Monthly Electricity Sales Considering Coupled Multi-factor

NASA Astrophysics Data System (ADS)

Wang, Jiangbo; Liu, Junhui; Li, Tiantian; Yin, Shuo; He, Xinhui

2018-01-01

The monthly electricity sales forecasting is a basic work to ensure the safety of the power system. This paper presented a monthly electricity sales forecasting method which comprehensively considers the coupled multi-factors of temperature, economic growth, electric power replacement and business expansion. The mathematical model is constructed by using regression method. The simulation results show that the proposed method is accurate and effective.

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Developing a robust methodology for assessing the value of weather/climate services

NASA Astrophysics Data System (ADS)

Krijnen, Justin; Golding, Nicola; Buontempo, Carlo

2016-04-01

Increasingly, scientists involved in providing weather and climate services are expected to demonstrate the value of their work for end users in order to justify the costs of developing and delivering these services. This talk will outline different approaches that can be used to assess the socio-economic benefits of weather and climate services, including, among others, willingness to pay and avoided costs. The advantages and limitations of these methods will be discussed and relevant case-studies will be used to illustrate each approach. The choice of valuation method may be influenced by different factors, such as resource and time constraints and the end purposes of the study. In addition, there are important methodological differences which will affect the value assessed. For instance the ultimate value of a weather/climate forecast to a decision-maker will not only depend on forecast accuracy but also on other factors, such as how the forecast is communicated to and consequently interpreted by the end-user. Thus, excluding these additional factors may result in inaccurate socio-economic value estimates. In order to reduce the inaccuracies in this valuation process we propose an approach that assesses how the initial weather/climate forecast information can be incorporated within the value chain of a given sector, taking into account value gains and losses at each stage of the delivery process. By this we aim to more accurately depict the socio-economic benefits of a weather/climate forecast to decision-makers.

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.

Project 1990: Educational Planning at the Metropolitan Level.

ERIC Educational Resources Information Center

Swanson, Austin D.; Lamitie, Robert E.

This paper describes a project designed to provide educational decisionmakers with projections of and forecasts about future metropolitan conditions and problems, and information about the implications of alternative ways of solving metropolitan problems. Project components included (1) population and economic projections and forecasts, (2)…

Forecasting Austrian national elections: The Grand Coalition model

PubMed Central

Aichholzer, Julian; Willmann, Johanna

2014-01-01

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

Policy implications of trends in Turkey's meat sector with respect to 2023 vision.

PubMed

Yavuz, Fahri; Bilgic, Abdulbaki; Terin, Mustafa; Guler, Irfan O

2013-12-01

Turkey has become one of the leading emerging economies in the world being second after China as the highest economically growing country with 8.9% economic growth rate in 2010. Forecasting impacts of this development in coming 10 years might have very important policy implications for the meat sector in the framework of 2013 vision of Turkey. In this study, annual time series data which contain several key variables of meat sector in last 26 years (1987-2012) are used to forecast the variables of the coming twelve years (2013-2024) to drive policy implications by considering the impacts of high economic growths, crises and major policy changes. Forecasted future values of the variables for 2023 in the sector are assessed and compared with recent national and international values to drive policy implications. The results show that the economic growth results in the increase in per capita income and thus increased demand for meat seemed to foster the meat sector. Therefore, these macroeconomic indicators need to be better in addition to improvements at micro level for establishing competitive meat sector and thus reaching aimed consumption level of meat. Copyright © 2013 Elsevier Ltd. All rights reserved.

Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power

NASA Astrophysics Data System (ADS)

Bracale, Antonio; Carpinelli, Guido; Di Fazio, Annarita; Khormali, Shahab

2014-01-01

Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

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

PubMed

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 μg/m(3)) with good compliance yielded the greatest risk reduction. At the highest threshold levels (50 μg/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.

Economic impact of large public programs: The NASA experience

NASA Technical Reports Server (NTRS)

Ginzburg, E.; Kuhn, J. W.; Schnee, J.; Yavitz, B.

1976-01-01

The economic impact of NASA programs on weather forecasting and the computer and semiconductor industries is discussed. Contributions to the advancement of the science of astronomy are also considered.

Weighing costs and losses: A decision making game using probabilistic forecasts

NASA Astrophysics Data System (ADS)

Werner, Micha; Ramos, Maria-Helena; Wetterhall, Frederik; Cranston, Michael; van Andel, Schalk-Jan; Pappenberger, Florian; Verkade, Jan

2017-04-01

Probabilistic forecasts are increasingly recognised as an effective and reliable tool to communicate uncertainties. The economic value of probabilistic forecasts has been demonstrated by several authors, showing the benefit to using probabilistic forecasts over deterministic forecasts in several sectors, including flood and drought warning, hydropower, and agriculture. Probabilistic forecasting is also central to the emerging concept of risk-based decision making, and underlies emerging paradigms such as impact-based forecasting. Although the economic value of probabilistic forecasts is easily demonstrated in academic works, its evaluation in practice is more complex. The practical use of probabilistic forecasts requires decision makers to weigh the cost of an appropriate response to a probabilistic warning against the projected loss that would occur if the event forecast becomes reality. In this paper, we present the results of a simple game that aims to explore how decision makers are influenced by the costs required for taking a response and the potential losses they face in case the forecast flood event occurs. Participants play the role of one of three possible different shop owners. Each type of shop has losses of quite different magnitude, should a flood event occur. The shop owners are presented with several forecasts, each with a probability of a flood event occurring, which would inundate their shop and lead to those losses. In response, they have to decide if they want to do nothing, raise temporary defences, or relocate their inventory. Each action comes at a cost; and the different shop owners therefore have quite different cost/loss ratios. The game was played on four occasions. Players were attendees of the ensemble hydro-meteorological forecasting session of the 2016 EGU Assembly, professionals participating at two other conferences related to hydrometeorology, and a group of students. All audiences were familiar with the principles of forecasting and water-related risks, and one of the audiences comprised a group of experts in probabilistic forecasting. Results show that the different shop owners do take the costs of taking action and the potential losses into account in their decisions. Shop owners with a low cost/loss ratio were found to be more inclined to take actions based on the forecasts, though the absolute value of the losses also increased the willingness to take action. Little differentiation was found between the different groups of players.

12 CFR 380.8 - Predominantly engaged in activities that are financial or incidental thereto.

Code of Federal Regulations, 2014 CFR

2014-01-01

.... (iii) Providing financial, investment, or economic advisory services, including advising an investment... managing a closed-end investment company; (2) Furnishing general economic information and advice, general economic statistical forecasting services, and industry studies; (3) Providing advice in connection with...

SEASAT economic assessment. Volume 3: Offshore oil and natural gas industry case study and generalization

NASA Technical Reports Server (NTRS)

1975-01-01

The economic benefits of improved ocean condition, weather and ice forecasts by SEASAT satellites to the exploration, development and production of oil and natural gas in the offshore regions are considered. The results of case studies which investigate the effects of forecast accuracy on offshore operations in the North Sea, the Celtic Sea, and the Gulf of Mexico are reported. A methodology for generalizing the results to other geographic regions of offshore oil and natural gas exploration and development is described.

Documentation of volume 3 of the 1978 Energy Information Administration annual report to congress

NASA Astrophysics Data System (ADS)

1980-02-01

In a preliminary overview of the projection process, the relationship between energy prices, supply, and demand is addressed. Topics treated in detail include a description of energy economic interactions, assumptions regarding world oil prices, and energy modeling in the long term beyond 1995. Subsequent sections present the general approach and methodology underlying the forecasts, and define and describe the alternative projection series and their associated assumptions. Short term forecasting, midterm forecasting, long term forecasting of petroleum, coal, and gas supplies are included. The role of nuclear power as an energy source is also discussed.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience.

PubMed

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the 'economic fundamentals' and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience

PubMed Central

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the ‘economic fundamentals’ and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications. PMID:28877204

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2012 CFR

2012-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2014 CFR

2014-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2011 CFR

2011-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2013 CFR

2013-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

Establishing an Environmental Scanning/Forecasting System to Augment College and University Planning.

ERIC Educational Resources Information Center

Morrison, James L.

1987-01-01

The major benefit of an environmental scanning/forecasting system is in providing critical information for strategic planning. Such a system allows the institution to detect social, technological, economic, and political trends and potential events. The environmental scanning database developed by United Way of America is described. (MLW)

77 FR 1761 - Self-Regulatory Organizations; NYSE Arca, Inc.; Order Granting Approval of Proposed Rule Change...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-11

... quantitative research and evaluation process that forecasts economic excess sector returns (over/under the... proprietary SectorSAM quantitative research and evaluation process. \\8\\ The following convictions constitute... Allocation Methodology'' (``SectorSAM''), which is a proprietary quantitative analysis, to forecast each...

76 FR 67403 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-01

..., making business decisions, developing economic models and forecasts, conducting economic research, and... of information under the provisions of the Paperwork Reduction Act (44 U.S.C. chapter 35). Agency: U.... Economic census statistics serve as part of the framework for the national accounts and provide essential...

Limitations of JEDI Models | Jobs and Economic Development Impact Models |

Science.gov Websites

precise forecast. The Jobs and Economic Development Impact (JEDI) models are input-output based models for assessing economic impacts and jobs, including JEDI (see Chapter 5, pp. 136-142). The most not reflect many other economic impacts that could affect real-world impacts on jobs from the project

Modeling the Components of an Economy as a Complex Adaptive System

DTIC Science & Technology

principles of constrained optimization and fails to see economic variables as part of an interconnected network. While tools for forecasting economic...data sets such as the stock market . This research portrays the stock market as one component of a networked system of economic variables, with the

The Economics of Industrial Preparedness Planning and Raw Materials Stockpiling

DTIC Science & Technology

1982-05-01

concepts are sketched on page 6. Chapter II 1. Marshall, Alfred, Principles of Economics , Guillebaud ed., pages 330, 465, 366, 372, 374, and 377. 2... Principles of Economics , Guillebaud Edition, Vol. 1. New York: The MacMillan Co, 1961. Martino, Joseph P., Technological Forecasting For Decision- making

A Methodology for Forecasting Damage & Economic Consequences to Floods: Building on the National Flood Interoperability Experiment (NFIE)

NASA Astrophysics Data System (ADS)

Tootle, G. A.; Gutenson, J. L.; Zhu, L.; Ernest, A. N. S.; Oubeidillah, A.; Zhang, X.

2015-12-01

The National Flood Interoperability Experiment (NFIE) held June 3-July 17, 2015 at the National Water Center (NWC) in Tuscaloosa, Alabama sought to demonstrate an increase in flood predictive capacity for the coterminous United States (CONUS). Accordingly, NFIE-derived technologies and workflows offer the ability to forecast flood damage and economic consequence estimates that coincide with the hydrologic and hydraulic estimations these physics-based models generate. A model providing an accurate prediction of damage and economic consequences is a valuable asset when allocating funding for disaster response, recovery, and relief. Damage prediction and economic consequence assessment also offer an adaptation planning mechanism for defending particularly valuable or vulnerable structures. The NFIE, held at the NWC on The University of Alabama (UA) campus led to the development of this large scale flow and inundation forecasting framework. Currently, the system can produce 15-hour lead-time forecasts for the entire coterminous United States (CONUS). A concept which is anticipated to become operational as of May 2016 within the NWC. The processing of such a large-scale, fine resolution model is accomplished in a parallel computing environment using large supercomputing clusters. Traditionally, flood damage and economic consequence assessment is calculated in a desktop computing environment with a ménage of meteorology, hydrology, hydraulic, and damage assessment tools. In the United States, there are a range of these flood damage/ economic consequence assessment software's available to local, state, and federal emergency management agencies. Among the more commonly used and freely accessible models are the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA), Flood Impact Assessment (HEC-FIA), and Federal Emergency Management Agency's (FEMA's) United States Multi-Hazard (Hazus-MH). All of which exist only in a desktop environment. With this, authors submit an initial framework for estimating damage and economic consequences to floods using flow and inundation products from the NFIE framework. This adaptive system utilizes existing nationwide datasets describing location and use of structures and can take assimilate a range of data resolutions.

Linked population and economic models: some methodological issues in forecasting, analysis, and policy optimization.

PubMed

Madden, M; Batey Pwj

1983-05-01

Some problems associated with demographic-economic forecasting include finding models appropriate for a declining economy with unemployment, using a multiregional approach in an interregional model, finding a way to show differential consumption while endogenizing unemployment, and avoiding unemployment inconsistencies. The solution to these problems involves the construction of an activity-commodity framework, locating it within a group of forecasting models, and indicating possible ratios towards dynamization of the framework. The authors demonstrate the range of impact multipliers that can be derived from the framework and show how these multipliers relate to Leontief input-output multipliers. It is shown that desired population distribution may be obtained by selecting instruments from the economic sphere to produce, through the constraints vector of an activity-commodity framework, targets selected from demographic activities. The next step in this process, empirical exploitation, was carried out by the authors in the United Kingdom, linking an input-output model with a wide selection of demographic and demographic-economic variables. The generally tenuous control which government has over any variables in systems of this type, especially in market economies, makes application in the policy field of the optimization approach a partly conjectural exercise, although the analytic capacity of the approach can provide clear indications of policy directions.

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

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Hock-Eam, Lim

2012-09-01

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

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Enhanced seasonal forecast skill following stratospheric sudden warmings

NASA Astrophysics Data System (ADS)

Sigmond, M.; Scinocca, J. F.; Kharin, V. V.; Shepherd, T. G.

2013-02-01

Advances in seasonal forecasting have brought widespread socio-economic benefits. However, seasonal forecast skill in the extratropics is relatively modest, prompting the seasonal forecasting community to search for additional sources of predictability. For over a decade it has been suggested that knowledge of the state of the stratosphere can act as a source of enhanced seasonal predictability; long-lived circulation anomalies in the lower stratosphere that follow stratospheric sudden warmings are associated with circulation anomalies in the troposphere that can last up to two months. Here, we show by performing retrospective ensemble model forecasts that such enhanced predictability can be realized in a dynamical seasonal forecast system with a good representation of the stratosphere. When initialized at the onset date of stratospheric sudden warmings, the model forecasts faithfully reproduce the observed mean tropospheric conditions in the months following the stratospheric sudden warmings. Compared with an equivalent set of forecasts that are not initialized during stratospheric sudden warmings, we document enhanced forecast skill for atmospheric circulation patterns, surface temperatures over northern Russia and eastern Canada and North Atlantic precipitation. We suggest that seasonal forecast systems initialized during stratospheric sudden warmings are likely to yield significantly greater forecast skill in some regions.

Small city synthesis of transportation planning and economic development : user's guide

DOT National Transportation Integrated Search

1999-10-01

Using Alice, Texas, as a model, a template has been developed to increase the cooperation and communication between transportation planning and economic development groups. The template establishes a foundation for coordinating traffic forecasts with...

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.

Assessment of economic vulnerability to infectious disease crises.

PubMed

Sands, Peter; El Turabi, Anas; Saynisch, Philip A; Dzau, Victor J

2016-11-12

Infectious disease crises have substantial economic impact. Yet mainstream macroeconomic forecasting rarely takes account of the risk of potential pandemics. This oversight contributes to persistent underestimation of infectious disease risk and consequent underinvestment in preparedness and response to infectious disease crises. One reason why economists fail to include economic vulnerability to infectious disease threats in their assessments is the absence of readily available and digestible input data to inform such analysis. In this Viewpoint we suggest an approach by which the global health community can help to generate such inputs, and a framework to use these inputs to assess the economic vulnerability to infectious disease crises of individual countries and regions. We argue that incorporation of these risks in influential macroeconomic analyses such as the reports from the International Monetary Fund's Article IV consultations, rating agencies and risk consultancies would simultaneously improve the quality of economic risk forecasting and reinforce individual government and donor incentives to mitigate infectious disease risks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of forecasting and inventory control of raw material supplies in PT INDAC INT’L

NASA Astrophysics Data System (ADS)

Lesmana, E.; Subartini, B.; Riaman; Jabar, D. A.

2018-03-01

This study discusses the data forecasting sales of carbon electrodes at PT. INDAC INT L uses winters and double moving average methods, while for predicting the amount of inventory and cost required in ordering raw material of carbon electrode next period using Economic Order Quantity (EOQ) model. The result of error analysis shows that winters method for next period gives result of MAE, MSE, and MAPE, the winters method is a better forecasting method for forecasting sales of carbon electrode products. So that PT. INDAC INT L is advised to provide products that will be sold following the sales amount by the winters method.

Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

Uncertainty estimation of long-range ensemble forecasts of snowmelt flood characteristics

NASA Astrophysics Data System (ADS)

Kuchment, L.

2012-04-01

Long-range forecasts of snowmelt flood characteristics with the lead time of 2-3 months have important significance for regulation of flood runoff and mitigation of flood damages at almost all large Russian rivers At the same time, the application of current forecasting techniques based on regression relationships between the runoff volume and the indexes of river basin conditions can lead to serious errors in forecasting resulted in large economic losses caused by wrong flood regulation. The forecast errors can be caused by complicated processes of soil freezing and soil moisture redistribution, too high rate of snow melt, large liquid precipitation before snow melt. or by large difference of meteorological conditions during the lead-time periods from climatologic ones. Analysis of economic losses had shown that the largest damages could, to a significant extent, be avoided if the decision makers had an opportunity to take into account predictive uncertainty and could use more cautious strategies in runoff regulation. Development of methodology of long-range ensemble forecasting of spring/summer floods which is based on distributed physically-based runoff generation models has created, in principle, a new basis for improving hydrological predictions as well as for estimating their uncertainty. This approach is illustrated by forecasting of the spring-summer floods at the Vyatka River and the Seim River basins. The application of the physically - based models of snowmelt runoff generation give a essential improving of statistical estimates of the deterministic forecasts of the flood volume in comparison with the forecasts obtained from the regression relationships. These models had been used also for the probabilistic forecasts assigning meteorological inputs during lead time periods from the available historical daily series, and from the series simulated by using a weather generator and the Monte Carlo procedure. The weather generator consists of the stochastic models of daily temperature and precipitation. The performance of the probabilistic forecasts were estimated by the ranked probability skill scores. The application of Monte Carlo simulations using weather generator has given better results then using the historical meteorological series.

12 CFR Appendix A to Part 242 - Financial Activities for Purposes of Title I of the Dodd-Frank Act

Code of Federal Regulations, 2014 CFR

2014-01-01

... broker for purposes of the foregoing, in any state. (c) Providing financial, investment, or economic..., organizing, and managing a closed-end investment company; (ii) Furnishing general economic information and advice, general economic statistical forecasting services, and industry studies; (iii) Providing advice...

A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Reale, O.; Atlas, R.; Jusem, J. C.

2004-01-01

Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.

The Health Equity and Effectiveness of Policy Options to Reduce Dietary Salt Intake in England: Policy Forecast.

PubMed

Gillespie, Duncan O S; Allen, Kirk; Guzman-Castillo, Maria; Bandosz, Piotr; Moreira, Patricia; McGill, Rory; Anwar, Elspeth; Lloyd-Williams, Ffion; Bromley, Helen; Diggle, Peter J; Capewell, Simon; O'Flaherty, Martin

2015-01-01

Public health action to reduce dietary salt intake has driven substantial reductions in coronary heart disease (CHD) over the past decade, but avoidable socio-economic differentials remain. We therefore forecast how further intervention to reduce dietary salt intake might affect the overall level and inequality of CHD mortality. We considered English adults, with socio-economic circumstances (SEC) stratified by quintiles of the Index of Multiple Deprivation. We used IMPACTSEC, a validated CHD policy model, to link policy implementation to salt intake, systolic blood pressure and CHD mortality. We forecast the effects of mandatory and voluntary product reformulation, nutrition labelling and social marketing (e.g., health promotion, education). To inform our forecasts, we elicited experts' predictions on further policy implementation up to 2020. We then modelled the effects on CHD mortality up to 2025 and simultaneously assessed the socio-economic differentials of effect. Mandatory reformulation might prevent or postpone 4,500 (2,900-6,100) CHD deaths in total, with the effect greater by 500 (300-700) deaths or 85% in the most deprived than in the most affluent. Further voluntary reformulation was predicted to be less effective and inequality-reducing, preventing or postponing 1,500 (200-5,000) CHD deaths in total, with the effect greater by 100 (-100-600) deaths or 49% in the most deprived than in the most affluent. Further social marketing and improvements to labelling might each prevent or postpone 400-500 CHD deaths, but minimally affect inequality. Mandatory engagement with industry to limit salt in processed-foods appears a promising and inequality-reducing option. For other policy options, our expert-driven forecast warns that future policy implementation might reach more deprived individuals less well, limiting inequality reduction. We therefore encourage planners to prioritise equity.

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.

The weather roulette: assessing the economic value of seasonal wind speed predictions

NASA Astrophysics Data System (ADS)

Christel, Isadora; Cortesi, Nicola; Torralba-Fernandez, Veronica; Soret, Albert; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco

2016-04-01

Climate prediction is an emerging and highly innovative research area. For the wind energy sector, predicting the future variability of wind resources over the coming weeks or seasons is especially relevant to quantify operation and maintenance logistic costs or to inform energy trading decision with potential cost savings and/or economic benefits. Recent advances in climate predictions have already shown that probabilistic forecasting can improve the current prediction practices, which are based in the use of retrospective climatology and the assumption that what happened in the past is the best estimation of future conditions. Energy decision makers now have this new set of climate services but, are they willing to use them? Our aim is to properly explain the potential economic benefits of adopting probabilistic predictions, compared with the current practice, by using the weather roulette methodology (Hagedorn & Smith, 2009). This methodology is a diagnostic tool created to inform in a more intuitive and relevant way about the skill and usefulness of a forecast in the decision making process, by providing an economic and financial oriented assessment of the benefits of using a particular forecast system. We have selected a region relevant to the energy stakeholders where the predictions of the EUPORIAS climate service prototype for the energy sector (RESILIENCE) are skillful. In this region, we have applied the weather roulette to compare the overall prediction success of RESILIENCE's predictions and climatology illustrating it as an effective interest rate, an economic term that is easier to understand for energy stakeholders.

Forecasting electricity usage using univariate time series models

NASA Astrophysics Data System (ADS)

Hock-Eam, Lim; Chee-Yin, Yip

2014-12-01

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

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

PubMed

Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Thorburn, Peter J; Castellano, Michael J; Moore, Kenneth J; VanLoocke, Andrew; Heaton, Emily A; Archontoulis, Sotirios V

2018-01-01

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time ( R 2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity ( R 2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined ( n = 31) with an average error range of ±38 kg N ha -1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

PubMed Central

Puntel, Laila A.; Sawyer, John E.; Barker, Daniel W.; Thorburn, Peter J.; Castellano, Michael J.; Moore, Kenneth J.; VanLoocke, Andrew; Heaton, Emily A.; Archontoulis, Sotirios V.

2018-01-01

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost. PMID:29706974

Looking Forward: Visions of the Future of the South. The 1984 Report of the Committee on Southern Trends of the Southern Growth Policies Board.

ERIC Educational Resources Information Center

Rosenfeld, Stuart A., Ed.

Written for Southern policymakers, this report forecasts economic changes in the South. It addresses demographic factors, traditional economic concerns, and emerging economic realities which already influence, or are likely to influence, economic life in the South. Trends and issues include: (1) the aging of the population largely due to retirees…

Economic Impact of Fire Weather Forecasts

Treesearch

Don Gunasekera; Graham Mills; Mark Williams

2006-01-01

Southeastern Australia, where the State of Victoria is located is regarded as one of the most fire prone areas in the world. The Australian Bureau of Meteorology provides fire weather services in Victoria as part of a national framework for the provision of such services. These services range from fire weather warnings to special forecasts for hazard reduction burns....

Futures Research and the Strategic Planning Process: Implications for Higher Education. ASHE-ERIC Higher Education Research Report No. 9, 1984.

ERIC Educational Resources Information Center

Morrison, James L.; And Others

The use of futures research to improve a college's ability to deal with changes brought about by social, economic, political, and technological developments is discussed, with attention to new planning strategies and forecasting methods. While traditional long-range planning tracks and forecasts the institution's internal development, strategic…

Hydro-economic assessment of hydrological forecasting systems

NASA Astrophysics Data System (ADS)

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

2012-01-01

SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

Forecasting Electricity Prices in an Optimization Hydrothermal Problem

NASA Astrophysics Data System (ADS)

Matías, J. M.; Bayón, L.; Suárez, P.; Argüelles, A.; Taboada, J.

2007-12-01

This paper presents an economic dispatch algorithm in a hydrothermal system within the framework of a competitive and deregulated electricity market. The optimization problem of one firm is described, whose objective function can be defined as its profit maximization. Since next-day price forecasting is an aspect crucial, this paper proposes an efficient yet highly accurate next-day price new forecasting method using a functional time series approach trying to exploit the daily seasonal structure of the series of prices. For the optimization problem, an optimal control technique is applied and Pontryagin's theorem is employed.

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

PubMed Central

2014-01-01

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

Research of Coal Resources Reserves Prediction Based on GM (1, 1) Model

NASA Astrophysics Data System (ADS)

Xiao, Jiancheng

2018-01-01

Based on the forecast of China’s coal reserves, this paper uses the GM (1, 1) gray forecasting theory to establish the gray forecasting model of China’s coal reserves based on the data of China’s coal reserves from 2002 to 2009, and obtained the trend of coal resources reserves with the current economic and social development situation, and the residual test model is established, so the prediction model is more accurate. The results show that China’s coal reserves can ensure the use of production at least 300 years of use. And the results are similar to the mainstream forecast results, and that are in line with objective reality.

Climate Forecasts and Water Resource Management: Applications for a Developing Country

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

While the quantity of water on the planet earth is relatively constant, the demand for water is continuously increasing. Population growth leads to linear increases in water demand, and economic growth leads to further demand growth. Strzepek et al. calculate that with a United Nations mean population estimate of 8.5 billion people by 2025 and globally balanced economic growth, water use could increase by 70% over that time (Strzepek et al., 1995). For developing nations especially, supplying water for this growing demand requires the construction of new water supply infrastructure. The prospect of designing and constructing long life-span infrastructure is clouded by the uncertainty of future climate. The availability of future water resources is highly dependent on future climate. With realization of the nonstationarity of climate, responsible design emphasizes resiliency and robustness of water resource systems (IPCC, 1995; Gleick et al., 1999). Resilient systems feature multiple sources and complex transport and distribution systems, and so come at a high economic and environmental price. A less capital-intense alternative to creating resilient and robust water resource systems is the use of seasonal climate forecasts. Such forecasts provide adequate lead time and accuracy to allow water managers and water-based sectors such as agriculture or hydropower to optimize decisions for the expected water supply. This study will assess the use of seasonal climate forecasts from regional climate models as a method to improve water resource management in systems with limited water supply infrastructure

Effect of initial conditions of a catchment on seasonal streamflow prediction using ensemble streamflow prediction (ESP) technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar; Zammit, Christian; Hreinsson, Einar; Woods, Ross; Clark, Martyn; Hamlet, Alan

2013-04-01

Increased access to water is a key pillar of the New Zealand government plan for economic growths. Variable climatic conditions coupled with market drivers and increased demand on water resource result in critical decision made by water managers based on climate and streamflow forecast. Because many of these decisions have serious economic implications, accurate forecast of climate and streamflow are of paramount importance (eg irrigated agriculture and electricity generation). New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicate that the sensitivity of flow forecast to initial condition uncertainty is depend on the hydrological regime and season of forecast. However initial conditions do not have a large impact on seasonal flow uncertainties for snow dominated catchments. Further analysis indicates that this result is valid when the hindcast database is conditioned by ENSO classification. As a result hydrological forecasts based on ESP technique, where present initial conditions with histological forcing data are used may be plausible for New Zealand catchments.

Potential economic value of drought information to support early warning in Africa

NASA Astrophysics Data System (ADS)

Quiroga, S.; Iglesias, A.; Diz, A.; Garrote, L.

2012-04-01

We present a methodology to estimate the economic value of advanced climate information for food production in Africa under climate change scenarios. The results aim to facilitate better choices in water resources management. The methodology includes 4 sequential steps. First two contrasting management strategies (with and without early warning) are defined. Second, the associated impacts of the management actions are estimated by calculating the effect of drought in crop productivity under climate change scenarios. Third, the optimal management option is calculated as a function of the drought information and risk aversion of potential information users. Finally we use these optimal management simulations to compute the economic value of enhanced water allocation rules to support stable food production in Africa. Our results show how a timely response to climate variations can help reduce loses in food production. The proposed framework is developed within the Dewfora project (Early warning and forecasting systems to predict climate related drought vulnerability and risk in Africa) that aims to improve the knowledge on drought forecasting, warning and mitigation, and advance the understanding of climate related vulnerability to drought and to develop a prototype operational forecasting.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Operational value of ensemble streamflow forecasts for hydropower production: A Canadian case study

NASA Astrophysics Data System (ADS)

Boucher, Marie-Amélie; Tremblay, Denis; Luc, Perreault; François, Anctil

2010-05-01

Ensemble and probabilistic forecasts have many advantages over deterministic ones, both in meteorology and hydrology (e.g. Krzysztofowicz, 2001). Mainly, they inform the user on the uncertainty linked to the forecast. It has been brought to attention that such additional information could lead to improved decision making (e.g. Wilks and Hamill, 1995; Mylne, 2002; Roulin, 2007), but very few studies concentrate on operational situations involving the use of such forecasts. In addition, many authors have demonstrated that ensemble forecasts outperform deterministic forecasts in terms of performance (e.g. Jaun et al., 2005; Velazquez et al., 2009; Laio and Tamea, 2007). However, such performance is mostly assessed on the basis of numerical scoring rules, which compare the forecasts to the observations, and seldom in terms of management gains. The proposed case study adopts an operational point of view, on the basis that a novel forecasting system has value only if it leads to increase monetary and societal gains (e.g. Murphy, 1994; Laio and Tamea, 2007). More specifically, Environment Canada operational ensemble precipitation forecasts are used to drive the HYDROTEL distributed hydrological model (Fortin et al., 1995), calibrated on the Gatineau watershed located in Québec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Québec SOHO stochastic management optimization tool that automatically search for optimal operation decisions for the all reservoirs and hydropower plants located on the basin. The timeline of the study is the fall season of year 2003. This period is especially relevant because of high precipitations that nearly caused a major spill, and forced the preventive evacuation of a portion of the population located near one of the dams. We show that the use of the ensemble forecasts would have reduced the occurrence of spills and flooding, which is of particular importance for dams located in populous area, and increased hydropower production. The ensemble precipitation forecasts extend from March 1st of 2002 to December 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used within HYDROTEL in order to compare ensemble streamflow forecasts with their deterministic counterparts. Although this study does not incorporate all the sources of uncertainty, precipitation is certainly the most important input for hydrological modeling and conveys a great portion of the total uncertainty. References: Fortin, J.P., Moussa, R., Bocquillon, C. and Villeneuve, J.P. 1995: HYDROTEL, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, Revue des Sciences de l'Eau, 8(1), 94-124. Jaun, S., Ahrens, B., Walser, A., Ewen, T. and Schaer, C. 2008: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Natural Hazards and Earth System Sciences, 8 (2), 281-291. Krzysztofowicz, R. 2001: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9. Murphy, A.H. 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues, Meteorological Applications, 1, 69-73. Mylne, K.R. 2002: Decision-Making from probability forecasts based on forecast value, Meteorological Applications, 9, 307-315. Laio, F. and Tamea, S. 2007: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences, 11, 1267-1277. Roulin, E. 2007: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrology and Earth System Sciences, 11, 725-737. Velazquez, J.-A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V. and Anctil, F. 2009: An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrology and Earth System Sciences, 13(11), 2221-2231. Wilks, D.S. and Hamill, T.M. 1995: Potential economic value of ensemble-based surface weather forecasts, Monthly Weather Review, 123(12), 3565-3575.

Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models

NASA Astrophysics Data System (ADS)

Khin, Aye Aye; Chau, Wong Hong; Seong, Lim Chee; Bin, Raymond Ling Leh; Teng, Kevin Low Lock

2017-05-01

Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy.

SEASAT economic assessment. Volume 5: Coastal zones case study and generalization. [economic benefits of weather forecasting by SEASAT satellites to the coastal plains of the United States

NASA Technical Reports Server (NTRS)

1975-01-01

The economic losses sustained in the U.S. coastal zones were studied for the purpose of quantitatively establishing economic benefits as a consequence of improving the predictive quality of destructive phenomena in U.S. coastal zones. Improved prediction of hurricane landfall and improved experimental knowledge of hurricane seeding are discussed.

Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica

2017-12-01

Traditionally, navigation-related forecasts in central Europe cover short- to medium-range lead times linked to the travel times of vessels to pass the main waterway bottlenecks leaving the loading ports. Without doubt, this aspect is still essential for navigational users, but in light of the growing political intention to use the free capacity of the inland waterway transport in Europe, additional lead time supporting strategic decisions is more and more in demand. However, no such predictions offering extended lead times of several weeks up to several months currently exist for considerable parts of the European waterway network. This paper describes the set-up of a monthly to seasonal forecasting system for the German stretches of the international waterways of the Rhine, Danube and Elbe rivers. Two competitive forecast approaches have been implemented: the dynamical set-up forces a hydrological model with post-processed outputs from ECMWF general circulation model System 4, whereas the statistical approach is based on the empirical relationship (teleconnection) of global oceanic, climate and regional hydro-meteorological data with river flows. The performance of both forecast methods is evaluated in relation to the climatological forecast (ensemble of historical streamflow) and the well-known ensemble streamflow prediction approach (ESP, ensemble based on historical meteorology) using common performance indicators (correlation coefficient; mean absolute error, skill score; mean squared error, skill score; and continuous ranked probability, skill score) and an impact-based evaluation quantifying the potential economic gain. The following four key findings result from this study: (1) as former studies for other regions of central Europe indicate, the accuracy and/or skill of the meteorological forcing used has a larger effect than the quality of initial hydrological conditions for relevant stations along the German waterways. (2) Despite the predictive limitations on longer lead times in central Europe, this study reveals the existence of a valuable predictability of streamflow on monthly up to seasonal timescales along the Rhine, upper Danube and Elbe waterways, and the Elbe achieves the highest skill and economic value. (3) The more physically based and the statistical approach are able to improve the predictive skills and economic value compared to climatology and the ESP approach. The specific forecast skill highly depends on the forecast location, the lead time and the season. (4) Currently, the statistical approach seems to be most skilful for the three waterways investigated. The lagged relationship between the monthly and/or seasonal streamflow and the climatic and/or oceanic variables vary between 1 month (e.g. local precipitation, temperature and soil moisture) up to 6 months (e.g. sea surface temperature). Besides focusing on improving the forecast methodology, especially by combining the individual approaches, the focus is on developing useful forecast products on monthly to seasonal timescales for waterway transport and to operationalize the related forecasting service.

Summary results of the 2014-2015 DARPA Chikungunya challenge

DOE PAGES

Del Valle, Sara Y.; McMahon, Benjamin Hamilton; Asher, Jason; ...

2018-05-30

Here, emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting.

Summary results of the 2014-2015 DARPA Chikungunya challenge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Del Valle, Sara Y.; McMahon, Benjamin Hamilton; Asher, Jason

Here, emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting.

Multidisciplinary studies of the social, economic and political impact resulting from recent advances in satellite meteorology. Volume 6: Executive summary. [technological forecasting spacecraft control/attitude (inclination) -classical mechanics

NASA Technical Reports Server (NTRS)

1975-01-01

An assessment of the technological impact of modern satellite weather forecasting for the United States is presented. Topics discussed are: (1) television broadcasting of weather; (2) agriculture (crop production); (3) water resources; (4) urban development; (5) recreation; and (6) transportation.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

NASA Astrophysics Data System (ADS)

Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian

2016-08-01

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

Power Scaling of the Size Distribution of Economic Loss and Fatalities due to Hurricanes, Earthquakes, Tornadoes, and Floods in the USA

NASA Astrophysics Data System (ADS)

Tebbens, S. F.; Barton, C. C.; Scott, B. E.

2016-12-01

Traditionally, the size of natural disaster events such as hurricanes, earthquakes, tornadoes, and floods is measured in terms of wind speed (m/sec), energy released (ergs), or discharge (m3/sec) rather than by economic loss or fatalities. Economic loss and fatalities from natural disasters result from the intersection of the human infrastructure and population with the size of the natural event. This study investigates the size versus cumulative number distribution of individual natural disaster events for several disaster types in the United States. Economic losses are adjusted for inflation to 2014 USD. The cumulative number divided by the time over which the data ranges for each disaster type is the basis for making probabilistic forecasts in terms of the number of events greater than a given size per year and, its inverse, return time. Such forecasts are of interest to insurers/re-insurers, meteorologists, seismologists, government planners, and response agencies. Plots of size versus cumulative number distributions per year for economic loss and fatalities are well fit by power scaling functions of the form p(x) = Cx-β; where, p(x) is the cumulative number of events with size equal to and greater than size x, C is a constant, the activity level, x is the event size, and β is the scaling exponent. Economic loss and fatalities due to hurricanes, earthquakes, tornadoes, and floods are well fit by power functions over one to five orders of magnitude in size. Economic losses for hurricanes and tornadoes have greater scaling exponents, β = 1.1 and 0.9 respectively, whereas earthquakes and floods have smaller scaling exponents, β = 0.4 and 0.6 respectively. Fatalities for tornadoes and floods have greater scaling exponents, β = 1.5 and 1.7 respectively, whereas hurricanes and earthquakes have smaller scaling exponents, β = 0.4 and 0.7 respectively. The scaling exponents can be used to make probabilistic forecasts for time windows ranging from 1 to 1000 years. Forecasts show that on an annual basis, in the United States, the majority of events with 10 fatalities and greater are related to floods and tornadoes; while events with 100 fatalities and greater are less frequent and are dominated by hurricanes and earthquakes. Disaster mitigation strategies need to account for these differences.

Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

NASA Astrophysics Data System (ADS)

Brown, M. E.; Abbott, P. C.; Escobar, V. M.

2013-12-01

This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon Monitoring System (CMS). A general evaluation framework to determine the usefulness of the CMS products to various users and to the broader community interested in managing carbon is shown in Figure 2. The first step in conducting such an analysis is to develop an understanding of the history, institutions, behaviors and other factors setting the context of an application which CMS data products inform. Decision makers are identified (who may become early adopters), and the alternative decisions they might take are elaborated. Economic models informed by biophysical models would then predict the outcome of the engagement. The new information must then be linked to a revised decision, and that decision in turn must lead to better economic or social outcomes on average. The value of the information is estimated as the predicted increase in economic surplus (profit, cost, consumer welfare) or social outcome that is a direct result of that revised decision. Alternative Monte Carlo simulations would estimate averages of key outcomes under alternative circumstances, such as differing regulations or better data, hence capturing consequences of the changes induced. These approaches will be described in the context of NASA and satellite data.

Production data from five major geothermal fields in Nevada analysed using a physiostatistical algorithm developed for oil and gas: temperature decline forecasts and type curves

NASA Astrophysics Data System (ADS)

Kuzma, H. A.; Golubkova, A.; Eklund, C.

2015-12-01

Nevada has the second largest output of geothermal energy in the United States (after California) with 14 major power plants producing over 425 megawatts of electricity meeting 7% of the state's total energy needs. A number of wells, particularly older ones, have shown significant temperature and pressure declines over their lifetimes, adversely affecting economic returns. Production declines are almost universal in the oil and gas (O&G) industry. BetaZi (BZ) is a proprietary algorithm which uses a physiostatistical model to forecast production from the past history of O&G wells and to generate "type curves" which are used to estimate the production of undrilled wells. Although BZ was designed and calibrated for O&G, it is a general purpose diffusion equation solver, capable of modeling complex fluid dynamics in multi-phase systems. In this pilot study, it is applied directly to the temperature data from five Nevada geothermal fields. With the data appropriately normalized, BZ is shown to accurately predict temperature declines. The figure shows several examples of BZ forecasts using historic data from Steamboat Hills field near Reno. BZ forecasts were made using temperature on a normalized scale (blue) with two years of data held out for blind testing (yellow). The forecast is returned in terms of percentiles of probability (red) with the median forecast marked (solid green). Actual production is expected to fall within the majority of the red bounds 80% of the time. Blind tests such as these are used to verify that the probabilistic forecast can be trusted. BZ is also used to compute and accurate type temperature profile for wells that have yet to be drilled. These forecasts can be combined with estimated costs to evaluate the economics and risks of a project or potential capital investment. It is remarkable that an algorithm developed for oil and gas can accurately predict temperature in geothermal wells without significant recasting.

Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

NASA Astrophysics Data System (ADS)

Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

2013-04-01

The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of flood preparedness and crisis management for basins greater than 1.000 km2.

MULTIREGION: a simulation-forecasting model of BEA economic area population and employment. [Bureau of Economic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olsen, R.J.; Westley, G.W.; Herzog, H.W. Jr.

This report documents the development of MULTIREGION, a computer model of regional and interregional socio-economic development. The MULTIREGION model interprets the economy of each BEA economic area as a labor market, measures all activity in terms of people as members of the population (labor supply) or as employees (labor demand), and simultaneously simulates or forecasts the demands and supplies of labor in all BEA economic areas at five-year intervals. In general the outputs of MULTIREGION are intended to resemble those of the Water Resource Council's OBERS projections and to be put to similar planning and analysis purposes. This report hasmore » been written at two levels to serve the needs of multiple audiences. The body of the report serves as a fairly nontechnical overview of the entire MULTIREGION project; a series of technical appendixes provide detailed descriptions of the background empirical studies of births, deaths, migration, labor force participation, natural resource employment, manufacturing employment location, and local service employment used to construct the model.« less

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economicmore » dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.« less

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

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. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

Seasonal Climate Forecasts and Adoption by Agriculture

NASA Astrophysics Data System (ADS)

Garbrecht, Jurgen; Meinke, Holger; Sivakumar, Mannava V. K.; Motha, Raymond P.; Salinger, Michael J.

2005-06-01

Recent advances in atmospheric and ocean sciences and a better understanding of the global climate have led to skillful climate forecasts at seasonal to interannual timescales, even in midlatitudes. These scientific advances and forecasting capabilities have opened the door to practical applications that benefit society. The benefits include the reduction of weather/climate related risks and vulnerability, increased economic opportunities, enhanced food security, mitigation of adverse climate impacts, protection of environmental quality, and so forth. Agriculture in particular can benefit substantially from accurate long-lead seasonal climate forecasts. Indeed, agricultural production very much depends on weather, climate, and water availability, and unexpected departures from anticipated climate conditions can thwart the best laid management plans. Timely climate forecasts offer means to reduce losses in drought years, increase profitability in good years, deal more effectively with climate variability, and choose from targeted risk-management strategies. In addition to benefiting farmers, forecasts can also help marketing systems and downstream users prepare for anticipated production outcomes and associated consequences.

A cost of living longer: Projections of the effects of prospective mortality improvement on economic support ratios for 14 advanced economies.

PubMed

Parr, Nick; Li, Jackie; Tickle, Leonie

2016-07-01

The economic implications of increasing life expectancy are important concerns for governments in developed countries. The aims of this study were as follows: (i) to forecast mortality for 14 developed countries from 2010 to 2050, using the Poisson Common Factor Model; (ii) to project the effects of the forecast mortality patterns on support ratios; and (iii) to calculate labour force participation increases which could offset these effects. The forecast gains in life expectancy correlate negatively with current fertility. Pre-2050 support ratios are projected to fall most in Japan and east-central and southern Europe, and least in Sweden and Australia. A post-2050 recovery is projected for most east-central and southern European countries. The increases in labour force participation needed to counterbalance the effects of mortality improvement are greatest for Japan, Poland, and the Czech Republic, and least for the USA, Canada, Netherlands, and Sweden. The policy implications are discussed.

Gloom and doom? The future of marine capture fisheries

PubMed Central

Garcia, Serge M.; Grainger, Richard J. R.

2005-01-01

Predicting global fisheries is a high-order challenge but predictions have been made and updates are needed. Past forecasts, present trends and perspectives of key parameters of the fisheries—including potential harvest, state of stocks, supply and demand, trade, fishing technology and governance—are reviewed in detail, as the basis for new forecasts and forecasting performance assessment. The future of marine capture fisheries will be conditioned by the political, social and economic evolution of the world within which they operate. Consequently, recent global scenarios for the future world are reviewed, with the emphasis on fisheries. The main driving forces (e.g. global economic development, demography, environment, public awareness, information technology, energy, ethics) including aquaculture are described. Outlooks are provided for each aspect of the fishery sector. The conclusion puts these elements in perspective and offers the authors’ personal interpretation of the possible future pathway of fisheries, the uncertainty about it and the still unanswered questions of direct relevance in shaping that future. PMID:15713587

Impact of Brexit on the forest products industry of the United Kingdom and the rest of the world

Treesearch

Craig M. T. Johnston; Joseph Buongiorno

2016-01-01

The Global Forest Products Model was applied to forecast the effect of Brexit on the global forest products industry to2003 under two scenarios; an optimistic and pessimistic future storyline regarding the potential economic effect of Brexit. The forecasts integrated a range of gross domestic product growth rates using an average of the optimistic and...

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 3: Intensive use of living resources, agriculture. Part 3: The integrated impact of improved (ERS) information on US agricultural commodities

NASA Technical Reports Server (NTRS)

Seidel, A. D.

1974-01-01

The economic value of information produced by an assumed operational version of an earth resources survey satellite of the ERTS class is assessed. The theoretical capability of an ERTS system to provide improved agricultural forecasts is analyzed and this analysis is used as a reasonable input to the econometric methods derived by ECON. An econometric investigation into the markets for agricultural commodities is summarized. An overview of the effort including the objectives, scopes, and architecture of the analysis, and the estimation strategy employed is presented. The results and conclusions focus on the economic importance of improved crop forecasts, U.S. exports, and government policy operations. Several promising avenues of further investigation are suggested.

Assessment of seasonal soil moisture forecasts over Southern South America with emphasis on dry and wet events

NASA Astrophysics Data System (ADS)

Spennemann, Pablo; Rivera, Juan Antonio; Osman, Marisol; Saulo, Celeste; Penalba, Olga

2017-04-01

The importance of forecasting extreme wet and dry conditions from weeks to months in advance relies on the need to prevent considerable socio-economic losses, mainly in regions of large populations and where agriculture is a key value for the economies, like Southern South America (SSA). Therefore, to improve the understanding of the performance and uncertainties of seasonal soil moisture and precipitation forecasts over SSA, this study aims to: 1) perform a general assessment of the Climate Forecast System version-2 (CFSv2) soil moisture and precipitation forecasts; and 2) evaluate the CFSv2 ability to represent an extreme drought event merging observations with forecasted Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Anomalies (SSMA) based on GLDAS-2.0 simulations. Results show that both SPI and SSMA forecast skill are regionally and seasonally dependent. In general a fast degradation of the forecasts skill is observed as the lead time increases with no significant metrics for forecast lead times longer than 2 months. Based on the assessment of the 2008-2009 extreme drought event it is evident that the CFSv2 forecasts have limitations regarding the identification of drought onset, duration, severity and demise, considering both meteorological (SPI) and agricultural (SSMA) drought conditions. These results have some implications upon the use of seasonal forecasts to assist agricultural practices in SSA, given that forecast skill is still too low to be useful for lead times longer than 2 months.

Long term load forecasting accuracy in electric utility integrated resource planning

DOE PAGES

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.; ...

2018-05-23

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electricmore » utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.« less

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Long term load forecasting accuracy in electric utility integrated resource planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electricmore » utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.« less

Toward an Apartheid Economy?

ERIC Educational Resources Information Center

Freeman, Richard B.; And Others

1996-01-01

Suggests that in the last 20 years, the normal rate of inequality in the United States, except in the category of gender, has jumped. Cites specific examples of economic inequality and offers solutions to the problem. Responses are given by union representatives, economic researchers, the Secretary of Labor, a financial forecaster, and a bank…

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

The Health Equity and Effectiveness of Policy Options to Reduce Dietary Salt Intake in England: Policy Forecast

PubMed Central

Gillespie, Duncan O. S.; Allen, Kirk; Guzman-Castillo, Maria; Bandosz, Piotr; Moreira, Patricia; McGill, Rory; Anwar, Elspeth; Lloyd-Williams, Ffion; Bromley, Helen; Diggle, Peter J.; Capewell, Simon; O’Flaherty, Martin

2015-01-01

Background Public health action to reduce dietary salt intake has driven substantial reductions in coronary heart disease (CHD) over the past decade, but avoidable socio-economic differentials remain. We therefore forecast how further intervention to reduce dietary salt intake might affect the overall level and inequality of CHD mortality. Methods We considered English adults, with socio-economic circumstances (SEC) stratified by quintiles of the Index of Multiple Deprivation. We used IMPACTSEC, a validated CHD policy model, to link policy implementation to salt intake, systolic blood pressure and CHD mortality. We forecast the effects of mandatory and voluntary product reformulation, nutrition labelling and social marketing (e.g., health promotion, education). To inform our forecasts, we elicited experts’ predictions on further policy implementation up to 2020. We then modelled the effects on CHD mortality up to 2025 and simultaneously assessed the socio-economic differentials of effect. Results Mandatory reformulation might prevent or postpone 4,500 (2,900–6,100) CHD deaths in total, with the effect greater by 500 (300–700) deaths or 85% in the most deprived than in the most affluent. Further voluntary reformulation was predicted to be less effective and inequality-reducing, preventing or postponing 1,500 (200–5,000) CHD deaths in total, with the effect greater by 100 (−100–600) deaths or 49% in the most deprived than in the most affluent. Further social marketing and improvements to labelling might each prevent or postpone 400–500 CHD deaths, but minimally affect inequality. Conclusions Mandatory engagement with industry to limit salt in processed-foods appears a promising and inequality-reducing option. For other policy options, our expert-driven forecast warns that future policy implementation might reach more deprived individuals less well, limiting inequality reduction. We therefore encourage planners to prioritise equity. PMID:26131981

The regional geological hazard forecast based on rainfall and WebGIS in Hubei, China

NASA Astrophysics Data System (ADS)

Zheng, Guizhou; Chao, Yi; Xu, Hongwen

2008-10-01

Various disasters have been a serious threat to human and are increasing over time. The reduction and prevention of hazard is the largest problem faced by local governments. The study of disasters has drawn more and more attention mainly due to increasing awareness of the socio-economic impact of disasters. Hubei province, one of the highest economic developing provinces in China, suffered big economic losses from geo-hazards in recent years due to frequent geo-hazard events with the estimated damage of approximately 3000 million RMB. It is therefore important to establish an efficient way to mitigate potential damage and reduce losses of property and life derived from disasters. This paper presents the procedure of setting up a regional geological hazard forecast and information releasing system of Hubei province with the combination of advanced techniques such as World Wide Web (WWW), database online and ASP based on WEBGIS platform (MAPGIS-IMS) and rainfall information. A Web-based interface was developed using a three-tiered architecture based on client-server technology in this system. The study focused on the upload of the rainfall data, the definition of rainfall threshold values, the creation of geological disaster warning map and the forecast of geohazard relating to the rainfall. Its purposes are to contribute to the management of mass individual and regional geological disaster spatial data, help to forecast the conditional probabilities of occurrence of various disasters that might be posed by the rainfall, and release forecasting information of Hubei province timely via the internet throughout all levels of government, the private and nonprofit sectors, and the academic community. This system has worked efficiently and stably in the internet environment which is strongly connected with meteorological observatory. Environment Station of Hubei Province are making increased use of our Web-tool to assist in the decision-making process to analyze geo-hazard in Hubei Province. It would be more helpful to present the geo-hazard information for Hubei administrator.

Innovative Ideas in Introductory Economics. Volume 2. A Report Developed from the 1979-80 College and University Level Entries in the International Paper Company Foundation Awards Program for the Teaching of Economics.

ERIC Educational Resources Information Center

Nappi, Andrew T., Ed.; Suglia, Anthony F., Ed.

Award winning projects in K-12 and college level economics are described in this publication. There are two major sections. Section I describes winning projects for 1979-80. A senior research seminar in economics offered undergraduate students a chance to build inexpensive, simplified forecasting models of the U.S. economy. Each student develops…

An operational procedure for rapid flood risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Feyen, Luc

2017-07-01

The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Biological Invasions: A Challenge In Ecological Forecasting

NASA Technical Reports Server (NTRS)

Schnase, J. L.; Smith, J. A.; Stohlgren, T. J.; Graves, S.; Trees, C.; Rood, Richard (Technical Monitor)

2002-01-01

The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being considered by NASA's Earth Science Vision for 2025. The invasive species problem is complex and presents many challenges. Developing an invasive species predictive capability could significantly advance the science and technology of ecological forecasting.

[Forecast of costs of ecodependent cancer treatment for the development of management decisions].

PubMed

Krasovskiy, V O

2014-01-01

The methodical approach for probabilistic forecasting and differentiation of treatment of costs of ecodependent cancer cases has been elaborated. The modality is useful in the organization of medical aid to cancer patients, in developing management decisions for the reduction the occupational load on the population, as well as in solutions problems in compensation to the population economic and social loss from industrial plants.

The FY2014 Government Shutdown: Economic Effects

DTIC Science & Technology

2013-11-01

caused a decline in consumer, business, or investor confidence, it could have led consumers and businesses to postpone or cancel spending decisions...Even if lawmakers come to terms roughly as expected , political vitriol and repeated threats to shut government or not pay its bills have weighed...growth.14 Most forecasts taken before the shutdown expected a moderate pace of growth in the fourth quarter, and forecasters projected a slightly more

Handbook of Forecasting Techniques

DTIC Science & Technology

1975-12-01

economics , political science, and the other behavioral and social sciences is centrally dependent upon or makes heavy use of treL.d analysis and...General Equilibrium Analysis "The economic activities within a region may at times be grouped into a number of industries or sectors in order to facilitate... economic analysis . These sectors will not generally be independent of each other as there will usually be flows of goods and services between the

Mathematical model comparing of the multi-level economics systems

NASA Astrophysics Data System (ADS)

Brykalov, S. M.; Kryanev, A. V.

2017-12-01

The mathematical model (scheme) of a multi-level comparison of the economic system, characterized by the system of indices, is worked out. In the mathematical model of the multi-level comparison of the economic systems, the indicators of peer review and forecasting of the economic system under consideration can be used. The model can take into account the uncertainty in the estimated values of the parameters or expert estimations. The model uses the multi-criteria approach based on the Pareto solutions.

Jobs and Economic Development from New Transmission and Generation in Wyoming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lantz, E.; Tegen, S.

2011-03-01

This report is intended to inform policymakers, local government officials, and Wyoming residents about the jobs and economic development activity that could occur should new infrastructure investments in Wyoming move forward. The report and analysis presented is not a projection or a forecast of what will happen. Instead, the report uses a hypothetical deployment scenario and economic modeling tools to estimate the jobs and economic activity likely associated with these projects if or when they are built.

Jobs and Economic Development from New Transmission and Generation in Wyoming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lantz, Eric; Tegen, Suzanne

2011-03-31

This report is intended to inform policymakers, local government officials, and Wyoming residents about the jobs and economic development activity that could occur should new infrastructure investments in Wyoming move forward. The report and analysis presented is not a projection or a forecast of what will happen. Instead, the report uses a hypothetical deployment scenario and economic modeling tools to estimate the jobs and economic activity likely associated with these projects if or when they are built.

Medium- and long-term electric power demand forecasting based on the big data of smart city

NASA Astrophysics Data System (ADS)

Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

2017-08-01

Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

Forecasting the development of nanotechnology with the help of science and technology indicators

NASA Astrophysics Data System (ADS)

Compañó, Ramón; Hullmann, Angela

2002-06-01

Nanotechnology is supposed to become one of the key enabling technologies of the 21st century. Its economic potential is forecast to be a market of several hundred billion Euros in the next decade. Therefore, nanotechnology has attracted the interest of many industry sectors and many companies redirecting internal activities to prepare themselves for this new challenge. At the same time governmental R&D decision makers all over the world are setting up new nanotechnology-specific research programmes aiming at putting their respective countries in a favourable position for the future. The aim of this paper is to use scientific and technological indicators to make predictions on economic development and to compare the situation in different countries.

76 FR 8330 - Hawaii Bottomfish and Seamount Groundfish Fisheries; Modification to Advance Notification Period...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-14

... precision in forecasting the closure date, and also facilitate adherence to the catch limit by reducing the... Business Administration that this proposed rule, if adopted, would not have a significant economic impact... disproportionate economic impacts between large and small entities, and the proposed action is not expected to have...

Multi-step-ahead crude oil price forecasting using a hybrid grey wave model

NASA Astrophysics Data System (ADS)

Chen, Yanhui; Zhang, Chuan; He, Kaijian; Zheng, Aibing

2018-07-01

Crude oil is crucial to the operation and economic well-being of the modern society. Huge changes of crude oil price always cause panics to the global economy. There are many factors influencing crude oil price. Crude oil price prediction is still a difficult research problem widely discussed among researchers. Based on the researches on Heterogeneous Market Hypothesis and the relationship between crude oil price and macroeconomic factors, exchange market, stock market, this paper proposes a hybrid grey wave forecasting model, which combines Random Walk (RW)/ARMA to forecast multi-step-ahead crude oil price. More specifically, we use grey wave forecasting model to model the periodical characteristics of crude oil price and ARMA/RW to simulate the daily random movements. The innovation also comes from using the information of the time series graph to forecast crude oil price, since grey wave forecasting is a graphical prediction method. The empirical results demonstrate that based on the daily data of crude oil price, the hybrid grey wave forecasting model performs well in 15- to 20-step-ahead prediction and it always dominates ARMA and Random Walk in correct direction prediction.

Attributing Predictable Signals at Subseasonal Timescales

NASA Astrophysics Data System (ADS)

Shelly, A.; Norton, W.; Rowlands, D.; Beech-Brandt, J.

2016-12-01

Subseasonal forecasts offer significant economic value in the management of energy infrastructure and through the associated financial markets. Models are now accurate enough to provide, for some occasions, good forecasts in the subseasonal range. However, it is often not clear what the drivers of these subseasonal signals are and if the forecasts could be more accurate with better representation of physical processes. Also what are the limits of predictability in the subseasonal range? To address these questions, we have run the ECMWF monthly forecast system over the 2015/16 winter with a set of 6 week ensemble integrations initialised every week over the period. In these experiments, we have relaxed the band 15N to 15S to reanalysis fields. Hence, we have a set of forecasts where the tropics is constrained to actual events and we can analyse the changes in predictability in middle latitudes - in particular in regions of high energy consumption like North America and Europe. Not surprisingly, the forecast of some periods are significantly improved while others show no improvement. We discuss events/patterns that have extended range predictability and also the tropical forecast errors which prevent the potential predictability in middle latitudes from being realised.

Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles

NASA Astrophysics Data System (ADS)

Hu, Qi; Pytlik Zillig, Lisa M.; Lynne, Gary D.; Tomkins, Alan J.; Waltman, William J.; Hayes, Michael J.; Hubbard, Kenneth G.; Artikov, Ikrom; Hoffman, Stacey J.; Wilhite, Donald A.

2006-09-01

Although the accuracy of weather and climate forecasts is continuously improving and new information retrieved from climate data is adding to the understanding of climate variation, use of the forecasts and climate information by farmers in farming decisions has changed little. This lack of change may result from knowledge barriers and psychological, social, and economic factors that undermine farmer motivation to use forecasts and climate information. According to the theory of planned behavior (TPB), the motivation to use forecasts may arise from personal attitudes, social norms, and perceived control or ability to use forecasts in specific decisions. These attributes are examined using data from a survey designed around the TPB and conducted among farming communities in the region of eastern Nebraska and the western U.S. Corn Belt. There were three major findings: 1) the utility and value of the forecasts for farming decisions as perceived by farmers are, on average, around 3.0 on a 0 7 scale, indicating much room to improve attitudes toward the forecast value. 2) The use of forecasts by farmers to influence decisions is likely affected by several social groups that can provide “expert viewpoints” on forecast use. 3) A major obstacle, next to forecast accuracy, is the perceived identity and reliability of the forecast makers. Given the rapidly increasing number of forecasts in this growing service business, the ambiguous identity of forecast providers may have left farmers confused and may have prevented them from developing both trust in forecasts and skills to use them. These findings shed light on productive avenues for increasing the influence of forecasts, which may lead to greater farming productivity. In addition, this study establishes a set of reference points that can be used for comparisons with future studies to quantify changes in forecast use and influence.

An experimental system for flood risk forecasting and monitoring at global scale

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

2017-04-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.

Coupling Recruitment Forecasts with Economics in the Gulf of Maine's American Lobster Fishery

NASA Astrophysics Data System (ADS)

Wahle, R.; Oppenheim, N.; Brady, D. C.; Dayton, A.; Sun, C. H. J.

2016-02-01

Accurate predictions of fishery recruitment and landings represent an important goal of fisheries science and management, but linking environmental drivers of fish population dynamics to financial markets remains a challenge. A fundamental step in that process is understanding the environmental drivers of fishery recruitment. American lobster (Homarus americanus) populations of the northwest Atlantic have been undergoing a dramatic surge, mostly driven by increases the Gulf of Maine. Settler-recruit models that track cohorts after larvae settle to the sea bed are proving useful in predicting subsequent fishery recruitment some 5-7 years later. Here we describe new recruitment forecasting models for the lobster fishery at 11 management areas from Southern New England to Atlantic Canada. We use an annual survey of juvenile year-class strength and environmental indicators to parameterize growth and mortality terms in the model. As a consequence of a recent widespread multi-year downturn in larval settlement, our models suggest that the peak in lobster abundance in the Gulf of Maine will be passed in the near future. We also present initial steps in the coupling of forecast data with economic models for the fishery. We anticipate that these models will give stakeholders and policy makers time to consider their management choices for this most valuable of the region's fisheries. Our vision is to couple our forecast model outputs to an economic model that captures the dynamics of market forces in the New England and Canadian Maritime lobster fisheries. It will then be possible to estimate the financial status of the fishery several years in advance. This early warning system could mitigate the adverse effects of a fluctuating fishery on the coastal communities that are perilously dependent upon it.

Techniques for water demand analysis and forecasting: Puerto Rico, a case study

USGS Publications Warehouse

Attanasi, E.D.; Close, E.R.; Lopez, M.A.

1975-01-01

The rapid economic growth of the Commonwealth-of Puerto Rico since 1947 has brought public pressure on Government agencies for rapid development of public water supply and waste treatment facilities. Since 1945 the Puerto Rico Aqueduct and Sewer Authority has had the responsibility for planning, developing and operating water supply and waste treatment facilities on a municipal basis. The purpose of this study was to develop operational techniques whereby a planning agency, such as the Puerto Rico Aqueduct and Sewer Authority, could project the temporal and spatial distribution of .future water demands. This report is part of a 2-year cooperative study between the U.S. Geological Survey and the Environmental Quality Board of the Commonwealth of Puerto Rico, for the development of systems analysis techniques for use in water resources planning. While the Commonwealth was assisted in the development of techniques to facilitate ongoing planning, the U.S. Geological Survey attempted to gain insights in order to better interface its data collection efforts with the planning process. The report reviews the institutional structure associated with water resources planning for the Commonwealth. A brief description of alternative water demand forecasting procedures is presented and specific techniques and analyses of Puerto Rico demand data are discussed. Water demand models for a specific area of Puerto Rico are then developed. These models provide a framework for making several sets of water demand forecasts based on alternative economic and demographic assumptions. In the second part of this report, the historical impact of water resources investment on regional economic development is analyzed and related to water demand .forecasting. Conclusions and future data needs are in the last section.

Future Weather Forecasting in the Year 2020-Investing in Technology Today: Improving Weather and Environmental Predictions

NASA Technical Reports Server (NTRS)

Anthes, Richard; Schoeberl, Mark

2000-01-01

Fast-forward twenty years to the nightly simultaneous TV/webcast. Accurate 8-14 day regional forecasts will be available as will be a whole host of linked products including economic impact, travel, energy usage, etc. On-demand, personalized street-level forecasts will be downloaded into your PDA. Your home system will automatically update the products of interest to you (e.g. severe storm forecasts, hurricane predictions, etc). Short and long range climate forecasts will be used by your "Quicken 2020" to make suggest changes in your "futures" investment portfolio. Through a lively and informative multi-media presentation, leading Space-Earth Science Researchers and Technologists will share their vision for the year 2020, offering a possible futuristic forecast enabled through the application of new technologies under development today. Copies of the 'broadcast' will be available on Beta Tape for your own future use. If sufficient interest exists, the program may also be made available for broadcasters wishing to do stand-ups with roll-ins from the San Francisco meeting for their viewers back home.

Does money matter in inflation forecasting?

NASA Astrophysics Data System (ADS)

Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

2010-11-01

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

The Creation and Application of Two Innovative Real-Time Delphi and Cross-Impact Simulation Approaches to Forecast the Future: Forecasting High-Speed Broadband Developments for the State of Hawai`i

ERIC Educational Resources Information Center

Bergo, Rolv Alexander

2013-01-01

Technology development is moving rapidly and our dependence on information services is growing. Building a broadband infrastructure that can support future demand and change is therefore critical to social, political, economic and technological developments. It is often up to local policy makers to find the best solutions to support this demand…

Seasonal forecasting of dolphinfish distribution in eastern Australia to aid recreational fishers and managers

NASA Astrophysics Data System (ADS)

Brodie, Stephanie; Hobday, Alistair J.; Smith, James A.; Spillman, Claire M.; Hartog, Jason R.; Everett, Jason D.; Taylor, Matthew D.; Gray, Charles A.; Suthers, Iain M.

2017-06-01

Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish (Coryphaena hippurus), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management.

Influence of Forecast Accuracy of Photovoltaic Power Output on Facility Planning and Operation of Microgrid under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Kato, Takeyoshi; Sone, Akihito; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on the demonstrative studies on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization and daily operation evaluated with the cost. The main results are as follows. The required capacity of NaS battery must be increased by 10-40% against the ideal situation without the forecast error of PVS power output. The influence of forecast error on the received grid electricity would not be so significant on annual basis because the positive and negative forecast error varies with days. The annual total cost of facility and operation increases by 2-7% due to the forecast error applied in this study. The impact of forecast error on the facility optimization and operation optimization is almost the same each other at a few percentages, implying that the forecast accuracy should be improved in terms of both the number of times with large forecast error and the average error.

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.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

NASA Astrophysics Data System (ADS)

Arnal, Louise; Ramos, Maria-Helena; Coughlan, Erin; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk-Jan; Pappenberger, Florian

2016-04-01

Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic forecasts over deterministic forecasts for a diversity of activities in the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of flood protection mitigation, called ``How much are you prepared to pay for a forecast?'', will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game showed that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological forecasting community in the future.

Challenges for operational forecasting and early warning of rainfall induced landslides

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto

2017-04-01

In many areas of the world, landslides occur every year, claiming lives and producing severe economic and environmental damage. Many of the landslides with human or economic consequences are the result of intense or prolonged rainfall. For this reason, in many areas the timely forecast of rainfall-induced landslides is of both scientific interest and social relevance. In the recent years, there has been a mounting interest and an increasing demand for operational landslide forecasting, and for associated landslide early warning systems. Despite the relevance of the problem, and the increasing interest and demand, only a few systems have been designed, and are currently operated. Inspection of the - limited - literature on operational landslide forecasting, and on the associated early warning systems, reveals that common criteria and standards for the design, the implementation, the operation, and the evaluation of the performances of the systems, are lacking. This limits the possibility to compare and to evaluate the systems critically, to identify their inherent strengths and weaknesses, and to improve the performance of the systems. Lack of common criteria and of established standards can also limit the credibility of the systems, and consequently their usefulness and potential practical impact. Landslides are very diversified phenomena, and the information and the modelling tools used to attempt landslide forecasting vary largely, depending on the type and size of the landslides, the extent of the geographical area considered, the timeframe of the forecasts, and the scope of the predictions. Consequently, systems for landslide forecasting and early warning can be designed and implemented at several different geographical scales, from the local (site or slope specific) to the regional, or even national scale. The talk focuses on regional to national scale landslide forecasting systems, and specifically on operational systems based on empirical rainfall threshold models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.

Probabilistic Forecasting of Life and Economic Losses due to Natural Disasters

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Score Matrix for HWBI Forecast Model

EPA Pesticide Factsheets

2000-2010 Annual State-Scale Service and Domain scores used to support the approach for forecasting EPA's Human Well-Being Index. A modeling approach was developed based relationship function equations derived from select economic, social and ecosystem final goods and service scores and calculated human well-being index and related domain scores. These data are being used in a secondary capacity. The foundational data and scoring techniques were originally described in: a) U.S. EPA. 2012. Indicators and Methods for Constructing a U.S. Human Well-being Index (HWBI) for Ecosystem Services Research. Report. EPA/600/R-12/023. pp. 121; and b) U.S. EPA. 2014. Indicators and Methods for Evaluating Economic, Ecosystem and Social Services Provisioning. Report. EPA/600/R-14/184. pp. 174. Mode Smith, L. M., Harwell, L. C., Summers, J. K., Smith, H. M., Wade, C. M., Straub, K. R. and J.L. Case (2014).This dataset is associated with the following publication:Summers , K., L. Harwell , and L. Smith. A Model For Change: An Approach for Forecasting Well-Being From Service-Based Decisions. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 69: 295-309, (2016).

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

PubMed Central

Zhao, Xiuli; Yiranbon, Ethel

2014-01-01

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

Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model

PubMed Central

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

2011-01-01

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

Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model.

PubMed

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

2011-07-01

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

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

PubMed

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

2014-01-01

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

Improved potential fishing zone forecast along East coast of India.

NASA Astrophysics Data System (ADS)

Srinivasa Rao, N.; Rao, M. V.; Chowdhary, S. B.; Rao, K. H.; Ramana, I. V.

Marine fisheries provide support to millions of fishermen community in terms of their living and livelihood Remote sensing technology proved to be useful for successful fishing in reducing time fuel and manpower because of its synoptic coverage Potential Fishing Zone PFZ forecast is being provided to the fishing industry in near-real time since 1992 using SST data derived from NOAA AVHRR thermal IR channel Retrieval of chlorophyll and its mapping has been done over Bay of Bengal from IRS-P4 OCM data Synergetic study of SST and chlorophyll has been established and implemented recently for an improved PFZ forecast The validation of these forecasts revealed 2-3 fold increases in fish catch along east coast of India This program provides socio-economic benefits to the fishermen living all along the Indian coast

What might we learn from climate forecasts?

PubMed Central

Smith, Leonard A.

2002-01-01

Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them “better,” estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as “chaos” prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system. PMID:11875200

Thin-Slice Forecasts of Gubernatorial Elections

PubMed Central

Benjamin, Daniel J.; Shapiro, Jesse M.

2010-01-01

We showed 10-second, silent video clips of unfamiliar gubernatorial debates to a group of experimental participants and asked them to predict the election outcomes. The participants’ predictions explain more than 20 percent of the variation in the actual two-party vote share across the 58 elections in our study, and their importance survives a range of controls, including state fixed effects. In a horse race of alternative forecasting models, participants’ forecasts significantly outperform economic variables in predicting vote shares, and are comparable in predictive power to a measure of incumbency status. Participants’ forecasts seem to rest on judgments of candidates’ personal attributes (such as likeability), rather than inferences about candidates’ policy positions. Though conclusive causal inference is not possible in our context, our findings may be seen as suggestive evidence of a causal effect of candidate appeal on election outcomes. PMID:20431718

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

NASA Astrophysics Data System (ADS)

Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj

2012-05-01

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2013-01-01

Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of themore » day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.« less

Short term load forecasting of anomalous load using hybrid soft computing methods

NASA Astrophysics Data System (ADS)

Rasyid, S. A.; Abdullah, A. G.; Mulyadi, Y.

2016-04-01

Load forecast accuracy will have an impact on the generation cost is more economical. The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Particle Swarm proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers.

Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008-2015

NASA Astrophysics Data System (ADS)

Min, Young-Mi; Kryjov, Vladimir N.; Oh, Sang Myeong; Lee, Hyun-Ju

2017-12-01

This paper assesses the real-time 1-month lead forecasts of 3-month (seasonal) mean temperature and precipitation on a monthly basis issued by the Asia-Pacific Economic Cooperation Climate Center (APCC) for 2008-2015 (8 years, 96 forecasts). It shows the current level of the APCC operational multi-model prediction system performance. The skill of the APCC forecasts strongly depends on seasons and regions that it is higher for the tropics and boreal winter than for the extratropics and boreal summer due to direct effects and remote teleconnections from boundary forcings. There is a negative relationship between the forecast skill and its interseasonal variability for both variables and the forecast skill for precipitation is more seasonally and regionally dependent than that for temperature. The APCC operational probabilistic forecasts during this period show a cold bias (underforecasting of above-normal temperature and overforecasting of below-normal temperature) underestimating a long-term warming trend. A wet bias is evident for precipitation, particularly in the extratropical regions. The skill of both temperature and precipitation forecasts strongly depends upon the ENSO strength. Particularly, the highest forecast skill noted in 2015/2016 boreal winter is associated with the strong forcing of an extreme El Nino event. Meanwhile, the relatively low skill is associated with the transition and/or continuous ENSO-neutral phases of 2012-2014. As a result the skill of real-time forecast for boreal winter season is higher than that of hindcast. However, on average, the level of forecast skill during the period 2008-2015 is similar to that of hindcast.

Influence of Forecast Accuracy of Photovoltaic Power Output on Capacity Optimization of Microgrid Composition under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Sone, Akihito; Kato, Takeyoshi; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). If a number of MGs are controlled to maintain the predetermined electricity demand including RE-based DGs as negative demand, they would contribute to supply-demand balancing of whole electric power system. For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on a demonstrative study on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization. Three forecast cases with different accuracy are compared. The main results are as follows. Even with no forecast error during every 30 min. as the ideal forecast method, the required capacity of NaS battery reaches about 40% of PVS capacity for mitigating the instantaneous forecast error within 30 min. The required capacity to compensate for the forecast error is doubled with the actual forecast method. The influence of forecast error can be reduced by adjusting the scheduled power output of controllable DGs according to the weather forecast. Besides, the required capacity can be reduced significantly if the error of balancing control in a MG is acceptable for a few percentages of periods, because the total periods of large forecast error is not so often.

When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes

PubMed Central

Yoon, Carolyn

2017-01-01

Although traditional economic and psychological theories imply that individual choice best scales to aggregate choice, primary components of choice reflected in neural activity may support even more generalizable forecasts. Crowdfunding represents a significant and growing platform for funding new and unique projects, causes, and products. To test whether neural activity could forecast market-level crowdfunding outcomes weeks later, 30 human subjects (14 female) decided whether to fund proposed projects described on an Internet crowdfunding website while undergoing scanning with functional magnetic resonance imaging. Although activity in both the nucleus accumbens (NAcc) and medial prefrontal cortex predicted individual choices to fund on a trial-to-trial basis in the neuroimaging sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Internet. Behavioral measures from the neuroimaging sample, however, did not forecast market funding outcomes. This pattern of associations was replicated in a second study. These findings demonstrate that a subset of the neural predictors of individual choice can generalize to forecast market-level crowdfunding outcomes—even better than choice itself. SIGNIFICANCE STATEMENT Forecasting aggregate behavior with individual neural data has proven elusive; even when successful, neural forecasts have not historically supplanted behavioral forecasts. In the current research, we find that neural responses can forecast market-level choice and outperform behavioral measures in a novel Internet crowdfunding context. Targeted as well as model-free analyses convergently indicated that nucleus accumbens activity can support aggregate forecasts. Beyond providing initial evidence for neuropsychological processes implicated in crowdfunding choices, these findings highlight the ability of neural features to forecast aggregate choice, which could inform applications relevant to business and policy. PMID:28821681

Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hongyu; Krad, Ibrahim; Florita, Anthony

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conductedmore » in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.« less

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.

Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

NASA Astrophysics Data System (ADS)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

Economic Models for Projecting Industrial Capacity for Defense Production: A Review

DTIC Science & Technology

1983-02-01

macroeconomic forecast to establish the level of civilian final demand; all use the DoD Bridge Table to allocate budget category outlays to industries. Civilian...output table.’ 3. Macroeconomic Assumptions and the Prediction of Final Demand All input-output models require as a starting point a prediction of final... macroeconomic fore- cast of GNP and its components and (2) a methodology to transform these forecast values of consumption, investment, exports, etc. into

Post-processing of a low-flow forecasting system in the Thur basin (Switzerland)

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Joerg-Hess, Stefanie; Bernhard, Luzi; Zappa, Massimiliano

2015-04-01

Low-flows and droughts are natural hazards with potentially severe impacts and economic loss or damage in a number of environmental and socio-economic sectors. As droughts develop slowly there is time to prepare and pre-empt some of these impacts. Real-time information and forecasting of a drought situation can therefore be an effective component of drought management. Although Switzerland has traditionally been more concerned with problems related to floods, in recent years some unprecedented low-flow situations have been experienced. Driven by the climate change debate a drought information platform has been developed to guide water resources management during situations where water resources drop below critical low-flow levels characterised by the indices duration (time between onset and offset), severity (cumulative water deficit) and magnitude (severity/duration). However to gain maximum benefit from such an information system it is essential to remove the bias from the meteorological forecast, to derive optimal estimates of the initial conditions, and to post-process the stream-flow forecasts. Quantile mapping methods for pre-processing the meteorological forecasts and improved data assimilation methods of snow measurements, which accounts for much of the seasonal stream-flow predictability for the majority of the basins in Switzerland, have been tested previously. The objective of this study is the testing of post-processing methods in order to remove bias and dispersion errors and to derive the predictive uncertainty of a calibrated low-flow forecast system. Therefore various stream-flow error correction methods with different degrees of complexity have been applied and combined with the Hydrological Uncertainty Processor (HUP) in order to minimise the differences between the observations and model predictions and to derive posterior probabilities. The complexity of the analysed error correction methods ranges from simple AR(1) models to methods including wavelet transformations and support vector machines. These methods have been combined with forecasts driven by Numerical Weather Prediction (NWP) systems with different temporal and spatial resolutions, lead-times and different numbers of ensembles covering short to medium to extended range forecasts (COSMO-LEPS, 10-15 days, monthly and seasonal ENS) as well as climatological forecasts. Additionally the suitability of various skill scores and efficiency measures regarding low-flow predictions will be tested. Amongst others the novel 2afc (2 alternatives forced choices) score and the quantile skill score and its decompositions will be applied to evaluate the probabilistic forecasts and the effects of post-processing. First results of the performance of the low-flow predictions of the hydrological model PREVAH initialised with different NWP's will be shown.

Airfreight forecasting methodology and results

NASA Technical Reports Server (NTRS)

1978-01-01

A series of econometric behavioral equations was developed to explain and forecast the evolution of airfreight traffic demand for the total U.S. domestic airfreight system, the total U.S. international airfreight system, and the total scheduled international cargo traffic carried by the top 44 foreign airlines. The basic explanatory variables used in these macromodels were the real gross national products of the countries involved and a measure of relative transportation costs. The results of the econometric analysis reveal that the models explain more than 99 percent of the historical evolution of freight traffic. The long term traffic forecasts generated with these models are based on scenarios of the likely economic outlook in the United States and 31 major foreign countries.

Forecasting carbon dioxide emissions.

PubMed

Zhao, Xiaobing; Du, Ding

2015-09-01

This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Economic evaluation of a solar hot-water-system

NASA Technical Reports Server (NTRS)

1981-01-01

Analysis shows economic benefits at six representative sites using actual data from Tempe, Arizona and San Diego, California installations. Model is two-tank cascade water heater with flat-plate collector array for single-family residences. Performances are forecast for Albuquerque, New Mexico; Fort Worth, Texas; Madison, Wisconsin; and Washington, D.C. Costs are compared to net energy savings using variables for each site's environmental conditions, loads, fuel costs, and other economic factors; uncertainty analysis is included.

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

NASA Astrophysics Data System (ADS)

Sanabria, Orlando

Guided by organizational theory, this phenomenological study explored the factors affecting forecast preparation and inaccuracies during the construction of fossil fuel-fired power plants in the United States. Forecast inaccuracies can create financial stress and uncertain profits during the project construction phase. A combination of purposeful and snowball sampling supported the selection of participants. Twenty project managers with over 15 years of experience in power generation and project experience across the United States were interviewed within a 2-month period. From the inductive codification and descriptive analysis, 5 themes emerged: (a) project monitoring, (b) cost control, (c) management review frequency, (d) factors to achieve a precise forecast, and (e) factors causing forecast inaccuracies. The findings of the study showed the factors necessary to achieve a precise forecast includes a detailed project schedule, accurate labor cost estimates, monthly project reviews and risk assessment, and proper utilization of accounting systems to monitor costs. The primary factors reported as causing forecast inaccuracies were cost overruns by subcontractors, scope gaps, labor cost and availability of labor, and equipment and material cost. Results of this study could improve planning accuracy and the effective use of resources during construction of power plants. The study results could contribute to social change by providing a framework to project managers to lessen forecast inaccuracies, and promote construction of power plants that will generate employment opportunities and economic development.

Management of business economic growth as function of resource rents

NASA Astrophysics Data System (ADS)

Prljić, Stefan; Nikitović, Zorana; Stojanović, Aleksandra Golubović; Cogoljević, Dušan; Pešić, Gordana; Alizamir, Meysam

2018-02-01

Economic profit could be influenced by economic rents. However natural resource rents provided different impact on the economic growth or economic profit. The main focus of the study was to evaluate the economic growth as function of natural resource rents. For such a purpose machine learning approach, artificial neural network, was used. The used natural resource rents were coal rents, forest rents, mineral rents, natural gas rents and oil rents. Based on the results it is concluded that the machine learning approach could be used as the tool for the economic growth evaluation as function of natural resource rents. Moreover the more advanced approaches should be incorporated to improve more the forecasting accuracy.

Applications of Principled Search Methods in Climate Influences and Mechanisms

NASA Technical Reports Server (NTRS)

Glymour, Clark

2005-01-01

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

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.

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

NASA Astrophysics Data System (ADS)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites andmore » for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.« less

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

USGS Publications Warehouse

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

2014-01-01

In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) 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.

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Probabilistic Nowcasting of Low-Visibility Procedure States at Vienna International Airport During Cold Season

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian J.; Mayr, Georg J.; Zeileis, Achim

2018-04-01

Airport operations are sensitive to visibility conditions. Low-visibility events may lead to capacity reduction, delays and economic losses. Different levels of low-visibility procedures (lvp) are enacted to ensure aviation safety. A nowcast of the probabilities for each of the lvp categories helps decision makers to optimally schedule their operations. An ordered logistic regression (OLR) model is used to forecast these probabilities directly. It is applied to cold season forecasts at Vienna International Airport for lead times of 30-min out to 2 h. Model inputs are standard meteorological measurements. The skill of the forecasts is accessed by the ranked probability score. OLR outperforms persistence, which is a strong contender at the shortest lead times. The ranked probability score of the OLR is even better than the one of nowcasts from human forecasters. The OLR-based nowcasting system is computationally fast and can be updated instantaneously when new data become available.

Modeling of Economy Considering Crisis

NASA Astrophysics Data System (ADS)

Petrov, Lev F.

2009-09-01

We discuss main modeling's problems of economy dynamic processes and the reason forecast's absence of economic crisis. We present a structure of complexity level of system and models and discuss expected results concerning crisis phenomena. We formulate the basic perspective directions of the mathematical modeling of economy, including possibility of the analysis of the pre crisis, crisis and post crisis phenomena in economic systems.

Impacts of Ramp-Up to Readiness™ after One Year of Implementation. REL 2017-241

ERIC Educational Resources Information Center

Lindsay, Jim; Davis, Elisabeth; Stephan, Jennifer; Proger, Amy

2017-01-01

College education is fundamental to students' upward mobility, states' economic growth, and the country's economic competitiveness. Researchers have forecast that 63 percent of future jobs will require a college degree, yet in the coming years the United States will likely produce 3 million fewer college graduates than are needed to fill workforce…

When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes.

PubMed

Genevsky, Alexander; Yoon, Carolyn; Knutson, Brian

2017-09-06

Although traditional economic and psychological theories imply that individual choice best scales to aggregate choice, primary components of choice reflected in neural activity may support even more generalizable forecasts. Crowdfunding represents a significant and growing platform for funding new and unique projects, causes, and products. To test whether neural activity could forecast market-level crowdfunding outcomes weeks later, 30 human subjects (14 female) decided whether to fund proposed projects described on an Internet crowdfunding website while undergoing scanning with functional magnetic resonance imaging. Although activity in both the nucleus accumbens (NAcc) and medial prefrontal cortex predicted individual choices to fund on a trial-to-trial basis in the neuroimaging sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Internet. Behavioral measures from the neuroimaging sample, however, did not forecast market funding outcomes. This pattern of associations was replicated in a second study. These findings demonstrate that a subset of the neural predictors of individual choice can generalize to forecast market-level crowdfunding outcomes-even better than choice itself. SIGNIFICANCE STATEMENT Forecasting aggregate behavior with individual neural data has proven elusive; even when successful, neural forecasts have not historically supplanted behavioral forecasts. In the current research, we find that neural responses can forecast market-level choice and outperform behavioral measures in a novel Internet crowdfunding context. Targeted as well as model-free analyses convergently indicated that nucleus accumbens activity can support aggregate forecasts. Beyond providing initial evidence for neuropsychological processes implicated in crowdfunding choices, these findings highlight the ability of neural features to forecast aggregate choice, which could inform applications relevant to business and policy. Copyright © 2017 Genevsky et al.

Establishing NWP capabilities in African Small Island States (SIDs)

NASA Astrophysics Data System (ADS)

Rögnvaldsson, Ólafur

2017-04-01

Íslenskar orkurannsóknir (ÍSOR), in collaboration with Belgingur Ltd. and the United Nations Economic Commission for Africa (UNECA) signed a Letter of Agreement in 2015 regarding collaboration in the "Establishing Operational Capacity for Building, Deploying and Using Numerical Weather and Seasonal Prediction Systems in Small Island States in Africa (SIDs)" project. The specific objectives of the collaboration were the following: - Build capacity of National Meteorological and Hydrology Services (NMHS) staff on the use of the WRF atmospheric model for weather and seasonal forecasting, interpretation of model results, and the use of observations to verify and improve model simulations. - Establish a platform for integrating short to medium range weather forecasts, as well as seasonal forecasts, into already existing infrastructure at NMHS and Regional Climate Centres. - Improve understanding of existing model results and forecast verification, for improving decision-making on the time scale of days to weeks. To meet these challenges the operational Weather On Demand (WOD) forecasting system, developed by Belgingur, is being installed in a number of SIDs countries (Cabo Verde, Guinea-Bissau, and Seychelles), as well as being deployed for the Pan-Africa region, with forecasts being disseminated to collaborating NMHSs.

SEASAT economic assessment. Volume 7: Marine transporation case study

NASA Technical Reports Server (NTRS)

1975-01-01

The studies conducted of the potential use of SEASAT ocean condition data and resulting forecasts by dry cargo ships and tankers reached the following conclusions. The SEASAT ocean condition data and resulting forecasts could be usefully employed to route ships around storms, thereby resulting in reduced adverse weather damage, time loss and the related operating costs, and occasional catastrophic losses. These benefits are incremental benefits beyond those which present and future conventional ship routing procedures can supply. The values of the benefits are listed.

Methodology for the Assessment of the Macroeconomic Impacts of Stricter CAFE Standards - Addendum

EIA Publications

2002-01-01

This assessment of the economic impacts of Corporate Average Fuel Economy (CAFÉ) standards marks the first time the Energy Information Administration has used the new direct linkage of the DRI-WEFA Macroeconomic Model to the National Energy Modeling System (NEMS) in a policy setting. This methodology assures an internally consistent solution between the energy market concepts forecast by NEMS and the aggregate economy as forecast by the DRI-WEFA Macroeconomic Model of the U.S. Economy.

A forecast of broadcast satellite communications

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.

1977-01-01

This paper presents forecasts of likely changes in broadcast satellite technology, the technology of ground terminals, and the technology of terrestrial communications competitive with satellites. The impacts of these changes in technology are then assessed, using a cross-impact model of U.S. domestic telecommunications, to determine the consequences of various possible changes in communications satellite technology. These consequences are discussed in terms of various possible services, for households, businesses, and specialized customers, which might become economically viable as a result of improvements in satellite technology.

Energy demand forecasting

NASA Astrophysics Data System (ADS)

Energy demand forecasting and its connection with national energy policies and decisions is examined in light of recent, sharply revised estimates of future energy requirements. Techniques of economic projects are examined. Modeling of energy demands is discussed. Renewable energy sources are discussed. The shift away from reliance of domestic users on oil and natural gas toward electricity as a primary energy resource is examined in the context of the need to conserve energy and expand generating capacity in order to avoid a significant electricity shortfall.

Benefits to world agriculture through remote sensing

NASA Technical Reports Server (NTRS)

Buffalano, A. C.; Kochanowski, P.

1976-01-01

Remote sensing of agricultural land permits crop classification and mensuration which can lead to improved forecasts of production. This technique is particularly important for nations which do not already have an accurate agricultural reporting system. Better forecasts have important economic effects. International grain traders can make better decisions about when to store, buy, and sell. Farmers can make better planting decisions by taking advantage of production estimates for areas out of phase with their own agricultural calendar. World economic benefits will accrue to both buyers and sellers because of increased food supply and price stabilization. This paper reviews the econometric models used to establish this scenario and estimates the dollar value of benefits for world wheat as 200 million dollars annually for the United States and 300 to 400 million dollars annually for the rest of the world.

Integration of Earth System Models and Workflow Management under iRODS for the Northeast Regional Earth System Modeling Project

NASA Astrophysics Data System (ADS)

Lengyel, F.; Yang, P.; Rosenzweig, B.; Vorosmarty, C. J.

2012-12-01

The Northeast Regional Earth System Model (NE-RESM, NSF Award #1049181) integrates weather research and forecasting models, terrestrial and aquatic ecosystem models, a water balance/transport model, and mesoscale and energy systems input-out economic models developed by interdisciplinary research team from academia and government with expertise in physics, biogeochemistry, engineering, energy, economics, and policy. NE-RESM is intended to forecast the implications of planning decisions on the region's environment, ecosystem services, energy systems and economy through the 21st century. Integration of model components and the development of cyberinfrastructure for interacting with the system is facilitated with the integrated Rule Oriented Data System (iRODS), a distributed data grid that provides archival storage with metadata facilities and a rule-based workflow engine for automating and auditing scientific workflows.

The Canadian seasonal forecast and the APCC exchange.

NASA Astrophysics Data System (ADS)

Archambault, B.; Fontecilla, J.; Kharin, V.; Bourgouin, P.; Ashok, K.; Lee, D.

2009-05-01

In this talk, we will first describe the Canadian seasonal forecast system. This system uses a 4 model ensemble approach with each of these models generating a 10 members ensemble. Multi-model issues related to this system will be describes. Secondly, we will describe an international multi-system initiative. The Asia-Pacific Economic Cooperation (APEC) is a forum for 21 Pacific Rim countries or regions including Canada. The APEC Climate Center (APCC) provides seasonal forecasts to their regional climate centers with a Multi Model Ensemble (MME) approach. The APCC MME is based on 13 ensemble prediction systems from different institutions including MSC(Canada), NCEP(USA), COLA(USA), KMA(Korea), JMA(Japan), BOM(Australia) and others. In this presentation, we will describe the basics of this international cooperation.

The transport forecast - an important stage of transport management

NASA Astrophysics Data System (ADS)

Dragu, Vasile; Dinu, Oana; Oprea, Cristina; Alina Roman, Eugenia

2017-10-01

The transport system is a powerful system with varying loads in operation coming from changes in freight and passenger traffic in different time periods. The variations are due to the specific conditions of organization and development of socio-economic activities. The causes of varying loads can be included in three groups: economic, technical and organizational. The assessing of transport demand variability leads to proper forecast and development of the transport system, knowing that the market price is determined on equilibrium between supply and demand. The reduction of transport demand variability through different technical solutions, organizational, administrative, legislative leads to an increase in the efficiency and effectiveness of transport. The paper presents a new way of assessing the future needs of transport through dynamic series. Both researchers and practitioners in transport planning can benefit from the research results. This paper aims to analyze in an original approach how a good transport forecast can lead to a better management in transport, with significant effects on transport demand full meeting in quality terms. The case study shows how dynamic series of statistics can be used to identify the size of future demand addressed to the transport system.

Interval forecasting of cyber-attacks on industrial control systems

NASA Astrophysics Data System (ADS)

Ivanyo, Y. M.; Krakovsky, Y. M.; Luzgin, A. N.

2018-03-01

At present, cyber-security issues of industrial control systems occupy one of the key niches in a state system of planning and management Functional disruption of these systems via cyber-attacks may lead to emergencies related to loss of life, environmental disasters, major financial and economic damage, or disrupted activities of cities and settlements. There is then an urgent need to develop protection methods against cyber-attacks. This paper studied the results of cyber-attack interval forecasting with a pre-set intensity level of cyber-attacks. Interval forecasting is the forecasting of one interval from two predetermined ones in which a future value of the indicator will be obtained. For this, probability estimates of these events were used. For interval forecasting, a probabilistic neural network with a dynamic updating value of the smoothing parameter was used. A dividing bound of these intervals was determined by a calculation method based on statistical characteristics of the indicator. The number of cyber-attacks per hour that were received through a honeypot from March to September 2013 for the group ‘zeppo-norcal’ was selected as the indicator.

Issues in midterm analysis and forecasting 1998

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1998-07-01

Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`smore » National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.« less

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting

NASA Astrophysics Data System (ADS)

Kann, A.

2012-05-01

A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.

Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

NASA Astrophysics Data System (ADS)

Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

2014-05-01

Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

USSR Report, Science and Technology Policy.

DTIC Science & Technology

1987-01-21

prerequisites for the effective application of mathematical economic methods and computer hardware for the predesigning forecasting of future generations of...M.I. Belkin, I. G. Bogorodskiy, et al.; EKONOMIKA I MATEMATICHESRTYE METODY, No 3, 1986) 1 Development of Mathematic Economics Instrumentarium at...ensuring the effectiveness of the automated elaboration of plans, it is essential, in the first place, to improve the skills of the planning specialists

Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

NASA Astrophysics Data System (ADS)

Verkade, J. S.; Brown, J. D.; Davids, F.; Reggiani, P.; Weerts, A. H.

2017-12-01

Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) 'dressing' of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) 'dressing' of an ensemble streamflow forecast by adding an estimate of hydrological uncertainty to each individual streamflow ensemble member. Both approaches aim to produce an estimate of the 'total uncertainty' that captures both the meteorological and hydrological uncertainties. They differ in the degree to which they make use of statistical post-processing techniques. In the 'lumped' approach, both sources of uncertainty are lumped by post-processing deterministic forecasts using their verifying observations. In the 'source-specific' approach, the meteorological uncertainties are estimated by an ensemble of weather forecasts. These ensemble members are routed through a hydrological model and a realization of the probability distribution of hydrological uncertainties (only) is then added to each ensemble member to arrive at an estimate of the total uncertainty. The techniques are applied to one location in the Meuse basin and three locations in the Rhine basin. Resulting forecasts are assessed for their reliability and sharpness, as well as compared in terms of multiple verification scores including the relative mean error, Brier Skill Score, Mean Continuous Ranked Probability Skill Score, Relative Operating Characteristic Score and Relative Economic Value. The dressed deterministic forecasts are generally more reliable than the dressed ensemble forecasts, but the latter are sharper. On balance, however, they show similar quality across a range of verification metrics, with the dressed ensembles coming out slightly better. Some additional analyses are suggested. Notably, these include statistical post-processing of the meteorological forecasts in order to increase their reliability, thus increasing the reliability of the streamflow forecasts produced with ensemble meteorological forcings.

The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilczak, James M.; Finley, Cathy; Freedman, Jeff

The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collectionmore » of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.« less

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.

How do I know if I’ve improved my continental scale flood early warning system?

NASA Astrophysics Data System (ADS)

Cloke, Hannah L.; Pappenberger, Florian; Smith, Paul J.; Wetterhall, Fredrik

2017-04-01

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.

Observed Impacts. Chapter 19

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia

1999-01-01

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds.

Long-run evolution of the global economy - Part 2: Hindcasts of innovation and growth

NASA Astrophysics Data System (ADS)

Garrett, T. J.

2015-10-01

Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolved societal dynamics. If this model is initialized with economic data from the 1950s, it yields hindcasts for how fast global economic production and energy consumption grew between 2000 and 2010 with skill scores > 90 % relative to a model of persistence in trends. The model appears to attain high skill partly because there was a strong impulse of discovery of fossil fuel energy reserves in the mid-twentieth century that helped civilization to grow rapidly as a deterministic physical response. Forecasting the coming century may prove more of a challenge because the effect of the energy impulse appears to have nearly run its course. Nonetheless, an understanding of the external forces that drive civilization may help development of constrained futures for the coupled evolution of civilization and climate during the Anthropocene.

Prediction of the demographic situation in urban districts as a factor of sustainable social and economic development of the transport infrastructure

NASA Astrophysics Data System (ADS)

Davydova, Tatyana; Zhutaeva, Evgeniya; Dubrovskaya, Tatyana

2017-10-01

Article considers the significance of the demographic forecast for the effective operation of the providing system of social and economic development of the urban transport infrastructure. Analysis of the factors which influence on the population of the city of Voronezh was performed and the population forecast for the year 2020 is presented on the basis of the classification by year of birth. Calculation was performed in three variants (with consideration of the use of classification by year of birth) in connection with an impact of modern social and economic situation on the negative tendencies formed in demographic processes. In the basis of variants were grounded different approaches to the dynamics of demographic processes. The main demographic indicators are the number of permanent residents, birth rates, death rates, migration rates. According to the results of the study, population of the urban district of the city of Voronezh is expected to increase in the specified period and migration inflow of the population has a dominant role in the formation in the formation of the number of the city population.

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

[Macro-economic calculation of spending versus micro-economic follow-up of costs of breast cancer].

PubMed

Borella, L; Paraponaris, A

2002-12-01

In the healthcare field, the ability to make economic forecasts requires knowledge of the costs of caring for major diseases. In the case of a semi-chronic condition like cancer, this cost covers all the episodes of care associated with a patient. An evaluation of a macro-economic method of calculating costs for treating non-metastatic cancer, covering all hospital episodes, is proposed. This method is based entirely on the use of annual hospital activity databases, linked to data concerning the incidence of cancer. It allows us to obtain the global cost of care for a neoplasm of a particular site, without the need to reconstruct the whole care pathway of the patients. The model was assessed by comparing it's own results, in the particular case of breast cancer to those issuing from a micro-economic follow-up of 115 patients. Data for macro-economic calculation are extracted from the national French hospital database for the year 1999 and from cancer incidence data. The prospective study was done in 1995, in a comprehensive cancer centre. Macro-economic calculation leads to a cost of 14,555 Euro, for primary breast cancer. Prospective follow-up showed a cost of 14,350 Euro (data corrected, 1999 value). With a difference of 1%, there was a clear cohesion of the two results, while a higher level of divergence was noticed (from 1 to 15%) in the comparison between therapeutic techniques. Accuracy and reliability of results were evaluated. This method may be extended to all types of neoplasms. This method cannot be used instead of follow-up studies, for cost-efficacy or cost-severity analysis, but may be interesting beyond economic forecasts, in the field of payment per pathology.

Demand for satellite-provided domestic communications services up to the year 2000

NASA Technical Reports Server (NTRS)

Stevenson, S.; Poley, W.; Lekan, J.; Salzman, J. A.

1984-01-01

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

AMMA 2005 Dakar International Conference to be Held November 28-December 2, 2005

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamb, Peter

2006-05-09

Consistent with the original proposal (dated April 14, 2005), the grant supported the participation in the above conference of a number of West African meteorologists, the majority of whom will be supporting the ARM Mobile Facility deployment in Niamey in various ways during 2006. The following seven individuals were fully funded (complete airfare, accommodation, registration, meals) to participate in the Conference –Yerima Ladan (Head, ASECNA Forecast Office, Niamey Airport); Dr. Ousmane Manga Adamou (University of Niamey); Abdou Adam Abdoul-Aziz Abebe (Forecasater, ASECNA Forecast Office, Niamey Airport); Hassane Abdou (Forecaster, ASECNA Forecast Office, Niamey Airport); Saley Diori (Forecaster, ASECNA Forecast Office,more » Niamey Airport); Alhassane Diallo (Meteorological Engineer, Burkina Faso Weather Service). The following three individuals were partly funded (for some of their airfare, accommodation, registration, meals) to participate in the Conference – Katiellou Lawan (International Relations, Niger Weather Service); Mamoutou Kouressy (Institute of Rural Economics, Niger Department of Agriculture); and Francis Dide (Benin Weather Service). I am confident that the participation of the above individuals in the Conference will facilitate both the smooth operation of the ARM Mobile Facility in Niamey during 2006 and the involvement of University of Niamey scientists in analysis of the data collected. We appreciate greatly this support from the ARM Program.« less

Demand for satellite-provided domestic communications services up to the year 2000

NASA Astrophysics Data System (ADS)

Stevenson, S.; Poley, W.; Lekan, J.; Salzman, J. A.

1984-11-01

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

Forecast Mekong: navigating changing waters

USGS Publications Warehouse

Powell, Janine

2011-01-01

The U.S. Geological Survey (USGS) is using research and data from the Mekong River Delta in Southeast Asia to compare restoration, conservation, and management efforts there with those done in other major river deltas, such as the Mississippi River Delta in the United States. The project provides a forum to engage regional partners in the Mekong Basin countries to share data and support local research efforts. Ultimately, Forecast Mekong will lead to more informed decisions about how to make the Mekong and Mississippi Deltas resilient in the face of climate change, economic stresses, and other impacts.

The large area crop inventory experiment: A major demonstration of space remote sensing

NASA Technical Reports Server (NTRS)

Macdonald, R. B.; Hall, F. G.

1977-01-01

Strategies are presented in agricultural technology to increase the resistance of crops to a wider range of meteorological conditions in order to reduce year-to-year variations in crop production. Uncertainties in agricultral production, together with the consumer demands of an increasing world population, have greatly intensified the need for early and accurate annual global crop production forecasts. These forecasts must predict fluctuation with an accuracy, timeliness and known reliability sufficient to permit necessary social and economic adjustments, with as much advance warning as possible.

Assessment of historical and projected segments of US and world civil and military rotorcraft markets, 1960 - 1990

NASA Technical Reports Server (NTRS)

Yates, W. J.

1981-01-01

The geographic climatic, political, economic and demographic environment of 75 countries was analyzed with respect to helicopter procurement history and usage. Key environmental indicators which are variables were projected into strengths and weaknesses of U.S. technology are reviewed. The civil market sensitivity to new technology is forecast with selected premises as to vehicle life, noise standards, fuel costs, GNP expansion and traffic growth. The forecast is based on a scenario of helicopter technology improvements resulting in increased size and performance.

Long-run evolution of the global economy: 2. Hindcasts of innovation and growth

NASA Astrophysics Data System (ADS)

Garrett, T. J.

2015-03-01

Long-range climate forecasts rely upon integrated assessment models that link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework, outlined in Part 1, that is based on physical principles rather than explicitly resolved societal dynamics. Relative to a reference model of persistence in trends, model hindcasts that are initialized with data from 1950 to 1960 reproduce trends in global economic production and energy consumption between 2000 and 2010 with a skill score greater than 90%. In part, such high skill appears to be because civilization has responded to an impulse of fossil fuel discovery in the mid-twentieth century. Forecasting the coming century will be more of a challenge because the effect of the impulse appears to have nearly run its course. Nonetheless, the model offers physically constrained futures for the coupled evolution of civilization and climate during the Anthropocene.

The causal link among militarization, economic growth, CO2 emission, and energy consumption.

PubMed

Bildirici, Melike E

2017-02-01

This paper examines the long-run and the causal relationship among CO 2 emissions, militarization, economic growth, and energy consumption for USA for the period 1960-2013. Using the bound test approach to cointegration, a short-run as well as a long-run relationship among the variables with a positive and a statistically significant relationship between CO 2 emissions and militarization was found. To determine the causal link, MWALD and Rao's F tests were applied. According to Rao's F tests, the evidence of a unidirectional causality running from militarization to CO 2 emissions, from energy consumption to CO 2 emissions, and from militarization to energy consumption all without a feedback was found. Further, the results determined that 26% of the forecast-error variance of CO 2 emissions was explained by the forecast error variance of militarization and 60% by energy consumption.

World Energy Projection System Plus (WEPS ): Global Activity Module

EIA Publications

2016-01-01

The World Energy Projection System Plus (WEPS ) is a comprehensive, mid?term energy forecasting and policy analysis tool used by EIA. WEPS projects energy supply, demand, and prices by country or region, given assumptions about the state of various economies, international energy markets, and energy policies. The Global Activity Module (GLAM) provides projections of economic driver variables for use by the supply, demand, and conversion modules of WEPS . GLAM’s baseline economic projection contains the economic assumptions used in WEPS to help determine energy demand and supply. GLAM can also provide WEPS with alternative economic assumptions representing a range of uncertainty about economic growth. The resulting economic impacts of such assumptions are inputs to the remaining supply and demand modules of WEPS .

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.

Performance of fuzzy approach in Malaysia short-term electricity load forecasting

NASA Astrophysics Data System (ADS)

Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee

2014-12-01

Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

Assessment of the Charging Policy in Energy Efficiency of the Enterprise

NASA Astrophysics Data System (ADS)

Shutov, E. A.; E Turukina, T.; Anisimov, T. S.

2017-04-01

The forecasting problem for energy facilities with a power exceeding 670 kW is currently one of the main. In connection with rules of the retail electricity market such customers also pay for actual energy consumption deviations from plan value. In compliance with the hierarchical stages of the electricity market a guaranteeing supplier is to respect the interests of distribution and generation companies that require load leveling. The answer to this question for industrial enterprise is possible only within technological process through implementation of energy-efficient processing chains with the adaptive function and forecasting tool. In such a circumstance the primary objective of a forecasting is reduce the energy consumption costs by taking account of the energy cost correlation for 24 hours for forming of pumping unit work schedule. The pumping unit virtual model with the variable frequency drive is considered. The forecasting tool and the optimizer are integrated into typical control circuit. Economic assessment of the optimization method was estimated.

Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhong, Ming; Cheng, Ling; Jin, Lu; Shen, Si

2018-02-01

In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

Forecasting the stochastic demand for inpatient care: the case of the Greek national health system.

PubMed

Boutsioli, Zoe

2010-08-01

The aim of this study is to estimate the unexpected demand of Greek public hospitals. A multivariate model with four explanatory variables is used. These are as follows: the weekend effect, the duty effect, the summer holiday and the official holiday. The method of the ordinary least squares is used to estimate the impact of these variables on the daily hospital emergency admissions series. The forecasted residuals of hospital regressions for each year give the estimated stochastic demand. Daily emergency admissions decline during weekends, summer months and official holidays, and increase on duty hospital days. Stochastic hospital demand varies both among hospitals and over the five-year time period under investigation. Variations among hospitals are larger than time variations. Hospital managers and health policy-makers can be availed by forecasting the future flows of emergent patients. The benefit can be both at managerial and economical level. More advanced models including additional daily variables such as the weather forecasts could provide more accurate estimations.

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

PubMed Central

Dai, Yonghui; Han, Dongmei; Dai, Weihui

2014-01-01

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

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting.

PubMed

Hu, Yi-Chung

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

PubMed Central

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants. PMID:28981548

The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

PubMed Central

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. PMID:27219341

The impact of implementing a demand forecasting system into a low-income country's supply chain.

PubMed

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright © 2016 Elsevier Ltd. All rights reserved.

Understanding the land-atmospheric interaction in drought forecast from CFSv2 for the 2011 Texas and 2012 Upper Midwest US droughts

NASA Astrophysics Data System (ADS)

Zhang, Y.; Roundy, J. K.; Ek, M. B.; Wood, E. F.

2015-12-01

Prediction and thus preparedness in advance of hydrological extremes, such as drought and flood events, is crucial for proactively reducing their social and economic impacts. In the summers of 2011 Texas, and 2012 the Upper Midwest, experienced intense droughts that affected crops and the food market in the US. It is expected that seasonal forecasts with sufficient skill would reduce the negative impacts through planning and preparation. However, the forecast skill from models such as Climate Forecast System Version 2 (CFSv2) from National Centers for Environmental Prediction (NCEP) is low over the US, especially during the warm season (Jun - Sep), which restricts their practical use for drought prediction. This study analyzes the processes that lead to premature termination of 2011 and 2012 US summer droughts in CFSv2 forecast resulting in its low forecast skill. Using the North American Land Data Assimilation System version 2 (NLDAS2) and Climate Forecast System Reanalysis (CFSR) as references, this study investigates the forecast skills of CFSv2 initialized at 00, 06, 12, 18z from May 15 - 31 (leads out to September) for each event in terms of land-atmosphere interaction, through a recently developed Coupling Drought Index (CDI), which is based on the Convective Triggering Potential-Humidity Index-soil moisture (CTP-HI-SM) classification of four climate regimes: wet coupling, dry coupling, transitional and atmospherically controlled. A recycling model is used to trace the moisture sources in the CFSv2 forecasts of anomalous precipitation, which lead to the breakdown of drought conditions and a lack of drought forecasting skills. This is then compared with tracing the moisture source in CFSR with the same recycling model, which is used as the verification for the same periods. This helps to identify the parameterization that triggered precipitation in CFSv2 during 2011 and 2012 summer in the US thus has the potential to improve the forecast skill of CSFv2.

An interdisciplinary approach for earthquake modelling and forecasting

NASA Astrophysics Data System (ADS)

Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.

2016-12-01

Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.

Forecasting the 2013–2014 influenza season using Wikipedia

DOE PAGES

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; ...

2015-05-14

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

Forecasting the 2013–2014 Influenza Season Using Wikipedia

PubMed Central

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.

2015-01-01

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed. PMID:25974758

Forecasting the 2013-2014 influenza season using Wikipedia.

PubMed

Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M; Deshpande, Alina; Del Valle, Sara Y

2015-05-01

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

NASA Astrophysics Data System (ADS)

Dreano, Denis; Tsiaras, Kostas; Triantafyllou, George; Hoteit, Ibrahim

2017-07-01

Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.

Forecasting the 2013–2014 influenza season using Wikipedia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

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.

SEASAT economic assessment

NASA Technical Reports Server (NTRS)

Hicks, K.; Steele, W.

1974-01-01

The SEASAT program will provide scientific and economic benefits from global remote sensing of the ocean's dynamic and physical characteristics. The program as presently envisioned consists of: (1) SEASAT A; (2) SEASAT B; and (3) Operational SEASAT. This economic assessment was to identify, rationalize, quantify and validate the economic benefits evolving from SEASAT. These benefits will arise from improvements in the operating efficiency of systems that interface with the ocean. SEASAT data will be combined with data from other ocean and atmospheric sampling systems and then processed through analytical models of the interaction between oceans and atmosphere to yield accurate global measurements and global long range forecasts of ocean conditions and weather.

Investment Strategies: A Checklist for an Uncertain Decade.

ERIC Educational Resources Information Center

Salem, David A.

1990-01-01

Advice on college and university endowment investing includes a general recommendation for caution and restraint and specific suggestions for approaching popular economic forecasts and assumptions, incentive fees, risk diversification, foreign options, especially in Europe, and energy investment. (MSE)

Proceedings of the Flat-Plate Solar Array Project Workshop on Low-Cost Polysilicon for Terrestrial Photovoltaic Solar-Cell Applications

NASA Technical Reports Server (NTRS)

1986-01-01

Sessions conducted included: polysilicon material requirements; economics; process development in the U.S.; international process development; and polysilicon market and forecasts. Twenty-one papers were presented and discussed.

The economic impact of longer range weather information on the production of peas in Wisconsin

NASA Technical Reports Server (NTRS)

Smith, K. R.; Torkelson, A. W.

1972-01-01

The extent of benefits which will be realized in the pea industry as a result of improved long range weather forecasts are outlined. Particular attention was given to planting and harvesting operations.

Development of a model-based flood emergency management system in Yujiang River Basin, South China

NASA Astrophysics Data System (ADS)

Zeng, Yong; Cai, Yanpeng; Jia, Peng; Mao, Jiansu

2014-06-01

Flooding is the most frequent disaster in China. It affects people's lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for flood control.

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

Asymmetric affective forecasting errors and their correlation with subjective well-being

PubMed Central

2018-01-01

Aims Social scientists have postulated that the discrepancy between achievements and expectations affects individuals' subjective well-being. Still, little has been done to qualify and quantify such a psychological effect. Our empirical analysis assesses the consequences of positive and negative affective forecasting errors—the difference between realized and expected subjective well-being—on the subsequent level of subjective well-being. Data We use longitudinal data on a representative sample of 13,431 individuals from the German Socio-Economic Panel. In our sample, 52% of individuals are females, average age is 43 years, average years of education is 11.4 and 27% of our sample lives in East Germany. Subjective well-being (measured by self-reported life satisfaction) is assessed on a 0–10 discrete scale and its sample average is equal to 6.75 points. Methods We develop a simple theoretical framework to assess the consequences of positive and negative affective forecasting errors—the difference between realized and expected subjective well-being—on the subsequent level of subjective well-being, properly accounting for the endogenous adjustment of expectations to positive and negative affective forecasting errors, and use it to derive testable predictions. Given the theoretical framework, we estimate two panel-data equations, the first depicting the association between positive and negative affective forecasting errors and the successive level of subjective well-being and the second describing the correlation between subjective well-being expectations for the future and hedonic failures and successes. Our models control for individual fixed effects and a large battery of time-varying demographic characteristics, health and socio-economic status. Results and conclusions While surpassing expectations is uncorrelated with subjective well-being, failing to match expectations is negatively associated with subsequent realizations of subjective well-being. Expectations are positively (negatively) correlated to positive (negative) forecasting errors. We speculate that in the first case the positive adjustment in expectations is strong enough to cancel out the potential positive effects on subjective well-being of beaten expectations, while in the second case it is not, and individuals persistently bear the negative emotional consequences of not achieving expectations. PMID:29513685

The Value of Weather Forecast in Irrigation

NASA Astrophysics Data System (ADS)

Cai, X.; Wang, D.

2007-12-01

This paper studies irrigation scheduling (when and how much water to apply during the crop growth season) in the Havana Lowlands region, Illinois, using meteorological, agronomic and agricultural production data from 2002. Irrigation scheduling determines the timing and amount of water applied to an irrigated cropland during the crop growing season. In this study a hydrologic-agronomic simulation is coupled with an optimization algorithm to search for the optimal irrigation schedule under various weather forecast horizons. The economic profit of irrigated corn from an optimized scheduling is compared to that from and the actual schedule, which is adopted from a pervious study. Extended and reliable climate prediction and weather forecast are found to be significantly valuable. If a weather forecast horizon is long enough to include the critical crop growth stage, in which crop yield bears the maximum loss over all stages, much economic loss can be avoided. Climate predictions of one to two months, which can cover the critical period, might be even more beneficial during a dry year. The other purpose of this paper is to analyze farmers' behavior in irrigation scheduling by comparing the "actual" schedule to the "optimized" ones. The ultimate goal of irrigation schedule optimization is to provide information to farmers so that they may modify their behavior. In practice, farmers' decision may not follow an optimal irrigation schedule due to the impact of various factors such as natural conditions, policies, farmers' habits and empirical knowledge, and the uncertain or inexact information that they receive. In this study farmers' behavior in irrigation decision making is analyzed by comparing the "actual" schedule to the "optimized" ones. This study finds that the identification of the crop growth stage with the most severe water stress is critical for irrigation scheduling. For the case study site in the year of 2002, framers' response to water stress was found to be late; they did not even respond appropriately to a major rainfall just 3 days ahead, which might be due to either an unreliable weather forecast or farmer's ignorance of the forecast.

Developing a user-friendly Drought Monitoring and Forecasting Tool for Doctors without Borders

NASA Astrophysics Data System (ADS)

Enenkel, Markus

2015-04-01

Humanitarian aid organizations that focus on drought-related emergency response and disaster preparedness need to take decisions under high uncertainty. Satellite-derived and modelled information can help to decrease this uncertainty. However, in order to benefit from the provided knowledge it is crucial to adapt datasets and tools to actual user requirements and existing organizational capacities. Furthermore, socio-economic vulnerabilities (e. g. current rates of malnutrition) and coping capacities (e. g. access to drought-resistant seeds) of the affected population need to be assessed to link environmental conditions (drought risk) to potential impacts (food insecurity). Forecasts with lead times up to several months are desirable from a logistic point of view, but naturally less accurate than short-term predictions. As a consequence, careful calibration is required to identify and balance forecasts with an acceptable accuracy and the risk of possible false alarms. Therefore, we calibrate modelled predictions of rainfall, temperature and soil moisture via satellite-derived observations. Field tests with Doctors without Borders in Ethiopia help to define critical thresholds, to interpret the information under real conditions and to collect the necessary additional socio-economic data via a smartphone app. The final risk maps need to be visualized in a way that is easy to interpret, but not oversimplified.

Chemical weather forecasting for the Yangtze River Delta

NASA Astrophysics Data System (ADS)

Xie, Y.; Xu, J.; Zhou, G.; Chang, L.; Chen, B.

2016-12-01

Shanghai is one of the largest megacities in the world. With rapid economic growth of the city and its surrounding areas in recent years, air pollution has posed adverse effects on public health and ecosystem. In winter heavy pollution episodes are often associated with PM exceedances under stagnant conditions or transport events, whereas in summer the region frequently experiences elevated O3 levels. Chemical weather prediction systems with the WRF-Chem and CMAQ models are being developed to support air quality and haze forecasting for Shanghai and the Yangtze River Delta region. We will present main components of the modeling system, forecasting products, as well as evaluation results. Evaluation of the WRF-Chem forecasts show the model has generally good ability to capture the temporal variations of O3 and PM2.5. Substantial regional differences exist, with the best performance in Shanghai. Meanwhile, the forecasts tend to degrade during highly polluted episodes and transitional time periods, which highlights the need to improve model representation of key process (e.g. meteorological fields and formation of secondary pollutants). Recent work includes using the ECMWF global model forecasts as chemical boundary conditions for our regional model. We investigate the impact of chemical downscaling, and also compare the results from different models participated in the PANDA (PArtnership with chiNa on space Data) project. Results from ongoing efforts (e.g. chemical weather forecasting driven by SMS regional high resolution NWP) will also be presented.

Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system

NASA Astrophysics Data System (ADS)

Liang, Ping; Lin, Hai

2018-02-01

A useful sub-seasonal forecast is of great societal and economical value in the highly populated East Asian region, especially during boreal summer when frequent extreme events such as heat waves and persistent heavy rainfalls occur. Despite recent interest and development in sub-seasonal prediction, it is still unclear how skillful dynamical forecasting systems are in East Asia beyond 2 weeks. In this study we evaluate the sub-seasonal prediction over East Asia during boreal summer in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC).Results show that the climatological intra-seasonal oscillation (CISO) of East Asian summer monsoonis reasonably well captured. Statistically significant forecast skill of 2-meter air temperature (T2m) is achieved for all lead times up to week 4 (days 26-32) over East China and Northeast Asia, which is consistent with the skill in 500 hPa geopotential height (Z500). Significant forecast skill of precipitation, however, is limited to the week of days 5-11. Possible sources of predictability on the sub-seasonal time scale are analyzed. The weekly mean T2m anomaly over East China is found to be linked to an eastward propagating extratropical Rossby wave from the North Atlantic across Europe to East Asia. The Madden-Julian Oscillation (MJO) and El Nino-Southern Oscillation (ENSO) are also likely to influence the forecast skill of T2m at the sub-seasonal timescale over East Asia.

Science from genes to landscapes

USGS Publications Warehouse

,

2015-08-26

The quality of life and economic strength in America hinges on healthy ecosystems that support living things and natural processes. Ecosystem science better enables society to understand how and why ecosystems change, to predict and forecast future changes, and to guide actions that can prevent damage to, and restore and sustain ecosystems. It is through this knowledge that informed decisions are made about natural resources that can enhance our Nation's economic and environmental well-being.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aabakken, J.

This report, prepared by NREL's Strategic Energy Analysis Center, includes up-to-date information on power technologies, including complete technology profiles. The data book also contains charts on electricity restructuring, power technology forecasts, electricity supply, electricity capability, electricity generation, electricity demand, prices, economic indicators, environmental indicators, and conversion factors.

Creating History: By Design or by Default.

ERIC Educational Resources Information Center

Baugher, Shirley L.

1989-01-01

The author presents social demographic forecasts for the future. She examines social, economic, and political transitions in U.S. society separately and argues that the transitions that society makes depend ultimately on the values upon which individuals choose to act. (CH)

Economic project perspectives: An overview of the impact resulting from recent advances in satellite meteorology

NASA Technical Reports Server (NTRS)

Smith, K. R.; Boness, F. H.

1972-01-01

The impact of advanced satellite meteorology on long range weather forecasts, agriculture, commerce, and resource utilization are examined. All data are geared to obtaining a picture of various user needs and possible benefits.

Development of an integrated economic, land use & transportation forecasting model for the state of Alabama

DOT National Transportation Integrated Search

2010-08-31

In 2007 the Alabama Department of Transportation (ALDOT) in cooperation with the Montgomery Area : Metropolitan Planning Organization (MPO) and Auburn University initiated a research project to explore : the potential of developing an integrated tran...

Megaregion freight movements : a case study of the Texas Triangle.

DOT National Transportation Integrated Search

2011-09-01

U.S. population growth is predicted to substantially increase over the next 40 years, particularly in areas with large regional economies forecasted to contain over two-thirds of the national economic activity. In Texas, population growth from 2000 t...

Mega-region freight movements : a case study of the Texas triangle.

DOT National Transportation Integrated Search

2011-09-01

U.S. population growth is predicted to substantially increase over the next 40 years, particularly in areas : with large regional economies forecasted to contain over two-thirds of the national economic activity. In : Texas, population growth from 20...

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

Evolution-informed forecasting of seasonal influenza A (H3N2)

PubMed Central

Du, Xiangjun; King, Aaron A.; Woods, Robert J.; Pascual, Mercedes

2018-01-01

Inter-pandemic or seasonal influenza exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus’ antigenic evolution. We propose here a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino-acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States over 10 years, we demonstrate the feasibility of prediction ahead of season and an accurate real-time forecast for the 2016/2017 influenza season. PMID:29070700

The probability forecast evaluation of hazard and storm wind over the territories of Russia and Europe

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2012-04-01

The results of the probability forecast methods of summer storm and hazard wind over territories of Russia and Europe are submitted at this paper. These methods use the hydrodynamic-statistical model of these phenomena. The statistical model was developed for the recognition of the situation involving these phenomena. For this perhaps the samples of the values of atmospheric parameters (n=40) for the presence and for the absence of these phenomena of storm and hazard wind were accumulated. The compressing of the predictors space without the information losses was obtained by special algorithm (k=7<19m/s, the values of 65%24m/s, the values of 75%29m/s or the area of the tornado and strong squalls. The evaluation of this probability forecast was provided by criterion of Brayer. The estimation was successful and was equal for the European part of Russia B=0,37. The application of the probability forecast of storm and hazard winds allows to mitigate the economic losses when the errors of the first and second kinds of storm wind categorical forecast are not so small. A lot of examples of the storm wind probability forecast are submitted at this report.

Economic analysis for transmission operation and planning

NASA Astrophysics Data System (ADS)

Zhou, Qun

2011-12-01

Restructuring of the electric power industry has caused dramatic changes in the use of transmission system. The increasing congestion conditions as well as the necessity of integrating renewable energy introduce new challenges and uncertainties to transmission operation and planning. Accurate short-term congestion forecasting facilitates market traders in bidding and trading activities. Cost sharing and recovery issue is a major impediment for long-term transmission investment to integrate renewable energy. In this research, a new short-term forecasting algorithm is proposed for predicting congestion, LMPs, and other power system variables based on the concept of system patterns. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce the forecasting error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm. Moreover, a negotiation methodology is developed to guide transmission investment for integrating renewable energy. Built on Nash Bargaining theory, the negotiation of investment plans and payment rate can proceed between renewable generation and transmission companies for cost sharing and recovery. The proposed approach is applied to Garver's six bus system. The numerical results demonstrate fairness and efficiency of the approach, and hence can be used as guidelines for renewable energy investors. The results also shed light on policy-making of renewable energy subsidies.

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

PubMed Central

Wang, Hongguang

2018-01-01

Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting. PMID:29768450

The method of planning the energy consumption for electricity market

NASA Astrophysics Data System (ADS)

Russkov, O. V.; Saradgishvili, S. E.

2017-10-01

The limitations of existing forecast models are defined. The offered method is based on game theory, probabilities theory and forecasting the energy prices relations. New method is the basis for planning the uneven energy consumption of industrial enterprise. Ecological side of the offered method is disclosed. The program module performed the algorithm of the method is described. Positive method tests at the industrial enterprise are shown. The offered method allows optimizing the difference between planned and factual consumption of energy every hour of a day. The conclusion about applicability of the method for addressing economic and ecological challenges is made.

Studies in short haul air transportation in the California corridor: Effects of design runway length; community acceptance; impact of return on investment and fuel cost increases. Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Shevell, R. S.; Jones, D. W., Jr.

1973-01-01

The development of a forecast model for short haul air transportation systems in the California Corridor is discussed. The factors which determine the level of air traffic demand are identified. A forecast equation for use in airport utilization analysis is developed. A mathematical model is submitted to show the relationship between population, employment, and income for indicating future air transportation utilization. Diagrams and tables of data are included to support the conclusions reached regarding air transportation economic factors.

Undecidability in macroeconomics

NASA Technical Reports Server (NTRS)

Chandra, Siddharth; Chandra, Tushar Deepak

1993-01-01

In this paper we study the difficulty of solving problems in economics. For this purpose, we adopt the notion of undecidability from recursion theory. We show that certain problems in economics are undecidable, i.e., cannot be solved by a Turing Machine, a device that is at least as powerful as any computational device that can be constructed. In particular, we prove that even in finite closed economies subject to a variable initial condition, in which a social planner knows the behavior of every agent in the economy, certain important social planning problems are undecidable. Thus, it may be impossible to make effective policy decisions. Philosophically, this result formally brings into question the Rational Expectations Hypothesis which assumes that each agent is able to determine what it should do if it wishes to maximize its utility. We show that even when an optimal rational forecast exists for each agency (based on the information currently available to it), agents may lack the ability to make these forecasts. For example, Lucas describes economic models as 'mechanical, artificial world(s), populated by ... interacting robots'. Since any mechanical robot can be at most as computationally powerful as a Turing Machine, such economies are vulnerable to the phenomenon of undecidability.

Conjunctive use of groundwater and surface water for irrigated agriculture: Risk aversion

USGS Publications Warehouse

Bredehoeft, John D.; Young, Richard A.

1983-01-01

In examining the South Platte system in Colorado where surface water and groundwater are used conjunctively for irrigation, we find the actual installed well capacity is approximately sufficient to irrigate the entire area. This would appear to be an overinvestment in well capacity. In this paper we examine to what extent groundwater is being developed as insurance against periods of low streamflow. Using a simulation model which couples the hydrology of a conjunctive stream aquifer system to a behavioral-economic model which incorporates farmer behavior in such a system, we have investigated the economics of an area patterned after a reach of the South Platte Valley in Colorado. The results suggest that under current economic conditions the most reasonable groundwater pumping capacity is a total capacity capable of irrigating the available acreage with groundwater. Installing sufficient well capacity to irrigate all available acreage has two benefits: (1) this capacity maximizes the expected net benefits and (2) this capacity also minimizes the variation in annual income: it reduces the variance to essentially zero. As pumping capacity is installed in a conjunctive use system, the value of flow forecasts is diminished. Poor forecasts are compensated for by pumping groundwater.

The effects of carbon tax on the Oregon economy and state greenhouse gas emissions

NASA Astrophysics Data System (ADS)

Rice, A. L.; Butenhoff, C. L.; Renfro, J.; Liu, J.

2014-12-01

Of the numerous mechanisms to mitigate greenhouse gas emissions on statewide, regional or national scales in the United States, a tax on carbon is perhaps one of the simplest. By taxing emissions directly, the costs of carbon emissions are incorporated into decision-making processes of market actors including consumers, energy suppliers and policy makers. A carbon tax also internalizes the social costs of climate impacts. In structuring carbon tax revenues to reduce corporate and personal income taxes, the negative incentives created by distortionary income taxes can be reduced or offset entirely. In 2008, the first carbon tax in North America across economic sectors was implemented in British Columbia through such a revenue-neutral program. In this work, we investigate the economic and environmental effects of a carbon tax in the state of Oregon with the goal of informing the state legislature, stakeholders and the public. The study investigates 70 different economic sectors in the Oregon economy and six geographical regions of the state. The economic model is built upon the Carbon Tax Analysis Model (C-TAM) to provide price changes in fuel with data from: the Energy Information Agency National Energy Modeling System (EIA-NEMS) Pacific Region Module which provides Oregon-specific energy forecasts; and fuel price increases imposed at different carbon fees based on fuel-specific carbon content and current and projected regional-specific electricity fuel mixes. CTAM output is incorporated into the Regional Economic Model (REMI) which is used to dynamically forecast economic impacts by region and industry sector including: economic output, employment, wages, fiscal effects and equity. Based on changes in economic output and fuel demand, we further project changes in greenhouse gas emissions resulting from economic activity and calculate revenue generated through a carbon fee. Here, we present results of this modeling effort under different scenarios of carbon fee and avenues for revenue repatriation.

Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany

NASA Astrophysics Data System (ADS)

Rieger, Daniel; Steiner, Andrea; Bachmann, Vanessa; Gasch, Philipp; Förstner, Jochen; Deetz, Konrad; Vogel, Bernhard; Vogel, Heike

2017-11-01

The importance for reliable forecasts of incoming solar radiation is growing rapidly, especially for those countries with an increasing share in photovoltaic (PV) power production. The reliability of solar radiation forecasts depends mainly on the representation of clouds and aerosol particles absorbing and scattering radiation. Especially under extreme aerosol conditions, numerical weather prediction has a systematic bias in the solar radiation forecast. This is caused by the design of numerical weather prediction models, which typically account for the direct impact of aerosol particles on radiation using climatological mean values and the impact on cloud formation assuming spatially and temporally homogeneous aerosol concentrations. These model deficiencies in turn can lead to significant economic losses under extreme aerosol conditions. For Germany, Saharan dust outbreaks occurring 5 to 15 times per year for several days each are prominent examples for conditions, under which numerical weather prediction struggles to forecast solar radiation adequately. We investigate the impact of mineral dust on the PV-power generation during a Saharan dust outbreak over Germany on 4 April 2014 using ICON-ART, which is the current German numerical weather prediction model extended by modules accounting for trace substances and related feedback processes. We find an overall improvement of the PV-power forecast for 65 % of the pyranometer stations in Germany. Of the nine stations with very high differences between forecast and measurement, eight stations show an improvement. Furthermore, we quantify the direct radiative effects and indirect radiative effects of mineral dust. For our study, direct effects account for 64 %, indirect effects for 20 % and synergistic interaction effects for 16 % of the differences between the forecast including mineral dust radiative effects and the forecast neglecting mineral dust.

Short-term load forecasting of power system

NASA Astrophysics Data System (ADS)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Multicomponent ensemble models to forecast induced seismicity

NASA Astrophysics Data System (ADS)

Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

2018-01-01

In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels of seismicity days before the occurrence of felt events.

Annual energy outlook 1998 : with projections to 2020

DOT National Transportation Integrated Search

1997-12-01

The analysis in AEO98 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes...

Markovian prediction of future values for food grains in the economic survey

NASA Astrophysics Data System (ADS)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

Regional economic forecasting models: Suitability for use in the National Acid Precipitation Assessment Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

South, D.W.; McDonald, J.F.; Oakland, W.H.

1990-02-01

In preparation for the Phase 1 test runs of the National Acid Precipitation Assessment Program Task Group B (TG-B) emissions model set, the need arose to provide regional economic data directly to the sector models in the model set and to the Argonne Regionalization Activity Module (ARAM). Candidate regional economic models were reviewed, and the Data Resources, Inc. (DRI), model was selected. This review of models, conducted during 1984--1985, is documented in this report. Even though considerable time has elapsed since then, the model descriptions and critique contained in this report are still fairly accurate and the recommendations should stillmore » be valid. There have been, however, some significant changes: (1) two of the economic consulting firms whose models were reviewed, Chase Econometrics and Wharton Econometric Forecasting Associates, have merged, (2) the DRI Regional Information System (DRI/RIS) now constructs a regional measure of industrial value of shipments, which will be used as the industrial activity variable (instead of employment) in the Phase 2 scenario analyses, and (3) based on recommendations from the third-party review of the TG-B model set, price-sensitive regional equations were developed to provide inputs, not already produced by the DRI/RIS model, directly to the sector models, thus eliminating the function served by ARAM. 44 refs., 12 figs., 44 tabs.« less

Appraisal of artificial neural network for forecasting of economic parameters

NASA Astrophysics Data System (ADS)

Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

2017-01-01

The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

Interval forecasting of cyberattack intensity on informatization objects of industry using probability cluster model

NASA Astrophysics Data System (ADS)

Krakovsky, Y. M.; Luzgin, A. N.; Mikhailova, E. A.

2018-05-01

At present, cyber-security issues associated with the informatization objects of industry occupy one of the key niches in the state management system. As a result of functional disruption of these systems via cyberattacks, an emergency may arise related to loss of life, environmental disasters, major financial and economic damage, or disrupted activities of cities and settlements. When cyberattacks occur with high intensity, in these conditions there is the need to develop protection against them, based on machine learning methods. This paper examines interval forecasting and presents results with a pre-set intensity level. The interval forecasting is carried out based on a probabilistic cluster model. This method involves forecasting of one of the two predetermined intervals in which a future value of the indicator will be located; probability estimates are used for this purpose. A dividing bound of these intervals is determined by a calculation method based on statistical characteristics of the indicator. Source data are used that includes a number of hourly cyberattacks using a honeypot from March to September 2013.

Current gaps in understanding and predicting space weather: An operations perspective

NASA Astrophysics Data System (ADS)

Murtagh, W. J.

2016-12-01

The NOAA Space Weather Prediction Center (SWPC), one of the nine National Weather Service (NWS) National Centers for Environmental Prediction, is the Nation's official source for space weather alerts and warnings. Space weather effects the technology that forms the backbone of global economic vitality and national security, including satellite and airline operations, communications networks, and the electric power grid. Many of SWPC's over 48,000 subscribers rely on space weather forecasts for critical decision making. But extraordinary gaps still exist in our ability to meet customer needs for accurate and timely space weather forecasts and warnings. The 2015 National Space Weather Strategy recognizes that it is imperative that we improve the fundamental understanding of space weather and increase the accuracy, reliability, and timeliness of space-weather observations and forecasts in support of the growing demands. In this talk we provide a broad perspective of the key challenges that currently limit the forecaster's ability to better understand and predict space weather. We also examine the impact of these limitations on the end-user community.

Environmental predictors of stunting among children under-five in Somalia: cross-sectional studies from 2007 to 2010.

PubMed

Kinyoki, Damaris K; Berkley, James A; Moloney, Grainne M; Odundo, Elijah O; Kandala, Ngianga-Bakwin; Noor, Abdisalan M

2016-07-28

Stunting among children under five years old is associated with long-term effects on cognitive development, school achievement, economic productivity in adulthood and maternal reproductive outcomes. Accurate estimation of stunting and tools to forecast risk are key to planning interventions. We estimated the prevalence and distribution of stunting among children under five years in Somalia from 2007 to 2010 and explored the role of environmental covariates in its forecasting. Data from household nutritional surveys in Somalia from 2007 to 2010 with a total of 1,066 clusters covering 73,778 children were included. We developed a Bayesian hierarchical space-time model to forecast stunting by using the relationship between observed stunting and environmental covariates in the preceding years. We then applied the model coefficients to environmental covariates in subsequent years. To determine the accuracy of the forecasting, we compared this model with a model that used data from all the years with the corresponding environmental covariates. Rainfall (OR = 0.994, 95 % Credible interval (CrI): 0.993, 0.995) and vegetation cover (OR = 0.719, 95 % CrI: 0.603, 0.858) were significant in forecasting stunting. The difference in estimates of stunting using the two approaches was less than 3 % in all the regions for all forecast years. Stunting in Somalia is spatially and temporally heterogeneous. Rainfall and vegetation are major drivers of these variations. The use of environmental covariates for forecasting of stunting is a potentially useful and affordable tool for planning interventions to reduce the high burden of malnutrition in Somalia.

Sensitivity of WRF precipitation field to assimilation sources in northeastern Spain

NASA Astrophysics Data System (ADS)

Lorenzana, Jesús; Merino, Andrés; García-Ortega, Eduardo; Fernández-González, Sergio; Gascón, Estíbaliz; Hermida, Lucía; Sánchez, José Luis; López, Laura; Marcos, José Luis

2015-04-01

Numerical weather prediction (NWP) of precipitation is a challenge. Models predict precipitation after solving many physical processes. In particular, mesoscale NWP models have different parameterizations, such as microphysics, cumulus or radiation schemes. These facilitate, according to required spatial and temporal resolutions, precipitation fields with increasing reliability. Nevertheless, large uncertainties are inherent to precipitation forecasting. Consequently, assimilation methods are very important. The Atmospheric Physics Group at the University of León in Spain and the Castile and León Supercomputing Center carry out daily weather prediction based on the Weather Research and Forecasting (WRF) model, covering the entire Iberian Peninsula. Forecasts of severe precipitation affecting the Ebro Valley, in the southern Pyrenees range of northeastern Spain, are crucial in the decision-making process for managing reservoirs or initializing runoff models. These actions can avert floods and ensure uninterrupted economic activity in the area. We investigated a set of cases corresponding to intense or severe precipitation patterns, using a rain gauge network. Simulations were performed with a dual objective, i.e., to analyze forecast improvement using a specific assimilation method, and to study the sensitivity of model outputs to different types of assimilation data. A WRF forecast model initialized by an NCEP SST analysis was used as the control run. The assimilation was based on the Meteorological Assimilation Data Ingest System (MADIS) developed by NOAA. The MADIS data used were METAR, maritime, ACARS, radiosonde, and satellite products. The results show forecast improvement using the suggested assimilation method, and differences in the accuracy of forecast precipitation patterns varied with the assimilation data source.

Understanding the Influence of Climate Forecasts on Farmer Decisions as Planned Behavior

NASA Astrophysics Data System (ADS)

Artikov, Ikrom; Hoffman, Stacey J.; Lynne, Gary D.; Pytlik Zillig, Lisa M.; Hu, Qi; Tomkins, Alan J.; Hubbard, Kenneth G.; Hayes, Michael J.; Waltman, William

2006-09-01

Results of a set of four regression models applied to recent survey data of farmers in eastern Nebraska suggest the causes that drive farmer intentions of using weather and climate information and forecasts in farming decisions. The model results quantify the relative importance of attitude, social norm, perceived behavioral control, and financial capability in explaining the influence of climate-conditions information and short-term and long-term forecasts on agronomic, crop insurance, and crop marketing decisions. Attitude, serving as a proxy for the utility gained from the use of such information, had the most profound positive influence on the outcome of all the decisions, followed by norms. The norms in the community, as a proxy for the utility gained from allowing oneself to be influenced by others, played a larger role in agronomic decisions than in insurance or marketing decisions. In addition, the interaction of controllability (accuracy, availability, reliability, timeliness of weather and climate information), self-efficacy (farmer ability and understanding), and general preference for control was shown to be a substantive cause. Yet control variables also have an economic side: The farm-sales variable as a measure of financial ability and motivation intensified and clarified the role of control while also enhancing the statistical robustness of the attitude and norms variables in better clarifying how they drive the influence. Overall, the integrated model of planned behavior from social psychology and derived demand from economics, that is, the “planned demand model,” is more powerful than models based on either of these approaches alone. Taken together, these results suggest that the “human dimension” needs to be better recognized so as to improve effective use of climate and weather forecasts and information for farming decision making.

Improving Arctic Sea Ice Observations and Data Access to Support Advances in Sea Ice Forecasting

NASA Astrophysics Data System (ADS)

Farrell, S. L.

2017-12-01

The economic and strategic importance of the Arctic region is becoming apparent. One of the most striking and widely publicized changes underway is the declining sea ice cover. Since sea ice is a key component of the climate system, its ongoing loss has serious, and wide-ranging, socio-economic implications. Increasing year-to-year variability in the geographic location, concentration, and thickness of the Arctic ice cover will pose both challenges and opportunities. The sea ice research community must be engaged in sustained Arctic Observing Network (AON) initiatives so as to deliver fit-for-purpose remote sensing data products to a variety of stakeholders including Arctic communities, the weather forecasting and climate modeling communities, industry, local, regional and national governments, and policy makers. An example of engagement is the work currently underway to improve research collaborations between scientists engaged in obtaining and assessing sea ice observational data and those conducting numerical modeling studies and forecasting ice conditions. As part of the US AON, in collaboration with the Interagency Arctic Research Policy Committee (IARPC), we are developing a strategic framework within which observers and modelers can work towards the common goal of improved sea ice forecasting. Here, we focus on sea ice thickness, a key varaible of the Arctic ice cover. We describe multi-sensor, and blended, sea ice thickness data products under development that can be leveraged to improve model initialization and validation, as well as support data assimilation exercises. We will also present the new PolarWatch initiative (polarwatch.noaa.gov) and discuss efforts to advance access to remote sensing satellite observations and improve communication with Arctic stakeholders, so as to deliver data products that best address societal needs.

Impacts of the Midwestern Drought Forecasts of 2000.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.

2002-10-01

In March of 2000 (and again in April and May) NOAA issued long-range forecasts indicating that an existing Midwestern drought would continue and intensify through the upcoming summer. These forecasts received extensive media coverage and wide public attention. If the drought persisted and intensified during the summer of 2000, significant agricultural and water supply problems would occur. However, in late May, June, and July heavy rains fell throughout most of the Midwest, ending the drought in most areas and revealing that the forecast was incorrect for most of the Midwest. Significant media coverage was devoted to the `failed' forecast, with considerable speculation that major economic hardship had resulted from the forecast. This study assesses the effects of the failed drought forecast on agricultural and water agency actions in the Midwest. Assessment of the agricultural and water management sectors revealed notable commonalities. Most people surveyed were aware of the drought forecasts, and the information sources were diverse. One-third of those surveyed indicated they did nothing as a result of the forecasts. The decisions and actions taken by others as a result of the forecasts provided mixed impacts. The water resource actions such as conserving water, seeking new sources, and convening state drought groups resulted in little cost and were considered to be beneficial. However, in the three areas of agricultural impacts (crop production shifts, crop insurance purchases, and grain market choices), mainly negative outcomes occurred. The 13 March issuance of the forecast was too late for producers to make sizable changes in production practices or to alter insurance coverage greatly, and most forecast-based actions taken in these two areas were considered to be negative but financially minor losses. However, 48% of the 1017 producers sampled altered their normal crop marketing practices, which in 84% of the cases led to sizable losses in revenue. This loss can be extrapolated as $1.1 billion for the entire Midwest if the sample statistics are representative of the region. A common result of the failed drought forecast among its users was a loss of credibility in climate predictions and a reluctance to use them in the future. Credibility is a fragile commodity that is difficult to obtain and is easy to lose.

Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

NASA Astrophysics Data System (ADS)

Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

2016-04-01

This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

PubMed

Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J

2018-01-22

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Operational Forecasting and Warning systems for Coastal hazards in Korea

NASA Astrophysics Data System (ADS)

Park, Kwang-Soon; Kwon, Jae-Il; Kim, Jin-Ah; Heo, Ki-Young; Jun, Kicheon

2017-04-01

Coastal hazards caused by both Mother Nature and humans cost tremendous social, economic and environmental damages. To mitigate these damages many countries have been running the operational forecasting or warning systems. Since 2009 Korea Operational Oceanographic System (KOOS) has been developed by the leading of Korea Institute of Ocean Science and Technology (KIOST) in Korea and KOOS has been operated in 2012. KOOS is consists of several operational modules of numerical models and real-time observations and produces the basic forecasting variables such as winds, tides, waves, currents, temperature and salinity and so on. In practical application systems include storm surges, oil spills, and search and rescue prediction models. In particular, abnormal high waves (swell-like high-height waves) have occurred in the East coast of Korea peninsula during winter season owing to the local meteorological condition over the East Sea, causing property damages and the loss of human lives. In order to improve wave forecast accuracy even very local wave characteristics, numerical wave modeling system using SWAN is established with data assimilation module using 4D-EnKF and sensitivity test has been conducted. During the typhoon period for the prediction of sever waves and the decision making support system for evacuation of the ships, a high-resolution wave forecasting system has been established and calibrated.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Advancing solar energy forecasting through the underlying physics

NASA Astrophysics Data System (ADS)

Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

2017-12-01

As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

Why a Letter of Transmittal Should Accompany Annual Financial Reports.

ERIC Educational Resources Information Center

Phipps, Bill W.

1986-01-01

Explains the importance of comprehensive annual financial reports, including introductory, financial, and statistical sections. Advises school districts to pay special attention to the letter of transmittal, which should provide information on services, financial highlights, economic forecasting, accounting principles used, and other pertinent…

STEM Vocational Socialization and Career Development in Middle Schools

ERIC Educational Resources Information Center

Kendall, Katherine A.

2017-01-01

Economic forecasts predict an unprecedented shortage of STEM workers in the United States. This study examined the vocational anticipatory socialization factors and classroom stratagems influencing middle school students' science, technology, engineering and mathematics (STEM) career development. Student attitudes towards STEM content areas and…

32 CFR Appendix F to Part 651 - Glossary

Code of Federal Regulations, 2013 CFR

2013-07-01

.... ASA(AL&T) Assistant Secretary of the Army (Acquisition, Logistics, and Technology). ASA(FM) Assistant.../Cost Analysis. EICS Environmental Impact Computer System. EIFS Economic Impact Forecast System. EIS... Record of Non-Applicability. RSC Regional Support Command. S&T Science and Technology. SA Secretary of...

32 CFR Appendix F to Part 651 - Glossary

Code of Federal Regulations, 2012 CFR

2012-07-01

.... ASA(AL&T) Assistant Secretary of the Army (Acquisition, Logistics, and Technology). ASA(FM) Assistant.../Cost Analysis. EICS Environmental Impact Computer System. EIFS Economic Impact Forecast System. EIS... Record of Non-Applicability. RSC Regional Support Command. S&T Science and Technology. SA Secretary of...

32 CFR Appendix F to Part 651 - Glossary

Code of Federal Regulations, 2014 CFR

2014-07-01

.... ASA(AL&T) Assistant Secretary of the Army (Acquisition, Logistics, and Technology). ASA(FM) Assistant.../Cost Analysis. EICS Environmental Impact Computer System. EIFS Economic Impact Forecast System. EIS... Record of Non-Applicability. RSC Regional Support Command. S&T Science and Technology. SA Secretary of...

THE U.S. EPA'S VISION FOR A BEACH FORECASTING TOOL

EPA Science Inventory

Beach closures due to water quality that exceeds standard limits occur frequently in the United States. These beach closures deprive the public of opportunities for recreational activities and can have a significant impact on local economics. Because of the large number of mari...

e-Business Innovation: The Next Decade

NASA Astrophysics Data System (ADS)

Marca, David A.

Innovation is invention or application of technologies or theories that radically alters business and the economy. For many years, innovation and the economy have been locked in 80-year cycles, which might imply that innovation is an economic driver, and vice versa. Based on this, some forecast that innovation and the economy might decrease sharply due to several forces: a) decreasing economic growth, b) increasing demand for custom services, c) more entrepreneurial work environments, and d) urban and environmental degradation. Should such forecasts hold true, business may need to alter its offerings, operations and organization to survive. Such a scenario may also require applied e-Business innovation by combining existing internet, wireless, broadband, and video technologies. One possible result: flexible front offices integrated with efficient back offices. Such an e-Business could comprise: a) a customer-based and transaction-based organization, b) functions for adaptive offerings that anticipate need, c) highly responsive, real-time, operations having no inventory, and d) value-based front-end, and automated back-end, decision making.

A dynamic factor model of the evaluation of the financial crisis in Turkey.

PubMed

Sezgin, F; Kinay, B

2010-01-01

Factor analysis has been widely used in economics and finance in situations where a relatively large number of variables are believed to be driven by few common causes of variation. Dynamic factor analysis (DFA) which is a combination of factor and time series analysis, involves autocorrelation matrices calculated from multivariate time series. Dynamic factor models were traditionally used to construct economic indicators, macroeconomic analysis, business cycles and forecasting. In recent years, dynamic factor models have become more popular in empirical macroeconomics. They have more advantages than other methods in various respects. Factor models can for instance cope with many variables without running into scarce degrees of freedom problems often faced in regression-based analysis. In this study, a model which determines the effect of the global crisis on Turkey is proposed. The main aim of the paper is to analyze how several macroeconomic quantities show an alteration before the evolution of the crisis and to decide if a crisis can be forecasted or not.

Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030.

PubMed

Bommer, Christian; Sagalova, Vera; Heesemann, Esther; Manne-Goehler, Jennifer; Atun, Rifat; Bärnighausen, Till; Davies, Justine; Vollmer, Sebastian

2018-05-01

Despite the importance of diabetes for global health, the future economic consequences of the disease remain opaque. We forecast the full global costs of diabetes in adults through the year 2030 and predict the economic consequences of diabetes if global targets under the Sustainable Development Goals (SDG) and World Health Organization Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 are met. We modeled the absolute and gross domestic product (GDP)-relative economic burden of diabetes in individuals aged 20-79 years using epidemiological and demographic data, as well as recent GDP forecasts for 180 countries. We assumed three scenarios: prevalence and mortality 1 ) increased only with urbanization and population aging (baseline scenario), 2 ) increased in line with previous trends (past trends scenario), and 3 ) achieved global targets (target scenario). The absolute global economic burden will increase from U.S. $1.3 trillion (95% CI 1.3-1.4) in 2015 to $2.2 trillion (2.2-2.3) in the baseline, $2.5 trillion (2.4-2.6) in the past trends, and $2.1 trillion (2.1-2.2) in the target scenarios by 2030. This translates to an increase in costs as a share of global GDP from 1.8% (1.7-1.9) in 2015 to a maximum of 2.2% (2.1-2.2). The global costs of diabetes and its consequences are large and will substantially increase by 2030. Even if countries meet international targets, the global economic burden will not decrease. Policy makers need to take urgent action to prepare health and social security systems to mitigate the effects of diabetes. © 2018 by the American Diabetes Association.

Chaos and Forecasting - Proceedings of the Royal Society Discussion Meeting

NASA Astrophysics Data System (ADS)

Tong, Howell

1995-04-01

The Table of Contents for the full book PDF is as follows: * Preface * Orthogonal Projection, Embedding Dimension and Sample Size in Chaotic Time Series from a Statistical Perspective * A Theory of Correlation Dimension for Stationary Time Series * On Prediction and Chaos in Stochastic Systems * Locally Optimized Prediction of Nonlinear Systems: Stochastic and Deterministic * A Poisson Distribution for the BDS Test Statistic for Independence in a Time Series * Chaos and Nonlinear Forecastability in Economics and Finance * Paradigm Change in Prediction * Predicting Nonuniform Chaotic Attractors in an Enzyme Reaction * Chaos in Geophysical Fluids * Chaotic Modulation of the Solar Cycle * Fractal Nature in Earthquake Phenomena and its Simple Models * Singular Vectors and the Predictability of Weather and Climate * Prediction as a Criterion for Classifying Natural Time Series * Measuring and Characterising Spatial Patterns, Dynamics and Chaos in Spatially-Extended Dynamical Systems and Ecologies * Non-Linear Forecasting and Chaos in Ecology and Epidemiology: Measles as a Case Study

Seasonal forecasting of discharge for the Raccoon River, Iowa

NASA Astrophysics Data System (ADS)

Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

2016-04-01

The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast weighting procedures based on the computed potential skill (historical forecast accuracy) of the different GCMs. We find that the models describe the year-to-year variability in streamflow accurately, as well as the overall tendency towards increasing (and more variable) discharge over time. Surprisingly, forecast skill does not decrease markedly with lead time, and high flows tend to be well predicted, suggesting that these forecasts may have considerable practical applications. Further, the seasonal flow forecast accuracy is substantially improved by weighting the contribution of individual GCMs to the forecasts, and also by the inclusion of antecedent precipitation. Our results can provide critical information for adaptation strategies aiming to mitigate the costs and disruptions arising from flood and drought conditions, and allow us to determine how far in advance skillful forecasts can be issued. The availability of these discharge forecasts would have major societal and economic benefits for hydrology and water resources management, agriculture, disaster forecasts and prevention, energy, finance and insurance, food security, policy-making and public authorities, and transportation.

Staged decision making based on probabilistic forecasting

NASA Astrophysics Data System (ADS)

Booister, Nikéh; Verkade, Jan; Werner, Micha; Cranston, Michael; Cumiskey, Lydia; Zevenbergen, Chris

2016-04-01

Flood forecasting systems reduce, but cannot eliminate uncertainty about the future. Probabilistic forecasts explicitly show that uncertainty remains. However, as - compared to deterministic forecasts - a dimension is added ('probability' or 'likelihood'), with this added dimension decision making is made slightly more complicated. A technique of decision support is the cost-loss approach, which defines whether or not to issue a warning or implement mitigation measures (risk-based method). With the cost-loss method a warning will be issued when the ratio of the response costs to the damage reduction is less than or equal to the probability of the possible flood event. This cost-loss method is not widely used, because it motivates based on only economic values and is a technique that is relatively static (no reasoning, yes/no decision). Nevertheless it has high potential to improve risk-based decision making based on probabilistic flood forecasting because there are no other methods known that deal with probabilities in decision making. The main aim of this research was to explore the ways of making decision making based on probabilities with the cost-loss method better applicable in practice. The exploration began by identifying other situations in which decisions were taken based on uncertain forecasts or predictions. These cases spanned a range of degrees of uncertainty: from known uncertainty to deep uncertainty. Based on the types of uncertainties, concepts of dealing with situations and responses were analysed and possible applicable concepts where chosen. Out of this analysis the concepts of flexibility and robustness appeared to be fitting to the existing method. Instead of taking big decisions with bigger consequences at once, the idea is that actions and decisions are cut-up into smaller pieces and finally the decision to implement is made based on economic costs of decisions and measures and the reduced effect of flooding. The more lead-time there is in flood event management, the more damage can be reduced. And with decisions based on probabilistic forecasts, partial decisions can be made earlier in time (with a lower probability) and can be scaled up or down later in time when there is more certainty; whether the event takes place or not. Partial decisions are often more cheap, or shorten the final mitigation-time at the moment when there is more certainty. The proposed method is tested on Stonehaven, on the Carron River in Scotland. Decisions to implement demountable defences in the town are currently made based on a very short lead-time due to the absence of certainty. Application showed that staged decision making is possible and gives the decision maker more time to respond to a situation. The decision maker is able to take a lower regret decision with higher uncertainty and less related negative consequences. Although it is not possible to quantify intangible effects, it is part of the analysis to reduce these effects. Above all, the proposed approach has shown to be a possible improvement in economic terms and opens up possibilities of more flexible and robust decision making.

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

Seasonal forecasts of impact-relevant climate information indices developed as part of the EUPORIAS project

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas

2015-04-01

Climate information indices (CIIs) represent a way to communicate climate conditions to specific sectors and the public. As such, CIIs provide actionable information to stakeholders in an efficient way. Due to their non-linear nature, such CIIs can behave differently than the underlying variables, such as temperature. At the same time, CIIs do not involve impact models with different sources of uncertainties. As part of the EU project EUPORIAS (EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale) we have developed examples of seasonal forecasts of CIIs. We present forecasts and analyses of the skill of seasonal forecasts for CIIs that are relevant to a variety of economic sectors and a range of stakeholders: heating and cooling degree days as proxies for energy demand, various precipitation and drought-related measures relevant to agriculture and hydrology, a wild fire index, a climate-driven mortality index and wind-related indices tailored to renewable energy producers. Common to all examples is the finding of limited forecast skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of forecast skill vary: often we find little skill in the underlying variable(s) precisely in those areas that are relevant for the CII, in other cases the nature of the CII is particularly demanding for predictions, as seen in the case of counting measures such as frost days or cool nights. On the other hand, several results suggest there may be some predictability in sub-regions for certain indices. Several of the exemplary analyses show potential for skillful forecasts and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII forecasts showed similar skill values as those of the underlying meteorological variables, forecasts of CIIs provide added value from a user perspective.

Drought forecasting in Luanhe River basin involving climatic indices

NASA Astrophysics Data System (ADS)

Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

2017-11-01

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the three proposed models outperform the two traditional models and involving large-scale climatic indices can improve the forecasting accuracy.

Prospects for development of unified global flood observation and prediction systems (Invited)

NASA Astrophysics Data System (ADS)

Lettenmaier, D. P.

2013-12-01

Floods are among the most damaging of natural hazards, with global flood losses in 2011 alone estimated to have exceeded $100B. Historically, flood economic damages have been highest in the developed world (due in part to encroachment on historical flood plains), but loss of life, and human impacts have been greatest in the developing world. However, as the 2011 Thailand floods show, industrializing countries, many of which do not have well developed flood protection systems, are increasingly vulnerable to economic damages as they become more industrialized. At present, unified global flood observation and prediction systems are in their infancy; notwithstanding that global weather forecasting is a mature field. The summary for this session identifies two evolving capabilities that hold promise for development of more sophisticated global flood forecast systems: global hydrologic models and satellite remote sensing (primarily of precipitation, but also of flood inundation). To this I would add the increasing sophistication and accuracy of global precipitation analysis (and forecast) fields from numerical weather prediction models. In this brief overview, I will review progress in all three areas, and especially the evolution of hydrologic data assimilation which integrates modeling and data sources. I will also comment on inter-governmental and inter-agency cooperation, and related issues that have impeded progress in the development and utilization of global flood observation and prediction systems.

Enhancements to the Economic Impact Forecast System (EIFS).

DTIC Science & Technology

1984-04-01

IU U .. A ILC.. Meww 4 """ Economia c. .- brmodc ’ The economic submodel is appropriately classified as an export base model that jointly determines...9 yes 3 Washington - 1963 State of Washington 27 no 4 Utah - 1963 State of Utah 39 yes 5 New Mexico - 1960 State of New Mexico 42 yes 6 Kansas - 1965... Mexico .311 .627 -.017 .360 .635 (13.266) (1.381) (8.507) Kansas .556 427 -.022 .616 .433 (11.270) (.854) (7156) Clinton .229 .681 -.005 .247 .677

Application of the CloudSat and NEXRAD Radars Toward Improvements in High Resolution Operational Forecasts

NASA Technical Reports Server (NTRS)

Molthan, A. L.; Haynes, J. A.; Case, J. L.; Jedlovec, G. L.; Lapenta, W. M.

2008-01-01

As computational power increases, operational forecast models are performing simulations with higher spatial resolution allowing for the transition from sub-grid scale cloud parameterizations to an explicit forecast of cloud characteristics and precipitation through the use of single- or multi-moment bulk water microphysics schemes. investments in space-borne and terrestrial remote sensing have developed the NASA CloudSat Cloud Profiling Radar and the NOAA National Weather Service NEXRAD system, each providing observations related to the bulk properties of clouds and precipitation through measurements of reflectivity. CloudSat and NEXRAD system radars observed light to moderate snowfall in association with a cold-season, midlatitude cyclone traversing the Central United States in February 2007. These systems are responsible for widespread cloud cover and various types of precipitation, are of economic consequence, and pose a challenge to operational forecasters. This event is simulated with the Weather Research and Forecast (WRF) Model, utilizing the NASA Goddard Cumulus Ensemble microphysics scheme. Comparisons are made between WRF-simulated and observed reflectivity available from the CloudSat and NEXRAD systems. The application of CloudSat reflectivity is made possible through the QuickBeam radiative transfer model, with cautious application applied in light of single scattering characteristics and spherical target assumptions. Significant differences are noted within modeled and observed cloud profiles, based upon simulated reflectivity, and modifications to the single-moment scheme are tested through a supplemental WRF forecast that incorporates a temperature dependent snow crystal size distribution.

Towards custom made seasonal/decadal forecasting

NASA Astrophysics Data System (ADS)

Mahlstein, Irina; Spirig, Christoph; Liniger, Mark

2014-05-01

Climate indices offer the possibility to deliver information to the end user that can be easily applied to their field of work. For instance, a 3-monthly mean average temperature does not say much about the Heating Degree Days of a season, or how many frost days there are to be expected. Hence, delivering aggregated climate information can be more useful to the consumer than just raw data. In order to ensure that the end-users actually get what they need, the providers need to know what exactly they need to deliver. Hence, the specific user-needs have to be identified. In the framework of EUPORIAS, interviews with the end-user were conducted in order to learn more about the types of information that are needed. But also to investigate what knowledge exists among the users about seasonal/decadal forecasting and in what way uncertainties are taken into account. It is important that we gain better knowledge of how forecasts/predictions are applied by the end-user to their specific situation and business. EUPORIAS, which is embedded in the framework of EU FP7, aims exactly to improve that knowledge and deliver very specific forecasts that are custom made. Here we present examples of seasonal forecasts and their skill of several climate impact indices with direct relevance for specific economic sectors, such as energy. The results are compared to the visualization of conventional depiction of seasonal forecasts, such as 3 monthly average temperature tercile probabilities and the differences are highlighted.

Global Impacts and Regional Actions: Preparing for the 1997-98 El Niño.

NASA Astrophysics Data System (ADS)

Buizer, James L.; Foster, Josh; Lund, David

2000-09-01

It has been estimated that severe El Niño-related flooding and droughts in Africa, Latin America, North America, and Southeast Asia resulted in more than 22 000 lives lost and in excess of $36 billion in damages during 1997-98. As one of the most severe events this century, the 1997-98 El Niño was unique not only in terms of physical magnitude, but also in terms of human response. This response was made possible by recent advances in climate-observing and forecasting systems, creation and dissemination of forecast information by institutions such as the International Research Institute for Climate Prediction and NOAA's Climate Prediction Center, and individuals in climate-sensitive sectors willing to act on forecast information by incorporating it into their decision-making. The supporting link between the forecasts and their practical application was a product of efforts by several national and international organizations, and a primary focus of the United States National Oceanic and Atmospheric Administration Office of Global Programs (NOAA/OGP).NOAA/OGP over the last decade has supported pilot projects in Latin America, the Caribbean, the South Pacific, Southeast Asia, and Africa to improve transfer of forecast information to climate sensitive sectors, study linkages between climate and human health, and distribute climate information products in certain areas. Working with domestic and international partners, NOAA/OGP helped organize a total of 11 Climate Outlook Fora around the world during the 1997-98 El Niño. At each Outlook Forum, climatologists and meteorologists created regional, consensus-based, seasonal precipitation forecasts and representatives from climate-sensitive sectors discussed options for applying forecast information. Additional ongoing activities during 1997-98 included research programs focused on the social and economic impacts of climate change and the regional manifestations of global-scale climate variations and their effect on decision-making in climate-sensitive sectors in the United States.The overall intent of NOAA/OGP's activities was to make experimental forecast information broadly available to potential users, and to foster a learning process on how seasonal-to-interannual forecasts could be applied in sectors susceptible to climate variability. This process allowed users to explore the capabilities and limitations of climate forecasts currently available, and forecast producers to receive feedback on the utility of their products. Through activities in which NOAA/OGP and its partners were involved, it became clear that further application of forecast information will be aided by improved forecast accuracy and detail, creation of common validation techniques, continued training in forecast generation and application, alternate methods for presenting forecast information, and a systematic strategy for creation and dissemination of forecast products.The overall intent of NOAA/OGP's activities was to make experimental forecast information broadly available to potential users, and to foster a learning process on how seasonal-to-interannual forecasts could be applied in sectors susceptible to climate variability. This process allowed users to explore the capabilities and limitations of climate forecasts currently available, and forecast producers to receive feedback on the utility of their products. Through activities in which NOAA/OGP and its partners were involved, it became clear that further application of forecast information will be aided by improved forecast accuracy and detail, creation of common validation techniques, continued training in forecast generation and application, alternate methods for presenting forecast information, and a systematic strategy for creation and dissemination of forecast products.

Perspective On Income Security and Social Services and An Agenda for Analysis.

DTIC Science & Technology

1981-08-13

economic stability of America and the continued viability of the U.S. social system. This report provides a prespective on many of the major issues, identifies present concerns, forecasts future developments, and briefly discusses GAO’s approach to addressing these issues.

The future of transportation planning : dynamic travel behavior analyses based on stochastic decision-making styles : final report.

DOT National Transportation Integrated Search

2003-08-01

Over the past half-century, the progress of travel behavior research and travel demand forecasting has been spear : headed and continuously propelled by the micro-economic theories, specifically utility maximization. There is no : denial that the tra...

Assessing Efficiency of GDP Revisions in South Africa

ERIC Educational Resources Information Center

Ncwadi, Ronney

2016-01-01

Gross Domestic Product of any country often influence economic decisions by policy makers, market participants and econometricians on policy recommendations, evaluation and forecasting. However these decisions are often based on preliminary data announcements by statistical agencies. It is therefore important to ensure that the preliminary GDP…

A parametric study of the value of hydrological information for irrigation and hydropower management of the Feather River

NASA Technical Reports Server (NTRS)

Wetzler, E.; Sand, F.; Stevenson, P.; Putnam, M.

1975-01-01

A case study analysis is presented of the relationships between improvements in the accuracy, frequency, and timeliness of information used in making hydrological forecasts and economic benefits in the areas of hydropower and irrigation. The area chosen for the case study is the Oroville Dam and Reservoir. Emphasis is placed on the use of timely and accurate mapping of the aerial extent of snow in the basin by earth resources survey systems such as LANDSAT. The subject of benefits resulting from improved runoff forecasts is treated in a generalized way without specifying the source of the improvements.

Forecasting system predicts presence of sea nettles in Chesapeake Bay

NASA Astrophysics Data System (ADS)

Brown, Christopher W.; Hood, Raleigh R.; Li, Zhen; Decker, Mary Beth; Gross, Thomas F.; Purcell, Jennifer E.; Wang, Harry V.

Outbreaks of noxious biota, which occur in both aquatic and terrestrial systems, can have considerable negative economic impacts. For example, an increasing frequency of harmful algal blooms worldwide has negatively affected the tourism industry in many regions. Such impacts could be mitigated if the conditions that give rise to these outbreaks were known and could be monitored. Recent advances in technology and communications allow us to continuously measure and model many environmental factors that are responsible for outbreaks of certain noxious organisms. A new prototype ecological forecasting system predicts the likelihood of occurrence of the sea nettle (Chrysaora quinquecirrha), a stinging jellyfish, in the Chesapeake Bay.

Energy Resources Program of the U.S. Geological Survey

USGS Publications Warehouse

Weedman, Suzanne

2001-01-01

Our Nation faces the simultaneous challenges of increasing demand for energy, declining domestic production from existing oil and gas fields, and increasing expectations for environmental protection. The Energy Information Administration (2000) forecasts that worldwide energy consumption will increase 32 percent between 1999 and 2020 because of growth of the world economy. Forecasts indicate that in the same time period, U.S. natural gas consumption will increase 62 percent, petroleum consumption will increase 33 percent, and coal consumption will increase 22 percent. The U.S. Geological Survey provides the objective scientific information our society needs for sound decisions regarding land management, environmental quality, and economic, energy, and strategic policy.

Leveraging Past and Current Measurements to Probabilistically Nowcast Low Visibility Procedures at an Airport

NASA Astrophysics Data System (ADS)

Mayr, G. J.; Kneringer, P.; Dietz, S. J.; Zeileis, A.

2016-12-01

Low visibility or low cloud ceiling reduce the capacity of airports by requiring special low visibility procedures (LVP) for incoming/departing aircraft. Probabilistic forecasts when such procedures will become necessary help to mitigate delays and economic losses.We compare the performance of probabilistic nowcasts with two statistical methods: ordered logistic regression, and trees and random forests. These models harness historic and current meteorological measurements in the vicinity of the airport and LVP states, and incorporate diurnal and seasonal climatological information via generalized additive models (GAM). The methods are applied at Vienna International Airport (Austria). The performance is benchmarked against climatology, persistence and human forecasters.

Estimates of the long-term U.S. economic impacts of global climate change-induced drought.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ehlen, Mark Andrew; Loose, Verne W.; Warren, Drake E.

2010-01-01

While climate-change models have done a reasonable job of forecasting changes in global climate conditions over the past decades, recent data indicate that actual climate change may be much more severe. To better understand some of the potential economic impacts of these severe climate changes, Sandia economists estimated the impacts to the U.S. economy of climate change-induced impacts to U.S. precipitation over the 2010 to 2050 time period. The economists developed an impact methodology that converts changes in precipitation and water availability to changes in economic activity, and conducted simulations of economic impacts using a large-scale macroeconomic model of themore » U.S. economy.« less

Use of medium-range weather forecasts for drought mitigation and adaptation under a Mediterranean area

NASA Astrophysics Data System (ADS)

Lahlou, Ouiam; Imani, Yasmina; Bennasser Alaoui, Si; Dutra, Emanuel; DiGiuseppe, Francesca; Pappenberger, Florian; Wetterhall, Fredrik

2014-05-01

Use of medium-range weather forecasts for drought mitigation and adaptation under a Mediterranean area Authors: Ouiam Lahlou1, Yasmina Imani1, Si Bennasser Alaoui1, Emmanuel Dutra 2, Francesca Di Guiseppe2, Florian Pappenberger2, Fredrik Wetterhall2 1: Institut Agronomique et Vétérinaire Hassan II (IAV Hassan II) 2: European Center for Medium-Range Weather Forecasts (ECMWF) The main pillar of economic development in Morocco is the agricultural sector employing 40% of the active workforce. Agriculture is still mainly dominated by rainfed agriculture which is vulnerable to an increasing frequency and severity of drought events. In rainfed agriculture, there are few interventions possible once crops are planted. Medium to long range weather forecasts could therefore provide valid information for crop selection and sowing time at the onset of the yield season and later to plan mitigation measures during dry-spell episodes. More than 600 daily forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasting system were analyzed in terms of probabilistic skills scores. Results show that, while daily and weekly accumulated precipitation are poorly predicted there is good skill in the forecast of occurrence and extent of dry periods. The availability of this information to decision makers in the agricultural sector would mean moving from a reactive drought management plan to a proactive one. This is very important, especially for the remote areas where often the needed help comes late. A simulation case-study involving farmers who were made aware of the availability of forecasts for the next seasons, show that medium-range forecasts will allow i) governments and relief agencies to position themselves for more effective and cost-efficient drought interventions, ii) producers to be more aware of their production options and insure their payment rate, iii) Herders, to cope with higher food costs for their cattle iv) farmers to better plan the pre-season agronomic corrections, to schedule the most appropriate timing for the unique complementary irrigation that they can provide to cereals, and to better schedule the harvesting date. Since failing on these mitigation actions due to a lack of forecast availability would be highly priced for the rural Marocco economy, we stress that forecasting drought onset, especially under the high variability of the Mediterranean climate, is of a paramount importance.

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful between the 5th and the 8th day of the prediction. The information obtained is then included in an early warning system, designed in the framework of the European project iCoast (ECHO/SUB/2013/661009) with the aim of set alarms in coastal areas depending on the wave conditions, the sea level, the flooding and the run up in the coast.

Linking seasonal climate forecasts with crop models in Iberian Peninsula

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Performance assessment of deterministic and probabilistic weather predictions for the short-term optimization of a tropical hydropower reservoir

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Alvarado, Rodolfo; Assis dos Reis, Alberto; Naumann, Steffi; Collischonn, Walter

2016-04-01

Hydropower is the most important electricity source in Brazil. During recent years, it accounted for 60% to 70% of the total electric power supply. Marginal costs of hydropower are lower than for thermal power plants, therefore, there is a strong economic motivation to maximize its share. On the other hand, hydropower depends on the availability of water, which has a natural variability. Its extremes lead to the risks of power production deficits during droughts and safety issues in the reservoir and downstream river reaches during flood events. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for the short-term reservoir management, the use of probabilistic ensemble forecasts and stochastic optimization techniques receives growing attention and a number of researches have shown its benefit. The present work shows one of the first hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project (HPP) Três Marias, located in southeast Brazil. The HPP reservoir is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control at Pirapora City located 120 km downstream of the dam. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts with 50 ensemble members of the ECMWF are used as forcing of the MGB-IPH hydrological model to generate streamflow forecasts over a period of 2 years. The online optimization depends on a deterministic and multi-stage stochastic version of a model predictive control scheme. Results for the perfect forecasts show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts with shorter lead times of up to 15 days shows the practical benefit of actual operational data. It appears that the use of stochastic optimization combined with ensemble forecasts leads to a significant higher level of flood protection without compromising the HPP's energy production.

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.

PubMed

Withanage, Gayan P; Viswakula, Sameera D; Nilmini Silva Gunawardena, Y I; Hapugoda, Menaka D

2018-04-24

Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike's information criterion, Bayesian information criterion and residual analysis. The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month's dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

Adaptive Regulation of the Northern California Reservoir System for Water, Energy, and Environmental Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.; Kistenmacher, M.; Yao, H.; Georgakakos, K. P.

2014-12-01

The 2014 National Climate Assessment of the US Global Change Research Program emphasizes that water resources managers and planners in most US regions will have to cope with new risks, vulnerabilities, and opportunities, and recommends the development of adaptive capacity to effectively respond to the new water resources planning and management challenges. In the face of these challenges, adaptive reservoir regulation is becoming all the more ncessary. Water resources management in Northern California relies on the coordinated operation of several multi-objective reservoirs on the Trinity, Sacramento, American, Feather, and San Joaquin Rivers. To be effective, reservoir regulation must be able to (a) account for forecast uncertainty; (b) assess changing tradeoffs among water uses and regions; and (c) adjust management policies as conditions change; and (d) evaluate the socio-economic and environmental benefits and risks of forecasts and policies for each region and for the system as a whole. The Integrated Forecast and Reservoir Management (INFORM) prototype demonstration project operated in Northern California through the collaboration of several forecast and management agencies has shown that decision support systems (DSS) with these attributes add value to stakeholder decision processes compared to current, less flexible management practices. Key features of the INFORM DSS include: (a) dynamically downscaled operational forecasts and climate projections that maintain the spatio-temporal coherence of the downscaled land surface forcing fields within synoptic scales; (b) use of ensemble forecast methodologies for reservoir inflows; (c) assessment of relevant tradeoffs among water uses on regional and local scales; (d) development and evaluation of dynamic reservoir policies with explicit consideration of hydro-climatic forecast uncertainties; and (e) focus on stakeholder information needs.This article discusses the INFORM integrated design concept, underlying methodologies, and selected applications with the California water resources system.

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme

NASA Astrophysics Data System (ADS)

Owens, Mathew J.; Riley, Pete

2017-11-01

Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme.

PubMed

Owens, Mathew J; Riley, Pete

2017-11-01

Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

Wintertime component of the THORPEX Pacific-Asian Regional Campaign (T-PARC)

NASA Astrophysics Data System (ADS)

Song, Y.; Toth, Z.; Asuma, Y.; Reynolds, C.; Lngland, R.; Szunyogh, I.; Colle, B.; Chang, E.; Doyle, C.; Kats, A.

2009-04-01

The winter component of the T-PARC is an international field project that aims at improving high impact weather event forecasts for North America. The main objective is to understand how perturbations from the tropics, Eurasia and polar fronts travel through waveguide and turn into high impact weather events. Through adaptive observations by using manned aircrafts (NOAA G-IV and US Air force C-130s) and Russian rawinsonde network over data sparse regions, it is expected that accurate initial conditions will improve the numerical weather forecasts. Non-adaptive aircraft measurements over the Pacific Rim and part of India are also deployed through E-AMDAR program, which is expected to improve the background field over Asia where perturbations are initiated. The campaign is led by NOAA and joined by agencies and universities from US, Canada, Mexico, Japan, ECWMF, and Russia. While most observational data will be assimilated by operational centers to improve real time numerical weather predictions, post field studies will focus on aspects such as: data impact on forecast and analysis, dry and moist processes that affect the formation and propagation of perturbations, meso-scale storm structure, error growth, forecast "busts" under certain atmospheric regimes, and socio-economic applications such as costs and benefits of improved forecasts and their use by the public for high impact weather events. In particular, a Winter Olympics demonstration project (February 12 - February 28) is expected to be a test bed during winter T-PARC for real user outreach and application purposes. Effectiveness of existing targeting methods as well as new targeting methods in the 3-5 day lead time range will be pursued and other aspects related to data assimilation and numerical forecasts (both deterministic and ensemble forecasts) will be investigated within this project as well.

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional “Upwind” Scheme

PubMed Central

Riley, Pete

2017-01-01

Abstract Long lead‐time space‐weather forecasting requires accurate prediction of the near‐Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near‐Sun solar wind and magnetic field conditions provide the inner boundary condition to three‐dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics‐based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near‐Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near‐Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near‐Sun solar wind speed at a range of latitudes about the sub‐Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun‐Earth line. Propagating these conditions to Earth by a three‐dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one‐dimensional “upwind” scheme is used. The variance in the resulting near‐Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996–2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large). PMID:29398982

Analysis of the environmental and economic indicators of the industrial enterprise

NASA Astrophysics Data System (ADS)

Mikhailov, V. G.; Kiseleva, T. V.

2018-05-01

In the paper the features of the analysis of the environmental and economic indicators of the industrial enterprise are considered. The purpose of the study is to improve the system of environmental and economic analysis at the enterprise for more accurate forecasting of its main environmental and economic indicators. The study of the main approaches to the implementation of environmental and economic analysis based on the corresponding systems of indicators with identification of the most significant factors was carried out. The main result of the study is the choice of a system for analyzing the environmental and economic indicators, maximally oriented to a specific enterprise, taking into account its production specific features. The practical significance of the study consists in the selection of an adequate system of indicators at enterprises to improve the effectiveness from preparation of an environmentally safe management decision.

Mitigating randomness of consumer preferences under certain conditional choices

NASA Astrophysics Data System (ADS)

Bothos, John M. A.; Thanos, Konstantinos-Georgios; Papadopoulou, Eirini; Daveas, Stelios; Thomopoulos, Stelios C. A.

2017-05-01

Agent-based crowd behaviour consists a significant field of research that has drawn a lot of attention in recent years. Agent-based crowd simulation techniques have been used excessively to forecast the behaviour of larger or smaller crowds in terms of certain given conditions influenced by specific cognition models and behavioural rules and norms, imposed from the beginning. Our research employs conditional event algebra, statistical methodology and agent-based crowd simulation techniques in developing a behavioural econometric model about the selection of certain economic behaviour by a consumer that faces a spectre of potential choices when moving and acting in a multiplex mall. More specifically we try to analyse the influence of demographic, economic, social and cultural factors on the economic behaviour of a certain individual and then we try to link its behaviour with the general behaviour of the crowds of consumers in multiplex malls using agent-based crowd simulation techniques. We then run our model using Generalized Least Squares and Maximum Likelihood methods to come up with the most probable forecast estimations, regarding the agent's behaviour. Our model is indicative about the formation of consumers' spectre of choices in multiplex malls under the condition of predefined preferences and can be used as a guide for further research in this area.

Extracting Value from Ensembles for Cloud-Free Forecasting

DTIC Science & Technology

2011-09-01

event. It should follow that the decision maker also prefers the movie over the sporting event. 3) If the movie ticket comes with popcorn , neither...the popcorn nor the movie ticket can be valued more than the movie ticket and popcorn together, assuming there are no other economic factors to

Toward 2000. Trends Report II: Elementary-Secondary Projections.

ERIC Educational Resources Information Center

Press, Harold L.

Comprehensive, systematic planning provides the overall direction for education through the development of policies and objectives. An understanding of demographic, social, and economic trends is necessary for educators to make decisions for the future. The 1986 demographic forecasts for the province of Newfoundland are updated in this report,…

Benefit assessment of NASA space technology goals

NASA Technical Reports Server (NTRS)

1976-01-01

The socio-economic benefits to be derived from system applications of space technology goals developed by NASA were assessed. Specific studies include: electronic mail; personal telephone communications; weather and climate monitoring, prediction, and control; crop production forecasting and water availability; planetary engineering of the planet Venus; and planetary exploration.

78 FR 46783 - Federal Acquisition Regulation; Documenting Contractor Performance

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-01

... determinations in FAR subpart 9.1 and the impact of a contractor with more than one contract to have a negative... forecasting and cost controlling and the impact on future contracts. This respondent felt that a contractor... (including potential economic, environmental, public health and safety effects, distributive impacts, and...

The Promise and the Problems of Community Colleges in California.

ERIC Educational Resources Information Center

Craig, William G.; McIntyre, Charles

The requirements of urban sprawl, a highly industrialized and technical economy, and an unpredictable job market apply great pressure to the educational community for diversity of offerings, quality, and accountability. However, despite the extraordinary demands for programs and services, the economic forecast for higher education is pessimistic.…

The Future of African-Americans to the Year 2000. Summary Report.

ERIC Educational Resources Information Center

Congressional Task Force on the Future of African-Americans, Washington, DC.

This summary report highlights the major features of a comprehensive analysis and forecast of the future of African-Americans. Section 1 discusses the future of the United States. Section 2, "The Past and Present," covers the following topics: (1) "Employment and Economic Development"; (2) "Health"; (3)…

SMAP Impact Analysis of Early Adopter Research-Two Case studies on the scientific and societal benefits of SMAP data

NASA Astrophysics Data System (ADS)

Escobar, V. M.; Wu, H. T.; Moran, S.; O'Neill, P. E.

2016-12-01

To document and evaluate the use of SMAP science products in applications, the SMAP Phase E Applications Plan proposes to "conduct case studies to address a basic question: How are SMAP science products used in decision support systems and how does the new data stream affect the system performance?" The objective is to determine the value of SMAP data to the six categories of applications based on Early Adopters' experiences, where value is defined as the scientific and/or societal benefit. Since SMAP is the first mission with a pre-launch Early Adopter Program, the post-launch case study is also unprecedented. In this talk, we will show some results of the SMAP Early Adopters, with focus on the two case studies in the applications of agriculture and weather forecasting, respectively. For agriculture, we will show the work of USDA/NASS (National Agriculture Statistics Service) scientists (Zhengwei Yang and Rick Mueller). Using SMAP soil moisture products, they have been working on the establishment of a visualization, analytics, and dissemination tool to support and improve US national crop condition monitoring. Scientifically, this study will improve our understanding on the impact of crop canopy on the SMAP SM retrieval and on the mapping relation between SMAP SM and NASS soil moisture survey results. Socio-economically, the use of SMAP data and web-based tool will improve the consistency, reliability, objectivity, and efficiency of cropland soil moisture monitoring and assessment, which will benefit the current end users of the NASS weekly report including farmers, insurance companies, and financial institutes. For weather, we will show the work of NOAA scientists (Xiwu Zhan, Weizhong Zheng, and Mike Ek) on the transition of NASA SMAP research products to NOAA operational numerical weather and seasonal climate predictions and research hydrological forecasts. Results of initial analyses and validation of the assimilation of SMAP soil moisture in NOAA's Global Forecast System are promising. The implementation of SMAP data into NOAA's operational forecasting systems is expected to increase the skill and confidence level of our weather, seasonal climate, and hydrological forecasts, which has huge socio-economic benefit.

Helping Resource Managers Understand Hydroclimatic Variability and Forecasts: A Case Study in Research Equity

NASA Astrophysics Data System (ADS)

Hartmann, H. C.; Pagano, T. C.; Sorooshian, S.; Bales, R.

2002-12-01

Expectations for hydroclimatic research are evolving as changes in the contract between science and society require researchers to provide "usable science" that can improve resource management policies and practices. However, decision makers have a broad range of abilities to access, interpret, and apply scientific research. "High-end users" have technical capabilities and operational flexibility capable of readily exploiting new information and products. "Low-end users" have fewer resources and are less likely to change their decision making processes without clear demonstration of benefits by influential early adopters (i.e., high-end users). Should research programs aim for efficiency, targeting high-end users? Should they aim for impact, targeting decisions with high economic value or great influence (e.g., state or national agencies)? Or should they focus on equity, whereby outcomes benefit groups across a range of capabilities? In this case study, we focus on hydroclimatic variability and forecasts. Agencies and individuals responsible for resource management decisions have varying perspectives about hydroclimatic variability and opportunities for using forecasts to improve decision outcomes. Improper interpretation of forecasts is widespread and many individuals find it difficult to place forecasts in an appropriate regional historical context. In addressing these issues, we attempted to mitigate traditional inequities in the scope, communication, and accessibility of hydroclimatic research results. High-end users were important in prioritizing information needs, while low-end users were important in determining how information should be communicated. For example, high-end users expressed hesitancy to use seasonal forecasts in the absence of quantitative performance evaluations. Our subsequently developed forecast evaluation framework and research products, however, were guided by the need for a continuum of evaluation measures and interpretive materials to enable low-end users to increase their understanding of probabilistic forecasts, credibility concepts, and implications for decision making. We also developed an interactive forecast assessment tool accessible over the Internet, to support resource decisions by individuals as well as agencies. The tool provides tutorials for guiding forecast interpretation, including quizzes that allow users to test their forecast interpretation skills. Users can monitor recent and historical observations for selected regions, communicated using terminology consistent with available forecast products. The tool also allows users to evaluate forecast performance for the regions, seasons, forecast lead times, and performance criteria relevant to their specific decision making situations. Using consistent product formats, the evaluation component allows individuals to use results at the level they are capable of understanding, while offering opportunity to shift to more sophisticated criteria. Recognizing that many individuals lack Internet access, the forecast assessment webtool design also includes capabilities for customized report generation so extension agents or other trusted information intermediaries can provide material to decision makers at meetings or site visits.

Microgrid optimal scheduling considering impact of high penetration wind generation

NASA Astrophysics Data System (ADS)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

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

PubMed

Bildirici, Melike; Ersin, Özgür

2014-01-01

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

Analysis of recurrent neural networks for short-term energy load forecasting

NASA Astrophysics Data System (ADS)

Di Persio, Luca; Honchar, Oleksandr

2017-11-01

Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.

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

PubMed Central

Bildirici, Melike; Ersin, Özgür

2014-01-01

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

Climate science and famine early warning

USGS Publications Warehouse

Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning.

PubMed

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-11-29

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning

PubMed Central

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Financial Risk Reduction and Management of Water Reservoirs Using Forecasts: A Case for Pernambuco, Brazil

NASA Astrophysics Data System (ADS)

Kumar, I.; Josset, L.; e Silva, E. C.; Possas, J. M. C.; Asfora, M. C.; Lall, U.

2017-12-01

The financial health and sustainability, ensuring adequate supply, and adapting to climate are fundamental challenges faced by water managers. These challenges are worsened in semi-arid regions with socio-economic pressures, seasonal supply of water, and projected increase in intensity and frequency of droughts. Over time, probabilistic rainfall forecasts are improving and for water managers, it could be key in addressing the above challenges. Using forecasts can also help make informed decisions about future infrastructure. The study proposes a model to minimize cost of water supply (including cost of deficit) given ensemble forecasts. The model can be applied to seasonal to annual ensemble forecasts, to determine the least cost solution. The objective of the model is to evaluate the resiliency and cost associated to supplying water. A case study is conducted in one of the largest reservoirs (Jucazinho) in Pernambuco state, Brazil, and four other reservoirs, which provide water to nineteen municipalities in the Jucazinho system. The state has been in drought since 2011, and the Jucazinho reservoir, has been empty since January 2017. The importance of climate adaptation along with risk management and financial sustainability are important to the state as it is extremely vulnerable to droughts, and has seasonal streamflow. The objectives of the case study are first, to check if streamflow forecasts help reduce future supply costs by comparing k-nearest neighbor ensemble forecasts with a fixed release policy. Second, to determine the value of future infrastructure, a new source of supply from Rio São Francisco, considered to mitigate drought conditions. The study concludes that using forecasts improve the supply and financial sustainability of water, by reducing cost of failure. It also concludes that additional infrastructure can help reduce the risks of failure significantly, but does not guarantee supply during prolonged droughts like the one experienced currently.

The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg K.; Devoli, Graziella; Colleuille, Hervé; Boje, Søren; Sund, Monica; Engen, Inger Karin

2018-05-01

The Norwegian Water Resources and Energy Directorate (NVE) have run a national flood forecasting and warning service since 1989. In 2009, the directorate was given the responsibility of also initiating a national forecasting service for rainfall-induced landslides. Both services are part of a political effort to improve flood and landslide risk prevention. The Landslide Forecasting and Warning Service was officially launched in 2013 and is developed as a joint initiative across public agencies between NVE, the Norwegian Meteorological Institute (MET), the Norwegian Public Road Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The main goal of the service is to reduce economic and human losses caused by landslides. The service performs daily a national landslide hazard assessment describing the expected awareness level at a regional level (i.e. for a county and/or group of municipalities). The service is operative 7 days a week throughout the year. Assessments and updates are published at the warning portal http://www.varsom.no/ at least twice a day, for the three coming days. The service delivers continuous updates on the current situation and future development to national and regional stakeholders and to the general public. The service is run in close cooperation with the flood forecasting service. Both services are based on the five pillars: automatic hydrological and meteorological stations, landslide and flood historical database, hydro-meteorological forecasting models, thresholds or return periods, and a trained group of forecasters. The main components of the service are herein described. A recent evaluation, conducted on the 4 years of operation, shows a rate of over 95 % correct daily assessments. In addition positive feedbacks have been received from users through a questionnaire. The capability of the service to forecast landslides by following the hydro-meteorological conditions is illustrated by an example from autumn 2017. The case shows how the landslide service has developed into a well-functioning system providing useful information, effectively and on time.

Forecasting the Revenues of Local Public Health Departments in the Shadows of the ‘Great Recession’

PubMed Central

Reschovsky, Andrew; Zahner, Susan J.

2015-01-01

Context The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the “Great Recession” on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy-makers. This study represents a novel research application for these econometric methods. Methods Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an eight year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in “base case” estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. Findings The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014 inflation-adjusted revenues will decline by $8 million, a reduction of 4.7 percent. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6 percent between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. Conclusions The impact of the Great Recession continues to haunt LHDs. Multi-year revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided. PMID:23531611

Forecasting the Revenues of Local Public Health Departments in the Shadows of the "Great Recession".

PubMed

Reschovsky, Andrew; Zahner, Susan J

2016-01-01

The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the "Great Recession" on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy makers. This study represents a novel research application for these econometric methods. Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an 8-year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in "base case" estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014, inflation-adjusted revenues will decline by $8 million, a reduction of 4.7%. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6% between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. The impact of the Great Recession continues to haunt LHDs. Multiyear revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided.

Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

NASA Astrophysics Data System (ADS)

Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

2015-12-01

The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast. The period of analysis is from 1985-2010 (over which the reforecasts of GEFS is available) and the focus season is October-November-December. We examine the improvement (if any) in long-term skill, and present results for several recent drought events in the region.

Experiences from coordinated national-level landslide and flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

2015-04-01

While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

Predictability of the European heat and cold waves

NASA Astrophysics Data System (ADS)

Lavaysse, Christophe; Naumann, Gustavo; Alfieri, Lorenzo; Salamon, Peter; Vogt, Jürgen

2018-06-01

Heat and cold waves may have considerable human and economic impacts in Europe. Recent events, like the heat waves observed in France in 2003 and Russia in 2010, illustrated the major consequences to be expected. Reliable Early Warning Systems for extreme temperatures would, therefore, be of high value for decision makers. However, they require a clear definition and robust forecasts of these events. This study analyzes the predictability of heat and cold waves over Europe, defined as at least three consecutive days of {T}_{min} and {T}_{max} above the quantile Q90 (under Q10), using the extended ensemble system of ECMWF. The results show significant predictability for events within a 2-week lead time, but with a strong decrease of the predictability during the first week of forecasts (from 80 to 40% of observed events correctly forecasted). The scores show a higher predictive skill for the cold waves (in winter) than for the heat waves (in summer). The uncertainties and the sensitivities of the predictability are discussed on the basis of tests conducted with different spatial and temporal resolutions. Results demonstrate the negligible effect of the temporal resolution (very few errors due to bad timing of the forecasts), and a better predictability of large-scale events. The onset and the end of the waves are slightly less predictable with an average of about 35% (30%) of observed heat (cold) waves onsets or ends correctly forecasted with a 5-day lead time. Finally, the forecasted intensities show a correlation of about 0.65 with those observed, revealing the challenge to predict this important characteristic.

Forecasting model for Pea seed-borne mosaic virus epidemics in field pea crops in a Mediterranean-type environment.

PubMed

Congdon, B S; Coutts, B A; Jones, R A C; Renton, M

2017-09-15

An empirical model was developed to forecast Pea seed-borne mosaic virus (PSbMV) incidence at a critical phase of the annual growing season to predict yield loss in field pea crops sown under Mediterranean-type conditions. The model uses pre-growing season rainfall to calculate an index of aphid abundance in early-August which, in combination with PSbMV infection level in seed sown, is used to forecast virus crop incidence. Using predicted PSbMV crop incidence in early-August and day of sowing, PSbMV transmission from harvested seed was also predicted, albeit less accurately. The model was developed so it provides forecasts before sowing to allow sufficient time to implement control recommendations, such as having representative seed samples tested for PSbMV transmission rate to seedlings, obtaining seed with minimal PSbMV infection or of a PSbMV-resistant cultivar, and implementation of cultural management strategies. The model provides a disease forecast risk indication, taking into account predicted percentage yield loss to PSbMV infection and economic factors involved in field pea production. This disease risk forecast delivers location-specific recommendations regarding PSbMV management to end-users. These recommendations will be delivered directly to end-users via SMS alerts with links to web support that provide information on PSbMV management options. This modelling and decision support system approach would likely be suitable for use in other world regions where field pea is grown in similar Mediterranean-type environments. Copyright © 2017 Elsevier B.V. All rights reserved.

Probabilistic drought intensification forecasts using temporal patterns of satellite-derived drought indicators

NASA Astrophysics Data System (ADS)

Park, Sumin; Im, Jungho; Park, Seonyeong

2016-04-01

A drought occurs when the condition of below-average precipitation in a region continues, resulting in prolonged water deficiency. A drought can last for weeks, months or even years, so can have a great influence on various ecosystems including human society. In order to effectively reduce agricultural and economic damage caused by droughts, drought monitoring and forecasts are crucial. Drought forecast research is typically conducted using in situ observations (or derived indices such as Standardized Precipitation Index (SPI)) and physical models. Recently, satellite remote sensing has been used for short term drought forecasts in combination with physical models. In this research, drought intensification was predicted using satellite-derived drought indices such as Normalized Difference Drought Index (NDDI), Normalized Multi-band Drought Index (NMDI), and Scaled Drought Condition Index (SDCI) generated from Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) products over the Korean Peninsula. Time series of each drought index at the 8 day interval was investigated to identify drought intensification patterns. Drought condition at the previous time step (i.e., 8 days before) and change in drought conditions between two previous time steps (e.g., between 16 days and 8 days before the time step to forecast) Results show that among three drought indices, SDCI provided the best performance to predict drought intensification compared to NDDI and NMDI through qualitative assessment. When quantitatively compared with SPI, SDCI showed a potential to be used for forecasting short term drought intensification. Finally this research provided a SDCI-based equation to predict short term drought intensification optimized over the Korean Peninsula.

Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

NASA Astrophysics Data System (ADS)

Gunda, T.; Bazuin, J. T.; Nay, J.; Yeung, K. L.

2017-03-01

Access to seasonal climate forecasts can benefit farmers by allowing them to make more informed decisions about their farming practices. However, it is unclear whether farmers realize these benefits when crop choices available to farmers have different and variable costs and returns; multiple countries have programs that incentivize production of certain crops while other crops are subject to market fluctuations. We hypothesize that the benefits of forecasts on farmer livelihoods will be moderated by the combined impact of differing crop economics and changing climate. Drawing upon methods and insights from both physical and social sciences, we develop a model of farmer decision-making to evaluate this hypothesis. The model dynamics are explored using empirical data from Sri Lanka; primary sources include survey and interview information as well as game-based experiments conducted with farmers in the field. Our simulations show that a farmer using seasonal forecasts has more diversified crop selections, which drive increases in average agricultural income. Increases in income are particularly notable under a drier climate scenario, when a farmer using seasonal forecasts is more likely to plant onions, a crop with higher possible returns. Our results indicate that, when water resources are scarce (i.e. drier climate scenario), farmer incomes could become stratified, potentially compounding existing disparities in farmers’ financial and technical abilities to use forecasts to inform their crop selections. This analysis highlights that while programs that promote production of certain crops may ensure food security in the short-term, the long-term implications of these dynamics need careful evaluation.

Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

2016-12-01

In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

A coupled human-natural system to assess the operational value of weather and climate services for agriculture

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2017-09-01

Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

Configuring calendar variation based on time series regression method for forecasting of monthly currency inflow and outflow in Central Java

NASA Astrophysics Data System (ADS)

Setiawan, Suhartono, Ahmad, Imam Safawi; Rahmawati, Noorgam Ika

2015-12-01

Bank Indonesia (BI) as the central bank of Republic Indonesiahas a single overarching objective to establish and maintain rupiah stability. This objective could be achieved by monitoring traffic of inflow and outflow money currency. Inflow and outflow are related to stock and distribution of money currency around Indonesia territory. It will effect of economic activities. Economic activities of Indonesia,as one of Moslem country, absolutely related to Islamic Calendar (lunar calendar), that different with Gregorian calendar. This research aims to forecast the inflow and outflow money currency of Representative Office (RO) of BI Semarang Central Java region. The results of the analysis shows that the characteristics of inflow and outflow money currency influenced by the effects of the calendar variations, that is the day of Eid al-Fitr (moslem holyday) as well as seasonal patterns. In addition, the period of a certain week during Eid al-Fitr also affect the increase of inflow and outflow money currency. The best model based on the value of the smallestRoot Mean Square Error (RMSE) for inflow data is ARIMA model. While the best model for predicting the outflow data in RO of BI Semarang is ARIMAX model or Time Series Regression, because both of them have the same model. The results forecast in a period of 2015 shows an increase of inflow money currency happened in August, while the increase in outflow money currency happened in July.

Benchmarking an operational procedure for rapid flood mapping and risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Salamon, Peter; Kalas, Milan; Bianchi, Alessandra; Feyen, Luc

2016-04-01

The development of real-time methods for rapid flood mapping and risk assessment is crucial to improve emergency response and mitigate flood impacts. This work describes the benchmarking of an operational procedure for rapid flood risk assessment based on the flood predictions issued by the European Flood Awareness System (EFAS). The daily forecasts produced for the major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations, based on the hydro-meteorological dataset of EFAS. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in near real-time in terms of flood prone areas, potential economic damage, affected population, infrastructures and cities. An extensive testing of the operational procedure is carried out using the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-derived flood footprints, while ground-based estimations of economic damage and affected population is compared against modelled estimates. We evaluated the skill of flood hazard and risk estimations derived from EFAS flood forecasts with different lead times and combinations. The assessment includes a comparison of several alternative approaches to produce and present the information content, in order to meet the requests of EFAS users. The tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management.

Development of modelling algorithm of technological systems by statistical tests

NASA Astrophysics Data System (ADS)

Shemshura, E. A.; Otrokov, A. V.; Chernyh, V. G.

2018-03-01

The paper tackles the problem of economic assessment of design efficiency regarding various technological systems at the stage of their operation. The modelling algorithm of a technological system was performed using statistical tests and with account of the reliability index allows estimating the level of machinery technical excellence and defining the efficiency of design reliability against its performance. Economic feasibility of its application shall be determined on the basis of service quality of a technological system with further forecasting of volumes and the range of spare parts supply.

Improved (ERTS) information and its impact on U.S. markets for agricultural commodities: A quantitiative economic investigation of production, distribution and net export effects

NASA Technical Reports Server (NTRS)

1974-01-01

An econometric investigation into the markets for agricultural commodities is summarized. An overview of the effort including the objectives, scope, and architecture of the analysis and the estimation strategy employed is presented. The major empirical results and policy conclusions are set forth. These results and conclusions focus on the economic importance of improved crop forecasts, U.S. exports, and government policy operations. A number of promising avenues of further investigation are suggested.

Lognormal field size distributions as a consequence of economic truncation

USGS Publications Warehouse

Attanasi, E.D.; Drew, L.J.

1985-01-01

The assumption of lognormal (parent) field size distributions has for a long time been applied to resource appraisal and evaluation of exploration strategy by the petroleum industry. However, frequency distributions estimated with observed data and used to justify this hypotheses are conditional. Examination of various observed field size distributions across basins and over time shows that such distributions should be regarded as the end result of an economic filtering process. Commercial discoveries depend on oil and gas prices and field development costs. Some new fields are eliminated due to location, depths, or water depths. This filtering process is called economic truncation. Economic truncation may occur when predictions of a discovery process are passed through an economic appraisal model. We demonstrate that (1) economic resource appraisals, (2) forecasts of levels of petroleum industry activity, and (3) expected benefits of developing and implementing cost reducing technology are sensitive to assumptions made about the nature of that portion of (parent) field size distribution subject to economic truncation. ?? 1985 Plenum Publishing Corporation.

Research Talent in the Natural Sciences and Engineering: Supply and Demand Projections to 1990.

ERIC Educational Resources Information Center

Natural Sciences and Engineering Research Council, Ottawa (Ontario).

This report presents conditional forecasts of the research talent required for the Canadian government's economic growth and research and development (R&D) targets. A number of alternative scenarios are also assessed. The study limits itself to postgraduate manpower in the natural sciences and engineering. Following an executive summary and…

State Budgetary Assumptions. State Fiscal Brief No. 36.

ERIC Educational Resources Information Center

Boyd, Donald J.; Davis, Elizabeth I.

When states prepare their budgets, they usually base revenue and expenditure projections upon forecasts of national and state economic and demographic trends. This brief presents findings of a Center for the Study of the States survey that asked state budget offices what they were assuming for many key variables. The survey obtained 41 state…

78 FR 28012 - Tier One Environmental Impact Statement for the Rochester, Minnesota to Twin Cities, Minnesota...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-13

... detail. Project Background and Study Area: Based upon travel demand and growth between the two regional... corridors in the region had been established. The number of jobs currently supported by Rochester employers... transportation alternative that will meet forecasted population and economic growth mobility demands in the...

Educational Planning and Human Resources Development with Reference to Arab Countries.

ERIC Educational Resources Information Center

Galaleldin, Mohamed Al Awad

Human resources development sees human beings as the means to socioeconomic development. This differs from human development which sees human beings as the immediate and ultimate goals and ends of socio-economic development. Arab states have tended to utilize the human resources development approach as part of their forecasting of manpower…

Demographic Trends and Projections Affecting Higher Education.

ERIC Educational Resources Information Center

Zuniga, Robin Etter

1997-01-01

A dramatic increase in the pool of college-age students in the next 20 years is inevitable, but this will not necessarily lead to dramatic increases in higher education enrollment. Enrollment forecasters must ask how economic growth, tuition increases, or an increase in standardized test requirements will affect demand and be clear about…

75 FR 1568 - Endangered and Threatened Wildlife and Plants; Proposed Designation of Critical Habitat for...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-12

... designation. The majority of the total future baseline impacts are associated with development projects ($6.4... development projects. The DEA estimates that total potential incremental economic impacts in areas proposed as..., the range in total incremental impacts is due to the range in development forecasts. The lack of...

Impact of the "Steel Collar" Revolution and Robotics upon Higher Education. AIR 1983 Annual Forum Paper.

ERIC Educational Resources Information Center

Todd, Edward S.

The need for higher education to plan curricula based upon generalizable human, analytical, and technical skills is discussed in view of historical and economic changes, productivity questions, demographic projections, and employment forecasts. Questions are posed regarding the form of undergraduate education that will best prepare the college…

Using DCOM to support interoperability in forest ecosystem management decision support systems

Treesearch

W.D. Potter; S. Liu; X. Deng; H.M. Rauscher

2000-01-01

Forest ecosystems exhibit complex dynamics over time and space. Management of forest ecosystems involves the need to forecast future states of complex systems that are often undergoing structural changes. This in turn requires integration of quantitative science and engineering components with sociopolitical, regulatory, and economic considerations. The amount of data...

Developing the U.S. Wildland Fire Decision Support System

Treesearch

Erin Noonan-Wright; Tonja S. Opperman; Mark A. Finney; Tom Zimmerman; Robert C. Seli; Lisa M. Elenz; David E. Calkin; John R. Fiedler

2011-01-01

A new decision support tool, the Wildland Fire Decision Support System (WFDSS) has been developed to support risk-informed decision-making for individual fires in the United States. WFDSS accesses national weather data and forecasts, fire behavior prediction, economic assessment, smoke management assessment, and landscape databases to efficiently formulate and apply...

The Migration of Foreign Students to Russia

ERIC Educational Resources Information Center

Pis'mennaia, E. E.

2010-01-01

According to forecasts, the competitive struggle on the worldwide scale to attract foreign students is going to get more intense, as many countries see students as the most desirable category of migrants. The world market of educational services has been estimated at $50-60 billion. The economically developed countries, such as the United States,…

Vector autoregressive model approach for forecasting outflow cash in Central Java

NASA Astrophysics Data System (ADS)

hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita

2018-05-01

Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.

Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

NASA Astrophysics Data System (ADS)

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

2012-12-01

Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate the performances of electricity demand forecast performed with predicted variables on Italian regions with encouraging results on the South of Italy. This work gives an initial assessment on the predictability of electricity demand on seasonal time scale, evaluating the relevance of climate information provided by seasonal forecasts for electricity management during high-demand periods.;

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

Time series modelling of global mean temperature for managerial decision-making.

PubMed

Romilly, Peter

2005-07-01

Climate change has important implications for business and economic activity. Effective management of climate change impacts will depend on the availability of accurate and cost-effective forecasts. This paper uses univariate time series techniques to model the properties of a global mean temperature dataset in order to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon. Although the model is estimated on global temperature data, the methodology could also be applied to temperature data at more localised levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks, as well as ARIMA and GARCH modelling. A forecasting evaluation shows that the chosen model performs well against rival models. The estimation results confirm the findings of a number of previous studies, namely that global mean temperatures increased significantly throughout the 20th century. The use of GARCH modelling also shows the presence of volatility clustering in the temperature data, and a positive association between volatility and global mean temperature.

Forecasting the impact of transport improvements on commuting and residential choice

NASA Astrophysics Data System (ADS)

Elhorst, J. Paul; Oosterhaven, Jan

2006-03-01

This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.

Barents-Kara sea ice and the winter NAO in the DePreSys3 Met Office Seasonal forecast model

NASA Astrophysics Data System (ADS)

Warner, J.; Screen, J.

2017-12-01

Accurate seasonal forecasting leads to a wide range of socio-economic benefits and increases resilience to prolonged bouts of extreme weather. This work looks at how November Barents-Kara sea ice may affect the winter northern hemisphere atmospheric circulation, using various compositing methods in the DePreSys3 ensemble model, with lag to argue better a relationship between the two. In particular, the NAO (North Atlantic Oscillation) is focused on given its implications on European weather. Using this large hindcast dataset comprised of 35 years with 30 available ensemble members, it is found that low Barents-Kara sea ice leads to a negative NAO tendency in all composite methods, with increased mean sea level pressure in higher latitudes. The significance of this varies between composites. This is preliminary analysis of a larger PhD project to further understand how Arctic Sea ice may play a role in seasonal forecasting skill through its connection/influence on mid-latitude weather.

Model documentation, Coal Market Module of the National Energy Modeling System

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The internationalmore » area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.« less

Predictability of extremes in non-linear hierarchically organized systems

NASA Astrophysics Data System (ADS)

Kossobokov, V. G.; Soloviev, A.

2011-12-01

Understanding the complexity of non-linear dynamics of hierarchically organized systems progresses to new approaches in assessing hazard and risk of the extreme catastrophic events. In particular, a series of interrelated step-by-step studies of seismic process along with its non-stationary though self-organized behaviors, has led already to reproducible intermediate-term middle-range earthquake forecast/prediction technique that has passed control in forward real-time applications during the last two decades. The observed seismic dynamics prior to and after many mega, great, major, and strong earthquakes demonstrate common features of predictability and diverse behavior in course durable phase transitions in complex hierarchical non-linear system of blocks-and-faults of the Earth lithosphere. The confirmed fractal nature of earthquakes and their distribution in space and time implies that many traditional estimations of seismic hazard (from term-less to short-term ones) are usually based on erroneous assumptions of easy tractable analytical models, which leads to widespread practice of their deceptive application. The consequences of underestimation of seismic hazard propagate non-linearly into inflicted underestimation of risk and, eventually, into unexpected societal losses due to earthquakes and associated phenomena (i.e., collapse of buildings, landslides, tsunamis, liquefaction, etc.). The studies aimed at forecast/prediction of extreme events (interpreted as critical transitions) in geophysical and socio-economical systems include: (i) large earthquakes in geophysical systems of the lithosphere blocks-and-faults, (ii) starts and ends of economic recessions, (iii) episodes of a sharp increase in the unemployment rate, (iv) surge of the homicides in socio-economic systems. These studies are based on a heuristic search of phenomena preceding critical transitions and application of methodologies of pattern recognition of infrequent events. Any study of rare phenomena of highly complex origin, by their nature, implies using problem oriented methods, which design breaks the limits of classical statistical or econometric applications. The unambiguously designed forecast/prediction algorithms of the "yes or no" variety, analyze the observable quantitative integrals and indicators available to a given date, then provides unambiguous answer to the question whether a critical transition should be expected or not in the next time interval. Since the predictability of an originating non-linear dynamical system is limited in principle, the probabilistic component of forecast/prediction algorithms is represented by the empirical probabilities of alarms, on one side, and failures-to-predict, on the other, estimated on control sets achieved in the retro- and prospective experiments. Predicting in advance is the only decisive test of forecast/predictions and the relevant on-going experiments are conducted in the case seismic extremes, recessions, and increases of unemployment rate. The results achieved in real-time testing keep being encouraging and confirm predictability of the extremes.

Development of energy consumption and energy efficiency potential in the Brazilian industrial sector according to the Integrated Energy Planning Model (IEPM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tolmasquim, M.T.; Szklo, A.S.; Cohen, C.

This paper presents the development of energy consumption in the Brazilian industrial sector and energy efficiency potential based on the analysis undertaken through a model developed in the Energy Planning Program at COPPE/UFRJ, known as the Integrated Energy Planning Model (IEPM). The study starts by presenting the IEPM, which is a technical and economic parameter-based model designed to forecast energy supplies and consumption for all economic sectors in Brazil, within three scenarios. Outlines of all three scenarios are presented, as they were constructed according to certain specific assumptions. The industrial sector was broken down into eleven sub-sectors: food and beverages,more » ceramics, cement, iron and steel, mining and pelletizing, ferroalloys, non-ferrous metals and others (metallurgy), chemicals, pulp and paper, textiles and other industries (MME, 1998). All these sub-sectors will also be presented as well as the results of the scenario forecasts. Results deriving from these forecasts come from very specific studies that analyze all process steps in each sub-sector in order to propose energy replacements, efficiency improvements of structural production alterations that result in major potential energy consumption reductions. Last but not least, this paper gives the development forecasts deriving from the three scenarios over ten years, with their contributions to energy efficiency in the Brazilian industrial sector, showing that the authors can reduce energy consumption in the Brazilian industrial sector by: substituting less efficient processes by more efficient ones, through the conversion of final energy into usable energy, basically, in the cement and aluminum industries; replacing equipment and energy sources; modifying product mix of several industries (pulp and paper), assigning top priority to producing goods with higher added value that are less energy intensive, and, finally, reducing the share held by some energy intensive sectors in the industrial output.« less

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

USDA Foreign Agricultural Service overview for operational monitoring of current crop conditions and production forecasts.

NASA Astrophysics Data System (ADS)

Crutchfield, J.

2016-12-01

The presentation will discuss the current status of the International Production Assessment Division of the USDA ForeignAgricultural Service for operational monitoring and forecasting of current crop conditions, and anticipated productionchanges to produce monthly, multi-source consensus reports on global crop conditions including the use of Earthobservations (EO) from satellite and in situ sources.United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) International Production AssessmentDivision (IPAD) deals exclusively with global crop production forecasting and agricultural analysis in support of the USDAWorld Agricultural Outlook Board (WAOB) lockup process and contributions to the World Agricultural Supply DemandEstimates (WASE) report. Analysts are responsible for discrete regions or countries and conduct in-depth long-termresearch into national agricultural statistics, farming systems, climatic, environmental, and economic factors affectingcrop production. IPAD analysts become highly valued cross-commodity specialists over time, and are routinely soughtout for specialized analyses to support governmental studies. IPAD is responsible for grain, oilseed, and cotton analysison a global basis. IPAD is unique in the tools it uses to analyze crop conditions around the world, including customweather analysis software and databases, satellite imagery and value-added image interpretation products. It alsoincorporates all traditional agricultural intelligence resources into its forecasting program, to make the fullest use ofavailable information in its operational commodity forecasts and analysis. International travel and training play animportant role in learning about foreign agricultural production systems and in developing analyst knowledge andcapabilities.

Earth Observations and the Role of UAVs: A Capabilities Assessment. Version 1.1

NASA Technical Reports Server (NTRS)

Cox, Timothy H.; Somers, Ivan; Fratello, David J.

2006-01-01

This document provides an assessment of the civil UAV missions and technologies and is intended to parallel the Office of the Secretary of Defense UAV Roadmap. The intent of this document is four-fold: 1. Determine and document desired future missions of Earth observation UAVs based on user-defined needs 2. Determine and document the technologies necessary to support those missions 3. Discuss the present state of the platform capabilities and required technologies, identifying those in progress, those planned, and those for which no current plans exist 4. Provide the foundations for development of a comprehensive civil UAV roadmap to complement the Department of Defense (DoD) effort (http://www.acq.osd.mil/uas/). Two aspects of the President's Management Agenda (refer to the document located at: www.whitehouse.gov/omb/budget/fy2002/mgmt.pdf ) are supported by this undertaking. First, it is one that will engage multiple Agencies in the effort as stakeholders and benefactors of the systems. In that sense, the market will be driven by the user requirements and applications. The second aspect is one of supporting economic development in the commercial sector. Market forecasts for the civil use of UAVs have indicated an infant market stage at present with a sustained forecasted growth. There is some difficulty in quantifying the value of the market since the typical estimate excludes system components other than the aerial platforms. Section 2.4 addresses the civil UAV market forecast and lists several independent forecasts. One conclusion that can be drawn from these forecasts is that all show a sustained growth for the duration of each long-term forecast.

Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: A case study of Beijing.

PubMed

Wei, Yigang; Wang, Zhichao; Wang, Huiwen; Yao, Tang; Li, Yan

2018-09-01

Water is centrally important for agricultural security, environment, people's livelihoods, and socio-economic development, particularly in the face of extreme climate changes. Due to water shortages in many cities, the conflicts between various stakeholders and sectors over water use and allocation are becoming more common and intense. Effective inclusive governance of water use is critical for relieving water use conflicts. In addition, reliable forecasting of the structure of water usage among different sectors is a basic need for effective water governance planning. Although a large number of studies have attempted to forecast water use, little is known about the forecasted structure and trends of water use in the future. This paper aims to develop a forecasting model for the structure of water usage based on compositional data. Compositional data analysis is an effective approach for investigating the internal structure of a system. A host of data transformation methods and forecasting models were adopted and compared in order to derive the best-performing model. According to mean absolute percent error for compositional data (CoMAPE), a hyperspherical-transformation-based vector autoregression model for compositional data (VAR-DRHT) is the best-performing model. The proportions of the agricultural, industrial, domestic and environmental water will be 6.11%, 5.01%, 37.48% and 51.4% by 2020. Several recommendations for water inclusive development are provided to give a better account for the optimization of the water use structure, alleviation of water shortages, and improving stake holders' wellbeing. Overall, although we focus on groundwater, this study presents a powerful framework broadly applicable to resource management. Copyright © 2018 Elsevier B.V. All rights reserved.

The 2015-16 El Nino - Manifestation and US Impacts

NASA Astrophysics Data System (ADS)

Farnham, D. J.; Lall, U.; Meschke, L.; Sutter, S.; Sayeed, S.; Orozco, L.; Jain, S.; Barry, O.; Montano, R.; Kirby, T., Jr.; Caraveo, B.; Homa, K.

2016-12-01

The impacts of ENSO, especially in the USA, are well studied. Given the past success at forecasting El Niño events and identifying their teleconnections to different parts of the country, there is a relatively well publicized understanding of the potential impacts, and the possible directions of management response. Consequently, a special course at Columbia University took advantage of the ongoing El Niño event in Winter/Spring 2016 to assess the forecasts of the potential impacts, forms of forecast-related communication, and the actual outcomes in selected sectors and regions of the USA. The students reviewed past literature and were exposed to webinars from leading scientists, NOAA specialists, and water managers from across the country. From their research and observations, the students developed a series of papers documenting and assessing the trajectory of the El Niño event with respect to climate forecasting, impacts in a specific location, and/or impacts on a specific sector. This presentation will highlight the findings of the group as well as consider the process of conducting such a course. Key points covered by the student research were: 1) the accuracy of forecasts of the significant teleconnections, and where and what may be responsible for the divergence from expectation; 2) how the forecasts were communicated by NOAA and others, and how they were manifest in formal and informal media; 3) the impacts on the water systems in California and Florida, and how water agencies adapted; 4) impacts on marine biology, fisheries and coral reef bleaching; 5) impacts on the skiing industry in the Western USA; 6) changes in the spatio-temporal influenza outcomes; 7) impacts on the winter/spring vegetable and orange production in California and Florida; and 8) an assessment of the overall economic impacts.

Contribution of suppression difficulty and lessons learned in forecasting fire suppression operations productivity: A methodological approach

Treesearch

Francisco Rodríguez y Silva; Armando González-Cabán

2016-01-01

We propose an economic analysis using utility and productivity, and efficiency theories to provide fire managers a decision support tool to determine the most efficient fire management programs levels. By incorporating managers’ accumulated fire suppression experiences (capitalized experience) in the analysis we help fire managers...

Tidal Wave II Revisited: A Review of Earlier Enrollment Projections for California Higher Education.

ERIC Educational Resources Information Center

Hayward, Gerald C.; Breneman, David W.; Estrada, Leobardo F.

This report examined enrollment projections for higher education institutions in California in relation to earlier projections conducted in the mid-1990s that forecasted steep declines in enrollment. It notes that California's remarkable economic recovery over the last several years has allowed it to fund higher education enrollment growth at a…

Forecasting economic gains from intensive plantation management using unrealistic yield over input curves

Treesearch

David B. South; Curtis L. VanderSchaaf; Larry D. Teeter

2006-01-01

Some researchers claim that continuously increasing intensive plantation management will increase profits and reduce the unit cost of wood production while others believe in the law of diminishing returns. We developed four hypothetical production models where yield is a function of silvicultural effort. Models that produced unrealistic results were (1) an exponential...

Religious Environmental Education? The New School Curriculum in Indonesia

ERIC Educational Resources Information Center

Parker, Lyn

2017-01-01

Indonesia is now considered as a lower-middle-income country (The World Bank 2014) with decades of sustained economic growth. Recent forecasts predict that by 2050 it will be the fourth largest economy in the world (PWC 2015). Indonesia is well endowed with tropical rainforests, coral reefs, and other priority ecological systems, but there is…

Proceedings of NECC/2 National Educational Computing Conference 1980 (Norfolk, Virginia, June 23-25, 1980).

ERIC Educational Resources Information Center

Harris, Diana, Ed.; Collison, Beth, Ed.

This proceedings, which includes 52 papers and abstracts of 13 invited and nine tutorial sessions, provides an overview of the current status of computer usage in education and offers substantive forecasts for academic computing. Papers are presented under the following headings: Business--Economics, Tools and Techniques for Instruction, Computers…

Base Redevelopment Planning for BRAC Sites

DTIC Science & Technology

2006-05-01

or the private sector, also may supplement core staff responsibilities. Pro - fessional consultants may provide legal, planning, real estate...Opportunities, and Threats ( SWOT ) Analysis: An evaluation of a community’s economic, social, and physical environmental strengths, weaknesses, opportunities...in this area compare to those elsewhere? Forecasted changes in employment and population trends can be translated into pro - jected demand for housing

I PASS: an interactive policy analysis simulation system.

Treesearch

Doug Olson; Con Schallau; Wilbur Maki

1984-01-01

This paper describes an interactive policy analysis simulation system(IPASS) that can be used to analyze the long-term economic and demographic effects of alternative forest resource management policies. The IPASS model is a dynamic analytical tool that forecasts growth and development of an economy. It allows the user to introduce changes in selected parameters based...

Uncertain Forecast: Education Adjusts to a New Economic Reality. Quality Counts, 2011

ERIC Educational Resources Information Center

Education Week, 2011

2011-01-01

This year's "Quality Counts" report, the 15th edition of this annual report produced through the joint efforts of the "Education Week" newsroom and the Editorial Projects in Education Research Center, arrives at a time of continued fiscal anxiety and education policy ferment in the wake of what has been widely described as the…

An Analysis of the Tuition Advance Fund Bill.

ERIC Educational Resources Information Center

National Association of Independent Colleges and Universities, Washington, DC. National Inst. of Independent Colleges and Universities.

The Tuition Advance Fund (TAF) bill is analyzed on theoretical economic grounds, and forecasts of the net costs of the proposed program up to 1990 are offered. The TAF bill proposes the establishment of a new system of college loans as an addition to existing programs to allow students to borrow tuition plus an allowance for other costs. Novel…

Predicting the impacts of new technology aircraft on international air transportation demand

NASA Technical Reports Server (NTRS)

Ausrotas, R. A.

1981-01-01

International air transportation to and from the United States was analyzed. Long term and short term effects and causes of travel are described. The applicability of econometric methods to forecast passenger travel is discussed. A nomograph is developed which shows the interaction of economic growth, airline yields, and quality of service in producing international traffic.

The comparative analysis of the forecasts of development of rocket propulsion in past and now

NASA Astrophysics Data System (ADS)

Nedaivoda, A.; Prisniakov, V.

2001-03-01

Consideration is being given to use the known long and short forecasts of development of rocket engines in past - at the beginning of development of a missile engineering (K. Tsiolkovsky etc. pioneers of rocket propulsion); on the eve of launching of the artificial satellite of Earth (A. Blagonravov); after manned flight of Yu. Gagarin (V. Gluchko); after manned flight on Moon (" The Forecasts on 2001 " on materials of readings R. Goddard in USA); in middle of 70-s' years (D. Sevruk, V. Prisniakov) and at the end of 20 centure. Last years under the initiative R. Beichel and M. Pouliquen IAA. Advanced Propulsion Working Group carries out large researches on definition of the tendencies of development of rocket propulsion for the next forty years, the outcomes which one will be used in the report. The comparison of development of rocket propulsion expected to the end of 20 century and real-life is given. The report analyses the errors of the forecasts of the past - the absence reliable prognostic procedure; the euphoria of the maiden successes of conquest of space; dominance of military and political- propaganda motives of implementation of the space programs before economical; to keep developments secret; competition of two super-powers USSR and USA etc.

Futures of global urban expansion: uncertainties and implications for biodiversity conservation

NASA Astrophysics Data System (ADS)

Güneralp, B.; Seto, K. C.

2013-03-01

Urbanization will place significant pressures on biodiversity across the world. However, there are large uncertainties in the amount and location of future urbanization, particularly urban land expansion. Here, we present a global analysis of urban extent circa 2000 and probabilistic forecasts of urban expansion for 2030 near protected areas and in biodiversity hotspots. We estimate that the amount of urban land within 50 km of all protected area boundaries will increase from 450 000 km2 circa 2000 to 1440 000 ± 65 000 km2 in 2030. Our analysis shows that protected areas around the world will experience significant increases in urban land within 50 km of their boundaries. China will experience the largest increase in urban land near protected areas with 304 000 ± 33 000 km2 of new urban land to be developed within 50 km of protected area boundaries. The largest urban expansion in biodiversity hotspots, over 100 000 ± 25 000 km2, is forecasted to occur in South America. Uncertainties in the forecasts of the amount and location of urban land expansion reflect uncertainties in their underlying drivers including urban population and economic growth. The forecasts point to the need to reconcile urban development and biodiversity conservation strategies.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PubMed Central

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. PMID:28708836

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

PubMed

Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

Monitoring and Predicting the African Climate for Food Security

NASA Astrophysics Data System (ADS)

Thiaw, W. M.

2015-12-01

Drought is one of the greatest challenges in Africa due to its impact on access to sanitary water and food. In response to this challenge, the international community has mobilized to develop famine early warning systems (FEWS) to bring safe food and water to populations in need. Over the past several decades, much attention has focused on advance risk planning in agriculture and water. This requires frequent updates of weather and climate outlooks. This paper describes the active role of NOAA's African Desk in FEWS. Emphasis is on the operational products from short and medium range weather forecasts to subseasonal and seasonal outlooks in support of humanitarian relief programs. Tools to provide access to real time weather and climate information to the public are described. These include the downscaling of the U.S. National Multi-model Ensemble (NMME) to improve seasonal forecasts in support of Regional Climate Outlook Forums (RCOFs). The subseasonal time scale has emerged as extremely important to many socio-economic sectors. Drawing from advances in numerical models that can now provide a better representation of the MJO, operational subseasonal forecasts are included in the African Desk product suite. These along with forecasts skill assessment and verifications are discussed. The presentation will also highlight regional hazards outlooks basis for FEWSNET food security outlooks.

Soft computing prediction of economic growth based in science and technology factors

NASA Astrophysics Data System (ADS)

Marković, Dušan; Petković, Dalibor; Nikolić, Vlastimir; Milovančević, Miloš; Petković, Biljana

2017-01-01

The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to forecast the gross domestic product (GDP) growth rate. In this study the GDP growth was analyzed based on ten science and technology factors. These factors were: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ELM results were compared with genetic programming (GP), artificial neural network (ANN) and fuzzy logic results. Based upon simulation results, it is demonstrated that ELM has better forecasting capability for the GDP growth rate.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Optimization modeling of U.S. renewable electricity deployment using local input variables

NASA Astrophysics Data System (ADS)

Bernstein, Adam

For the past five years, state Renewable Portfolio Standard (RPS) laws have been a primary driver of renewable electricity (RE) deployments in the United States. However, four key trends currently developing: (i) lower natural gas prices, (ii) slower growth in electricity demand, (iii) challenges of system balancing intermittent RE within the U.S. transmission regions, and (iv) fewer economical sites for RE development, may limit the efficacy of RPS laws over the remainder of the current RPS statutes' lifetime. An outsized proportion of U.S. RE build occurs in a small number of favorable locations, increasing the effects of these variables on marginal RE capacity additions. A state-by-state analysis is necessary to study the U.S. electric sector and to generate technology specific generation forecasts. We used LP optimization modeling similar to the National Renewable Energy Laboratory (NREL) Renewable Energy Development System (ReEDS) to forecast RE deployment across the 8 U.S. states with the largest electricity load, and found state-level RE projections to Year 2031 significantly lower than thoseimplied in the Energy Information Administration (EIA) 2013 Annual Energy Outlook forecast. Additionally, the majority of states do not achieve their RPS targets in our forecast. Combined with the tendency of prior research and RE forecasts to focus on larger national and global scale models, we posit that further bottom-up state and local analysis is needed for more accurate policy assessment, forecasting, and ongoing revision of variables as parameter values evolve through time. Current optimization software eliminates much of the need for algorithm coding and programming, allowing for rapid model construction and updating across many customized state and local RE parameters. Further, our results can be tested against the empirical outcomes that will be observed over the coming years, and the forecast deviation from the actuals can be attributed to discrete parameter variances.

Economic and environmental costs of regulatory uncertainty for coal-fired power plants.

PubMed

Patiño-Echeverri, Dalia; Fischbeck, Paul; Kriegler, Elmar

2009-02-01

Uncertainty about the extent and timing of CO2 emissions regulations for the electricity-generating sector exacerbates the difficulty of selecting investment strategies for retrofitting or alternatively replacing existent coal-fired power plants. This may result in inefficient investments imposing economic and environmental costs to society. In this paper, we construct a multiperiod decision model with an embedded multistage stochastic dynamic program minimizing the expected total costs of plant operation, installations, and pollution allowances. We use the model to forecast optimal sequential investment decisions of a power plant operator with and without uncertainty about future CO2 allowance prices. The comparison of the two cases demonstrates that uncertainty on future CO2 emissions regulations might cause significant economic costs and higher air emissions.

Energy performance standards for new buildings: Economic analysis

NASA Astrophysics Data System (ADS)

1980-01-01

The major economic impacts of the implementations of the standards on affected groups were assessed and the effectiveness of the standards as an investment in energy conservation was evaluated. The methodology used to evaluate the standards for the various building types and perspectives is described. The net economic effect of changes in building cost and energy use are discussed for three categories of buildings: single family residential, commercial and multifamily residential, and mobile homes. Forecasts of energy savings and national costs and benefits both with and without implementation of the standards are presented. The effects of changes in energy consumption and construction of new buildings on the national economy, including such factors as national income, investment, employment, and balance of trade are assessed.

Development of Aspen: A microanalytic simulation model of the US economy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pryor, R.J.; Basu, N.; Quint, T.

1996-02-01

This report describes the development of an agent-based microanalytic simulation model of the US economy. The microsimulation model capitalizes on recent technological advances in evolutionary learning and parallel computing. Results are reported for a test problem that was run using the model. The test results demonstrate the model`s ability to predict business-like cycles in an economy where prices and inventories are allowed to vary. Since most economic forecasting models have difficulty predicting any kind of cyclic behavior. These results show the potential of microanalytic simulation models to improve economic policy analysis and to provide new insights into underlying economic principles.more » Work already has begun on a more detailed model.« less

Economic Benefits, Carbon Dioxide (CO2) Emissions Reductions, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in Maine (Fact Sheet)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

2008-10-01

The U.S. Department of Energy?s Wind Powering America Program is committed to educating state-level policymakers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Maine. Although construction and operation of 1000 MW of wind power is a significant effort, six states have already reached the 1000-MW mark. We forecast the cumulative economic benefits from 1000 MW of development in Maine to be $1.3 billion, annual CO2 reductions are estimated at 2.8 million tons, and annual water savings are 1,387 million gallons.

Economic Benefits, Carbon Dioxide (CO2) Emissions Reductions, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in Wisconsin (Fact Sheet)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

2008-10-01

The U.S. Department of Energy?s Wind Powering America Program is committed to educating state-level policymakers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Wisconsin. Although construction and operation of 1000 MW of wind power is a significant effort, six states have already reached the 1000-MW mark. We forecast the cumulative economic benefits from 1000 MW of development in Wisconsin to be $1.1 billion, annual CO2 reductions are estimated at 3.2 million tons, and annual water savings are 1,476 million gallons.

The economic impact of NASA R and D spending Appendices

NASA Technical Reports Server (NTRS)

Evans, M. K.

1976-01-01

Seven appendices related to a previous report on the economic impact of NASA R and D spending were presented. They dealt with: (1) theoretical and empirical development of aggregate production functions, (2) the calculation of the time series for the rate of technological progress, (3) the calculation of the industry mix variable, (4) the estimation of distributed lags, (5) the estimation of the equations for gamma, (6) a ten-year forecast of the U.S. economy, (7) simulations of the macroeconomic model for increases in NASA R and D spending of $1.0, $.0.5, and 0.1 billions.

Solid waste forecasting using modified ANFIS modeling.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; K N A, Maulud

2015-10-01

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98. To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

NASA Astrophysics Data System (ADS)

Li, Xiwang

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.

Time-varying loss forecast for an earthquake scenario in Basel, Switzerland

NASA Astrophysics Data System (ADS)

Herrmann, Marcus; Zechar, Jeremy D.; Wiemer, Stefan

2014-05-01

When an unexpected earthquake occurs, people suddenly want advice on how to cope with the situation. The 2009 L'Aquila quake highlighted the significance of public communication and pushed the usage of scientific methods to drive alternative risk mitigation strategies. For instance, van Stiphout et al. (2010) suggested a new approach for objective evacuation decisions on short-term: probabilistic risk forecasting combined with cost-benefit analysis. In the present work, we apply this approach to an earthquake sequence that simulated a repeat of the 1356 Basel earthquake, one of the most damaging events in Central Europe. A recent development to benefit society in case of an earthquake are probabilistic forecasts of the aftershock occurrence. But seismic risk delivers a more direct expression of the socio-economic impact. To forecast the seismic risk on short-term, we translate aftershock probabilities to time-varying seismic hazard and combine this with time-invariant loss estimation. Compared with van Stiphout et al. (2010), we use an advanced aftershock forecasting model and detailed settlement data to allow us spatial forecasts and settlement-specific decision-making. We quantify the risk forecast probabilistically in terms of human loss. For instance one minute after the M6.6 mainshock, the probability for an individual to die within the next 24 hours is 41 000 times higher than the long-term average; but the absolute value remains at minor 0.04 %. The final cost-benefit analysis adds value beyond a pure statistical approach: it provides objective statements that may justify evacuations. To deliver supportive information in a simple form, we propose a warning approach in terms of alarm levels. Our results do not justify evacuations prior to the M6.6 mainshock, but in certain districts afterwards. The ability to forecast the short-term seismic risk at any time-and with sufficient data anywhere-is the first step of personal decision-making and raising risk awareness among the public. Reference Van Stiphout, T., S. Wiemer, and W. Marzocchi (2010). 'Are short-term evacuations warranted? Case of the 2009 L'Aquila earthquake'. In: Geophysical Research Letters 37.6, pp. 1-5. url: http://onlinelibrary.wiley.com/doi/10.1029/ 2009GL042352/abstract.

Ethical issues in forecasting of natural hazards

NASA Astrophysics Data System (ADS)

Tinti, Stefano

2014-05-01

Natural hazards have by definition a large impact on the society and, therefore, since the beginning of science one of the major aspiration of mankind has been the prediction of natural calamities in the attempt to avoid or to mitigate their effects. In modern societies where science and technology have gained a foundational role, forecasts and predictions have become part of the every-day life and may also influence state policies and economic development. And in parallel with the growing importance of forecasting, even ethical problems for forecasters and for forecasters communities have started to appear. In this work two of the many geo-ethical issues are considered mostly: 1) how to cope with uncertainties that are inherently associated with any forecast statement; 2) how to handle predictions in scientific journals and scientific conferences The former issue is mainly related to the impact of predictions on the general public and on managers and operators in the civil protection field. Forecasters operate in specific contexts that 1) may change from country to country, depending on the local adopted best practices, but also, which is more constraining, on the local legal regulations and laws; 2) may change from discipline to discipline according to the development of the specific knowhow and the range of the forecast (from minutes to centuries) The second issue has to do with the communication of the scientific results on predictions and on prediction methods to the audience mainly composed of scientists, and involves one of the basic elements of science. In principle, scientists should use scientific communication means (papers in scientific journals, conferences, …) to illustrate results that are sound and certain, or the methods by means of which they conduct their research. But scientists involved in predictions have inherently to do with uncertainties, and, since there is no common agreement on how to deal with them, there is the risk that scientific results may be confused with opinions and opinions with scientific results, which creates confusion in the scientific community, in the science divulgators and in turn in the general public.

Communicating likelihoods and probabilities in forecasts of volcanic eruptions

NASA Astrophysics Data System (ADS)

Doyle, Emma E. H.; McClure, John; Johnston, David M.; Paton, Douglas

2014-02-01

The issuing of forecasts and warnings of natural hazard events, such as volcanic eruptions, earthquake aftershock sequences and extreme weather often involves the use of probabilistic terms, particularly when communicated by scientific advisory groups to key decision-makers, who can differ greatly in relative expertise and function in the decision making process. Recipients may also differ in their perception of relative importance of political and economic influences on interpretation. Consequently, the interpretation of these probabilistic terms can vary greatly due to the framing of the statements, and whether verbal or numerical terms are used. We present a review from the psychology literature on how the framing of information influences communication of these probability terms. It is also unclear as to how people rate their perception of an event's likelihood throughout a time frame when a forecast time window is stated. Previous research has identified that, when presented with a 10-year time window forecast, participants viewed the likelihood of an event occurring ‘today’ as being of less than that in year 10. Here we show that this skew in perception also occurs for short-term time windows (under one week) that are of most relevance for emergency warnings. In addition, unlike the long-time window statements, the use of the phrasing “within the next…” instead of “in the next…” does not mitigate this skew, nor do we observe significant differences between the perceived likelihoods of scientists and non-scientists. This finding suggests that effects occurring due to the shorter time window may be ‘masking’ any differences in perception due to wording or career background observed for long-time window forecasts. These results have implications for scientific advice, warning forecasts, emergency management decision-making, and public information as any skew in perceived event likelihood towards the end of a forecast time window may result in an underestimate of the likelihood of an event occurring ‘today’ leading to potentially inappropriate action choices. We thus present some initial guidelines for communicating such eruption forecasts.

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Priya, S.; Young, W.; Avasthi, A.; Clayton, T. D.; Brakenridge, G. R.; Birkett, C. M.; Riddle, E. E.; Broman, D.; Boehnert, J.; Sampson, K. M.; Kettner, A.; Singh, D.

2017-12-01

During the 2017 South Asia monsoon, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children, across the Ganges-Brahmaputra-Meghna (GBM) basins. The basin is recognized as one of the world's most disaster-prone regions, with severe floods occurring almost annually causing extreme loss of life and property. In light of this vulnerability, the World Bank and collaborators have contributed toward reducing future flood impacts through recent developments to improve operational preparedness for such events, as well as efforts in more general preparedness and resilience building through planning based on detailed risk assessments. With respect to improved event-specific flood preparedness through operational warnings, we discuss a new forecasting system that provides probability-based flood forecasts developed for more than 85 GBM locations. Forecasts are available online, along with near-real-time data maps of rainfall (predicted and actual) and river levels. The new system uses multiple data sets and multiple models to enhance forecasting skill, and provides improved forecasts up to 16 days in advance of the arrival of high waters. These longer lead times provide the opportunity to save both lives and livelihoods. With sufficient advance notice, for example, farmers can harvest a threatened rice crop or move vulnerable livestock to higher ground. Importantly, the forecasts not only predict future water levels but indicate the level of confidence in each forecast. Knowing whether the probability of a danger-level flood is 10 percent or 90 percent helps people to decide what, if any, action to take. With respect to efforts in general preparedness and resilience building, we also present a recent flood risk assessment, and how it provides, for the first time, a numbers-based view of the impacts of different size floods across the Ganges basin. The findings help identify priority areas for tackling flood risks (for example, relocating levees, improving flood warning systems, or boosting overall economic resilience). The assessment includes the locations and numbers of people at risk, as well as the locations and value of buildings, roads and railways, and crops at risk. An accompanying atlas includes easy-to-use risk maps and tables for the Ganges basins.

Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolinger, Mark; Wiser, Ryan; Golove, William

2003-08-13

Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projectedmore » costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply contracts) to contemporaneous forecasts of spot natural gas prices, with the purpose of identifying any systematic differences between the two. Although our data set is quite limited, we find that over the past three years, forward gas prices for durations of 2-10 years have been considerably higher than most natural gas spot price forecasts, including the reference case forecasts developed by the Energy Information Administration (EIA). This difference is striking, and implies that resource planning and modeling exercises based on these forecasts over the past three years have yielded results that are biased in favor of gas-fired generation (again, presuming that long-term stability is desirable). As discussed later, these findings have important ramifications for resource planners, energy modelers, and policy-makers.« less

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

Climate and Salmon Restoration in the Columbia River Basin: The Role and Usability of Seasonal Forecasts.

NASA Astrophysics Data System (ADS)

Pulwarty, Roger S.; Redmond, Kelly T.

1997-03-01

The Pacific Northwest is dependent on the vast and complex Columbia River system for power production, irrigation, navigation, flood control, recreation, municipal and industrial water supplies, and fish and wildlife habitat. In recent years Pacific salmon populations in this region, a highly valued cultural and economic resource, have declined precipitously. Since 1980, regional entities have embarked on the largest effort at ecosystem management undertaken to date in the United States, primarily aimed at balancing hydropower demands with salmon restoration activities. It has become increasingly clear that climatically driven fluctuations in the freshwater and marine environments occupied by these fish are an important influence on population variability. It is also clear that there are significant prospects of climate predictability that may prove advantageous in managing the water resources shared by the long cast of regional interests. The main thrusts of this study are 1) to describe the climate and management environments of the Columbia River basin, 2) to assess the present degree of use and benefits of available climate information, 3) to identify new roles and applications made possible by recent advances in climate forecasting, and 4) to understand, from the point of view of present and potential users in specific contexts of salmon management, what information might be needed, for what uses, and when, where, and how it should be provided. Interviews were carried out with 32 individuals in 19 organizations involved in salmon management decisions. Primary needs were in forecasting runoff volume and timing, river transit times, and stream temperatures, as much as a year or more in advance. Most respondents desired an accuracy of 75% for a seasonal forecast. Despite the significant influence of precipitation and its subsequent hydrologic impacts on the regional economy, no specific use of the present climate forecasts was uncovered. Understanding the limitations to information use forms a major component of this study. The complexity of the management environment, the lack of well-defined linkages among potential users and forecasters, and the lack of supplementary background information relating to the forecasts pose substantial barriers to future use of forecasts. Recommendations to address these problems are offered. The use of climate information and forecasts to reduce the uncertainty inherent in managing large systems for diverse needs bears significant promise.

Automated system for smoke dispersion prediction due to wild fires in Alaska

NASA Astrophysics Data System (ADS)

Kulchitsky, A.; Stuefer, M.; Higbie, L.; Newby, G.

2007-12-01

Community climate models have enabled development of specific environmental forecast systems. The University of Alaska (UAF) smoke group was created to adapt a smoke forecast system to the Alaska region. The US Forest Service (USFS) Missoula Fire Science Lab had developed a smoke forecast system based on the Weather Research and Forecasting (WRF) Model including chemistry (WRF/Chem). Following the successful experience of USFS, which runs their model operationally for the contiguous U.S., we develop a similar system for Alaska in collaboration with scientists from the USFS Missoula Fire Science Lab. Wildfires are a significant source of air pollution in Alaska because the climate and vegetation favor annual summer fires that burn huge areas. Extreme cases occurred in 2004, when an area larger than Maryland (more than 25000~km2) burned. Small smoke particles with a diameter less than 10~μm can penetrate deep into lungs causing health problems. Smoke also creates a severe restriction to air transport and has tremendous economical effect. The smoke dispersion and forecast system for Alaska was developed at the Geophysical Institute (GI) and the Arctic Region Supercomputing Center (ARSC), both at University of Alaska Fairbanks (UAF). They will help the public and plan activities a few days in advance to avoid dangerous smoke exposure. The availability of modern high performance supercomputers at ARSC allows us to create and run high-resolution, WRF-based smoke dispersion forecast for the entire State of Alaska. The core of the system is a Python program that manages the independent pieces. Our adapted Alaska system performs the following steps \\begin{itemize} Calculate the medium-resolution weather forecast using WRF/Met. Adapt the near real-time satellite-derived wildfire location and extent data that are received via direct broadcast from UAF's "Geographic Information Network of Alaska" (GINA) Calculate fuel moisture using WRF forecasts and National Fire Danger Rating System (NFDRS) fuel maps Calculate smoke emission components using a first order fire emission model Model the smoke plume rise yielding a vertically distribution that accounts for one-dimensional (vertical) concentrations of smoke constituents in the atmosphere above the fire Run WRF/Chem at high resolution for the forecast Use standard graphical tools to provide accessible smoke dispersion The system run twice each day at ARSC. The results will be freely available from a dedicated wildfire smoke web portal at ARSC.

Remote and Local Influences in Forecasting Pacific SST: a Linear Inverse Model and a Multimodel Ensemble Study

NASA Astrophysics Data System (ADS)

Faggiani Dias, D.; Subramanian, A. C.; Zanna, L.; Miller, A. J.

2017-12-01

Sea surface temperature (SST) in the Pacific sector is well known to vary on time scales from seasonal to decadal, and the ability to predict these SST fluctuations has many societal and economical benefits. Therefore, we use a suite of statistical linear inverse models (LIMs) to understand the remote and local SST variability that influences SST predictions over the North Pacific region and further improve our understanding on how the long-observed SST record can help better guide multi-model ensemble forecasts. Observed monthly SST anomalies in the Pacific sector (between 15oS and 60oN) are used to construct different regional LIMs for seasonal to decadal prediction. The forecast skills of the LIMs are compared to that from two operational forecast systems in the North American Multi-Model Ensemble (NMME) revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The forecast skill with LIM is also influenced by the verification period utilized to make the predictions, likely due to the changing character of El Niño in the 20th century. The North Pacific seems to be a source of predictability for the Tropics on seasonal to interannual time scales, while the Tropics act to worsen the skill for the forecast in the North Pacific. The data were also bandpassed into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. For the decadal component, this coupling occurs the other way around: Tropics seem to be a source of predictability for the Extratropics, but the Extratropics don't improve the predictability for the Tropics. These results indicate the importance of temporal scale interactions in improving predictability on decadal timescales. Hence, we show that LIMs are not only useful as benchmarks for estimates of statistical skill, but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components.

Europe's Skill Challenge: Lagging Skill Demand Increases Risks of Skill Mismatch. Briefing Note

ERIC Educational Resources Information Center

Cedefop - European Centre for the Development of Vocational Training, 2012

2012-01-01

The main findings of Cedefop's latest skill demand and supply forecast for the European Union (EU) for 2010-20, indicate that although further economic troubles will affect the projected number of job opportunities, the major trends, including a shift to more skill-intensive jobs and more jobs in services, will continue. Between 2008 and 2010…

Hot Academic Jobs of the Future: Try These Fields

ERIC Educational Resources Information Center

Roberts, Lee

2009-01-01

At a time when the academic job market is looking bleak, the author asked career experts and economic forecasters to predict where faculty job growth could come in the next decade. Many agreed that job prospects will be dim because of budget cuts and diminishing faculty pension funds that have made professors less likely to retire. In addition,…

Planning in the 90's for Community Colleges: Coping with Rapid Change by Linking Futures Research with Professional Development.

ERIC Educational Resources Information Center

Kelly, Diana K.

The rate of the change now occurring outside of community colleges has made long-range planning an especially difficult task. Futures research, which attempts to forecast future scenarios by studying societal, economic, and demographic trends, can be used effectively to facilitate the institutional planning process by anticipating both internal…

Recreational fishing in the Southeast United States: a demand project analysis

Treesearch

Neelam C. Poudyal; J.M. Bowker

2008-01-01

The objective of this paper is to first develop an economic model of demand for recreational fishing in the Southeastern United States, and then project the demand for fishing in the region during the next few decades. The findings from this study will be useful to understand the factors behind declining people's participation and also to forecast the license...

Alternative Methods of Base Level Demand Forecasting for Economic Order Quantity Items,

DTIC Science & Technology

1975-12-01

Note .. . . . . . . . . . . . . . . . . . . . . . . . 21 AdaptivC Single Exponential Smooti-ing ........ 21 Choosing the Smoothiing Constant... methodology used in the study, an analysis of results, .And a detailed summary. Chapter I. Methodology , contains a description o the data, a...Chapter IV. Detailed Summary, presents a detailed summary of the findings, lists the limitations inherent in the 7’" research methodology , and

Economic and Technology Forecasts for the 80's: Implications for Careers. Proceedings of the Patterns Conference (Rochester Institute of Technology, May 1979).

ERIC Educational Resources Information Center

Nystrom, Dennis; And Others

Selected technological and research areas that may affect emerging careers and work expectations were discussed in the five seminar presentations contained in this proceedings. In the introductory section, Dennis Nystrom outlines the goals of the conference and suggests possible implications. Sar Levitan, in the keynote address, examines the…

Forecasting the Effects of Higher Education Appropriations on Local Economies. AIR 1986 Annual Forum Paper.

ERIC Educational Resources Information Center

Prewitt, Sidney A.; And Others

An economic model of the effects of colleges on their communities was developed. The Texas Input-Output Model was modified into a higher education budgetary model. Included were the positive benefits of tax savings and estimates of the net effect on various communities in which state-supported colleges and universities are located. The output…

Girls in Science and Technology in Secondary and Post-Secondary Education: The Case of France

ERIC Educational Resources Information Center

Stevanovic, Biljana

2014-01-01

Based on surveys undertaken by the Institut national de la statistique et des études économiques (France's National Institute of Statistics and Economic Studies) and by the Direction de l'évaluation de la prospective et de la performance (Directorate of Evaluation, Forecasting and Performance), this article examines the evolution of female student…

The US aviation system to the year 2000

NASA Technical Reports Server (NTRS)

Austrotas, R. A.

1982-01-01

The aviation system of the U.S. is described. Growth of the system over the past twenty years is analyzed. Long term and short term causes of air travel are discussed. The interaction of economic growth, airline yields, and quality of service in producing domestic traffic is shown. Forecasts are made for airline and general aviation growth. Potential airline scenarios are presented.

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Ying; Zhou, Zhi; Botterud, Audun

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixedmore » integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.« less

Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Younes, Mohammed Y

2016-09-01

Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%. Copyright © 2015 Elsevier Ltd. All rights reserved.

Non-Economic Determinants of Energy Use in Rural Areas of South Africa

DOE Office of Scientific and Technical Information (OSTI.GOV)

Annecke, W.

1999-03-29

This project will begin to determine the forces and dimensions in rural energy-use patterns and begin to address policy and implementation needs for the future. This entails: Forecasting the social and economic benefits that electrification is assumed to deliver regarding education and women's lives; Assessing negative perceptions of users, which have been established through the slow uptake of electricity; Making recommendations as to how these perceptions could be addressed in policy development and in the continuing electrification program; Making recommendations to policy makers on how to support and make optimal use of current energy-use practices where these are socio-economically sound;more » Identifying misinformation and wasteful practices; and Other recommendations, which will significantly improve the success of the rural electrification program in a socio-economically sound manner, as identified in the course of the work.« less

GPACC program cost work breakdown structure-dictionary. General purpose aft cargo carrier study, volume 3

NASA Technical Reports Server (NTRS)

1985-01-01

The results of detailed cost estimates and economic analysis performed on the updated Model 101 configuration of the general purpose Aft Cargo Carrier (ACC) are given. The objective of this economic analysis is to provide the National Aeronautics and Space Administration (NASA) with information on the economics of using the ACC on the Space Transportation System (STS). The detailed cost estimates for the ACC are presented by a work breakdown structure (WBS) to ensure that all elements of cost are considered in the economic analysis and related subsystem trades. Costs reported by WBS provide NASA with a basis for comparing competing designs and provide detailed cost information that can be used to forecast phase C/D planning for new projects or programs derived from preliminary conceptual design studies. The scope covers all STS and STS/ACC launch vehicle cost impacts for delivering payloads to a 160 NM low Earth orbit (LEO).

DACC program cost and work breakdown structure-dictionary. General purpose aft cargo carrier study, volume 2

NASA Technical Reports Server (NTRS)

1985-01-01

Results of detailed cost estimates and economic analysis performed on the updated 201 configuration of the dedicated Aft Cargo Carrier (DACC) are given. The objective of this economic analysis is to provide the National Aeronautics and Space Administration (NASA) with information on the economics of using the DACC on the Space Transportation System (STS). The detailed cost estimates for the DACC are presented by a work breakdown structure (WBS) to ensure that all elements of cost are considered in the economic analysis and related subsystem trades. Costs reported by WBS provide NASA with a basis for comparing competing designs and provide detailed cost information that can be used to forecast phase C/D planning for new projects or programs derived from preliminary conceptual design studies. The scope covers all STS and STS/DACC launch vehicle cost impacts for delivering an orbital transfer vehicle to a 120 NM low Earth orbit (LEO).

Forecasting of monthly inflow and outflow currency using time series regression and ARIMAX: The Idul Fitri effect

NASA Astrophysics Data System (ADS)

Ahmad, Imam Safawi; Setiawan, Suhartono, Masun, Nunun Hilyatul

2015-12-01

Currency plays an important role in economic transactions of Indonesian society. In order to guarantee the availability of currency, Bank Indonesia needs to develop demand and supply planning of currency. The purpose of this study is to get model and predict inflow and outflow of currency in KPW BI Region IV (East Java) with ARIMA method, time series regression and ARIMAX. The data of monthly inflow and outflow is used of currency in KPW BI Surabaya, Malang, Kediri and Jember.The observation period starting from January 2003 to December 2014. Based on the smallest values of out-sample RMSE and SMAPE, ARIMA is the best model to predict the outflow of currency in KPW BI Surabaya and ARIMAX for KPW BI Malang, Kediri and Jember. The best forecasting model for inflow of currency in KPW BI Surabaya, Malang, Kediri and Jember chronologically as follows are calendar variation model, transfer function, ARIMA, and time series regression. These results indicates that the more complex models may not necessarily produce a more accurate forecast as the result of M3-Competition.

Evolution-informed forecasting of seasonal influenza A (H3N2).

PubMed

Du, Xiangjun; King, Aaron A; Woods, Robert J; Pascual, Mercedes

2017-10-25

Interpandemic or seasonal influenza A, currently subtypes H3N2 and H1N1, exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus' antigenic evolution. We propose a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States for more than 10 years, we demonstrate the feasibility of skillful prediction for total cases ahead of season, with a tendency to underpredict monthly peak epidemic size, and an accurate real-time forecast for the 2016/2017 influenza season. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

q-Gaussian distributions of leverage returns, first stopping times, and default risk valuations

NASA Astrophysics Data System (ADS)

Katz, Yuri A.; Tian, Li

2013-10-01

We study the probability distributions of daily leverage returns of 520 North American industrial companies that survive de-listing during the financial crisis, 2006-2012. We provide evidence that distributions of unbiased leverage returns of all individual firms belong to the class of q-Gaussian distributions with the Tsallis entropic parameter within the interval 1

Modelling the costs of care of hypertension in patients with metabolic syndrome and its consequences, in Germany, Spain and Italy.

PubMed

Wille, Eberhard; Scholze, Jürgen; Alegria, Eduardo; Ferri, Claudio; Langham, Sue; Stevens, Warren; Jeffries, David; Uhl-Hochgraeber, Kerstin

2011-06-01

The presence of metabolic syndrome in patients with hypertension significantly increases the risk of cardiovascular disease, type 2 diabetes and mortality. Our aim is to estimate the economic burden to the health service of metabolic syndrome (MetS) in patients with hypertension and its consequences, in three European countries in 2008, and to forecast future economic burden in 2020 using projected demographic estimates and assumptions around the growth of MetS. An age-, sex- and risk group-structured prevalence-based cost of illness model was developed using the United States Adult Treatment Panel III of the National Cholesterol Education Program criteria to define MetS. Data sources included published information and public use databases on disease prevalence, incidence of cardiovascular events, prevalence of type 2 diabetes, treatment patterns and cost of management in Germany, Spain and Italy. The economic burden to the health service of MetS in patients with hypertension has been estimated at 24,427 euro, 1,900 euro and 4,877 euro million in Germany, Spain and Italy, and is forecast to rise by 59, 179 and 157%, respectively, by 2020. The largest components of costs included the management of prevalent type 2 diabetes and incident cardiovascular events. Mean annual costs per hypertensive patient were around three-fold higher in subjects with MetS compared to those without and rose incrementally with the additional number of MetS components present. In conclusion, the presence of MetS in patients with hypertension significantly inflates economic burden, and costs are likely to increase in the future due to an aging population and an increase in the prevalence of components of MetS.

Does NASA SMAP Improve the Accuracy of Power Outage Models?

NASA Astrophysics Data System (ADS)

Quiring, S. M.; McRoberts, D. B.; Toy, B.; Alvarado, B.

2016-12-01

Electric power utilities make critical decisions in the days prior to hurricane landfall that are primarily based on the estimated impact to their service area. For example, utilities must determine how many repair crews to request from other utilities, the amount of material and equipment they will need to make repairs, and where in their geographically expansive service area to station crews and materials. Accurate forecasts of the impact of an approaching hurricane within their service area are critical for utilities in balancing the costs and benefits of different levels of resources. The Hurricane Outage Prediction Model (HOPM) are a family of statistical models that utilize predictions of tropical cyclone windspeed and duration of strong winds, along with power system and environmental variables (e.g., soil moisture, long-term precipitation), to forecast the number and location of power outages. This project assesses whether using NASA SMAP soil moisture improves the accuracy of power outage forecasts as compared to using model-derived soil moisture from NLDAS-2. A sensitivity analysis is employed since there have been very few tropical cyclones making landfall in the United States since SMAP was launched. The HOPM is used to predict power outages for 13 historical tropical cyclones and the model is run using twice, once with NLDAS soil moisture and once with SMAP soil moisture. Our results demonstrate that using SMAP soil moisture can have a significant impact on power outage predictions. SMAP has the potential to enhance the accuracy of power outage forecasts. Improved outage forecasts reduce the duration of power outages which reduces economic losses and accelerates recovery.

Using pattern recognition as a method for predicting extreme events in natural and socio-economic systems

NASA Astrophysics Data System (ADS)

Intriligator, M.

2011-12-01

Vladimir (Volodya) Keilis-Borok has pioneered the use of pattern recognition as a technique for analyzing and forecasting developments in natural as well as socio-economic systems. Keilis-Borok's work on predicting earthquakes and landslides using this technique as a leading geophysicist has been recognized around the world. Keilis-Borok has also been a world leader in the application of pattern recognition techniques to the analysis and prediction of socio-economic systems. He worked with Allan Lichtman of American University in using such techniques to predict presidential elections in the U.S. Keilis-Borok and I have worked together with others on the use of pattern recognition techniques to analyze and to predict socio-economic systems. We have used this technique to study the pattern of macroeconomic indicators that would predict the end of an economic recession in the U.S. We have also worked with officers in the Los Angeles Police Department to use this technique to predict surges of homicides in Los Angeles.

Investment Appeal of the Recreational Potential of the Region

NASA Astrophysics Data System (ADS)

Galanina, T. V.; Mikhailov, V. G.; Golofastova, N. N.; Koroleva, T. G.

2017-01-01

The article deals with the issue of environmental and economic assessment of natural and recreational potential of Kuzbass. The purpose of the research is to study the trends in the development of the regional recreational potential, as well as to develop the methodological bases for creation of resort and recreational areas to improve the health of the population and increase the investment appeal of Kemerovo region. The constraints to improve the investment, environmental and economic performance of regional resorts are identified. The main result of the research is the development of the project to improve the stability of the socio-economic system of the region, on the basis of the mitigation of environmental and economic risks and the systematic development of recreational infrastructure. The practical significance of the study consists of the recommendations for regional and federal authorities concerning the development of socio-economic plans and forecasts, including the provision of a rationale for the status of priority development territories.

HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

2015-04-01

In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the forecasts against observed discharge. Analysis should be specifically oriented to the maximisation of hydroelectricity production. Thus, verification metrics should include economic measures like cost loss approaches. The final step will include the transfer of the HEPS system to several hydropower systems, the connection with the energy market prices and the development of probabilistic multi-reservoir production and management optimizations guidelines. The baseline model chain yielding three-days forecasts established for a hydropower system in southern-Switzerland will be presented alongside with the work-plan to achieve seasonal ensemble predictions.

CARICOF - The Caribbean Regional Climate Outlook Forum

NASA Astrophysics Data System (ADS)

Van Meerbeeck, Cedric

2013-04-01

Regional Climate Outlook Forums (RCOFs) are viewed as a critical building block in the Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO). The GFCS seeks to extend RCOFs to all vulnerable regions of the world such as the Caribbean, of which the entire population is exposed to water- and heat-related natural hazards. An RCOF is initially intended to identify gaps in information and technical capability; facilitate research cooperation and data exchange within and between regions, and improve coordination within the climate forecasting community. A focus is given on variations in climate conditions on a seasonal timescale. In this view, the relevance of a Caribbean RCOF (CARICOF) is the following: while the seasonality of the climate in the Caribbean has been well documented, major gaps in knowledge exist in terms of the drivers in the shifts of amplitude and phase of seasons (as evidenced from the worst region-wide drought period in recent history during 2009-2010). To address those gaps, CARICOF has brought together National Weather Services (NWSs) from 18 territories under the coordination of the Caribbean Institute for Meteorology and Hydrology (CIMH), to produce region-wide, consensus, seasonal climate outlooks since March 2012. These outlooks include tercile rainfall forecasts, sea and air surface temperature forecasts as well as the likely evolution of the drivers of seasonal climate variability in the region, being amongst others the El Niño Southern Oscillation or tropical Atlantic and Caribbean Sea temperatures. Forecasts for both the national-scale forecasts made by the NWSs and CIMH's regional-scale forecast amalgamate output from several forecasting tools. These currently include: (1) statistical models such as Canonical Correlation Analysis run with the Climate Predictability Tool, providing tercile rainfall forecasts at weather station scale; (2) a global outlooks published by the WMO appointed Global Producing Centres (GPCs). Indications are that the current seasonal forecasting system used by CARICOF has produced reliable outlooks than previously available. Nevertheless, through its forum platform, areas for further development are continuously being defined, which are then implemented through efficient information exchanges between and hands-on training of forecasters. Finally, the disaster research and emergency management communities have shown that effective early warnings of impending hazards need to be complemented by information on the risks actually posed by the hazards and pathways for action. CARICOF is to address this issue by designing the outputs of the seasonal climate outlooks such that they can then effectively feed into an early warning information system of seasonal climate variability related hazards to its constituent countries' and territories major socio-economic sectors.

Nitrogen-Use Efficiency, Nitrous Oxide Emissions, and Cereal Production in Brazil: Current Trends and Forecasts.

PubMed

Pires, Marcel Viana; da Cunha, Dênis Antônio; de Matos Carlos, Sabrina; Costa, Marcos Heil

2015-01-01

The agriculture sector has historically been a major source of greenhouse gas (GHG) emissions into the atmosphere. Although the use of synthetic fertilizers is one of the most common widespread agricultural practices, over-fertilization can lead to negative economic and environmental consequences, such as high production costs, depletion of energy resources, and increased GHG emissions. Here, we provide an analysis to understand the evolution of cereal production and consumption of nitrogen (N) fertilizers in Brazil and to correlate N use efficiency (NUE) with economic and environmental losses as N2O emissions. Our results show that the increased consumption of N fertilizers is associated with a large decrease in NUE in recent years. The CO2 eq. of N2O emissions originating from N fertilization for cereal production were approximately 12 times higher in 2011 than in 1970, indicating that the inefficient use of N fertilizers is directly related to environmental losses. The projected N fertilizer forecasts are 2.09 and 2.37 million ton for 2015 and 2023, respectively. An increase of 0.02% per year in the projected NUE was predicted for the same time period. However, decreases in the projected CO2 eq. emissions for future years were not predicted. In a hypothetical scenario, a 2.39% increase in cereal NUE would lead to $ 21 million savings in N fertilizer costs. Thus, increases in NUE rates would lead not only to agronomic and environmental benefits but also to economic improvement.

Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic

NASA Astrophysics Data System (ADS)

Shevnina, Elena; Kourzeneva, Ekaterina; Kovalenko, Viktor; Vihma, Timo

2017-05-01

Climate warming has been more acute in the Arctic than at lower latitudes and this tendency is expected to continue. This generates major challenges for economic activity in the region. Among other issues is the long-term planning and development of socio-economic infrastructure (dams, bridges, roads, etc.), which require climate-based forecasts of the frequency and magnitude of detrimental flood events. To estimate the cost of the infrastructure and operational risk, a probabilistic form of long-term forecasting is preferable. In this study, a probabilistic model to simulate the parameters of the probability density function (PDF) for multi-year runoff based on a projected climatology is applied to evaluate changes in extreme floods for the territory of the Russian Arctic. The model is validated by cross-comparison of the modelled and empirical PDFs using observations from 23 sites located in northern Russia. The mean values and coefficients of variation (CVs) of the spring flood depth of runoff are evaluated under four climate scenarios, using simulations of six climate models for the period 2010-2039. Regions with substantial expected changes in the means and CVs of spring flood depth of runoff are outlined. For the sites located within such regions, it is suggested to account for the future climate change in calculating the maximal discharges of rare occurrence. An example of engineering calculations for maximal discharges with 1 % exceedance probability is provided for the Nadym River at Nadym.

Nitrogen-Use Efficiency, Nitrous Oxide Emissions, and Cereal Production in Brazil: Current Trends and Forecasts

PubMed Central

Pires, Marcel Viana; da Cunha, Dênis Antônio; de Matos Carlos, Sabrina; Costa, Marcos Heil

2015-01-01

The agriculture sector has historically been a major source of greenhouse gas (GHG) emissions into the atmosphere. Although the use of synthetic fertilizers is one of the most common widespread agricultural practices, over-fertilization can lead to negative economic and environmental consequences, such as high production costs, depletion of energy resources, and increased GHG emissions. Here, we provide an analysis to understand the evolution of cereal production and consumption of nitrogen (N) fertilizers in Brazil and to correlate N use efficiency (NUE) with economic and environmental losses as N2O emissions. Our results show that the increased consumption of N fertilizers is associated with a large decrease in NUE in recent years. The CO2 eq. of N2O emissions originating from N fertilization for cereal production were approximately 12 times higher in 2011 than in 1970, indicating that the inefficient use of N fertilizers is directly related to environmental losses. The projected N fertilizer forecasts are 2.09 and 2.37 million ton for 2015 and 2023, respectively. An increase of 0.02% per year in the projected NUE was predicted for the same time period. However, decreases in the projected CO2 eq. emissions for future years were not predicted. In a hypothetical scenario, a 2.39% increase in cereal NUE would lead to $ 21 million savings in N fertilizer costs. Thus, increases in NUE rates would lead not only to agronomic and environmental benefits but also to economic improvement. PMID:26252377

Development and Evaluation of Season-ahead Precipitation and Streamflow Predictions for Sectoral Management in Western Ethiopia

NASA Astrophysics Data System (ADS)

Block, P. J.; Alexander, S.; WU, S.

2017-12-01

Skillful season-ahead predictions conditioned on local and large-scale hydro-climate variables can provide valuable knowledge to farmers and reservoir operators, enabling informed water resource allocation and management decisions. In Ethiopia, the potential for advancing agriculture and hydropower management, and subsequently economic growth, is substantial, yet evidence suggests a weak adoption of prediction information by sectoral audiences. To address common critiques, including skill, scale, and uncertainty, probabilistic forecasts are developed at various scales - temporally and spatially - for the Finchaa hydropower dam and the Koga agricultural scheme in an attempt to promote uptake and application. Significant prediction skill is evident across scales, particularly for statistical models. This raises questions regarding other potential barriers to forecast utilization at community scales, which are also addressed.

Implications of Water Budget Deficits on Socio-Economic Stability and Food Security in the Arabian Peninsula and in North Africa

NASA Astrophysics Data System (ADS)

Mazzoni, A.; Heggy, E.; Scabbia, G.

2017-12-01

Water scarcity in the Arabian Peninsula and North Africa is accentuated by forecasted climatic variability, decreasing precipitation volumes and projected population growth, urbanization and economic development, increasing water demand. These factors impose uncertainties on food security and socio-economic stability in the region. We develop a water-budget model combining hydrologic, climatic and economic data to quantify water deficit volumes and groundwater depletion rates for the main aquifer systems in the area, taking into account three different climatic scenarios, and calculated from the precipitation forecast elaborated in the CSIRO, ECHAM4 and HADCM3 global circulation models from 2016 to 2050 over 1-year intervals. Water demand comprises water requirements for each economic sector, derived from data such as population, GDP, cropland cover and electricity production, and is based upon the five different SSPs. Conventional and non-conventional water resource supply data are retrieved from FAO Aquastat and institutional databases. Our results suggest that in the next 35 years, in North Africa, only Egypt and Libya will exhibit severe water deficits with respectively 44% and 89.7% of their current water budgets by 2050 (SSP2-AVG climatic scenario), while all the countries in the Arabian Peninsula will be subjected to water stress; the majority of small-size aquifers in the Arabian Peninsula will reach full depletion by 2050. In North Africa, the fossil aquifers' volume loss will be 1-15% by 2050, and total depletion within 200-300 years. Our study suggests that (1) anthropogenic drivers on water resources are harsher than projected climatic variability; (2) the estimated water deficit will induce substantial rise in domestic food production's costs, causing higher dependency on food imports; and (3) projected water deficits will most strongly impact the nations with the lowest GDPP, namely Egypt, Yemen and Libya.

Management of the volcanic crises of Galeras volcano: Social, economic and institutional aspects

NASA Astrophysics Data System (ADS)

Cardona, Omar D.

1997-05-01

This paper presents a summary of the institutional management of the volcanic hazard and risk in the areas that surround Galeras volcano, Colombia, during its recent activity. The social and economic problems discussed have stemmed from difficulties in forecasting the behavior of the volcano and the inadequate management of the warnings by various government bodies and the media. The Galeras situation had economic, social, and psychological effects that contributed to resistance in implementing mitigation measures. Furthermore, the political authorities were reluctant to accept the volcanic risk. At regional and local levels, certain business organizations and a large part of the population also were inadequately prepared to accept the risk, despite the effort and insistence at the national level to implement a volcano emergency preparedness plan.

A framework for probabilistic pluvial flood nowcasting for urban areas

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Murla, Damian; Wang, Lipen; Foresti, Loris; Reyniers, Maarten; Delobbe, Laurent; Van Herk, Kristine; Van Ootegem, Luc; Willems, Patrick

2016-04-01

Pluvial flood nowcasting is gaining ground not least because of the advancements in rainfall forecasting schemes. Short-term forecasts and applications have benefited from the availability of such forecasts with high resolution in space (~1km) and time (~5min). In this regard, it is vital to evaluate the potential of nowcasting products for urban inundation applications. One of the most advanced Quantitative Precipitation Forecasting (QPF) techniques is the Short-Term Ensemble Prediction System, which was originally co-developed by the UK Met Office and Australian Bureau of Meteorology. The scheme was further tuned to better estimate extreme and moderate events for the Belgian area (STEPS-BE). Against this backdrop, a probabilistic framework has been developed that consists of: (1) rainfall nowcasts; (2) sewer hydraulic model; (3) flood damage estimation; and (4) urban inundation risk mapping. STEPS-BE forecasts are provided at high resolution (1km/5min) with 20 ensemble members with a lead time of up to 2 hours using a 4 C-band radar composite as input. Forecasts' verification was performed over the cities of Leuven and Ghent and biases were found to be small. The hydraulic model consists of the 1D sewer network and an innovative 'nested' 2D surface model to model 2D urban surface inundations at high resolution. The surface components are categorized into three groups and each group is modelled using triangular meshes at different resolutions; these include streets (3.75 - 15 m2), high flood hazard areas (12.5 - 50 m2) and low flood hazard areas (75 - 300 m2). Functions describing urban flood damage and social consequences were empirically derived based on questionnaires to people in the region that were recently affected by sewer floods. Probabilistic urban flood risk maps were prepared based on spatial interpolation techniques of flood inundation. The method has been implemented and tested for the villages Oostakker and Sint-Amandsberg, which are part of the larger city of Gent, Belgium. After each of the different above-mentioned components were evaluated, they were combined and tested for recent historical flood events. The rainfall nowcasting, hydraulic sewer and 2D inundation modelling and socio-economical flood risk results each could be partly evaluated: the rainfall nowcasting results based on radar data and rain gauges; the hydraulic sewer model results based on water level and discharge data at pumping stations; the 2D inundation modelling results based on limited data on some recent flood locations and inundation depths; the results for the socio-economical flood consequences of the most extreme events based on claims in the database of the national disaster agency. Different methods for visualization of the probabilistic inundation results are proposed and tested.

The flood event of 10-12 November 2013 on the Tiber River basin (central Italy): real-time flood forecasting with uncertainty supporting risk management and decision-making

NASA Astrophysics Data System (ADS)

Berni, Nicola; Brocca, Luca; Barbetta, Silvia; Pandolfo, Claudia; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Italian national hydro-meteorological early warning system is composed by 21 regional offices (Functional Centres, CF). Umbria Region (central Italy) CF provides early warning for floods and landslides, real-time monitoring and decision support systems (DSS) for the Civil Defence Authorities when significant events occur. The alert system is based on hydrometric and rainfall thresholds with detailed procedures for the management of critical events in which different roles of authorities and institutions involved are defined. The real-time flood forecasting system is based also on different hydrological and hydraulic forecasting models. Among these, the MISDc rainfall-runoff model ("Modello Idrologico SemiDistribuito in continuo"; Brocca et al., 2011) and the flood routing model named STAFOM-RCM (STAge Forecasting Model-Rating Curve Model; Barbetta et al., 2014) are continuously operative in real-time providing discharge and stage forecasts, respectively, with lead-times up to 24 hours (when quantitative precipitation forecasts are used) in several gauged river sections in the Upper-Middle Tiber River basin. Models results are published in real-time in the open source CF web platform: www.cfumbria.it. MISDc provides discharge and soil moisture forecasts for different sub-basins while STAFOM-RCM provides stage forecasts at hydrometric sections. Moreover, through STAFOM-RCM the uncertainty of the forecast stage hydrograph is provided in terms of 95% Confidence Interval (CI) assessed by analyzing the statistical properties of model output in terms of lateral. In the period 10th-12th November 2013, a severe flood event occurred in Umbria mainly affecting the north-eastern area and causing significant economic damages, but fortunately no casualties. The territory was interested by intense and persistent rainfall; the hydro-meteorological monitoring network recorded locally rainfall depth over 400 mm in 72 hours. In the most affected area, the recorded rainfall depths correspond approximately to a return period of 200 years. Most rivers in Umbria have been involved, exceeding hydrometric thresholds and causing flooding (e.g. Chiascio river). The flood event was continuously monitored at the Umbria Region CF and the possible evolution predicted and assessed on the basis of the model forecasts. The predictions provided by MISDc and STAFOM-RCM were found useful to support real-time decision-making addressed to flood risk management. Moreover, the quantification of the uncertainty affecting the deterministic forecast stages was found consistent with the level of confidence selected and had practical utility corroborating the need of coupling deterministic forecast and 'uncertainty' when the model output is used to support decisions about flood management. REFERENCES Barbetta, S., Moramarco, T., Brocca, L., Franchini, M., Melone, F. (2014). Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3), 729-743. Brocca, L., Melone, F., Moramarco, T. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. Hydrological Processes, 25 (18), 2801-2813

Integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts

NASA Astrophysics Data System (ADS)

Zischg, Andreas Paul; Mosimann, Markus; Weingartner, Rolf

2016-04-01

A key aspect of disaster prevention is flood discharge forecasting which is used for early warning and therefore as a decision support for intervention forces. Hereby, the phase between the issued forecast and the time when the expected flood occurs is crucial for an optimal planning of the intervention. Typically, river discharge forecasts cover the regional level only, i.e. larger catchments. However, it is important to note that these forecasts are not useable directly for specific target groups on local level because these forecasts say nothing about the consequences of the predicted flood in terms of affected areas, number of exposed residents and houses. For this, on one hand simulations of the flooding processes and on the other hand data of vulnerable objects are needed. Furthermore, flood modelling in a high spatial and temporal resolution is required for robust flood loss estimation. This is a resource-intensive task from a computing time point of view. Therefore, in real-time applications flood modelling in 2D is not suited. Thus, forecasting flood losses in the short-term (6h-24h in advance) requires a different approach. Here, we propose a method to downscale the river discharge forecast to a spatially-explicit flood loss forecast. The principal procedure is to generate as many flood scenarios as needed in advance to represent the flooded areas for all possible flood hydrographs, e.g. very high peak discharges of short duration vs. high peak discharges with high volumes. For this, synthetic flood hydrographs were derived from the hydrologic time series. Then, the flooded areas of each scenario were modelled with a 2D flood simulation model. All scenarios were intersected with the dataset of vulnerable objects, in our case residential, agricultural and industrial buildings with information about the number of residents, the object-specific vulnerability, and the monetary value of the objects. This dataset was prepared by a data-mining approach. For each flood scenario, the resulting number of affected residents, houses and therefore the losses are computed. This integral assessment leads to a hydro-economical characterisation of each floodplain. Based on that, a transfer function between discharge forecast and damages can be elaborated. This transfer function describes the relationship between predicted peak discharge, flood volume and the number of exposed houses, residents and the related losses. It also can be used to downscale the regional discharge forecast to a local level loss forecast. In addition, a dynamic map delimiting the probable flooded areas on the basis of the forecasted discharge can be prepared. The predicted losses and the delimited flooded areas provide a complementary information for assessing the need of preventive measures on one hand on the long-term timescale and on the other hand 6h-24h in advance of a predicted flood. To conclude, we can state that the transfer function offers the possibility for an integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts. The procedure has been developed and tested in the alpine and pre-alpine environment of the Aare river catchment upstream of Bern, Switzerland.

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2016-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

A Model For Change: An Approach for Forecasting Well-Being ...

EPA Pesticide Factsheets

Every community decision incorporates a "forecasting" strategy (whether formal or implicit) to help visualize expected results and evaluate the potential “feelings” that people living in that community may have about those results. With more communities seeking to make decisions based on sustainable alternatives, forecasting efforts that examine potential impacts of decisions on overall community well-being may prove to be valuable for not only gaging future benefits and trade-offs, but also for recognizing a community’s affective response to the outcomes of those decisions. This paper describes a forecasting approach based on concepts introduced in the development of the U.S. Environmental Protection Agency’s (US EPA) Human Well-Being Index (HWBI) (Smith, et. al. 2014; Summers et al. 2014). The approach examines the relationships among selected economic, environmental and social services that can be directly impacted by community decisions and eight domains of human well-being. Using models developed from constructed- or fixed-effect step-wise and multiple regressions and eleven years of data (2000-2010), these relationship functions may be used to characterize likely direct impacts of decisions on future well-being as well as the possible intended and unintended secondary and tertiary effects relative to any main decision effects. This paper describes an approach to using HWBI in decision making models to characterize likely impacts of decisions on fut

Global Disease Monitoring and Forecasting with Wikipedia

PubMed Central

Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid

2014-01-01

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art. PMID:25392913

Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

NASA Astrophysics Data System (ADS)

Dumitrache, Rodica Claudia; Iriza, Amalia; Maco, Bogdan Alexandru; Barbu, Cosmin Danut; Hirtl, Marcus; Mantovani, Simone; Nicola, Oana; Irimescu, Anisoara; Craciunescu, Vasile; Ristea, Alina; Diamandi, Andrei

2016-10-01

The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information - e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).

Global disease monitoring and forecasting with Wikipedia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: accessmore » logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.« less

Global disease monitoring and forecasting with Wikipedia.

PubMed

Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y; Priedhorsky, Reid

2014-11-01

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

PubMed Central

Hansen, James W

2005-01-01

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate–crop–economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based ‘discussion support systems’ contribute to learning and farmer–researcher dialogue. Integrated climate–crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis. PMID:16433092