A Novel Flood Forecasting Method Based on Initial State Variable Correction

Directory of Open Access Journals (Sweden)

Kuang Li

2017-12-01

Full Text Available The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction. The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

State-level electricity demand forecasting model. [For 1980, 1985, 1990

Energy Technology Data Exchange (ETDEWEB)

Nguyen, H. D.

1978-01-01

This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.

Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment

International Nuclear Information System (INIS)

Delarue, Erik; Van Den Bosch, Pieterjan; D'haeseleer, William

2010-01-01

This paper discusses and quantifies the so-called loss of profit (i.e., the sub-optimality of profit) that can be expected in a Price Based Unit Commitment (PBUC), when incorrect price forecasts are used. For this purpose, a PBUC model has been developed and utilized, using Mixed Integer Linear Programming (MILP). Simulations are used to determine the relationship between the Mean Absolute Percentage Error (MAPE) of a certain price forecast and the loss of profit, for four different types of power plants. A Combined Cycle (CC) power plant and a pumped storage unit show highest sensitivity to incorrect forecasts. A price forecast with a MAPE of 15%, on average, yields 13.8% and 12.1% profit loss, respectively. A classic thermal power plant (coal fired) and cascade hydro unit are less affected by incorrect forecasts, with only 2.4% and 2.0% profit loss, respectively, at the same price forecast MAPE. This paper further demonstrates that if price forecasts show an average bias (upward or downward), using the MAPE as measure of the price forecast might not be sufficient to quantify profit loss properly. Profit loss in this case has been determined as a function of both shift and MAPE of the price forecast. (author)

Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

International Nuclear Information System (INIS)

Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

2009-01-01

The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

Forecast of the energy final consumption for Minas Gerais State

International Nuclear Information System (INIS)

Almeida, P.E.F. de; Bechtlufft, P.C.T.; Araujo, M.E.A.; Vasconcelos, E.C.; Las Casas, H.B. de; Monteiro, M.A.G.

1990-01-01

This paper is included among the activities of the Energy Planning of Minas Gerais State and presents a forecast of the energy final consumption for the State up to year 2010. Two Scenarios are presented involving brazilian economy's evolution, the State's demography and its sectors: residential, services, transportation, agriculture and cattle-breeding and industry. Finally, it shows two forecast on energy final consumption for Minas Gerais State. (author)

An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern United States: observational-based analysis of surface temperature

Directory of Open Access Journals (Sweden)

J. Zhang

2016-05-01

Full Text Available A major continental-scale biomass burning smoke event from 28–30 June 2015, spanning central Canada through the eastern seaboard of the United States, resulted in unforecasted drops in daytime high surface temperatures on the order of 2–5  °C in the upper Midwest. This event, with strong smoke gradients and largely cloud-free conditions, provides a natural laboratory to study how aerosol radiative effects may influence numerical weather prediction (NWP forecast outcomes. Here, we describe the nature of this smoke event and evaluate the differences in observed near-surface air temperatures between Bismarck (clear and Grand Forks (overcast smoke, to evaluate to what degree solar radiation forcing from a smoke plume introduces daytime surface cooling, and how this affects model bias in forecasts and analyses. For this event, mid-visible (550 nm smoke aerosol optical thickness (AOT, τ reached values above 5. A direct surface cooling efficiency of −1.5 °C per unit AOT (at 550 nm, τ550 was found. A further analysis of European Centre for Medium-Range Weather Forecasts (ECMWF, National Centers for Environmental Prediction (NCEP, United Kingdom Meteorological Office (UKMO near-surface air temperature forecasts for up to 54 h as a function of Moderate Resolution Imaging Spectroradiometer (MODIS Dark Target AOT data across more than 400 surface stations, also indicated the presence of the daytime aerosol direct cooling effect, but suggested a smaller aerosol direct surface cooling efficiency with magnitude on the order of −0.25 to −1.0 °C per unit τ550. In addition, using observations from the surface stations, uncertainties in near-surface air temperatures from ECMWF, NCEP, and UKMO model runs are estimated. This study further suggests that significant daily changes in τ550 above 1, at which the smoke-aerosol-induced direct surface cooling effect could be comparable in magnitude with model uncertainties, are rare events

United States Registered Nurse Workforce Report Card and Shortage Forecast: A Revisit.

Science.gov (United States)

Zhang, Xiaoming; Tai, Daniel; Pforsich, Hugh; Lin, Vernon W

This is a reevaluation of registered nurse (RN) supply and demand from 2016 to 2030 using a previously published work forecast model and grading methodology with more recent workforce data. There will be a shortage of 154 018 RNs by 2020 and 510 394 RNs by 2030; the South and West regions will have higher shortage ratios than Northeast and Midwest regions. This reflects a nearly 50% overall improvement when compared with the authors' prior study, and the low-performing states have improved from 18 "D" and 12 "F" grades as published earlier to 13 "D" and 1 "F" in this study. Although progress has been made, efforts to foster the pipelines for improving the nursing workforce need to be continued.

Climate forecasts for corn producer decision making

Science.gov (United States)

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertai...

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

Energy Technology Data Exchange (ETDEWEB)

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.

Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

Energy Technology Data Exchange (ETDEWEB)

Hodge, B.

2013-12-01

Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

The WRF model forecast-derived low-level wind shear climatology over the United States great plains

Energy Technology Data Exchange (ETDEWEB)

Storm, B. [Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States); Basu, S. [Atmospheric Science Group, Department of Geosciences, Texas Tech University, Lubbock, TX (United States)

2010-07-01

For wind resource assessment projects, it is common practice to use a power-law relationship (U(z) {proportional_to} z{sup {alpha}}) and a fixed shear exponent ({alpha} = 1/7) to extrapolate the observed wind speed from a low measurement level to high turbine hub-heights. However, recent studies using tall-tower observations have found that the annual average shear exponents at several locations over the United States Great Plains (USGP) are significantly higher than 1/7. These findings highlight the critical need for detailed spatio-temporal characterizations of wind shear climatology over the USGP, where numerous large wind farms will be constructed in the foreseeable future. In this paper, a new generation numerical weather prediction model - the Weather Research and Forecasting (WRF) model, a fast and relatively inexpensive alternative to time-consuming and costly tall-tower projects, is utilized to determine whether it can reliably estimate the shear exponent and the magnitude of the directional shear at any arbitrary location over the USGP. Our results indicate that the WRF model qualitatively captures several low-level wind shear characteristics. However, there is definitely room for physics parameterization improvements for the WRF model to reliably represent the lower part of the atmospheric boundary layer. (author)

A hierarchical spatiotemporal analog forecasting model for count data.

Science.gov (United States)

McDermott, Patrick L; Wikle, Christopher K; Millspaugh, Joshua

2018-01-01

Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model-based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Directory of Open Access Journals (Sweden)

Jui-Yi Ho

2015-04-01

Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.

Forecasting the electricity consumption of the Mexican border states maquiladoras

Energy Technology Data Exchange (ETDEWEB)

Flores, C.E.; Phelan, P.E. [Arizona State Univ., Dept. of Mechanical and Aerospace Engineering, Tempe, AZ (United States); Mou, J.-I. [Taiwan Semiconductor Manufacturing Co., Operation Planning Div., Hsin-Chu (Taiwan); Bryan, H. [Arizona State Univ., School of Architecture, Tempe, AZ (United States)

2004-07-01

The consumption of electricity by maquiladora industries in the Mexican border states is an important driver for determining future powerplant needs in that area. An industrial electricity forecasting model is developed for the border states' maquiladoras, and the outputs are compared with a reference forecasting model developed for the US industrial sector, for which considerably more data are available. This model enables the prediction of the effect of implementing various energy efficiency measures in the industrial sector. As an illustration, here the impact of implementing energy-efficient lighting and motors in the Mexican border states' maquiladoras was determined to be substantial. Without such energy efficiency measures, electricity consumption for these industries is predicted to rise by 64% from 2001 to 2010, but if these measures are implemented on a gradual basis over the same time period, electricity consumption is forecast to rise by only 36%. (Author)

Wind power forecasting : state-of-the-art 2009.

Energy Technology Data Exchange (ETDEWEB)

Monteiro, C.; Bessa, R.; Miranda, V.; Botterud, A.; Wang, J.; Conzelmann, G.; Decision and Information Sciences; INESC Porto

2009-11-20

Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and

Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

DEFF Research Database (Denmark)

Bork, Lasse; Møller, Stig Vinther

2015-01-01

We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

Bayesian analyses of seasonal runoff forecasts

Science.gov (United States)

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.

31 CFR 515.321 - United States; continental United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States; continental United... General Definitions § 515.321 United States; continental United States. The term United States means the United States and all areas under the jurisdiction or authority thereof, including the Trust Territory of...

31 CFR 500.321 - United States; continental United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States; continental United... General Definitions § 500.321 United States; continental United States. The term United States means the United States and all areas under the jurisdiction or authority thereof, including U.S. trust territories...

31 CFR 535.321 - United States; continental United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States; continental United... General Definitions § 535.321 United States; continental United States. The term United States means the United States and all areas under the jurisdiction or authority thereof including the Trust Territory of...

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Zhang, Yingchen

2016-11-14

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

Sub-Seasonal Climate Forecast Rodeo

Science.gov (United States)

Webb, R. S.; Nowak, K.; Cifelli, R.; Brekke, L. D.

2017-12-01

The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, benefits from skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is an area with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a real-time S2S forecasting competition. For a year, solvers are submitting forecasts of temperature and precipitation for weeks 3&4 and 5&6 every two weeks on a 1x1 degree grid for the 17 western state domain where Reclamation operates. The competition began on April 18, 2017 and the final real-time forecast is due April 3, 2018. Forecasts are evaluated once observational data become available using spatial anomaly correlation. Scores are posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). The leaderboard can be accessed at: https://www.drought.gov/drought/sub-seasonal-climate-forecast-rodeo. To be eligible for cash prizes - which total $800,000 - solvers must outperform two benchmark forecasts during the real-time competition as well as in a required 11-year hind-cast. To receive a prize, competitors must grant a non-exclusive license to practice their forecast technique and make it available as open source software. At approximately one quarter complete, there are teams outperforming the benchmarks in three of the four competition categories. With prestige and monetary incentives on the line, it is hoped that the competition will spur innovation of improved S2S forecasts through novel approaches, enhancements to established models, or otherwise. Additionally, the competition aims to raise

Geothermal wells: a forecast of drilling activity

Energy Technology Data Exchange (ETDEWEB)

Brown, G.L.; Mansure, A.J.; Miewald, J.N.

1981-07-01

Numbers and problems for geothermal wells expected to be drilled in the United States between 1981 and 2000 AD are forecasted. The 3800 wells forecasted for major electric power projects (totaling 6 GWe of capacity) are categorized by type (production, etc.), and by location (The Geysers, etc.). 6000 wells are forecasted for direct heat projects (totaling 0.02 Quads per year). Equations are developed for forecasting the number of wells, and data is presented. Drilling and completion problems in The Geysers, The Imperial Valley, Roosevelt Hot Springs, the Valles Caldera, northern Nevada, Klamath Falls, Reno, Alaska, and Pagosa Springs are discussed. Likely areas for near term direct heat projects are identified.

The Value of Seasonal Climate Forecasts in Managing Energy Resources.

Science.gov (United States)

Brown Weiss, Edith

1982-04-01

Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

Rapanos v. United States & Carabell v. United States

Science.gov (United States)

Documents associated with guidance for implementing the definition of waters of the United States under the Clean Water Act following the Rapanos v. United States, and Carabell v. United States Supreme Court decision.

Weather Forecasts are for Wimps. Why Water Resource Managers Do Not Use Climate Forecasts

Energy Technology Data Exchange (ETDEWEB)

Rayner, S. [James Martin Institute of Science and Civilization, Said Business School, University of Oxford, OX1 1HP (United Kingdom); Lach, D. [Oregon State University, Corvallis, OR, 97331-4501 (United States); Ingram, H. [School of Social Ecology, University of California Irvine, Irvine, CA, 92697-7075 (United States)

2005-04-15

Short-term climate forecasting offers the promise of improved hydrologic management strategies. However, water resource managers in the United States have proven reluctant to incorporate them in decision making. While managers usually cite poor reliability of the forecasts as the reason for this, they are seldom able to demonstrate knowledge of the actual performance of forecasts or to consistently articulate the level of reliability that they would require. Analysis of three case studies in California, the Pacific Northwest, and metro Washington DC identifies institutional reasons that appear to lie behind managers reluctance to use the forecasts. These include traditional reliance on large built infrastructure, organizational conservatism and complexity, mismatch of temporal and spatial scales of forecasts to management needs, political disincentives to innovation, and regulatory constraints. The paper concludes that wider acceptance of the forecasts will depend on their being incorporated in existing organizational routines and industrial codes and practices, as well as changes in management incentives to innovation. Finer spatial resolution of forecasts and the regional integration of multi-agency functions would also enhance their usability. The title of this article is taken from an advertising slogan for the Oldsmobile Bravura SUV.

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

Science.gov (United States)

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

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases

The contemporary cement cycle of the United States

Science.gov (United States)

Kapur, A.; Van Oss, H. G.; Keoleian, G.; Kesler, S.E.; Kendall, A.

2009-01-01

A country-level stock and flow model for cement, an important construction material, was developed based on a material flow analysis framework. Using this model, the contemporary cement cycle of the United States was constructed by analyzing production, import, and export data for different stages of the cement cycle. The United States currently supplies approximately 80% of its cement consumption through domestic production and the rest is imported. The average annual net addition of in-use new cement stock over the period 2000-2004 was approximately 83 million metric tons and amounts to 2.3 tons per capita of concrete. Nonfuel carbon dioxide emissions (42 million metric tons per year) from the calcination phase of cement manufacture account for 62% of the total 68 million tons per year of cement production residues. The end-of-life cement discards are estimated to be 33 million metric tons per year, of which between 30% and 80% is recycled. A significant portion of the infrastructure in the United States is reaching the end of its useful life and will need to be replaced or rehabilitated; this could require far more cement than might be expected from economic forecasts of demand for cement. ?? 2009 Springer Japan.

Space Weather Forecasting and Supporting Research in the USA

Science.gov (United States)

Pevtsov, A. A.

2017-12-01

In the United State, scientific research in space weather is funded by several Government Agencies including the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA). For civilian and commercial purposes, space weather forecast is done by the Space Weather Prediction Center (SWPC) of the National Oceanic and Atmospheric Administration (NOAA). Observational data for modeling come from the network of groundbased observatories funded via various sources, as well as from the instruments on spacecraft. Numerical models used in forecast are developed in framework of individual research projects. The article provides a brief review of current state of space weather-related research and forecasting in the USA.

Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

Science.gov (United States)

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.

Geothermal power generation in the United States 1985 through 1989

International Nuclear Information System (INIS)

Rannels, J.E.; McLarty, L.

1990-01-01

The United States has used geothermal energy for the production of electricity since 1960 and has the largest installed capacity of any country in the world. During the 1980s, expansion at The Geysers and emergence of the hot water segment of the industry fueled explosive growth in generating capacity. In this paper geothermal development in the U.S. during the second half of the decade is reviewed, and development over the next five years is forecast

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

Science.gov (United States)

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

United States geological survey's reserve-growth models and their implementation

Science.gov (United States)

Klett, T.R.

2005-01-01

The USGS has developed several mathematical models to forecast reserve growth of fields both in the United States (U.S.) and the world. The models are based on historical reserve growth patterns of fields in the U.S. The patterns of past reserve growth are extrapolated to forecast future reserve growth. Changes of individual field sizes through time are extremely variable, therefore, the reserve growth models take on a statistical approach whereby volumetric changes for populations of fields are used in the models. Field age serves as a measure of the field-development effort that is applied to promote reserve growth. At the time of the USGS World Petroleum Assessment 2000, a reserve growth model for discovered fields of the world was not available. Reserve growth forecasts, therefore, were made based on a model of historical reserve growth of fields of the U.S. To test the feasibility of such an application, reserve growth forecasts were made of 186 giant oil fields of the world (excluding the U.S. and Canada). In addition, forecasts were made for these giant oil fields subdivided into those located in and outside of Organization of Petroleum Exporting Countries (OPEC). The model provided a reserve-growth forecast that closely matched the actual reserve growth that occurred from 1981 through 1996 for the 186 fields as a whole, as well as for both OPEC and non-OPEC subdivisions, despite the differences in reserves definition among the fields of the U.S. and the rest of the world. ?? 2005 International Association for Mathematical Geology.

A successful forecast of an El Nino winter

International Nuclear Information System (INIS)

Kerr, R.A.

1992-01-01

This year, for the first time, weather forecasters used signs of a warming in the tropical Pacific as the basis for a long-range prediction of winter weather patterns across the United States. Now forecasters are talking about the next step: stretching the lead time for such forecasts by a year or more. That seems feasible because although this Pacific warming was unmistakable by the time forecasters at the National Weather Service's Climate Analysis Center (CAC) in Camp Springs, Maryland, issued their winter forecast, the El Nino itself had been predicted almost 2 years in advance by a computer model. Next time around, the CAC may well be listening to the modelers and predicting El Nino-related patterns of warmth and flooding seasons in advance

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Deterministic Echo State Networks Based Stock Price Forecasting

Directory of Open Access Journals (Sweden)

Jingpei Dan

2014-01-01

Full Text Available Echo state networks (ESNs, as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500 demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

Incorporating energy efficiency into electric power transmission planning: A western United States case study

International Nuclear Information System (INIS)

Barbose, Galen L.; Sanstad, Alan H.; Goldman, Charles A.

2014-01-01

Driven by system reliability goals and the need to integrate significantly increased renewable power generation, long-range, bulk-power transmission planning processes in the United States are undergoing major changes. At the same time, energy efficiency is an increasing share of the electricity resource mix in many regions, and has become a centerpiece of many utility resource plans and state policies as a means of meeting electricity demand, complementing supply-side sources, and reducing carbon dioxide emissions from the electric power system. The paper describes an innovative project in the western United States to explicitly incorporate end-use efficiency into load forecasts – projections of electricity consumption and demand – that are a critical input into transmission planning and transmission planning studies. Institutional and regulatory background and context are reviewed, along with a detailed discussion of data sources and analytical procedures used to integrate efficiency into load forecasts. The analysis is intended as a practical example to illustrate the kinds of technical and institutional issues that must be addressed in order to incorporate energy efficiency into regional transmission planning activities. - Highlights: • Incorporating energy efficiency into electric power transmission planning is an emergent analytical and policy priority. • A new methodology for this purpose was developed and applied in the western U.S. transmission system. • Efficiency scenarios were created and incorporated into multiple load forecasts. • Aggressive deployment of efficiency policies and programs can significantly reduce projected load. • The approach is broadly applicable in long-range transmission planning

Forecasting behavioral response to a repository from stated intent data

International Nuclear Information System (INIS)

Easterling, D.; Kunreuther, H.; Morwitz, V.

1991-01-01

To forecast repository-induced behavior from surveys of behavioral intention, we develop a model of the relation between stated intent and actual propensity. This model relies heavily on the notion of a latent true intent score. We also consider a number of factors that cause true intent to be, on average, a biased indicator of propensity. The forecasting strategy is applied to a survey of convention planners to estimate the proportion of conventions that Las Vegas would lose following various repository scenarios at the Yucca Mountain site

The Use of Ambient Humidity Conditions to Improve Influenza Forecast

Science.gov (United States)

Shaman, J. L.; Kandula, S.; Yang, W.; Karspeck, A. R.

2017-12-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast and provide further evidence that humidity modulates rates of influenza transmission.

Defining conservation priorities using fragmentation forecasts

Science.gov (United States)

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 rationality of EIA forecasts under symmetric and asymmetric loss

International Nuclear Information System (INIS)

Auffhammer, Maximilian

2007-01-01

The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

The rationality of EIA forecasts under symmetric and asymmetric loss

Energy Technology Data Exchange (ETDEWEB)

Auffhammer, Maximilian [Department of Agricultural and Resource Economics, University of California, 207 Giannini Hall 3310, Berkeley, CA 94720 (United States)

2007-05-15

The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

EU pharmaceutical expenditure forecast.

Science.gov (United States)

Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them

Short-term forecasting of internal migration.

Science.gov (United States)

Frees, E W

1993-11-01

A new methodological approach to the forecasting of short-term trends in internal migration in the United States is introduced. "Panel-data (or longitudinal-data) models are used to represent the relationship between destination-specific out-migration and several explanatory variables. The introduction of this methodology into the migration literature is possible because of some new and improved databases developed by the U.S. Bureau of the Census.... Data from the Bureau of Economic Analysis are used to investigate the incorporation of exogenous factors as variables in the model." The exogenous factors considered include employment and unemployment, income, population size of state, and distance between states. The author concludes that "when one...includes additional parameters that are estimable in longitudinal-data models, it turns out that there is little additional information in the exogenous factors that is useful for forecasting." excerpt

The use of ambient humidity conditions to improve influenza forecast.

Science.gov (United States)

Shaman, Jeffrey; Kandula, Sasikiran; Yang, Wan; Karspeck, Alicia

2017-11-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

Science.gov (United States)

Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

2013-01-01

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

Directory of Open Access Journals (Sweden)

V. R. N. Pauwels

2013-09-01

Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Financing the next generation of new reactors in the united states. Panel Discussion

International Nuclear Information System (INIS)

Turner, Kyle; Simard, Ron; Tran, K.C.; Kelly, Patrick; Green, Barrett E.; Quinn, Edward L.; Stamos, John

2001-01-01

Full text of publication follows: With the California energy shortage and new growth forecasts in the United States, significant new base-load generation will be needed in the near future to meet electricity demands. New figures for growth in electricity demand for the United States rose significantly because of Internet and related business expansion. Lack of sufficient natural gas supplies to support new generation in some regions is causing a renewed interest in building new nuclear plants. Speakers will address the current status of available and near-term design options including both the U.S. Department of Energy Generation III and IV design packages, infrastructure challenges, and financial models that show that nuclear is competitive with alternatives and a prudent and profitable investment. (authors)

Worldwide satellite market demand forecast

Science.gov (United States)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-01-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

State-space forecasting of Schistosoma haematobium time-series in Niono, Mali.

Science.gov (United States)

Medina, Daniel C; Findley, Sally E; Doumbia, Seydou

2008-08-13

Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with infectious diseases. The incidence of Schistosoma sp.-which are neglected tropical diseases exposing and infecting more than 500 and 200 million individuals in 77 countries, respectively-is rising because of 1) numerous irrigation and hydro-electric projects, 2) steady shifts from nomadic to sedentary existence, and 3) ineffective control programs. Notwithstanding the colossal scope of these parasitic infections, less than 0.5% of Schistosoma sp. investigations have attempted to predict their spatial and or temporal distributions. Undoubtedly, public health programs in developing countries could benefit from parsimonious forecasting and early warning systems to enhance management of these parasitic diseases. In this longitudinal retrospective (01/1996-06/2004) investigation, the Schistosoma haematobium time-series for the district of Niono, Mali, was fitted with general-purpose exponential smoothing methods to generate contemporaneous on-line forecasts. These methods, which are encapsulated within a state-space framework, accommodate seasonal and inter-annual time-series fluctuations. Mean absolute percentage error values were circa 25% for 1- to 5-month horizon forecasts. The exponential smoothing state-space framework employed herein produced reasonably accurate forecasts for this time-series, which reflects the incidence of S. haematobium-induced terminal hematuria. It obliquely captured prior non-linear interactions between disease dynamics and exogenous covariates (e.g., climate, irrigation, and public health interventions), thus obviating the need for more complex forecasting methods in the district of Niono, Mali. Therefore, this framework could assist with managing and assessing S. haematobium transmission and intervention impact, respectively, in this district and potentially elsewhere in the Sahel.

The use of ambient humidity conditions to improve influenza forecast.

Directory of Open Access Journals (Sweden)

Jeffrey Shaman

2017-11-01

Full Text Available Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

National Forecast Charts

Science.gov (United States)

code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. National Forecast Charts

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

Energy Technology Data Exchange (ETDEWEB)

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 and 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.

Forecasting Inflation Using Interest-Rate and Time-Series Models: Some International Evidence.

OpenAIRE

Hafer, R W; Hein, Scott E

1990-01-01

It has been suggested that inflation forecasts derived from short-term interest rates are as accurate as time-series forecasts. Previous analyses of this notion have focused on U.S. data, providing mixed results. In this article, the authors extend previous work by testing the hypothesis using data taken from the United States and five other countries. Using monthly Eurocurrency rates and the consumer price index for the period 1967-86, their results indicate that time-series forecasts of inf...

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

Science.gov (United States)

Neufeld, K. N.; Keinath, A. P.; Gugino, B. K.; McGrath, M. T.; Sikora, E. J.; Miller, S. A.; Ivey, M. L.; Langston, D. B.; Dutta, B.; Keever, T.; Sims, A.; Ojiambo, P. S.

2017-11-01

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good

Fueling the dragon: Alternative Chinese oil futures and their implications for the United States

Science.gov (United States)

Eberling, George G.

This study examines how Chinese oil energy will likely shape future Sino-American relations under conditions of dependency and non-dependency. The study will list and describe three possible Chinese oil energy futures or scenarios (Competitive Dependency, Competitive Surplus and Cooperative Surplus) using Scenario Analysis to subsequently estimate their associated likelihoods using the PRINCE forecasting system and discuss and evaluate their strategic implications for the United States. Further, this study will determine the most likely oil energy future or scenario. Finally, the study will list and describe the most likely United States political, economic and/or military policy responses for each future or scenario. The study contributes to the literature on Chinese and United States energy security, foreign policy, political economy and political risk analysis by showing how China will most likely address its growing oil energy dependence and by determining what will be the most likely U.S. foreign policy consequences based on the most current literature available on energy security and foreign policy.

Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

Energy Technology Data Exchange (ETDEWEB)

Penning, Julie [Navigant Consulting Inc., Washington, DC (United States); Stober, Kelsey [Navigant Consulting Inc., Washington, DC (United States); Taylor, Victor [Navigant Consulting Inc., Washington, DC (United States); Yamada, Mary [Navigant Consulting Inc., Washington, DC (United States)

2016-09-01

The DOE report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, is a biannual report which models the adoption of LEDs in the U.S. general-lighting market, along with associated energy savings, based on the full potential DOE has determined to be technically feasible over time. This version of the report uses an updated 2016 U.S. lighting-market model that is more finely calibrated and granular than previous models, and extends the forecast period to 2035 from the 2030 limit that was used in previous editions.

Power systems simulations of the western United States region

International Nuclear Information System (INIS)

Conzelmann, G.; Koritarov, V.; Poch, L.; Thimmapuram, P.; Veselka, T.

2010-01-01

This report documents a part of a broad assessment of energy-water-related issues in the western United States. The full analysis involved three Department of Energy national laboratories: Argonne National Laboratory, Los Alamos National Laboratory, and Sandia National Laboratories. Argonne's objective in the overall project was to develop a regional power sector expansion forecast and a detailed unit-level operational (dispatch) analysis. With these two major analysis components, Argonne estimated current and future freshwater withdrawals and consumption related to the operation of U.S. thermal-electric power plants in the Western Electricity Coordinating Council (WECC) region for the period 2005-2025. Water is withdrawn and used primarily for cooling but also for environmental control, such as sulfur scrubbers. The current scope of the analysis included three scenarios: (1) Baseline scenario as a benchmark for assessing the adequacy and cost-effectiveness of water conservation options and strategies, (2) High nuclear scenario, and (3) High renewables scenario. Baseline projections are consistent with forecasts made by the WECC and the Energy Information Administration (EIA) in its Annual Energy Outlook (AEO) (EIA 2006a). Water conservation scenarios are currently limited to two development alternatives that focus heavily on constructing new generating facilities with zero water consumption. These technologies include wind farms and nuclear power plants with dry cooling. Additional water conservation scenarios and estimates of water use associated with fuel or resource extraction and processing will be developed in follow-on analyses.

State estimates and forecasts of the loop current in the Gulf of Mexico using the MITgcm and its adjoint

KAUST Repository

Gopalakrishnan, Ganesh

2013-07-01

An ocean state estimate has been developed for the Gulf of Mexico (GoM) using the MIT general circulation model and its adjoint. The estimate has been tested by forecasting loop current (LC) evolution and eddy shedding in the GoM. The adjoint (or four-dimensional variational) method was used to match the model evolution to observations by adjusting model temperature and salinity initial conditions, open boundary conditions, and atmospheric forcing fields. The model was fit to satellite-derived along-track sea surface height, separated into temporal mean and anomalies, and gridded sea surface temperature for 2 month periods. The optimized state at the end of the assimilation period was used to initialize the forecast for 2 months. Forecasts explore practical LC predictability and provide a cross-validation test of the state estimate by comparing it to independent future observations. The model forecast was tested for several LC eddy separation events, including Eddy Franklin in May 2010 during the deepwater horizon oil spill disaster in the GoM. The forecast used monthly climatological open boundary conditions, atmospheric forcing, and run-off fluxes. The model performance was evaluated by computing model-observation root-mean-square difference (rmsd) during both the hindcast and forecast periods. The rmsd metrics for the forecast generally outperformed persistence (keeping the initial state fixed) and reference (forecast initialized using assimilated Hybrid Coordinate Ocean Model 1/12° global analysis) model simulations during LC eddy separation events for a period of 1̃2 months.

State estimates and forecasts of the loop current in the Gulf of Mexico using the MITgcm and its adjoint

KAUST Repository

Gopalakrishnan, Ganesh; Cornuelle, Bruce D.; Hoteit, Ibrahim; Rudnick, Daniel L.; Owens, W. Brechner

2013-01-01

An ocean state estimate has been developed for the Gulf of Mexico (GoM) using the MIT general circulation model and its adjoint. The estimate has been tested by forecasting loop current (LC) evolution and eddy shedding in the GoM. The adjoint (or four-dimensional variational) method was used to match the model evolution to observations by adjusting model temperature and salinity initial conditions, open boundary conditions, and atmospheric forcing fields. The model was fit to satellite-derived along-track sea surface height, separated into temporal mean and anomalies, and gridded sea surface temperature for 2 month periods. The optimized state at the end of the assimilation period was used to initialize the forecast for 2 months. Forecasts explore practical LC predictability and provide a cross-validation test of the state estimate by comparing it to independent future observations. The model forecast was tested for several LC eddy separation events, including Eddy Franklin in May 2010 during the deepwater horizon oil spill disaster in the GoM. The forecast used monthly climatological open boundary conditions, atmospheric forcing, and run-off fluxes. The model performance was evaluated by computing model-observation root-mean-square difference (rmsd) during both the hindcast and forecast periods. The rmsd metrics for the forecast generally outperformed persistence (keeping the initial state fixed) and reference (forecast initialized using assimilated Hybrid Coordinate Ocean Model 1/12° global analysis) model simulations during LC eddy separation events for a period of 1̃2 months.

7 CFR 1220.615 - State and United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false State and United States. 1220.615 Section 1220.615... CONSUMER INFORMATION Procedures To Request a Referendum Definitions § 1220.615 State and United States. State and United States include the 50 States of the United States of America, the District of Columbia...

7 CFR 1220.129 - State and United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false State and United States. 1220.129 Section 1220.129... CONSUMER INFORMATION Soybean Promotion and Research Order Definitions § 1220.129 State and United States. The terms State and United States include the 50 States of the United States of America, the District...

Evaluation of the product ratio coherent model in forecasting mortality rates and life expectancy at births by States

Science.gov (United States)

Shair, Syazreen Niza; Yusof, Aida Yuzi; Asmuni, Nurin Haniah

2017-05-01

Coherent mortality forecasting models have recently received increasing attention particularly in their application to sub-populations. The advantage of coherent models over independent models is the ability to forecast a non-divergent mortality for two or more sub-populations. One of the coherent models was recently developed by [1] known as the product-ratio model. This model is an extension version of the functional independent model from [2]. The product-ratio model has been applied in a developed country, Australia [1] and has been extended in a developing nation, Malaysia [3]. While [3] accounted for coherency of mortality rates between gender and ethnic group, the coherency between states in Malaysia has never been explored. This paper will forecast the mortality rates of Malaysian sub-populations according to states using the product ratio coherent model and its independent version— the functional independent model. The forecast accuracies of two different models are evaluated using the out-of-sample error measurements— the mean absolute forecast error (MAFE) for age-specific death rates and the mean forecast error (MFE) for the life expectancy at birth. We employ Malaysian mortality time series data from 1991 to 2014, segregated by age, gender and states.

Forecasting manpower requirements for nuclear power plant construction

International Nuclear Information System (INIS)

Seltzer, N.; Schriver, W.R.

1978-01-01

This paper presents both the methodology and results of a segment of a comprehensive construction manpower demand forecasting system aimed at forecasting virtually all construction manpower requirements in the United States of America. The part of the system dealing with the demand for construction workers needed to build nuclear powered electricity generating plants is discussed here. The object of the system is to forecast manpower construction needs for each of 29 construction crafts on a monthly basis in each of 10 geographical regions of the United States. The method used is to establish profiles of the types of workers and time phasing required in the past. Profiling was done for different types of plants, different capacity classes, and different geographical locations. An appropriate worker profile matrix cannot simply be multiplied by the capacity of the proposed plant if the number of man-hours required per kilowatt of generating capacity is not constant. The value of this latter variable has changed considerably recently - presumably because of an increased awareness of environmental and safety considerations. Econometric techniques are used to forecast values for man-hours per kilowatt which are then multiplied by projected new capacity to be put in place. The resulting total man-hour requirement is then allocated over time and by craft through use of a worker profile matrix. The summary results indicate that 20 percent increases in man-hours required per kilowatt of capacity can be expected between 1977 and 1981. Total construction labour demand will rise from 65,700 work-years in 1977 to nearly 96,600 work-years in 1981. Forecasts of the actual number of different types of workers to be demanded in each month and in each region are available from the system. (author)

Device for forecasting reactor power-up routes

International Nuclear Information System (INIS)

Fukuzaki, Takaharu.

1980-01-01

Purpose: To improve the reliability and forecasting accuracy for a device forecasting the change of the state on line in BWR type reactors. Constitution: The present state in a nuclear reactor is estimated in a present state judging section based on measuring signals for thermal power, core flow rate, control rod density and the like from the nuclear reactor, and the estimated results are accumulated in an operation result collecting section. While on the other hand, a forecasting section forecasts the future state in the reactor based on the signals from the forecasting condition setting section. The actual result values from the collecting section and the forecasting results are compared to each other. If they are not equal, new setting signals are outputted from the setting section to perform the forecasting again. These procedures are repeated till the difference between the forecast results and the actual result values is minimized, by which accurate forecasting for the state of the reactor is made possible. (Furukawa, Y.)

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States.

Science.gov (United States)

Zhang, Hongliang; Chen, Gang; Hu, Jianlin; Chen, Shu-Hua; Wiedinmyer, Christine; Kleeman, Michael; Ying, Qi

2014-03-01

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0nitrate and sulfate concentrations are also well reproduced. The other unresolved PM2.5 components (OTHER) are significantly overestimated by more than a factor of two. No conclusive explanations can be made regarding the possible cause of this universal overestimation, which warrants a follow-up study to better understand this problem. Copyright © 2013 Elsevier B.V. All rights reserved.

The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

Science.gov (United States)

Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

2010-09-01

Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events

On density forecast evaluation

NARCIS (Netherlands)

Diks, C.

2008-01-01

Traditionally, probability integral transforms (PITs) have been popular means for evaluating density forecasts. For an ideal density forecast, the PITs should be uniformly distributed on the unit interval and independent. However, this is only a necessary condition, and not a sufficient one, as

Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment

Science.gov (United States)

David N. Wear

2011-01-01

Accurately forecasting future forest conditions and the implications for ecosystem services depends on understanding land use dynamics. In support of the 2010 Renewable Resources Planning Act (RPA) Assessment, we forecast changes in land uses for the coterminous United States in response to three scenarios. Our land use models forecast urbanization in response to the...

SOFTWARE SOLUTIONS FOR MEASURING AND FORECASTING THE CASH GENERATING UNIT FLOWS RELATED TO INTANGIBLE ASSETS

Directory of Open Access Journals (Sweden)

Veronica R GROSU

2016-08-01

Full Text Available In light of the difficulties encountered in assessing the value of the CGU (Cash Generating Unit and of the cash flows associated with goodwill or other intangible assets of a company and after performing the impairment test as provided by the IAS 36-Intangibile Asset and the forecasts related to it, the aim of this paper is to identify and suggest software instruments that would assist in the measurement and forecasting of these elements. The employment of the SPSS and the NeuroShell programmes in analyzing and forecasting the changes in CGU and CGU flows has helped compare the results and the ensuing error margins, thus giving the business entity the possibility to select the best software option, depending on certain variables identified on a micro or a macroeconomic level that may affect the depreciation or the increases in value of the underlying assets for CGU or CGU flows.

Evaluation of bias-correction methods for ensemble streamflow volume forecasts

Directory of Open Access Journals (Sweden)

T. Hashino

2007-01-01

Full Text Available Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

Rebuttal of "Polar bear population forecasts: a public-policy forecasting audit"

Science.gov (United States)

Amstrup, Steven C.; Caswell, Hal; DeWeaver, Eric; Stirling, Ian; Douglas, David C.; Marcot, Bruce G.; Hunter, Christine M.

2009-01-01

Observed declines in the Arctic sea ice have resulted in a variety of negative effects on polar bears (Ursus maritimus). Projections for additional future declines in sea ice resulted in a proposal to list polar bears as a threatened species under the United States Endangered Species Act. To provide information for the Department of the Interior's listing-decision process, the US Geological Survey (USGS) produced a series of nine research reports evaluating the present and future status of polar bears throughout their range. In response, Armstrong et al. [Armstrong, J. S., K. C. Green, W. Soon. 2008. Polar bear population forecasts: A public-policy forecasting audit. Interfaces 38(5) 382–405], which we will refer to as AGS, performed an audit of two of these nine reports. AGS claimed that the general circulation models upon which the USGS reports relied were not valid forecasting tools, that USGS researchers were not objective or lacked independence from policy decisions, that they did not utilize all available information in constructing their forecasts, and that they violated numerous principles of forecasting espoused by AGS. AGS (p. 382) concluded that the two USGS reports were "unscientific and inconsequential to decision makers." We evaluate the AGS audit and show how AGS are mistaken or misleading on every claim. We provide evidence that general circulation models are useful in forecasting future climate conditions and that corporate and government leaders are relying on these models to do so. We clarify the strict independence of the USGS from the listing decision. We show that the allegations of failure to follow the principles of forecasting espoused by AGS are either incorrect or are based on misconceptions about the Arctic environment, polar bear biology, or statistical and mathematical methods. We conclude by showing that the AGS principles of forecasting are too ambiguous and subjective to be used as a reliable basis for auditing scientific

7 CFR 1209.21 - State and United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false State and United States. 1209.21 Section 1209.21... Definitions § 1209.21 State and United States. (a) State means any of the several States, the District of Columbia, and the Commonwealth of Puerto Rico. (b) United States means collectively the several States of...

National Water Model assessment for water management needs over the Western United States.

Science.gov (United States)

Viterbo, F.; Thorstensen, A.; Cifelli, R.; Hughes, M.; Johnson, L.; Gochis, D.; Wood, A.; Nowak, K.; Dahm, K.

2017-12-01

The NOAA National Water Model (NWM) became operational in August 2016, providing the first ever, real-time distributed high-resolution forecasts for the continental United States. Since the model predictions occur at the CONUS scale, there is a need to evaluate the NWM in different regions to assess the wide variety and heterogeneity of hydrological processes that are included (e.g., snow melting, ice freezing, flash flooding events). In particular, to address water management needs in the western U.S., a collaborative project between the Bureau of Reclamation, NOAA, and NCAR is ongoing to assess the NWM performance for reservoir inflow forecasting needs and water management operations. In this work, the NWM is evaluated using different forecast ranges (short to medium) and retrospective historical runs forced by North American Land Data Assimilation System (NLDAS) analysis to assess the NWM skills over key headwaters watersheds in the western U.S. that are of interest to the Bureau of Reclamation. The streamflow results are analyzed and compared with the available observations at the gauge sites, evaluating different NWM operational versions together with the already existing local River Forecast Center forecasts. The NWM uncertainty is also considered, evaluating the propagation of the precipitation forcing uncertainties in the resulting hydrograph. In addition, the possible advantages of high-resolution distributed output variables (such as soil moisture, evapotranspiration fluxes) are investigated, to determine the utility of such information for water managers in terms of watershed characteristics in areas that traditionally have not had any forecast information. The results highlight the NWM's ability to provide high-resolution forecast information in space and time. As anticipated, the performance is best in regions that are dominated by natural flows and where the model has benefited from efforts toward parameter calibration. In highly regulated basins, the

Disaggregate energy consumption and industrial output in the United States

International Nuclear Information System (INIS)

Ewing, Bradley T.; Sari, Ramazan; Soytas, Ugur

2007-01-01

This paper investigates the effect of disaggregate energy consumption on industrial output in the United States. Most of the related research utilizes aggregate data which may not indicate the relative strength or explanatory power of various energy inputs on output. We use monthly data and employ the generalized variance decomposition approach to assess the relative impacts of energy and employment on real output. Our results suggest that unexpected shocks to coal, natural gas and fossil fuel energy sources have the highest impacts on the variation of output, while several renewable sources exhibit considerable explanatory power as well. However, none of the energy sources explain more of the forecast error variance of industrial output than employment

Space-time wind speed forecasting for improved power system dispatch

KAUST Repository

Zhu, Xinxin

2014-02-27

To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.

Forecasting biodiversity in breeding birds using best practices

Science.gov (United States)

Taylor, Shawn D.; White, Ethan P.

2018-01-01

Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods. PMID:29441230

Forecasting biodiversity in breeding birds using best practices

Directory of Open Access Journals (Sweden)

David J. Harris

2018-02-01

Full Text Available Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods.

Evaluation of Lightning Jumps as a Predictor of Severe Weather in the Northeastern United States

Science.gov (United States)

Eck, Pamela

Severe weather events in the northeastern United States can be challenging to forecast, given how the evolution of deep convection can be influenced by complex terrain and the lack of quality observations in complex terrain. To supplement existing observations, this study explores using lightning to forecast severe convection in areas of complex terrain in the northeastern United States. A sudden increase in lightning flash rate by two standard deviations (2sigma), also known as a lightning jump, may be indicative of a strengthening updraft and an increased probability of severe weather. This study assesses the value of using lightning jumps to forecast severe weather during July 2015 in the northeastern United States. Total lightning data from the National Lightning Detection Network (NLDN) is used to calculate lightning jumps using a 2sigma lightning jump algorithm with a minimum threshold of 5 flashes min-1. Lightning jumps are used to predict the occurrence of severe weather, as given by whether a Storm Prediction Center (SPC) severe weather report occurred 45 min after a lightning jump in the same cell. Results indicate a high probability of detection (POD; 85%) and a high false alarm rate (FAR; 89%), suggesting that lightning jumps occur in sub-severe storms. The interaction between convection and complex terrain results in a locally enhanced updraft and an increased probability of severe weather. Thus, it is hypothesized that conditioning on an upslope variable may reduce the FAR. A random forest is introduced to objectively combine upslope flow, calculated using data from the High Resolution Rapid Refresh (HRRR), flash rate (FR), and flash rate changes with time (DFRDT). The random forest, a machine-learning algorithm, uses pattern recognition to predict a severe or non-severe classification based on the predictors. In addition to upslope flow, FR, and DFRDT, Next-Generation Radar (NEXRAD) Level III radar data was also included as a predictor to compare its

Operational aerial snow surveying in the United States

Energy Technology Data Exchange (ETDEWEB)

Peck, E L; Carroll, T R; Vandemark, S C

1980-03-01

An airborne gamma radiation detector and data acquisition system has been designed for rapid measurement of the snow cover water equivalent over large open areas. Research and field tests conducted prior to the implementation of an operational snow measurement system in the United States are reviewed. Extensive research test flights were conducted over large river basins of the north-central plains and in the high mountain valleys of the inter-mountain West. Problems encountered during development include: (1) error in the gross gamma flux produced by atmospheric radon gas daughters; (2) spatial and temporal variability in soil moisture; and (3) errors in gamma radiation count rate introduced by aircraft and cosmic background radiation. Network design of operational flight line and ground observation data used in a river forecasting system are discussed. 22 references, 4 figures, 2 tables.

EXCHANGE-RATES FORECASTING: EXPONENTIAL SMOOTHING TECHNIQUES AND ARIMA MODELS

Directory of Open Access Journals (Sweden)

Dezsi Eva

2011-07-01

Full Text Available Exchange rates forecasting is, and has been a challenging task in finance. Statistical and econometrical models are widely used in analysis and forecasting of foreign exchange rates. This paper investigates the behavior of daily exchange rates of the Romanian Leu against the Euro, United States Dollar, British Pound, Japanese Yen, Chinese Renminbi and the Russian Ruble. Smoothing techniques are generated and compared with each other. These models include the Simple Exponential Smoothing technique, as the Double Exponential Smoothing technique, the Simple Holt-Winters, the Additive Holt-Winters, namely the Autoregressive Integrated Moving Average model.

Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

Science.gov (United States)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

7 CFR 1160.104 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 9 2010-01-01 2009-01-01 true United States. 1160.104 Section 1160.104 Agriculture... Definitions § 1160.104 United States. United States means the 48 contiguous states in the continental United States and the District of Columbia, except that United States means the 50 states of the United States...

About the National Forecast Chart

Science.gov (United States)

code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. The National Forecast Charts

The strategy of professional forecasting

DEFF Research Database (Denmark)

Ottaviani, Marco; Sørensen, Peter Norman

2006-01-01

We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts...... and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting...... contest with pre-specified rules. In a winner-take-all contest, equilibrium forecasts are excessively differentiated...

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Science.gov (United States)

Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

2018-02-01

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems

DEFF Research Database (Denmark)

Löwe, Roland; Mikkelsen, Peter Steen; Rasmussen, Michael Robdrup

2013-01-01

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input...... improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time...

31 CFR 800.225 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 800.225 Section 800... TAKEOVERS BY FOREIGN PERSONS Definitions § 800.225 United States. The term United States or U.S. means the United States of America, the States of the United States, the District of Columbia, and any commonwealth...

Examining Atmospheric and Ecological Drivers of Wildfires, Modeling Wildfire Occurrence in the Southwest United States, and Using Atmospheric Sounding Observations to Verify National Weather Service Spot Forecasts

Science.gov (United States)

Nauslar, Nicholas J.

This dissertation is comprised of three different papers that all pertain to wildland fire applications. The first paper performs a verification analysis on mixing height, transport winds, and Haines Index from National Weather Service spot forecasts across the United States. The final two papers, which are closely related, examine atmospheric and ecological drivers of wildfire for the Southwest Area (SWA) (Arizona, New Mexico, west Texas, and Oklahoma panhandle) to better equip operational fire meteorologists and managers to make informed decisions on wildfire potential in this region. The verification analysis here utilizes NWS spot forecasts of mixing height, transport winds and Haines Index from 2009-2013 issued for a location within 50 km of an upper sounding location and valid for the day of the fire event. Mixing height was calculated from the 0000 UTC sounding via the Stull, Holzworth, and Richardson methods. Transport wind speeds were determined by averaging the wind speed through the boundary layer as determined by the three mixing height methods from the 0000 UTC sounding. Haines Index was calculated at low, mid, and high elevation based on the elevation of the sounding and spot forecast locations. Mixing height forecasts exhibited large mean absolute errors and biased towards over forecasting. Forecasts of transport wind speeds and Haines Index outperformed mixing height forecasts with smaller errors relative to their respective means. The rainfall and lightning associated with the North American Monsoon (NAM) can vary greatly intra- and inter-annually and has a large impact on wildfire activity across the SWA by igniting or suppressing wildfires. NAM onset thresholds and subsequent dates are determined for the SWA and each Predictive Service Area (PSA), which are sub-regions used by operational fire meteorologists to predict wildfire potential within the SWA, April through September from 1995-2013. Various wildfire activity thresholds using the number

Real-time forecasting of the April 11, 2012 Sumatra tsunami

Science.gov (United States)

Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; ,

2012-01-01

The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

7 CFR 1150.106 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 9 2010-01-01 2009-01-01 true United States. 1150.106 Section 1150.106 Agriculture... Order Definitions § 1150.106 United States. United States means the 48 contiguous States in the continental United States. ...

Malaria Treatment (United States)

Science.gov (United States)

... Providers, Emergency Consultations, and General Public. Contact Us Malaria Treatment (United States) Recommend on Facebook Tweet Share Compartir Treatment of Malaria: Guidelines For Clinicians (United States) Download PDF version ...

Origins of forecast skill of weather and climate events on verifiable time scales

CSIR Research Space (South Africa)

Landman, WA

2012-07-01

Full Text Available specific location between the predictor or the predictand and their respective canonical component time series (rj and sk) Barnett, T. P., and Preisendorfer, R. W. 1987: Origins and levels of monthly and seasonal forecast skill for United States air...

7 CFR 65.255 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 3 2010-01-01 2010-01-01 false United States. 65.255 Section 65.255 Agriculture..., PEANUTS, AND GINSENG General Provisions Definitions § 65.255 United States. United States means the 50... United States. ...

7 CFR 1250.308 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1250.308 Section 1250.308 Agriculture... Research and Promotion Order Definitions § 1250.308 United States. United States means the 48 contiguous States of the United States of America and the District of Columbia. ...

7 CFR 1205.23 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1205.23 Section 1205.23 Agriculture... Procedures for Conduct of Sign-up Period Definitions § 1205.23 United States. The term United States means the 50 states of the United States of America. Procedures ...

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

Science.gov (United States)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

31 CFR 597.318 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 597.318 Section 597... General Definitions § 597.318 United States. The term United States means the United States, its territories, states, commonwealths, districts, and possessions, and all areas under the jurisdiction or...

7 CFR 1219.26 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1219.26 Section 1219.26 Agriculture..., AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.26 United States. United States means collectively the several 50 States of the United States, the District of...

A geographical information system-based web model of arbovirus transmission risk in the continental United States of America

Directory of Open Access Journals (Sweden)

Sarah K. Konrad

2012-11-01

Full Text Available A degree-day (DD model of West Nile virus capable of forecasting real-time transmission risk in the continental United States of America up to one week in advance using a 50-km grid is available online at https://sites. google.com/site/arbovirusmap/. Daily averages of historical risk based on temperatures for 1994-2003 are available at 10- km resolution. Transmission risk maps can be downloaded from 2010 to the present. The model can be adapted to work with any arbovirus for which the temperature-related parameters are known, e.g. Rift Valley fever virus. To more effectively assess virus establishment and transmission, the model incorporates “compound risk” maps and forecasts, which includes livestock density as a parameter.

7 CFR 1212.31 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1212.31 Section 1212.31 Agriculture..., Consumer Education, and Industry Information Order Definitions § 1212.31 United States. “United States... territories and possessions of the United States. ...

22 CFR 120.13 - United States.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false United States. 120.13 Section 120.13 Foreign... United States. United States, when used in the geographical sense, includes the several states, the Commonwealth of Puerto Rico, the insular possessions of the United States, the District of Columbia, the...

31 CFR 592.311 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 592.311 Section 592... § 592.311 United States. The term United States, when used in the geographic sense, means the several States, the District of Columbia, and any commonwealth, territory, or possession of the United States. ...

7 CFR 1205.313 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1205.313 Section 1205.313 Agriculture... Research and Promotion Order Definitions § 1205.313 United States. United States means the 50 States of the United States of America. [31 FR 16758, Dec. 31, 1966. Redesignated at 56 FR 64472, Dec. 10, 1991] ...

Science implementation of Forecast Mekong for food and environmental security

Science.gov (United States)

Turnipseed, D. Phil

2012-01-01

Forecast Mekong is a significant international thrust under the Delta Research and Global Observation Network (DRAGON) of the U.S. Geological Survey (USGS) and was launched in 2009 by the U.S. Department of State and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam under U.S. Department of State Secretary Hillary R. Clinton's Lower Mekong Initiative to enhance U.S. engagement with countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. Since 2009, the USGS has worked closely with the U.S. Department of State; personnel from Cambodia, Laos, Thailand, and Vietnam; nongovernmental organizations; and academia to collect and use research and data from the Lower Mekong River Basin to provide hands-on results that will help decisionmakers in future planning and design for restoration, conservation, and management efforts in the Lower Mekong River Basin. In 2012 Forecast Mekong is highlighting the increasing cooperation between the United States and Lower Mekong River Basin countries in the areas of food and environmental security. Under the DRAGON, Forecast Mekong continues work in interactive data integration, modeling, and visualization system by initiating three-dimensional bathymetry and river flow data along with a pilot study of fish distribution, population, and migratory patterns in the Lower Mekong River Basin. When fully developed by the USGS, in partnership with local governments and universities throughout the Mekong River region, Forecast Mekong will provide valuable planning tools to visualize the consequences of climate change and river management.

Gaussian process regression for forecasting battery state of health

Science.gov (United States)

Richardson, Robert R.; Osborne, Michael A.; Howey, David A.

2017-07-01

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

Verification of Global Radiation Forecasts from the Ensemble Prediction System at DMI

DEFF Research Database (Denmark)

Lundholm, Sisse Camilla

To comply with an increasing demand for sustainable energy sources, a solar heating unit is being developed at the Technical University of Denmark. To make optimal use — environmentally and economically —, this heating unit is equipped with an intelligent control system using forecasts of the heat...... consumption of the house and the amount of available solar energy. In order to make the most of this solar heating unit, accurate forecasts of the available solar radiation are esstential. However, because of its sensitivity to local meteorological conditions, the solar radiation received at the surface...... of the Earth can be highly fluctuating and challenging to forecast accurately. To comply with the accuracy requirements to forecasts of both global, direct, and diffuse radiation, the uncertainty of these forecasts is of interest. Forecast uncertainties can become accessible by running an ensemble of forecasts...

31 CFR 542.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 542.310 Section 542.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....310 United States. The term United States means the United States, its territories and possessions...

31 CFR 548.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 548.310 Section 548.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....310 United States. The term United States means the United States, its territories and possessions...

31 CFR 546.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 546.310 Section 546.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....310 United States. The term United States means the United States, its territories and possessions...

31 CFR 586.318 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 586.318 Section 586...) KOSOVO SANCTIONS REGULATIONS General Definitions § 586.318 United States. The term United States means the United States, its territories and possessions, and all areas under the jurisdiction or authority...

31 CFR 537.318 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 537.318 Section 537.318 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....318 United States. The term United States means the United States, its territories and possessions...

31 CFR 585.316 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 585.316 Section 585.316 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... General Definitions § 585.316 United States. The term United States means the United States, its...

31 CFR 575.319 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 575.319 Section 575.319 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....319 United States. The term United States means the United States, its territories and possessions...

31 CFR 539.312 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 539.312 Section 539.312 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... General Definitions § 539.312 United States. The term United States means the United States, its...

31 CFR 551.309 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 551.309 Section 551.309 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....309 United States. The term United States means the United States, its territories and possessions...

31 CFR 587.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 587.310 Section 587...) MILOSEVIC SANCTIONS REGULATIONS General Definitions § 587.310 United States. The term United States means the United States, its territories and possessions, and all areas under the jurisdiction or authority...

31 CFR 547.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 547.310 Section 547.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... General Definitions § 547.310 United States. The term United States means the United States, its...

Electricity Demand Forecasting Using a Functional State Space Model

OpenAIRE

Nagbe , Komi; Cugliari , Jairo; Jacques , Julien

2018-01-01

In the last past years the liberalization of the electricity supply, the increase variability of electric appliances and their use, and the need to respond to the electricity demand in the real time had made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All this sources make hard electricity demand forecasting. To forecast the electr...

State of the Science for Sub-Seasonal to Seasonal Precipitation Forecasting in Support of Water Resource Managers

Science.gov (United States)

DeWitt, D. G.

2017-12-01

Water resource managers are one of the communities that would strongly benefit from highly-skilled sub-seasonal to seasonal precipitation forecasts. Unfortunately, the current state of the art prediction tools frequently fail to provide a level of skill sufficient to meet the stakeholders needs, especially on the monthly and seasonal timescale. On the other hand, the skill of precipitation forecasts on the week-2 timescale are relatively high and arguably useful in many decision-making contexts. This talk will present a comparison of forecast skill for the week-2 through the first season timescale and describe current efforts within NOAA and elsewhere to try to improve forecast skill beyond week-2, including research gaps that need to be addressed in order to make progress.

State-and-transition simulation models: a framework for forecasting landscape change

Science.gov (United States)

Daniel, Colin; Frid, Leonardo; Sleeter, Benjamin M.; Fortin, Marie-Josée

2016-01-01

SummaryA wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features.We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states.We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years.State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of

31 CFR 598.317 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 598.317 Section 598.317 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 598.317 United States. The term United States means the United States, its territories and...

31 CFR 596.312 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 596.312 Section 596.312 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... General Definitions § 596.312 United States. The term United States means the United States, including its...

31 CFR 538.314 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 538.314 Section 538.314 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 538.314 United States. The term United States means the United States, its territories and possessions...

31 CFR 543.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 543.310 Section 543.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 543.310 United States. The term United States means the United States, its territories and...

31 CFR 594.313 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 594.313 Section 594.313 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 594.313 United States. The term United States means the United States, its territories and...

31 CFR 588.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 588.310 Section 588.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 588.310 United States. The term United States means the United States, its territories and...

31 CFR 536.315 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 536.315 Section 536.315 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 536.315 United States. The term United States means the United States, its territories and...

31 CFR 544.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 544.310 Section 544.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... REGULATIONS General Definitions § 544.310 United States. The term United States means the United States, its...

31 CFR 545.313 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 545.313 Section 545.313 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 545.313 United States. The term United States means the United States, its territories and...

31 CFR 595.314 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 595.314 Section 595.314 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 595.314 United States. The term United States means the United States, its territories and possessions...

31 CFR 560.307 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 560.307 Section 560.307 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 560.307 United States. The term United States means the United States, including its territories and...

31 CFR 593.311 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 593.311 Section 593.311 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... REGULATIONS General Definitions § 593.311 United States. The term United States means the United States, its...

31 CFR 541.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 541.310 Section 541.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 541.310 United States. The term United States means the United States, its territories and possessions...

31 CFR 540.313 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 540.313 Section 540.313 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... REGULATIONS General Definitions § 540.313 United States. The term United States means the United States, its...

Aggregated wind power generation probabilistic forecasting based on particle filter

International Nuclear Information System (INIS)

Li, Pai; Guan, Xiaohong; Wu, Jiang

2015-01-01

Highlights: • A new method for probabilistic forecasting of aggregated wind power generation. • A dynamic system is established based on a numerical weather prediction model. • The new method handles the non-Gaussian and time-varying wind power uncertainties. • Particle filter is applied to forecast predictive densities of wind generation. - Abstract: Probability distribution of aggregated wind power generation in a region is one of important issues for power system daily operation. This paper presents a novel method to forecast the predictive densities of the aggregated wind power generation from several geographically distributed wind farms, considering the non-Gaussian and non-stationary characteristics in wind power uncertainties. Based on a mesoscale numerical weather prediction model, a dynamic system is established to formulate the relationship between the atmospheric and near-surface wind fields of geographically distributed wind farms. A recursively backtracking framework based on the particle filter is applied to estimate the atmospheric state with the near-surface wind power generation measurements, and to forecast the possible samples of the aggregated wind power generation. The predictive densities of the aggregated wind power generation are then estimated based on these predicted samples by a kernel density estimator. In case studies, the new method presented is tested on a 9 wind farms system in Midwestern United States. The testing results that the new method can provide competitive interval forecasts for the aggregated wind power generation with conventional statistical based models, which validates the effectiveness of the new method

EU pharmaceutical expenditure forecast

OpenAIRE

Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

Background and Objectives: With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States’ ph...

Forecasting the Seasonal Timing of Maine's Lobster Fishery

Directory of Open Access Journals (Sweden)

Katherine E. Mills

2017-11-01

Full Text Available The fishery for American lobster is currently the highest-valued commercial fishery in the United States, worth over US$620 million in dockside value in 2015. During a marine heat wave in 2012, the fishery was disrupted by the early warming of spring ocean temperatures and subsequent influx of lobster landings. This situation resulted in a price collapse, as the supply chain was not prepared for the early and abundant landings of lobsters. Motivated by this series of events, we have developed a forecast of when the Maine (USA lobster fishery will shift into its high volume summer landings period. The forecast uses a regression approach to relate spring ocean temperatures derived from four NERACOOS buoys along the coast of Maine to the start day of the high landings period of the fishery. Tested against conditions in past years, the forecast is able to predict the start day to within 1 week of the actual start, and the forecast can be issued 3–4 months prior to the onset of the high-landings period, providing valuable lead-time for the fishery and its associated supply chain to prepare for the upcoming season. Forecast results are conveyed in a probabilistic manner and are updated weekly over a 6-week forecasting period so that users can assess the certainty and consistency of the forecast and factor the uncertainty into their use of the information in a given year. By focusing on the timing of events, this type of seasonal forecast provides climate-relevant information to users at time scales that are meaningful for operational decisions. As climate change alters seasonal phenology and reduces the reliability of past experience as a guide for future expectations, this type of forecast can enable fishing industry participants to better adjust to and prepare for operating in the context of climate change.

Use of Markov chains for forecasting labor requirements in black coal mines

Energy Technology Data Exchange (ETDEWEB)

Penar, L.; Przybyla, H.

1987-01-01

Increasing mining depth, deterioration of mining conditions and technology development are causes of changes in labor requirements. In mines with stable coal output these changes in most cases are of a qualitative character, in mines with an increasing or decreasing coal output they are of a quantitative character. Methods for forecasting personnel needs, in particular professional requirements, are discussed. Quantitative and qualitative changes are accurately described by heterogenous Markov chains. A structure consisting of interdependent variables is the subject of a forecast. Changes that occur within the structure of time units is the subject of investigations. For a homogenous Markov chain probabilities of a transition from the i-state to the j-state are determined (the probabilities being time independent). For a heterogenous Markov chain probabilities of a transition from the i-state to the j-state are non-conditioned. The method was developed for the ODRA 1325 computers. 8 refs.

Earthquake Forecasting Methodology Catalogue - A collection and comparison of the state-of-the-art in earthquake forecasting and prediction methodologies

Science.gov (United States)

Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

2015-04-01

Earthquake forecasting and prediction has been one of the key struggles of modern geosciences for the last few decades. A large number of approaches for various time periods have been developed for different locations around the world. A categorization and review of more than 20 of new and old methods was undertaken to develop a state-of-the-art catalogue in forecasting algorithms and methodologies. The different methods have been categorised into time-independent, time-dependent and hybrid methods, from which the last group represents methods where additional data than just historical earthquake statistics have been used. It is necessary to categorize in such a way between pure statistical approaches where historical earthquake data represents the only direct data source and also between algorithms which incorporate further information e.g. spatial data of fault distributions or which incorporate physical models like static triggering to indicate future earthquakes. Furthermore, the location of application has been taken into account to identify methods which can be applied e.g. in active tectonic regions like California or in less active continental regions. In general, most of the methods cover well-known high-seismicity regions like Italy, Japan or California. Many more elements have been reviewed, including the application of established theories and methods e.g. for the determination of the completeness magnitude or whether the modified Omori law was used or not. Target temporal scales are identified as well as the publication history. All these different aspects have been reviewed and catalogued to provide an easy-to-use tool for the development of earthquake forecasting algorithms and to get an overview in the state-of-the-art.

7 CFR 1206.23 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1206.23 Section 1206.23 Agriculture... INFORMATION Mango Promotion, Research, and Information Order Definitions § 1206.23 United States. United... Rico, and the territories and possessions of the United States. ...

Comparison of Standards and Technical Requirements of Grid-Connected Wind Power Plants in China and the United States

Energy Technology Data Exchange (ETDEWEB)

Gao, David Wenzhong [Alternative Power Innovations, LLC; Muljadi, Eduard [National Renewable Energy Lab. (NREL), Golden, CO (United States); Tian, Tian [National Renewable Energy Lab. (NREL), Golden, CO (United States); Miller, Mackay [National Renewable Energy Lab. (NREL), Golden, CO (United States); Wang, Weisheng [China Electric Power Research Inst. (China)

2016-09-01

The rapid deployment of wind power has made grid integration and operational issues focal points in industry discussions and research. Compliance with grid connection standards for wind power plants (WPPs) is crucial to ensuring the reliable and stable operation of the electric power grid. This report compares the standards for grid-connected WPPs in China to those in the United States to facilitate further improvements in wind power standards and enhance the development of wind power equipment. Detailed analyses of power quality, low-voltage ride-through capability, active power control, reactive power control, voltage control, and wind power forecasting are provided to enhance the understanding of grid codes in the two largest markets of wind power. This study compares WPP interconnection standards and technical requirements in China to those in the United States.

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

DEFF Research Database (Denmark)

López, Erick; Allende, Héctor; Gil, Esteban

2018-01-01

involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed...

7 CFR 1215.20 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1215.20 Section 1215.20 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... United States. United States means all of the States. Popcorn Board ...

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

Trade agreements with side-effects? : European Union and United States to negotiate Transatlantic Trade and Investment Partnership

OpenAIRE

Mildner, Stormy-Annika; Schmucker, Claudia

2013-01-01

"At the G8 summit in Northern Ireland on June 17, the European Union and the United States kicked off the negotiations for a comprehensive Transatlantic Trade and Investment Partnership (TTIP) to reduce tariffs and non-tariff trade barriers. While the expected economic benefits for both sides would be more than welcome in an era of gloomy growth forecasts, a TTIP is not entirely without risks for global trade and the multilateral trading system. The talks could tie up a considerable portion o...

Ecological forecasts: An emerging imperative

Science.gov (United States)

James S. Clark; Steven R. Carpenter; Mary Barber; Scott Collins; Andy Dobson; Jonathan A. Foley; David M. Lodge; Mercedes Pascual; Roger Pielke; William Pizer; Cathy Pringle; Walter V. Reid; Kenneth A. Rose; Osvaldo Sala; William H. Schlesinger; Diana H. Wall; David Wear

2001-01-01

Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts...

7 CFR 1280.127 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1280.127 Section 1280.127 Agriculture... INFORMATION ORDER Lamb Promotion, Research, and Information Order Definitions § 1280.127 United States. United States means collectively the 50 States and the District of Columbia. ...

The energy markets to 1995 - sector demand forecasts and summary. [United Kingdom

Energy Technology Data Exchange (ETDEWEB)

Buckley, J

1983-01-01

Energy demand forecasts are often based on assumptions which are uncertain and dependent upon both political and economic factors. However, there is a need for long-term energy forecasting for the benefit of industry and commerce. CIRS (Cambridge Information and Research Services Limited) have tried to fulfill this need, based on forecasts of useful heat demand sector by sector which are then converted to heat energy supply and primary requirements. The first such forecast was produced in 1975. This 1983 updated projection examines coal, oil and gas supplies in the UK to the year 1995.

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

Science.gov (United States)

Koster, R. D.; Walker, G. K.

2015-01-01

The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

7 CFR 1260.108 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1260.108 Section 1260.108 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... Promotion and Research Order Definitions § 1260.108 United States. United States means the 50 States and the...

7 CFR 1221.32 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1221.32 Section 1221.32 Agriculture... INFORMATION ORDER Sorghum Promotion, Research, and Information Order Definitions § 1221.32 United States. United States or U.S. means collectively the 50 States, the District of Columbia, the Commonwealth of...

7 CFR 1216.30 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1216.30 Section 1216.30 Agriculture... INFORMATION ORDER Peanut Promotion, Research, and Information Order Definitions § 1216.30 United States. United States means collectively the 50 states, the District of Columbia, the Commonwealth of Puerto Rico...

7 CFR 1218.22 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1218.22 Section 1218.22 Agriculture... INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.22 United States. United States means collectively the 50 states, the District of Columbia, the Commonwealth of Puerto Rico...

Analog forecasting with dynamics-adapted kernels

Science.gov (United States)

Zhao, Zhizhen; Giannakis, Dimitrios

2016-09-01

Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.

Seasonal forecasting of discharge for the Raccoon River, Iowa

Science.gov (United States)

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

Shelters. History, state and forecasting

International Nuclear Information System (INIS)

Geras'ko, V.N.; Klyuchnikov, A.A.; Korneev, A.A.

1997-01-01

For the first time were made attempt to systematize the result of scientific investigations in object 'Shelter' for the ten last years since the accident on 4-th unit of Chernobyl NPP. These materials contain the detailed analyses of the accident, the diagnostic of destroyed unit, the description of control systems, the results of investigations fuel-containing materials characteristics, there placement and state of nuclear and radiation safety. The results of inspect the building constructions, which are base of new defence construction present in this book. Were made the analyse of the influence on environment the 'Shelter' and estimated the consequences of accident on the object. Were estimated the projects, presented on the competition of implementation object 'Shelter', examined the perspective on realization of these projects

Forecasting with periodic autoregressive time series models

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

1999-01-01

textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

7 CFR 1210.315 - United States.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States. 1210.315 Section 1210.315 Agriculture... PLAN Watermelon Research and Promotion Plan Definitions § 1210.315 United States. United States means each of the several States and the District of Columbia. [60 FR 10797, Feb. 28, 1995] National...

Regional-seasonal weather forecasting

Energy Technology Data Exchange (ETDEWEB)

Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

1980-08-01

In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

Performance of the ocean state forecast system at Indian National Centre for Ocean Information Services

Digital Repository Service at National Institute of Oceanography (India)

Nair, T.M.B.; Sirisha, P.; Sandhya, K.G.; Srinivas, K.; SanilKumar, V.; Sabique, L.; Nherakkol, A.; KrishnaPrasad, B.; RakhiKumari; Jeyakumar, C.; Kaviyazhahu, K.; RameshKumar, M.; Harikumar, R.; Shenoi, S.S.C.; Nayak, S.

The reliability of the operational Ocean State Forecast system at the Indian National Centre for Ocean Information Services (INCOIS) during tropical cyclones that affect the coastline of India is described in this article. The performance...

Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences

International Nuclear Information System (INIS)

Mamatzakis, E.; Koutsomanoli-Filippaki, A.

2014-01-01

This paper examines the rationality of the price forecasts for energy commodities of the United States Department of Energy's (DOE), departing from the common assumption in the literature that DOE's forecasts are based on a symmetric underlying loss function with respect to positive vs. negative forecast errors. Instead, we opt for the methodology of Elliott et al. (2005) that allows testing the joint hypothesis of an asymmetric loss function and rationality and reveals the underlying preferences of the forecaster. Results indicate the existence of asymmetries in the shape of the loss function for most energy categories with preferences leaning towards optimism. Moreover, we also examine whether there is a structural break in those preferences over the examined period, 1997–2012. - Highlights: • Examine the rationality of DOE energy forecasts. • Departing from a symmetric underlying loss function. • Asymmetries exist in most energy prices. • Preferences lean towards optimism. • Examine structural breaks in those preferences

Use of temperature to improve West Nile virus forecasts.

Directory of Open Access Journals (Sweden)

Nicholas B DeFelice

2018-03-01

Full Text Available Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

Short-term Forecasting Tools for Agricultural Nutrient Management.

Science.gov (United States)

Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew

2017-11-01

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Healthcare. State Report

Science.gov (United States)

Carnevale, Anthony P.; Smith, Nicole; Gulish, Artem; Beach, Bennett H.

2012-01-01

This report projects education requirements linked to forecasted job growth in healthcare by state and the District of Columbia from 2010 through 2020. It complements a larger national report which projects educational demand for healthcare for the same time period. The national report shows that with or without Obamacare, the United States will…

State-space based analysis and forecasting of macroscopic road safety trends in Greece.

Science.gov (United States)

Antoniou, Constantinos; Yannis, George

2013-11-01

In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960-2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009-2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fuzzy approach for short term load forecasting

Energy Technology Data Exchange (ETDEWEB)

Chenthur Pandian, S.; Duraiswamy, K.; Kanagaraj, N. [Electrical and Electronics Engg., K.S. Rangasamy College of Technology, Tiruchengode 637209, Tamil Nadu (India); Christober Asir Rajan, C. [Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry (India)

2006-04-15

The main objective of short term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. The STLF is needed to supply necessary information for the system management of day-to-day operations and unit commitment. In this paper, the 'time' and 'temperature' of the day are taken as inputs for the fuzzy logic controller and the 'forecasted load' is the output. The input variable 'time' has been divided into eight triangular membership functions. The membership functions are Mid Night, Dawn, Morning, Fore Noon, After Noon, Evening, Dusk and Night. Another input variable 'temperature' has been divided into four triangular membership functions. They are Below Normal, Normal, Above Normal and High. The 'forecasted load' as output has been divided into eight triangular membership functions. They are Very Low, Low, Sub Normal, Moderate Normal, Normal, Above Normal, High and Very High. Case studies have been carried out for the Neyveli Thermal Power Station Unit-II (NTPS-II) in India. The fuzzy forecasted load values are compared with the conventional forecasted values. The forecasted load closely matches the actual one within +/-3%. (author)

Construction Safety Forecast for ITER

Energy Technology Data Exchange (ETDEWEB)

cadwallader, lee charles

2006-11-01

The International Thermonuclear Experimental Reactor (ITER) project is poised to begin its construction activity. This paper gives an estimate of construction safety as if the experiment was being built in the United States. This estimate of construction injuries and potential fatalities serves as a useful forecast of what can be expected for construction of such a major facility in any country. These data should be considered by the ITER International Team as it plans for safety during the construction phase. Based on average U.S. construction rates, ITER may expect a lost workday case rate of < 4.0 and a fatality count of 0.5 to 0.9 persons per year.

Optimal operation and forecasting policy for pump storage plants in day-ahead markets

International Nuclear Information System (INIS)

Muche, Thomas

2014-01-01

Highlights: • We investigate unit commitment deploying stochastic and deterministic approaches. • We consider day-ahead markets, its forecast and weekly price based unit commitment. • Stochastic and deterministic unit commitment are identical for the first planning day. • Unit commitment and bidding policy can be based on the deterministic approach. • Robust forecasting models should be estimated based on the whole planning horizon. - Abstract: Pump storage plants are an important electricity storage technology at present. Investments in this technology are expected to increase. The necessary investment valuation often includes expected cash flows from future price-based unit commitment policies. A price-based unit commitment policy has to consider market price uncertainty and the information revealing nature of electricity markets. For this environment stochastic programming models are suggested to derive the optimal unit commitment policy. For the considered day-ahead price electricity market stochastic and deterministic unit commitment policies are comparable suggesting an application of easier implementable deterministic models. In order to identify suitable unit commitment and forecasting policies, deterministic unit commitment models are applied to actual day-ahead electricity prices of a whole year. As a result, a robust forecasting model should consider the unit commitment planning period. This robust forecasting models result in expected cash flows similar to realized ones allowing a reliable investment valuation

Adapting National Water Model Forecast Data to Local Hyper-Resolution H&H Models During Hurricane Irma

Science.gov (United States)

Singhofen, P.

2017-12-01

The National Water Model (NWM) is a remarkable undertaking. The foundation of the NWM is a 1 square kilometer grid which is used for near real-time modeling and flood forecasting of most rivers and streams in the contiguous United States. However, the NWM falls short in highly urbanized areas with complex drainage infrastructure. To overcome these shortcomings, the presenter proposes to leverage existing local hyper-resolution H&H models and adapt the NWM forcing data to them. Gridded near real-time rainfall, short range forecasts (18-hour) and medium range forecasts (10-day) during Hurricane Irma are applied to numerous detailed H&H models in highly urbanized areas of the State of Florida. Coastal and inland models are evaluated. Comparisons of near real-time rainfall data are made with observed gaged data and the ability to predict flooding in advance based on forecast data is evaluated. Preliminary findings indicate that the near real-time rainfall data is consistently and significantly lower than observed data. The forecast data is more promising. For example, the medium range forecast data provides 2 - 3 days advanced notice of peak flood conditions to a reasonable level of accuracy in most cases relative to both timing and magnitude. Short range forecast data provides about 12 - 14 hours advanced notice. Since these are hyper-resolution models, flood forecasts can be made at the street level, providing emergency response teams with valuable information for coordinating and dispatching limited resources.

Expert forecasts and the emergence of water scarcity on public agendas

Science.gov (United States)

Graffy, E.A.

2006-01-01

Expert forecasts of worldwide water scarcity depict conditions that call for proactive, preventive, coordinated water governance, but they have not been matched by public agendas of commensurate scope and urgency in the United States. This disconnect can not be adequately explained without some attention to attributes of forecasts themselves. I propose that the institutional fragmentation of water expertise and prevailing patterns of communication about water scarcity militate against the formulation of a common public definition of the problem and encourage reliance on unambiguous crises to stimulate social and policy agenda setting. I do not argue that expert forecasts should drive public agendas deterministically, but if their purpose is to help prevent water crises (not just predict them), then a greater effort is needed to overcome the barriers to meaningful public scrutiny of expert claims and evaluation of water strategies presently in place. Copyright ?? 2006 Taylor & Francis Group, LLC.

Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

Energy Technology Data Exchange (ETDEWEB)

none,

2014-08-29

With declining production costs and increasing technical capabilities, LED adoption has recently gained momentum in general illumination applications. This is a positive development for our energy infrastructure, as LEDs use significantly less electricity per lumen produced than many traditional lighting technologies. The U.S. Department of Energy’s Energy Savings Forecast of Solid-State Lighting in General Illumination Applications examines the expected market penetration and resulting energy savings of light-emitting diode, or LED, lamps and luminaires from today through 2030.

Toll Facilities in the United States - Toll Facilities in the United States

Data.gov (United States)

Department of Transportation — Biennial report containing selected information on toll facilities in the United States that has been provided to FHWA by the States and/or various toll authorities...

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

Energy Technology Data Exchange (ETDEWEB)

Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability

Oil Vulnerabilities and United States Strategy

Science.gov (United States)

2007-02-08

Mazda, Mercedes - Benz , Ford, Mercury, and Nissan offer flexible fuel vehicles in the United States. Ethanol is currently produced in the United States...USAWC STRATEGY RESEARCH PROJECT OIL VULNERABILITIES AND UNITED STATES STRATEGY by Colonel Shawn P. Walsh...Colleges and Schools, 3624 Market Street, Philadelphia, PA 19104, (215) 662-5606. The Commission on Higher Education is an institutional accrediting

Continuous hydrological modelling in the context of real time flood forecasting in alpine Danube tributary catchments

International Nuclear Information System (INIS)

Stanzel, Ph; Kahl, B; Haberl, U; Herrnegger, M; Nachtnebel, H P

2008-01-01

A hydrological modelling framework applied within operational flood forecasting systems in three alpine Danube tributary basins, Traisen, Salzach and Enns, is presented. A continuous, semi-distributed rainfall-runoff model, accounting for the main hydrological processes of snow accumulation and melt, interception, evapotranspiration, infiltration, runoff generation and routing is set up. Spatial discretization relies on the division of watersheds into subbasins and subsequently into hydrologic response units based on spatial information on soil types, land cover and elevation bands. The hydrological models are calibrated with meteorological ground measurements and with meteorological analyses incorporating radar information. Operationally, each forecasting sequence starts with the re-calculation of the last 24 to 48 hours. Errors between simulated and observed runoff are minimized by optimizing a correction factor for the input to provide improved system states. For the hydrological forecast quantitative 48 or 72 hour forecast grids of temperature and precipitation - deterministic and probabilistic - are used as input. The forecasted hydrograph is corrected with an autoregressive model. The forecasting sequences are repeated each 15 minutes. First evaluations of resulting hydrological forecasts are presented and reliability of forecasts with different lead times is discussed.

31 CFR 596.313 - United States person.

Science.gov (United States)

2010-07-01

... FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY TERRORISM LIST GOVERNMENTS SANCTIONS REGULATIONS General Definitions § 596.313 United States person. The term United States person means any United States...

Nuclear development in the United States

International Nuclear Information System (INIS)

Brewer, S.

1983-01-01

The history of the nuclear development in the United States has been one of international cooperation relations so far. The United States is to offer the technical information on atomic energy utilization to foreign countries in exchange for the guarantee that they never attempt to have or develop nuclear weapons. Actually, the United States has supplied the technologies on nuclear fuel cycle and other related fields to enable other countries to achieve economical and social progress. The Department of Energy clarified the public promise of the United States regarding the idea of international energy community. The ratio of nuclear power generation to total electric power supply in the United States exceeded 12%, and will exceed 20% by 1990. Since 1978, new nuclear power station has not been ordered, and some of the contracted power stations were canceled. The atomic energy industry in the United States prospered at the beginning of 1970s, but lost the spirit now, mainly due to the institutional problems rather than the technical ones. As the policy of the government to eliminate the obstacles, the improvement of the procedure for the permission and approval, the establishment of waste disposal capability, the verification of fast breeder reactor technology and the promotion of commercial fuel reprocessing were proposed. The re-establishment of the United States as the reliable supplier of atomic energy service is the final aim. (Kako, I.)

Use of Temperature to Improve West Nile Virus Forecasts

Science.gov (United States)

Shaman, J. L.; DeFelice, N.; Schneider, Z.; Little, E.; Barker, C.; Caillouet, K.; Campbell, S.; Damian, D.; Irwin, P.; Jones, H.; Townsend, J.

2017-12-01

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether the inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that were on average 5%, 10%, 12%, and 6% more accurate, respectively, than the baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperatures influence rates of WNV transmission. The findings help build a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

78 FR 70274 - United States Travel and Tourism Advisory Board: Meeting of the United States Travel and Tourism...

Science.gov (United States)

2013-11-25

... DEPARTMENT OF COMMERCE International Trade Administration United States Travel and Tourism Advisory Board: Meeting of the United States Travel and Tourism Advisory Board AGENCY: International Trade... the schedule and agenda for an open meeting of the United States Travel and Tourism Advisory Board...

78 FR 3398 - United States Travel and Tourism Advisory Board: Meeting of the United States Travel and Tourism...

Science.gov (United States)

2013-01-16

... DEPARTMENT OF COMMERCE International Trade Administration United States Travel and Tourism Advisory Board: Meeting of the United States Travel and Tourism Advisory Board AGENCY: International Trade... the schedule and agenda for an open meeting of the United States Travel and Tourism Advisory Board...

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

Science.gov (United States)

Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

2016-07-01

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

United States housing, 2012

Science.gov (United States)

Delton Alderman

2013-01-01

Provides current and historical information on housing market in the United States. Information includes trends for housing permits and starts, housing completions for single and multifamily units, and sales and construction. This report will be updated annually.

A fuzzy inference model for short-term load forecasting

International Nuclear Information System (INIS)

Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

2009-01-01

This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

Effects of recent energy system changes on CO2 projections for the United States.

Science.gov (United States)

Lenox, Carol S; Loughlin, Daniel H

2017-09-21

Recent projections of future United States carbon dioxide (CO 2 ) emissions are considerably lower than projections made just a decade ago. A myriad of factors have contributed to lower forecasts, including reductions in end-use energy service demands, improvements in energy efficiency, and technological innovations. Policies that have encouraged these changes include renewable portfolio standards, corporate vehicle efficiency standards, smart growth initiatives, revisions to building codes, and air and climate regulations. Understanding the effects of these and other factors can be advantageous as society evaluates opportunities for achieving additional CO 2 reductions. Energy system models provide a means to develop such insights. In this analysis, the MARKet ALlocation (MARKAL) model was applied to estimate the relative effects of various energy system changes that have happened since the year 2005 on CO 2 projections for the year 2025. The results indicate that transformations in the transportation and buildings sectors have played major roles in lowering projections. Particularly influential changes include improved vehicle efficiencies, reductions in projected travel demand, reductions in miscellaneous commercial electricity loads, and higher efficiency lighting. Electric sector changes have also contributed significantly to the lowered forecasts, driven by demand reductions, renewable portfolio standards, and air quality regulations.

ON-BOARD MONITORING OF TECHNICAL STATE FOR POWER UNITS OF WHEELED AND TRACKED VEHICLES

Directory of Open Access Journals (Sweden)

Yu. D. Karpievich

2016-01-01

Full Text Available The paper considers new methodologies pertaining to on-board diagnosis of wear-out rate for friction linings of a clutch driven disk and friction discs of a hydraulic press clutch of transmission gear boxes which are based on physical process that uses friction work as an integrated indicator. A new methodology in determination of life-span rate for engine oil has been developed in the paper. The paper presents block schematic diagrams for on-board monitoring of technical state for power units of wheeled and tracked vehicles. Usage of friction work as an integrated indicator for determination of wear-out rate for friction linings of clutch driven disk and friction discs of a haydraulic press clutch makes it possible timely at any operational period of wheeled and tracked vehicles to determine their residual operation life and forecast their replacement.While taking volume of the used fuel for determination of engine oil life-span rate it permits quickly and effectively at any operational period of wheeled and tracked vehicles to determine residual useful life of the engine oil and also forecast its replacement.

2000-2010 Annual State-Scale Service and Domain Scores for Forecasting Well-Being from Service-Based Decisions

Data.gov (United States)

U.S. Environmental Protection Agency — 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...

31 CFR 500.520 - Payments from accounts of United States citizens in employ of United States in foreign countries...

Science.gov (United States)

2010-07-01

... States citizens in employ of United States in foreign countries and certain other persons. 500.520..., Authorizations and Statements of Licensing Policy § 500.520 Payments from accounts of United States citizens in employ of United States in foreign countries and certain other persons. (a) Banking institutions within...

31 CFR 515.520 - Payments from accounts of United States citizens in employ of United States in foreign countries...

Science.gov (United States)

2010-07-01

... States citizens in employ of United States in foreign countries and certain other persons. 515.520..., Authorizations, and Statements of Licensing Policy § 515.520 Payments from accounts of United States citizens in employ of United States in foreign countries and certain other persons. (a) Banking institutions within...

A quality assessment of the MARS crop yield forecasting system for the European Union

Science.gov (United States)

van der Velde, Marijn; Bareuth, Bettina

2015-04-01

Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

TRAINING OF THE STATE PRESIDENT'S UNIT

African Journals Online (AJOL)

The primary function of the State President's Unit is to protect the head of state - not his person as is generally believed, but his authority over the state. Ironically, the ceremonial performances of the State President's Unit lead people to believe that they are only capable of doing drill exer- cises. However, upon investigating.

Forecasting world natural gas supply

International Nuclear Information System (INIS)

Al-Fattah, S. M.; Startzman, R. A.

2000-01-01

Using the multi-cyclic Hubert approach, a 53 country-specific gas supply model was developed which enables production forecasts for virtually all of the world's gas. Supply models for some organizations such as OPEC, non-OPEC and OECD were also developed and analyzed. Results of the modeling study indicate that the world's supply of natural gas will peak in 2014, followed by an annual decline at the rate of one per cent per year. North American gas production is reported to be currently at its peak with 29 Tcf/yr; Western Europe will reach its peak supply in 2002 with 12 Tcf. According to this forecast the main sources of natural gas supply in the future will be the countries of the former Soviet Union and the Middle East. Between them, they possess about 62 per cent of the world's ultimate recoverable natural gas (4,880 Tcf). It should be noted that these estimates do not include unconventional gas resulting from tight gas reservoirs, coalbed methane, gas shales and gas hydrates. These unconventional sources will undoubtedly play an important role in the gas supply in countries such as the United States and Canada. 18 refs., 2 tabs., 18 figs

Forecasting timber, biomass, and tree carbon pools with the output of state and transition models

Science.gov (United States)

Xiaoping Zhou; Miles A. Hemstrom

2012-01-01

The Integrated Landscape Assessment Project (ILAP) uses spatial vegetation data and state and transition models (STM) to forecast future vegetation conditions and the interacting effects of natural disturbances and management activities. Results from ILAP will help land managers, planners, and policymakers evaluate management strategies that reduce fire risk, improve...

Development of a downed woody debris forecasting tool using strategic-scale multiresource forest inventories

Science.gov (United States)

Matthew B. Russell; Christopher W. Woodall

2017-01-01

The increasing interest in forest biomass for energy or carbon cycle purposes has raised the need for forest resource managers to refine their understanding of downed woody debris (DWD) dynamics. We developed a DWD forecasting tool using field measurements (mean size and stage of stage of decay) for three common forest types across the eastern United States using field...

Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

Science.gov (United States)

KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

2013-12-01

It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more

Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

Science.gov (United States)

Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

2015-01-01

A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

Draft forecast of the final report for the comparison to 40 CFR Part 191, Subpart B, for the Waste Isolation Pilot Plant

Energy Technology Data Exchange (ETDEWEB)

Bertram-Howery, S.G.; Marietta, M.G.; Anderson, D.R.; Gomez, L.S.; Rechard, R.P. (Sandia National Labs., Albuquerque, NM (USA)); Brinster, K.F.; Guzowski, R.V. (Science Applications International Corp., Albuquerque, NM (USA))

1989-12-01

The United States Department of Energy is planning to dispose of transuranic wastes, which have been generated by defense programs, at the Waste Isolation Pilot Plant. The WIPP Project will assess compliance with the requirements of the United States Environmental Protection Agency. This report forecasts the planned 1992 document, Comparison to 40 CFR, Part 191, Subpart B, for the Waste Isolation Pilot Plant (WIPP). 130 refs., 36 figs., 11 tabs.

Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Directory of Open Access Journals (Sweden)

Stepčenko Artūrs

2016-12-01

Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.

Forecasting metal prices: Do forecasters herd?

DEFF Research Database (Denmark)

Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

2013-01-01

We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

Forecasting resource-allocation decisions under climate uncertainty: fire suppression with assessment of net benefits of research

Science.gov (United States)

Jeffrey P. Prestemon; Geoffrey H. Donovan

2008-01-01

Making input decisions under climate uncertainty often involves two-stage methods that use expensive and opaque transfer functions. This article describes an alternative, single-stage approach to such decisions using forecasting methods. The example shown is for preseason fire suppression resource contracting decisions faced by the United States Forest Service. Two-...

Mercury emissions from municipal solid waste combustors. An assessment of the current situation in the United States and forecast of future emissions

Energy Technology Data Exchange (ETDEWEB)

None

1993-05-01

This report examines emissions of mercury (Hg) from municipal solid waste (MSW) combustion in the United States (US). It is projected that total annual nationwide MSW combustor emissions of mercury could decrease from about 97 tonnes (1989 baseline uncontrolled emissions) to less than about 4 tonnes in the year 2000. This represents approximately a 95 percent reduction in the amount of mercury emitted from combusted MSW compared to the 1989 mercury emissions baseline. The likelihood that routinely achievable mercury emissions removal efficiencies of about 80 percent or more can be assured; it is estimated that MSW combustors in the US could prove to be a comparatively minor source of mercury emissions after about 1995. This forecast assumes that diligent measures to control mercury emissions, such as via use of supplemental control technologies (e.g., carbon adsorption), are generally employed at that time. However, no present consensus was found that such emissions control measures can be implemented industry-wide in the US within this time frame. Although the availability of technology is apparently not a limiting factor, practical implementation of necessary control technology may be limited by administrative constraints and other considerations (e.g., planning, budgeting, regulatory compliance requirements, etc.). These projections assume that: (a) about 80 percent mercury emissions reduction control efficiency is achieved with air pollution control equipment likely to be employed by that time; (b) most cylinder-shaped mercury-zinc (CSMZ) batteries used in hospital applications can be prevented from being disposed into the MSW stream or are replaced with alternative batteries that do not contain mercury; and (c) either the amount of mercury used in fluorescent lamps is decreased to an industry-wide average of about 27 milligrams of mercury per lamp or extensive diversion from the MSW stream of fluorescent lamps that contain mercury is accomplished.

Educational Forecasting Methodologies: State of the Art, Trends, and Highlights.

Science.gov (United States)

Hudson, Barclay; Bruno, James

This overview of both quantitative and qualitative methods of educational forecasting is introduced by a discussion of a general typology of forecasting methods. In each of the following sections, discussion follows the same general format: a number of basic approaches are identified (e.g. extrapolation, correlation, systems modelling), and each…

Electric power supply and demand 1979 to 1988 for the contiguous United States as projected by the Regional Electric Reliability Councils in their April 1, 1979 long-range coordinated planning reports to the Department of Energy

Energy Technology Data Exchange (ETDEWEB)

Savage, N.; Graban, W.

1979-12-01

Information concerning bulk electric power supply and demand is summarized and reviewed. Electric-utility power-supply systems are composed of power sources, transmission and distribution facilities, and users of electricity. In the United States there are three such systems of large geographic extent that together cover the entire country. Subjects covered are: energy forecasts, peak demand forecasts, generating-capacity forecasts, purchases and sales of capacity, and transmission. Extensive data are compiled in 17 tables. Information in two appendices includes a general description of the Regional Electric Reliability Councils and US generating capacity as of June 30, 1979. 3 figures, 17 tables.

High Resolution Map of Water Supply and Demand for North East United States

Science.gov (United States)

Ehsani, N.; Vorosmarty, C. J.; Fekete, B. M.

2012-12-01

Accurate estimates of water supply and demand are crucial elements in water resources management and modeling. As part of our NSF-funded EaSM effort to build a Northeast Regional Earth System Model (NE-RESM) as a framework to improve our understanding and capacity to forecast the implications of planning decisions on the region's environment, ecosystem services, energy and economic systems through the 21st century, we are producing a high resolution map (3' x 3' lat/long) of estimated water supply and use for the north east region of United States. Focusing on water demand, results from this study enables us to quantify how demand sources affect the hydrology and thermal-chemical water pollution across the region. In an attempt to generate this 3-minute resolution map in which each grid cell has a specific estimated monthly domestic, agriculture, thermoelectric and industrial water use. Estimated Use of Water in the United States in 2005 (Kenny et al., 2009) is being coupled to high resolution land cover and land use, irrigation, power plant and population data sets. In addition to water demands, we tried to improve estimates of water supply from the WBM model by improving the way it controls discharge from reservoirs. Reservoirs are key characteristics of the modern hydrologic system, with a particular impact on altering the natural stream flow, thermal characteristics, and biogeochemical fluxes of rivers. Depending on dam characteristics, watershed characteristics and the purpose of building a dam, each reservoir has a specific optimum operating rule. It means that literally 84,000 dams in the National Inventory of Dams potentially follow 84,000 different sets of rules for storing and releasing water which must somehow be accounted for in our modeling exercise. In reality, there is no comprehensive observational dataset depicting these operating rules. Thus, we will simulate these rules. Our perspective is not to find the optimum operating rule per se but to find

31 CFR 515.334 - United States national.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States national. 515.334 Section 515.334 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE... of the United States, and which has its principal place of business in the United States. [61 FR...

7 CFR 1212.32 - United States Customs Service.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false United States Customs Service. 1212.32 Section 1212... § 1212.32 United States Customs Service. “United States Customs Service” or “Customs” means the United States Customs and Border Protection, an agency of the Department of Homeland Security. Honey Packers and...

The development rainfall forecasting using kalman filter

Science.gov (United States)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

Navy Mobility Fuels Forecasting System. Phase I report

Energy Technology Data Exchange (ETDEWEB)

Davis, R.M.; Hadder, G.R.; Singh, S.P.N.; Whittle, C.

1985-07-01

The Department of the Navy (DON) requires an improved capability to forecast mobility fuel availability and quality. The changing patterns in fuel availability and quality are important in planning the Navy's Mobility Fuels R and D Program. These changes come about primarily because of the decline in the quality of crude oil entering world markets as well as the shifts in refinery capabilities domestically and worldwide. The DON requested ORNL's assistance in assembling and testing a methodology for forecasting mobility fuel trends. ORNL reviewed and analyzed domestic and world oil reserve estimates, production and price trends, and recent refinery trends. Three publicly available models developed by the Department of Energy were selected as the basis of the Navy Mobility Fuels Forecasting System. The system was used to analyze the availability and quality of jet fuel (JP-5) that could be produced on the West Coast of the United States under an illustrative business-as-usual and a world oil disruption scenario in 1990. Various strategies were investigated for replacing the lost JP-5 production. This exercise, which was strictly a test case for the forecasting system, suggested that full recovery of lost fuel production could be achieved by relaxing the smoke point specifications or by increasing the refiners' gate price for the jet fuel. A more complete analysis of military mobility fuel trends is currently under way.

On the internal consistency of the term structure of forecasts of housing starts

DEFF Research Database (Denmark)

Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

2013-01-01

We use the term structure of forecasts of housing starts to test for rationality of forecasts. Our test is based on the idea that short-term and long-term forecasts should be internally consistent. We test the internal consistency of forecasts using data for Australia, Canada, Japan and the United...

Improving operational flood forecasting through data assimilation

Science.gov (United States)

Rakovec, Oldrich; Weerts, Albrecht; Uijlenhoet, Remko; Hazenberg, Pieter; Torfs, Paul

2010-05-01

Accurate flood forecasts have been a challenging topic in hydrology for decades. Uncertainty in hydrological forecasts is due to errors in initial state (e.g. forcing errors in historical mode), errors in model structure and parameters and last but not least the errors in model forcings (weather forecasts) during the forecast mode. More accurate flood forecasts can be obtained through data assimilation by merging observations with model simulations. This enables to identify the sources of uncertainties in the flood forecasting system. Our aim is to assess the different sources of error that affect the initial state and to investigate how they propagate through hydrological models with different levels of spatial variation, starting from lumped models. The knowledge thus obtained can then be used in a data assimilation scheme to improve the flood forecasts. This study presents the first results of this framework and focuses on quantifying precipitation errors and its effect on discharge simulations within the Ourthe catchment (1600 km2), which is situated in the Belgian Ardennes and is one of the larger subbasins of the Meuse River. Inside the catchment, hourly rain gauge information from 10 different locations is available over a period of 15 years. Based on these time series, the bootstrap method has been applied to generate precipitation ensembles. These were then used to simulate the catchment's discharges at the outlet. The corresponding streamflow ensembles were further assimilated with observed river discharges to update the model states of lumped hydrological models (R-PDM, HBV) through Residual Resampling. This particle filtering technique is a sequential data assimilation method and takes no prior assumption of the probability density function for the model states, which in contrast to the Ensemble Kalman filter does not have to be Gaussian. Our further research will be aimed at quantifying and reducing the sources of uncertainty that affect the initial

Modeling of the fatigue damage accumulation processes in the material of NPP design units under thermomechanical unstationary effects. Estimation of spent life and forecast of residual life

International Nuclear Information System (INIS)

Kiriushin, A.I.; Korotkikh, Yu.G.; Gorodov, G.F.

2002-01-01

Full text: The estimation problems of spent life and forecast of residual life of NPP equipment design units, operated at unstationary thermal force loads are considered. These loads are, as a rule, unregular and cause rotation of main stress tensor platforms of the most loaded zones of structural elements and viscoelastic plastic deformation of material in the places of stresses concentrations. The existing engineering approaches to the damages accumulation processes calculation in the material of structural units, their advantages and disadvantages are analyzed. For the processes of fatigue damages accumulation a model is proposed, which allows to take into account the unregular pattern of deformation multiaxiality of stressed state, rotation of main platforms, non-linear summation of damages at the loading mode change. The model in based on the equations of damaged medium mechanics, including the equations of viscoplastic deformation of the material and evolutionary equations of damages accumulation. The algorithms of spent life estimation and residual life forecast of the controlled equipment and systems zones are made on the bases of the given model by the known real history of loading, which is determined by real model of NPP operation. The results of numerical experiments on the basis of given model for various processes of thermal force loads and their comparison with experimental results are presented. (author)

Nuclear power in the United States

International Nuclear Information System (INIS)

Johnston, J.B.

1985-01-01

All over the world except in the United States, nuclear energy is a low cost, secure, environmentally acceptable form of energy. In the United States, civilian nuclear power is dead. 112 nuclear power plants have been abandoned or cancelled in the last decade, and there has been no new order for nuclear plants since 1978. It will be fortunate to have 125 operating nuclear plants in the United States in the year 2000. There are almost 90 completed nuclear power plants and about 45 under construction in the United States, but several of those under construction will eventually be abandoned. About 20 % of the electricity in the United States will be generated by nuclear plants in 2000 as compared with 13 % supplied in the last year. Under the present regulatory and institutional arrangement, American electric utilities would not consider to order a new nuclear power plant. Post-TMI nuclear plants became very expensive, and there is also ideological opposition to nuclear power. Coal-firing plants are also in the similar situation. The uncertainty about electric power demand, the cost of money, the inflation of construction cost and regulation caused the situation. (Kako, I.)

Teen Pregnancy in the United States

Science.gov (United States)

... United States: the contribution of abstinence and improved contraceptive use. Am J Public Health. 2007;97(1):150-6. Lindberg LD, Santelli JS, Desai, S. Understanding the Decline in Adolescent Fertility in the United States, 2007–2012. J ...

Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics.

Directory of Open Access Journals (Sweden)

Wan Yang

2014-04-01

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

Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

Directory of Open Access Journals (Sweden)

W. Petersen

2011-09-01

Full Text Available A coastal observing system for Northern and Arctic Seas (COSYNA aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data

Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

Science.gov (United States)

Restrepo, P.; Schaake, J.

2009-04-01

Traditionally, the role of the Hydrology program of the National Weather Service has been centered around forecasting floods, in order to minimize loss of lives and damage to property as a result of floods as well as water levels for navigable rivers, and water supply in some areas of the country. A number of factors, including shifting population patterns, widespread drought and concerns about climate change have made it imperative to widen the focus to cover forecasting flows ranging from drought to floods and anything in between. Because of these concerns, it is imperative to develop models that rely more on the physical characteristics of the watershed for parameterization and less on historical observations. Furthermore, it is also critical to consider explicitly the sources of uncertainty in the forecasting process, including parameter values, model structure, forcings (both observations and forecasts), initial conditions, and streamflow observations. A consequence of more widespread occurrence of low flows as a result either of the already evident earlier snowmelt in the Western United States, or of the predicted changes in precipitation patterns, is the issue of water quality: lower flows will have higher concentrations of certain pollutants. This paper describes the current projects and future directions of research for hydrologic forecasting in the United States. Ongoing projects on quantitative precipitation and temperature estimates and forecasts, uncertainty modeling by the use of ensembles, data assimilation, verification, distributed conceptual modeling will be reviewed. Broad goals of the research directions are: 1) reliable modeling of the different sources of uncertainty. 2) a more expeditious and cost-effective approach by reducing the effort required in model calibration; 3) improvements in forecast lead-time and accuracy; 4) an approach for rapid adjustment of model parameters to account for changes in the watershed, both rapid as the result

Variational data assimilation for the optimized ozone initial state and the short-time forecasting

Directory of Open Access Journals (Sweden)

S.-Y. Park

2016-03-01

Full Text Available In this study, we apply the four-dimensional variational (4D-Var data assimilation to optimize initial ozone state and to improve the predictability of air quality. The numerical modeling systems used for simulations of atmospheric condition and chemical formation are the Weather Research and Forecasting (WRF model and the Community Multiscale Air Quality (CMAQ model. The study area covers the capital region of South Korea, where the surface measurement sites are relatively evenly distributed. The 4D-Var code previously developed for the CMAQ model is modified to consider background error in matrix form, and various numerical tests are conducted. The results are evaluated with an idealized covariance function for the appropriateness of the modified codes. The background error is then constructed using the NMC method with long-term modeling results, and the characteristics of the spatial correlation scale related to local circulation are analyzed. The background error is applied in the 4D-Var research, and a surface observational assimilation is conducted to optimize the initial concentration of ozone. The statistical results for the 12 h assimilation periods and the 120 observatory sites show a 49.4 % decrease in the root mean squared error (RMSE, and a 59.9 % increase in the index of agreement (IOA. The temporal variation of spatial distribution of the analysis increments indicates that the optimized initial state of ozone concentration is transported to inland areas by the clockwise-rotating local circulation during the assimilation windows. To investigate the predictability of ozone concentration after the assimilation window, a short-time forecasting is carried out. The ratios of the RMSE (root mean squared error with assimilation versus that without assimilation are 8 and 13 % for the +24 and +12 h, respectively. Such a significant improvement in the forecast accuracy is obtained solely by using the optimized initial state. The potential

Immigration Enforcement Within the United States

Science.gov (United States)

2006-04-06

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Policy Issues...Remained in the United States, (Washington: Center for Immigration Studies, May 2002). Immigration Enforcement Within the United States Introduction ...interior enforcement lack a border component. For example, fugitive taskforces, investigations of alien slavery and sweatshops , and employer sanctions do

Objective Lightning Probability Forecasts for East-Central Florida Airports

Science.gov (United States)

Crawford, Winfred C.

2013-01-01

The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.

Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California: A Framework for Objectively Leveraging Weather and Climate Forecasts in a Decision Support Environment

Science.gov (United States)

Delaney, C.; Hartman, R. K.; Mendoza, J.; Whitin, B.

2017-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC. The ESP hindcast was developed using Global Ensemble Forecast System version 10 precipitation reforecasts processed with the Hydrologic Ensemble Forecast System to generate daily reforecasts of 61 flow ensemble members for a 15-day forecast horizon. Model simulation results demonstrate that the EFO alternative may improve water supply reliability for Lake Mendocino yet not increase flood risk for downstream areas. The developed operations framework can directly leverage improved skill in the second week of the forecast and is extendable into the S2S time domain given the demonstration of improved skill through a reliable reforecast of adequate historical duration and consistent with operationally available numerical weather predictions.

United States advanced technologies

International Nuclear Information System (INIS)

Longenecker, J.R.

1985-01-01

In the United States, the advanced technologies have been applied to uranium enrichment as a means by which it can be assured that nuclear fuel cost will remain competitive in the future. The United States is strongly committed to the development of advanced enrichment technology, and has brought both advanced gas centrifuge (AGC) and atomic vapor laser isotope separation (AVLIS) programs to a point of significant technical refinement. The ability to deploy advanced technologies is the basis for the confidence in competitive future price. Unfortunately, the development of advanced technologies is capital intensive. The year 1985 is the key year for advanced technology development in the United States, since the decision on the primary enrichment technology for the future, AGC or AVLIS, will be made shortly. The background on the technology selection process, the highlights of AGC and AVLIS programs and the way to proceed after the process selection are described. The key objective is to maximize the sales volume and minimize the operating cost. This will help the utilities in other countries supply low cost energy on a reliable, long term basis. (Kako, I.)

The SEEC United Kingdom energy demand forecast (1993-2000)

Energy Technology Data Exchange (ETDEWEB)

Fouquet, R; Hawdon, D; Pearson, P; Robinson, C; Stevens, P

1993-12-16

The aims of this paper are to present the underlying determinants of fuel consumption, such as economic activity and prices, develop a series of simple yet reliable sectoral models of energy demand, which incorporate recent modelling developments; provide forecasts of energy demand and its environmental consequences; examine the effects of VAT on domestic fuel and increased competition in the electricity sector; and aid the present debate on energy markets. The paper analyses world oil prices, with a particular focus on Iraq's role, reviews energy policy in the UK and discusses SEEC's expectations about UK fuel prices in coming years and how they vary among sectors. It forecasts final user demand in the domestic, iron and steel, other industry, transport, agricultural, public administration and defence and miscellaneous sectors. The paper also examines the major changes that are underway in electricity generators' demand for fuel, and primary energy consumption and its environmental implications.

The United States and the Arab Gulf Monarchies

International Nuclear Information System (INIS)

Kechichian, J.A.

1999-01-01

The United States has enduring strategic interests in the Persian Gulf region. To understand these interests and the Usa policy towards the Arab Gulf Monarchies, the french institute of international relations (IFRI) proposes this document. The following chapters are detailed: the United States and the Arab Gulf Monarchies, overview, Chief Unites States Objective: Access to oil, re-evaluating United States Foreign Policy in the Gulf, the second term (Usa strategy). (A.L.B.)

Short-Term fo F2 Forecast: Present Day State of Art

Science.gov (United States)

Mikhailov, A. V.; Depuev, V. H.; Depueva, A. H.

An analysis of the F2-layer short-term forecast problem has been done. Both objective and methodological problems prevent us from a deliberate F2-layer forecast issuing at present. An empirical approach based on statistical methods may be recommended for practical use. A forecast method based on a new aeronomic index (a proxy) AI has been proposed and tested over selected 64 severe storm events. The method provides an acceptable prediction accuracy both for strongly disturbed and quiet conditions. The problems with the prediction of the F2-layer quiet-time disturbances as well as some other unsolved problems are discussed

National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

Energy Technology Data Exchange (ETDEWEB)

Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

1982-03-31

The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

78 FR 46686 - Privacy Act of 1974; Treasury/United States Mint .013-United States Mint National Electronic...

Science.gov (United States)

2013-08-01

... available publicly. FOR FURTHER INFORMATION CONTACT: For general questions and privacy issues, please... DEPARTMENT OF THE TREASURY Privacy Act of 1974; Treasury/United States Mint .013--United States... Privacy Act of 1974, as amended, 5 U.S.C. 552a, the Department of the Treasury (``Treasury'') and the...

Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

KAUST Repository

Butler, T.

2012-07-01

Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

39 CFR 221.1 - The United States Postal Service.

Science.gov (United States)

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false The United States Postal Service. 221.1 Section 221.1 Postal Service UNITED STATES POSTAL SERVICE ORGANIZATION AND ADMINISTRATION GENERAL ORGANIZATION § 221.1 The United States Postal Service. The United States Postal Service was established as an...

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

Science.gov (United States)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the Mizu

UNITED STATES DURING THE COLD WAR 1945-1990

Directory of Open Access Journals (Sweden)

Novita Mujiyati

2016-02-01

Full Text Available United States and the Soviet Union is a country on the part of allies who emerged as the winner during World War II. However, after reaching the Allied victory in the situation soon changed, man has become an opponent. United States and the Soviet Union are competing to expand the influence and power. To compete the United States strive continuously strengthen itself both in the economic and military by establishing a defense pact and aid agencies in the field of economy. During the Cold War the two are not fighting directly in one of the countries of the former Soviet Union and the United States. However, if understood, teradinya the Korean War and the Vietnam War is a result of tensions between the two countries and is a direct warfare conducted by the United States and the Soviet Union. Cold War ended in conflict with the collapse of the Soviet Union and the United States emerged as the winner of the country.

The Latest Forecast.

Science.gov (United States)

Laurence, David

2002-01-01

Discusses the "latest forecast" for the future of English departments. Addresses departmental and institutional staffing practices, employment opportunities for PhDs, the acceleration of change in the institution, and the general state of the study and teaching of English. (RS)

78 FR 27857 - United States Standards for Wheat

Science.gov (United States)

2013-05-13

... RIN 0580-AB12 United States Standards for Wheat AGENCY: Grain Inspection, Packers and Stockyards... (GIPSA) is revising the United States Standards for Wheat under the United States Grain Standards Act (USGSA) to change the definition of Contrasting classes (CCL) in the class Hard White wheat. This change...

Tuberculosis along the United States-Mexico border, 1993-2001.

Science.gov (United States)

Schneider, Eileen; Laserson, Kayla F; Wells, Charles D; Moore, Marisa

2004-07-01

Tuberculosis (TB) is a leading public health problem and a recognized priority for the federal Governments of both Mexico and the United States of America. The objectives of this research, primarily for the four states in the United States that are along the border with Mexico, were to: (1) describe the epidemiological situation of TB, (2) identify TB risk factors, and (3) discuss tuberculosis program strategies. We analyzed tuberculosis case reports collected from 1993 through 2001 by the tuberculosis surveillance system of the United States. We used those data to compare TB cases mainly among three groups: (1) Mexican-born persons in the four United States border states (Arizona, California, New Mexico, and Texas), (2) persons in those four border states who had been born in the United States, and (3) Mexican-born persons in the 46 other states of the United States, which do not border Mexico. For the period from 1993 through 2001, of the 16 223 TB cases reported for Mexican-born persons in the United States, 12 450 of them (76.7%) were reported by Arizona, California, New Mexico, and Texas. In those four border states overall in 2001, tuberculosis case rates for Mexican-born persons were 5.0 times as high as the rates for persons born in the United States; those four states have 23 counties that directly border on Mexico, and the ratio in those counties was 5.8. HIV seropositivity, drug and alcohol use, unemployment, and incarceration were significantly less likely to be reported in Mexican-born TB patients from the four border states and the nonborder states than in patients born in the United States from the four border states (P pulmonary tuberculosis patients who were 18-64 years of age and residing in the four border states, the Mexican-born patients were 3.6 times as likely as the United States-born patients were to have resistance to at least isoniazid and rifampin (i. e., to have multidrug-resistant TB) and twice as likely to have isoniazid resistance

Vintage errors: do real-time economic data improve election forecasts?

Directory of Open Access Journals (Sweden)

Mark Andreas Kayser

2015-07-01

Full Text Available Economic performance is a key component of most election forecasts. When fitting models, however, most forecasters unwittingly assume that the actual state of the economy, a state best estimated by the multiple periodic revisions to official macroeconomic statistics, drives voter behavior. The difference in macroeconomic estimates between revised and original data vintages can be substantial, commonly over 100% (two-fold for economic growth estimates, making the choice of which data release to use important for the predictive validity of a model. We systematically compare the predictions of four forecasting models for numerous US presidential elections using real-time and vintage data. We find that newer data are not better data for election forecasting: forecasting error increases with data revisions. This result suggests that voter perceptions of economic growth are influenced more by media reports about the economy, which are based on initial economic estimates, than by the actual state of the economy.

Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach

DEFF Research Database (Denmark)

Boldrini, Lorenzo

In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based......) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature...

Global Entrepreneurship and the United States

Science.gov (United States)

2010-09-01

Global Entrepreneurship and the United States by Zoltan J. Acs Laszlo Szerb Ruxton, MD 21204 for under contract number SBAHQ-09...SUBTITLE Global Entrepreneurship and the United States 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT...3 2.1. Assessing Entrepreneurship ..................................................................................4 2.2. Stages of Development

75 FR 25925 - United States Mint

Science.gov (United States)

2010-05-10

... Committee May 25, 2010 Public Meeting. SUMMARY: Pursuant to United States Code, Title 31, section 5135(b)(8... scheduled for May 25, 2010. Date: May 25, 2010. Time: 9 a.m. to 12 p.m. Location: 8th Floor Board Room, United States Mint, 801 9th Street, NW., Washington, DC 20220. Subject: Review and discuss obverse and...

31 CFR 515.330 - Person within the United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Person within the United States. 515... Definitions § 515.330 Person within the United States. (a) The term person within the United States, includes: (1) Any person, wheresoever located, who is a resident of the United States; (2) Any person actually...

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

Science.gov (United States)

Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent

2016-08-01

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

45 CFR 212.7 - Repayment to the United States.

Science.gov (United States)

2010-10-01

... 45 Public Welfare 2 2010-10-01 2010-10-01 false Repayment to the United States. 212.7 Section 212... UNITED STATES CITIZENS RETURNED FROM FOREIGN COUNTRIES § 212.7 Repayment to the United States. (a) An..., any or all of the cost of such assistance to the United States, except insofar as it is determined...

20 CFR 416.215 - You leave the United States.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false You leave the United States. 416.215 Section... Eligible § 416.215 You leave the United States. You lose your eligibility for SSI benefits for any month during all of which you are outside of the United States. If you are outside of the United States for 30...

37 CFR 1.412 - The United States Receiving Office.

Science.gov (United States)

2010-07-01

... Information § 1.412 The United States Receiving Office. (a) The United States Patent and Trademark Office is a Receiving Office only for applicants who are residents or nationals of the United States of America. (b) The... “United States Receiving Office” or by the abbreviation “RO/US.” (c) The major functions of the Receiving...

An Approach to Assess Observation Impact Based on Observation-Minus-Forecast Residuals

Science.gov (United States)

Todling, Ricardo

2009-01-01

Langland and Baker (2004) introduced an approach to assess the impact of observations on the forecasts. In that, a state-space aspect of the forecast is defined and a procedure is derived that relates changes in the aspect with changes in the initial conditions associated with the assimilation of observations) ultimately providing information about the impact of individual observations on the forecast. Some features of the approach are to be noted. The typical choice of forecast aspect employed in related works is rather arbitrary and leads to an incomplete assessment of the observing system. Furthermore, the state-space forecast aspect requires availability of a verification state that should ideally be uncorrelated with the forecast but in practice is not. Lastly, the approach involves the adjoint operator of the entire data assimilation system and as such it is constrained by the validity of this operator. In this presentation, an observation-space metric is used that, for a relatively time-homogeneous observing system, allows inferring observation impact on the forecast without some of the limitations above. Specifically, using observation-minus-forecast residuals leads to an approach with the following features: (i) it suggests a rather natural choice of forecast aspect, directly linked to the analysis system and providing full assessment of the observations; (ii) it naturally avoids introducing undesirable correlations in the forecast aspect by verifying against the observations; and (iii) it does not involve linearization and use of adjoints; therefore being applicable to any length of forecast. The state and observation-space approaches might be complementary to some degree, and involve different limitations and complexities. Illustrations are given using the NASA GEOS-5 data.

Using the FORE-SCE model to project land-cover change in the southeastern United States

Science.gov (United States)

Sohl, Terry; Sayler, Kristi L.

2008-01-01

A wide variety of ecological applications require spatially explicit current and projected land-use and land-cover data. The southeastern United States has experienced massive land-use change since European settlement and continues to experience extremely high rates of forest cutting, significant urban development, and changes in agricultural land use. Forest-cover patterns and structure are projected to change dramatically in the southeastern United States in the next 50 years due to population growth and demand for wood products [Wear, D.N., Greis, J.G. (Eds.), 2002. Southern Forest Resource Assessment. General Technical Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635 pp]. Along with our climate partners, we are examining the potential effects of southeastern U.S. land-cover change on regional climate. The U.S. Geological Survey (USGS) Land Cover Trends project is analyzing contemporary (1973-2000) land-cover change in the conterminous United States, providing ecoregion-by-ecoregion estimates of the rates of change, descriptive transition matrices, and changes in landscape metrics. The FORecasting SCEnarios of future land-cover (FORE-SCE) model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land-cover change through 2050 for the southeastern United States. Prescriptions for future proportions of land cover for this application were provided by ecoregion-based extrapolations of historical change. Logistic regression was used to develop relationships between suspected drivers of land-cover change and land cover, resulting in the development of probability-of-occurrence surfaces for each unique land-cover type. Forest stand age was initially established with Forest Inventory and Analysis (FIA) data and tracked through model iterations. The spatial allocation procedure placed patches of new land cover on the landscape until the scenario

Modeling and forecasting the volatility of Islamic unit trust in Malaysia using GARCH model

Science.gov (United States)

Ismail, Nuraini; Ismail, Mohd Tahir; Karim, Samsul Ariffin Abdul; Hamzah, Firdaus Mohamad

2015-10-01

Due to the tremendous growth of Islamic unit trust in Malaysia since it was first introduced on 12th of January 1993 through the fund named Tabung Ittikal managed by Arab-Malaysian Securities, vast studies have been done to evaluate the performance of Islamic unit trust offered in Malaysia's capital market. Most of the studies found that one of the factors that affect the performance of the fund is the volatility level. Higher volatility produces better performance of the fund. Thus, we believe that a strategy must be set up by the fund managers in order for the fund to perform better. By using a series of net asset value (NAV) data of three different types of fund namely CIMB-IDEGF, CIMB-IBGF and CIMB-ISF from a fund management company named CIMB Principal Asset Management Berhad over a six years period from 1st January 2008 until 31st December 2013, we model and forecast the volatility of these Islamic unit trusts. The study found that the best fitting models for CIMB-IDEGF, CIMB-IBGF and CIMB-ISF are ARCH(4), GARCH(3,3) and GARCH(3,1) respectively. Meanwhile, the fund that is expected to be the least volatile is CIMB-IDEGF and the fund that is expected to be the most volatile is CIMB-IBGF.

Demand Forecasting: An Evaluation of DODs Accuracy Metric and Navys Procedures

Science.gov (United States)

2016-06-01

dataset ci = unit cost for item i fi = demand forecast for item i 28 ai = actual demand for item i A close look at fCIMIP metric reveals a...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA MBA PROFESSIONAL REPORT DEMAND FORECASTING : AN EVALUATION OF DOD’S ACCURACY...June 2016 3. REPORT TYPE AND DATES COVERED MBA professional report 4. TITLE AND SUBTITLE DEMAND FORECASTING : AN EVALUATION OF DOD’S ACCURACY

United States experience in environmental cost-benefit analysis for nuclear power plants with implications for developing countries

International Nuclear Information System (INIS)

Spangler, M.B.

1980-08-01

Environmental cost-benefit analysis in the United States involves a comparison of diverse societal impacts of the proposed developments and its alternatives. Regarding nuclear power plant licensing actions, such analyses include the need for base-load electrical generating capacity versus the no-action alternative; alternative sources of energy; alternative sites for the proposed nuclear plants; and alternative technologies for mitigating environmental impacts. Many U.S. experiences and environmental assessment practices and comparative resource requirements presented in this report will not provide a wholly reliable reflection of the precise situation of each country. Nevertheless, the procedural and substantive issues encountered by the United States in nuclear power plant licensing may exhibit a number of important, if rough, parallelisms for other countries. Procedural issues dealt with include: the scoping of alternatives and impact issues; the problem of balancing incommensurable impacts; and treating uncertainty in measuring or forecasting certain kinds of environmental impacts. Although substantive environmental impact issues will vary appreciably among nations, it is to be expected that many of the substantive impact issues such as impacts on biota, community-related effects, and aesthetic impacts will also have some measure of universal interest to other countries

Development and validation of a regional coupled forecasting system for S2S forecasts

Science.gov (United States)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

Affective forecasting and self-rated symptoms of depression, anxiety, and hypomania: evidence for a dysphoric forecasting bias.

Science.gov (United States)

Hoerger, Michael; Quirk, Stuart W; Chapman, Benjamin P; Duberstein, Paul R

2012-01-01

Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problems. Pitting cognitive theory against depressive realism theory, we examined whether dysphoria was associated with negatively biased affective forecasts or greater accuracy. Participants (n=325) supplied predicted and actual emotional reactions for three days surrounding an emotionally evocative relational event, Valentine's Day. Predictions were made a month prior to the holiday. Consistent with cognitive theory, we found evidence for a dysphoric forecasting bias-the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalisations of dysphoria, and three time points of observation. Similar biases were not observed in analyses examining the independent effects of anxiety and hypomania. Findings provide empirical evidence for the long-assumed influence of depressive symptoms on future expectations. The present investigation has implications for affective forecasting studies examining information-processing constructs, decision making, and broader domains of psychopathology.

Affective Forecasting and Self-Rated Symptoms of Depression, Anxiety, and Hypomania: Evidence for a Dysphoric Forecasting Bias

Science.gov (United States)

Hoerger, Michael; Quirk, Stuart W.; Chapman, Benjamin P.; Duberstein, Paul R.

2011-01-01

Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problems. Pitting cognitive theory against depressive realism theory, we examined whether dysphoria was associated with negatively biased affective forecasts or greater accuracy. Participants (n = 325) supplied predicted and actual emotional reactions for three days surrounding an emotionally-evocative relational event, Valentine’s Day. Predictions were made a month prior to the holiday. Consistent with cognitive theory, we found evidence for a dysphoric forecasting bias – the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalizations of dysphoria, and three time points of observation. Similar biases were not observed in analyses examining the independent effects of anxiety and hypomania. Findings provide empirical evidence for the long assumed influence of depressive symptoms on future expectations. The present investigation has implications for affective forecasting studies examining information processing constructs, decision making, and broader domains of psychopathology. PMID:22397734

The United States and the Kurds: Case Studies in United States Engagement

National Research Council Canada - National Science Library

Lambert, Peter

1997-01-01

..., between 1969- 1975, and 1990-1996. Both eras saw the United States able to influence events relating to the Kurds in support of a larger regional policy, only to find no easy solution to the Kurdish quest for autonomy...

Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

Directory of Open Access Journals (Sweden)

Weide Li

2017-01-01

Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao

2016-01-01

Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.

United States rejoin ITER

International Nuclear Information System (INIS)

Roberts, M.

2003-01-01

Upon pressure from the United States Congress, the US Department of Energy had to withdraw from further American participation in the ITER Engineering Design Activities after the end of its commitment to the EDA in July 1998. In the years since that time, changes have taken place in both the ITER activity and the US fusion community's position on burning plasma physics. Reflecting the interest in the United States in pursuing burning plasma physics, the DOE's Office of Science commissioned three studies as part of its examination of the option of entering the Negotiations on the Agreement on the Establishment of the International Fusion Energy Organization for the Joint Implementation of the ITER Project. These were a National Academy Review Panel Report supporting the burning plasma mission; a Fusion Energy Sciences Advisory Committee (FESAC) report confirming the role of ITER in achieving fusion power production, and The Lehman Review of the ITER project costing and project management processes (for the latter one, see ITER CTA Newsletter, no. 15, December 2002). All three studies have endorsed the US return to the ITER activities. This historical decision was announced by DOE Secretary Abraham during his remarks to employees of the Department's Princeton Plasma Physics Laboratory. The United States will be working with the other Participants in the ITER Negotiations on the Agreement and is preparing to participate in the ITA

Prediction of winter wheat high yield from remote sensing based model: application in United States and Ukraine

Science.gov (United States)

Franch, B.; Vermote, E.; Roger, J. C.; Skakun, S.; Becker-Reshef, I.; Justice, C. O.

2017-12-01

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season and the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data. These methods were applied to MODIS CMG data in Ukraine, the US and China with errors around 10%. However, the NDVI is saturated for yield values higher than 4 MT/ha. As a consequence, the model had to be re-calibrated in each country and the validation of the national yields showed low correlation coefficients. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national yield of winter wheat in the United States and Ukraine from 2001 to 2016.

House Price Forecasts, Forecaster Herding, and the Recent Crisis

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Ruelke

2013-01-01

We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

United States Stateplane Zones - NAD83

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — U.S. State Plane Zones (NAD 1983) represents the State Plane Coordinate System (SPCS) Zones for the 1983 North American Datum within United States.

United States Stateplane Zones - NAD27

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — U.S. State Plane Zones (NAD 1927) represents the State Plane Coordinate System (SPCS) Zones for the 1927 North American Datum within United States.

United States-Mexican Borderlands: Facing tomorrow's challenges through USGS science

Science.gov (United States)

Updike, Randall G.; Ellis, Eugene G.; Page, William R.; Parker, Melanie J.; Hestbeck, Jay B.; Horak, William F.

2013-01-01

Along the nearly 3,200 kilometers (almost 2,000 miles) of the United States–Mexican border, in an area known as the Borderlands, we are witnessing the expression of the challenges of the 21st century. This circular identifies several challenge themes and issues associated with life and the environment in the Borderlands, listed below. The challenges are not one-sided; they do not originate in one country only to become problems for the other. The issues and concerns of each challenge theme flow in both directions across the border, and both nations feel their effects throughout the Borderlands and beyond. The clear message is that our two nations, the United States and Mexico, face the issues in these challenge themes together, and the U.S. Geological Survey (USGS) understands it must work with its counterparts, partners, and customers in both countries.Though the mission of the USGS is not to serve as land manager, law enforcer, or code regulator, its innovation and creativity and the scientific and technical depth of its capabilities can be directly applied to monitoring the conditions of the landscape. The ability of USGS scientists to critically analyze the monitored data in search of signals and trends, whether they lead to negative or positive results, allows us to reach significant conclusions—from providing factual conclusions to decisionmakers, to estimating how much of a natural resource exists in a particular locale, to predicting how a natural hazard phenomenon will unfold, to forecasting on a scale from hours to millennia how ecosystems will behave.None of these challenge themes can be addressed strictly by one or two science disciplines; all require well-integrated, cross-discipline thinking, data collection, and analyses. The multidisciplinary science themes that have become the focus of the USGS mission parallel the major challenges in the border region between Mexico and the United States. Because of this multidisciplinary approach, the USGS

Present state of electric power business in United States and Europe

International Nuclear Information System (INIS)

Onishi, Kenichi

2011-01-01

This article reported present state of nuclear power and electric power business in United States and Europe after Fukushima Daiichi Accident. As for the trend of demand and supply of electric power and policy, the accident forced Germany possibly to proceed with phase-out of nuclear power, but France and United States to sustain nuclear power with no great change of energy policy at this moment. As for the trend of electric power market, there was not state in United States with liberalized retail market of electric power after rolling blackouts occurred in California State in the early 2000s. In Germany proceeding with renewable energy introduction, renewable electricity fed into the grid was paid for by the network operators at fixed tariffs and the costs passed on to electricity consumers were increasing. Renewable Portfolio Standards (RPS) in United States forced the state to introduction of renewable energy to some ratio, and Feed-in Tariff (FIT) introduced in EU in 1990s lead to introduction of a large amount of renewable electricity targeted in 2020. Huge amount of wind power introduction brought about several problems to solve such that excess electric power above domestic demand had bad effects on grids in neighboring region. Enforcement of power transmission lines was also needed with increase of maximum electric power as well as introduction of a large amount of renewable electricity. (T. Tanaka)

What is the current state of scientific knowledge with regard to seasonal and decadal forecasting?

International Nuclear Information System (INIS)

Smith, Doug M; Scaife, Adam A; Kirtman, Ben P

2012-01-01

Environmental factors, such as the frequency, intensity and duration of extreme weather events, are important drivers of migration and displacement of people. There is therefore a growing need for regional climate predictions for the coming seasons to decades. This paper reviews the current state of the art of seasonal to decadal climate prediction, focusing on the potential sources of skill, forecasting techniques, current capability and future prospects. (letter)

House Price Forecasts, Forecaster Herding, and the Recent Crisis

Directory of Open Access Journals (Sweden)

Christian Pierdzioch

2012-11-01

Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

AREVA in the United States

International Nuclear Information System (INIS)

2007-01-01

In 2005, the United States had 297 million inhabitants (the 3. most populous country in the world) and a land area of 9.4 million km 2 (17 times larger than France). With a GDP of 10,996 billion dollars (under the economic conditions of the year 2000), the U.S. is the largest economic power in the world. It is also the largest consumer of energy, with primary energy consumption of 2,329 million metric tons, meaning that 25% of the world's energy is consumed by just 4% of its population. Although it has large domestic energy supplies, the U.S. is very far from achieving energy self-sufficiency. A decline of nearly 50% in oil production over a period of more than 30 years and the simultaneous stagnation of gas production have further weakened the U.S. energy balance. On a more general level, the increasing depletion of hydrocarbon resources (gas and oil), the concentration of the world's main resources in geo-politically unstable areas and the forecasted increase in the consumption and price of hydrocarbons, especially since 2005, mean that energy independence and supply security have become 2 of the top priorities of U.S. commercial and international policy. In 2007, the U.S. accounted for 22% of global CO 2 emissions, equaling those of China. In relation to population, the U.S. emits 8 metric tons/inhabitant compared to a world average of 4.2 metric tons/inhabitant. Although global warming is seen as a reality by the American public, it has only recently become a major argument in favor of a nuclear energy revival in the U.S. The context is, however, changing significantly. This is evidenced by America's adoption, in recent years, of measures to reduce greenhouse gases, particularly through the development of new, more environmentally friendly technologies. Since 2001, nearly 23 billion dollars in public funds have been devoted to climate research and the development of clean energy sources, notably renewable energies such as wind and solar, but also hydrogen and

Death in the United States, 2011

Science.gov (United States)

... Order from the National Technical Information Service NCHS Death in the United States, 2011 Recommend on Facebook ... 2011 SOURCE: National Vital Statistics System, Mortality. Do death rates vary by state? States experience different mortality ...

Forecasting the student–professor matches that result in unusually effective teaching

Science.gov (United States)

Gross, Jennifer; Lakey, Brian; Lucas, Jessica L; LaCross, Ryan; R Plotkowski, Andrea; Winegard, Bo

2015-01-01

Background Two important influences on students' evaluations of teaching are relationship and professor effects. Relationship effects reflect unique matches between students and professors such that some professors are unusually effective for some students, but not for others. Professor effects reflect inter-rater agreement that some professors are more effective than others, on average across students. Aims We attempted to forecast students' evaluations of live lectures from brief, video-recorded teaching trailers. Sample Participants were 145 college students (74% female) enrolled in introductory psychology courses at a public university in the Great Lakes region of the United States. Methods Students viewed trailers early in the semester and attended live lectures months later. Because subgroups of students viewed the same professors, statistical analyses could isolate professor and relationship effects. Results Evaluations were influenced strongly by relationship and professor effects, and students' evaluations of live lectures could be forecasted from students' evaluations of teaching trailers. That is, we could forecast the individual students who would respond unusually well to a specific professor (relationship effects). We could also forecast which professors elicited better evaluations in live lectures, on average across students (professor effects). Professors who elicited unusually good evaluations in some students also elicited better memory for lectures in those students. Conclusions It appears possible to forecast relationship and professor effects on teaching evaluations by presenting brief teaching trailers to students. Thus, it might be possible to develop online recommender systems to help match students and professors so that unusually effective teaching emerges. PMID:24953773

Forecasting global atmospheric CO2

International Nuclear Information System (INIS)

Agusti-Panareda, A.; Massart, S.; Boussetta, S.; Balsamo, G.; Beljaars, A.; Engelen, R.; Jones, L.; Peuch, V.H.; Chevallier, F.; Ciais, P.; Paris, J.D.; Sherlock, V.

2014-01-01

A new global atmospheric carbon dioxide (CO 2 ) real-time forecast is now available as part of the preoperational Monitoring of Atmospheric Composition and Climate - Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO 2 forecasting system is that the land surface, including vegetation CO 2 fluxes, is modelled online within the IFS. Other CO 2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO 2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO 2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO 2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO 2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO 2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO 2 fluxes also lead to accumulating errors in the CO 2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO 2 fluxes compared to total optimized fluxes and the atmospheric CO 2 compared to observations. The largest biases in the atmospheric CO 2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO 2 analyses based on the assimilation of CO 2 products retrieved from satellite

Visualization of ocean forecast in BYTHOS

Science.gov (United States)

Zhuk, E.; Zodiatis, G.; Nikolaidis, A.; Stylianou, S.; Karaolia, A.

2016-08-01

The Cyprus Oceanography Center has been constantly searching for new ideas for developing and implementing innovative methods and new developments concerning the use of Information Systems in Oceanography, to suit both the Center's monitoring and forecasting products. Within the frame of this scope two major online managing and visualizing data systems have been developed and utilized, those of CYCOFOS and BYTHOS. The Cyprus Coastal Ocean Forecasting and Observing System - CYCOFOS provides a variety of operational predictions such as ultra high, high and medium resolution ocean forecasts in the Levantine Basin, offshore and coastal sea state forecasts in the Mediterranean and Black Sea, tide forecasting in the Mediterranean, ocean remote sensing in the Eastern Mediterranean and coastal and offshore monitoring. As a rich internet application, BYTHOS enables scientists to search, visualize and download oceanographic data online and in real time. The recent improving of BYTHOS system is the extension with access and visualization of CYCOFOS data and overlay forecast fields and observing data. The CYCOFOS data are stored at OPENDAP Server in netCDF format. To search, process and visualize it the php and python scripts were developed. Data visualization is achieved through Mapserver. The BYTHOS forecast access interface allows to search necessary forecasting field by recognizing type, parameter, region, level and time. Also it provides opportunity to overlay different forecast and observing data that can be used for complex analyze of sea basin aspects.

31 CFR 103.39 - Person outside the United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Person outside the United States. 103... Person outside the United States. For the purposes of this subpart, a remittance or transfer of funds, or... the United States, shall be deemed to be a remittance or transfer to a person outside the United...

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

Science.gov (United States)

Chaney, Nathaniel W; Wood, Eric F; McBratney, Alexander B; Hempel, Jonathan W; Nauman, Travis; Brungard, Colby W.; Odgers, Nathan P

2016-01-01

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Forecasting in the presence of expectations

Science.gov (United States)

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.

Real-time data processing and inflow forecasting

International Nuclear Information System (INIS)

Olason, T.; Lafreniere, M.

1998-01-01

One of the key inputs into the short-term scheduling of hydroelectric generation is inflow forecasting which is needed for natural or unregulated inflows into various lakes, reservoirs and river sections. The forecast time step and time horizon are determined by the time step and the scheduling horizon. Acres International Ltd. has developed the Vista Decision Support System (DSS) in which the time step is one hour and the scheduling can be done up to two weeks into the future. This paper presents the basis of the operational flow-forecasting module of the Vista DSS software and its application to flow forecasting for 16 basins within Nova Scotia Power's hydroelectric system. Among the tasks performed by the software are collection and treatment of data (in real time) regarding meteorological forecasts, reviews and monitoring of hydro-meteorological data, updating of the state variables in the module, and the review and adjustment of sub-watershed forecasts

Energy problems of the United States

International Nuclear Information System (INIS)

Pertuzio, A.

2006-01-01

The united states are the third world producer of oil which accounts for 440% of world production and 20 million barrels/day of which 60% are imported. That dependence on imports is likely to increase in the next decades. Such supplies and their security are therefore a fundamental factor of the United States foreign policy in combination with their political, economic and strategic objectives in a world both unsure and dangerous

Household pesticide usage in the United States.

Science.gov (United States)

Savage, E P; Keefe, T J; Wheeler, H W; Mounce, L; Helwic, L; Applehans, F; Goes, E; Goes, T; Mihlan, G; Rench, J; Taylor, D K

1981-01-01

A total of 10,000 U.S. households in 25 standard metropolitan statistical areas and 25 counties were included in the United States. More than 8,200 households granted an interview. Nine of every ten households in the United States used some types of pesticide in their house, garden, or yard. Households in the southeastern United States used the most pesticides. Although more than 500 different pesticide formulations were used by the sampled households, 15 pesticides accounted for 65.5% of all pesticides reported in this study. Thirteen of these 15 pesticides were insecticides, one was a herbicide, and one was a rodenticide.

A Two-Dimensional Gridded Solar Forecasting System using Situation-Dependent Blending of Multiple Weather Models

Science.gov (United States)

Lu, S.; Hwang, Y.; Shao, X.; Hamann, H.

2015-12-01

Previously, we reported the application of a "weather situation" dependent multi-model blending approach to improve the forecast accuracy of solar irradiance and other atmospheric parameters. The approach uses machine-learning techniques to classify "weather situations" by a set of atmospheric parameters. The "weather situation" classification is location-dependent and each "weather situation" has characteristic forecast errors from a set of individual input numerical weather prediction (NWP) models. The input models are thus corrected or combined differently for different "weather situations" to minimize the overall forecast error. While the original implementation of the model-blending is applicable to only point-like locations having historical data of both measurements and forecasts, here we extend the approach to provide two-dimensional (2D) gridded forecasts. An experimental 2D forecasting system has been set up to provide gridded forecasts of solar irradiance (global horizontal irradiance), temperature, wind speed, and humidity for the contiguous United States (CONUS). Validation results show around 30% enhancement of 0 to 48 hour ahead solar irradiance forecast accuracy compared to the best input NWP model. The forecasting system may be leveraged by other site- or region-specific solar energy forecast products. To enable the 2D forecasting system, historical solar irradiance measurements from around 1,600 selected sites of the remote automated weather stations (RAWS) network have been employed. The CONUS was divided into smaller sub-regions, each containing a group of 10 to 20 RAWS sites. A group of sites, as classified by statistical analysis, have similar "weather patterns", i.e. the NWPs have similar "weather situation" dependent forecast errors for all sites in a group. The model-blending trained by the historical data from a group of sites is then applied for all locations in the corresponding sub-region. We discuss some key techniques developed for

Technology and demand forecasting for carbon capture and storage technology in South Korea

International Nuclear Information System (INIS)

Shin, Jungwoo; Lee, Chul-Yong; Kim, Hongbum

2016-01-01

Among the various alternatives available to reduce greenhouse gas (GHG) emissions, carbon capture and storage (CCS) is considered to be a prospective technology that could both improve economic growth and meet GHG emission reduction targets. Despite the importance of CCS, however, studies of technology and demand forecasting for CCS are scarce. This study bridges this gap in the body of knowledge on this topic by forecasting CCS technology and demand based on an integrated model. For technology forecasting, a logistic model and patent network analysis are used to compare the competitiveness of CCS technology for selected countries. For demand forecasting, a competition diffusion model is adopted to consider competition among renewable energies and forecast demand. The results show that the number of patent applications for CCS technology will increase to 16,156 worldwide and to 4,790 in Korea by 2025. We also find that the United States has the most competitive CCS technology followed by Korea and France. Moreover, about 5 million tCO_2e of GHG will be reduced by 2040 if CCS technology is adopted in Korea after 2020. - Highlights: • Carbon capture and storage (CCS) can help mitigate climate change globally. • It can both improve economic growth and meet GHG emission reduction targets. • We forecast CCS technology and demand based on an integrated model. • The US has the most competitive CCS technology followed by Korea and France. • 5 million tCO_2e of GHG will be reduced by 2040 if CCS is adopted in Korea.

Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting.

Science.gov (United States)

Schauberger, Bernhard; Gornott, Christoph; Wechsung, Frank

2017-11-01

Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical

Forecasting droughts in West Africa: Operational practice and refined seasonal precipitation forecasts

Science.gov (United States)

Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

2016-04-01

Precipitation forecasts for the upcoming rainy seasons are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform seasonal precipitation forecasts within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized seasonal precipitation forecasts for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation forecasts for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy season 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation forecasts from the Climate Forecast System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy season for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The forecasts of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO forecasts indicates skillful seasonal precipitation forecasts for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy season 2013 illustrates the high potential of a physically-based downscaling for this region but still shows

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 2: Technical report

Science.gov (United States)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Al-Kinani, G.

1983-08-01

The potential United States domestic telecommunications demand for satellite provided customer premises voice, data and video services through the year 2000 were forecast, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by Computer premises services systems, identification of that portion of the satellite market addressabble by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a market distribution model and a network optimization model. Forecasts were developed for; 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Climate Forecast System

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Web portal to all Federal, state and local government Web resources and services. The NCEP Climate when using the CFS Reanalysis (CFSR) data. Saha, Suranjana, and Coauthors, 2010: The NCEP Climate

75 FR 13345 - Pricing for Certain United States Mint Products

Science.gov (United States)

2010-03-19

... DEPARTMENT OF THE TREASURY United States Mint Pricing for Certain United States Mint Products AGENCY: United States Mint, Department of the Treasury. ACTION: Notice. SUMMARY: The United States Mint is announcing the price of First Spouse Bronze Medals and 2010 First Spouse Bronze Medal Series: Four...

22 CFR 22.3 - Remittances in the United States.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Remittances in the United States. 22.3 Section...-DEPARTMENT OF STATE AND FOREIGN SERVICE § 22.3 Remittances in the United States. (a) Type of remittance. Remittances shall be in the form of: (1) Check or bank draft drawn on a bank in the United States; (2) money...

Electrical Load Survey and Forecast for a Decentralized Hybrid ...

African Journals Online (AJOL)

Electrical Load Survey and Forecast for a Decentralized Hybrid Power System at Elebu, Kwara State, Nigeria. ... Nigerian Journal of Technology ... The paper reports the results of electrical load demand and forecast for Elebu rural community ...

Online updating procedures for a real-time hydrological forecasting system

International Nuclear Information System (INIS)

Kahl, B; Nachtnebel, H P

2008-01-01

Rainfall-runoff-models can explain major parts of the natural runoff pattern but never simulate the observed hydrograph exactly. Reasons for errors are various sources of uncertainties embedded in the model forecasting system. Errors are due to measurement errors, the selected time period for calibration and validation, the parametric uncertainty and the model imprecision. In on-line forecasting systems forecasted input data is used which additionally generates a major uncertainty for the hydrological forecasting system. Techniques for partially compensating these uncertainties are investigated in the recent study in a medium sized catchment in the Austrian part of the Danube basin. The catchment area is about 1000 km2. The forecasting system consists of a semi-distributed continuous rainfall-runoff model that uses quantitative precipitation and temperature forecasts. To provide adequate system states at the beginning of the forecasting period continuous simulation is required, especially in winter. In this study two online updating methods are used and combined for enhancing the runoff forecasts. The first method is used for updating the system states at the beginning of the forecasting period by changing the precipitation input. The second method is an autoregressive error model, which is used to eliminate systematic errors in the model output. In combination those two methods work together well as each method is more effective in different runoff situations.

Flood forecasting and uncertainty of precipitation forecasts

International Nuclear Information System (INIS)

Kobold, Mira; Suselj, Kay

2004-01-01

The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

The United Kingdom: Issues for the United States

National Research Council Canada - National Science Library

Archick, Kristin

2007-01-01

...; and more recently, from the UK's strong support in countering terrorism and confronting Iraq. The United States and Britain also share a mutually beneficial trade and economic relationship, and are each other's biggest foreign direct investors...

USA Nutrient managment forecasting via the "Fertilizer Forecaster": linking surface runnof, nutrient application and ecohydrology.

Science.gov (United States)

Drohan, Patrick; Buda, Anthony; Kleinman, Peter; Miller, Douglas; Lin, Henry; Beegle, Douglas; Knight, Paul

2017-04-01

USA and state nutrient management planning offers strategic guidance that strives to educate farmers and those involved in nutrient management to make wise management decisions. A goal of such programs is to manage hotspots of water quality degradation that threaten human and ecosystem health, water and food security. The guidance provided by nutrient management plans does not provide the day-to-day support necessary to make operational decisions, particularly when and where to apply nutrients over the short term. These short-term decisions on when and where to apply nutrients often make the difference between whether the nutrients impact water quality or are efficiently utilized by crops. Infiltrating rainfall events occurring shortly after broadcast nutrient applications are beneficial, given they will wash soluble nutrients into the soil where they are used by crops. Rainfall events that generate runoff shortly after nutrients are broadcast may wash off applied nutrients, and produce substantial nutrient losses from that site. We are developing a model and data based support tool for nutrient management, the Fertilizer Forecaster, which identifies the relative probability of runoff or infiltrating events in Pennsylvania (PA) landscapes in order to improve water quality. This tool will support field specific decisions by farmers and land managers on when and where to apply fertilizers and manures over 24, 48 and 72 hour periods. Our objectives are to: (1) monitor agricultural hillslopes in watersheds representing four of the five Physiographic Provinces of the Chesapeake Bay basin; (2) validate a high resolution mapping model that identifies soils prone to runoff; (3) develop an empirically based approach to relate state-of-the-art weather forecast variables to site-specific rainfall infiltration or runoff occurrence; (4) test the empirical forecasting model against alternative approaches to forecasting runoff occurrence; and (5) recruit farmers from the four

Assessing Variability and Errors in Historical Runoff Forecasting with Physical Models and Alternative Data Sources

Science.gov (United States)

Penn, C. A.; Clow, D. W.; Sexstone, G. A.

2017-12-01

Water supply forecasts are an important tool for water resource managers in areas where surface water is relied on for irrigating agricultural lands and for municipal water supplies. Forecast errors, which correspond to inaccurate predictions of total surface water volume, can lead to mis-allocated water and productivity loss, thus costing stakeholders millions of dollars. The objective of this investigation is to provide water resource managers with an improved understanding of factors contributing to forecast error, and to help increase the accuracy of future forecasts. In many watersheds of the western United States, snowmelt contributes 50-75% of annual surface water flow and controls both the timing and volume of peak flow. Water supply forecasts from the Natural Resources Conservation Service (NRCS), National Weather Service, and similar cooperators use precipitation and snowpack measurements to provide water resource managers with an estimate of seasonal runoff volume. The accuracy of these forecasts can be limited by available snowpack and meteorological data. In the headwaters of the Rio Grande, NRCS produces January through June monthly Water Supply Outlook Reports. This study evaluates the accuracy of these forecasts since 1990, and examines what factors may contribute to forecast error. The Rio Grande headwaters has experienced recent changes in land cover from bark beetle infestation and a large wildfire, which can affect hydrological processes within the watershed. To investigate trends and possible contributing factors in forecast error, a semi-distributed hydrological model was calibrated and run to simulate daily streamflow for the period 1990-2015. Annual and seasonal watershed and sub-watershed water balance properties were compared with seasonal water supply forecasts. Gridded meteorological datasets were used to assess changes in the timing and volume of spring precipitation events that may contribute to forecast error. Additionally, a

Radiation therapy facilities in the United States

International Nuclear Information System (INIS)

Ballas, Leslie K.; Elkin, Elena B.; Schrag, Deborah; Minsky, Bruce D.; Bach, Peter B.

2006-01-01

Purpose: About half of all cancer patients in the United States receive radiation therapy as a part of their cancer treatment. Little is known, however, about the facilities that currently deliver external beam radiation. Our goal was to construct a comprehensive database of all radiation therapy facilities in the United States that can be used for future health services research in radiation oncology. Methods and Materials: From each state's health department we obtained a list of all facilities that have a linear accelerator or provide radiation therapy. We merged these state lists with information from the American Hospital Association (AHA), as well as 2 organizations that audit the accuracy of radiation machines: the Radiologic Physics Center (RPC) and Radiation Dosimetry Services (RDS). The comprehensive database included all unique facilities listed in 1 or more of the 4 sources. Results: We identified 2,246 radiation therapy facilities operating in the United States as of 2004-2005. Of these, 448 (20%) facilities were identified through state health department records alone and were not listed in any other data source. Conclusions: Determining the location of the 2,246 radiation facilities in the United States is a first step in providing important information to radiation oncologists and policymakers concerned with access to radiation therapy services, the distribution of health care resources, and the quality of cancer care

Using snow data assimilation to improve ensemble streamflow forecasting for the Upper Colorado River Basin

Science.gov (United States)

Micheletty, P. D.; Perrot, D.; Day, G. N.; Lhotak, J.; Quebbeman, J.; Park, G. H.; Carney, S.

2017-12-01

Water supply forecasting in the western United States is inextricably linked to snowmelt processes, as approximately 70-85% of total annual runoff comes from water stored in seasonal mountain snowpacks. Snowmelt-generated streamflow is vital to a variety of downstream uses; the Upper Colorado River Basin (UCRB) alone provides water supply for 25 million people, irrigation water for 3.5 million acres, and drives hydropower generation at Lake Powell. April-July water supply forecasts produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC) are critical to basin water management. The primary objective of this project as part of the NASA Water Resources Applied Science Program, is to improve water supply forecasting for the UCRB by assimilating satellite and ground snowpack observations into a distributed hydrologic model at various times during the snow accumulation and melt seasons. To do this, we have built a framework that uses an Ensemble Kalman Filter (EnKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with spatially interpolated SNOTEL snow water equivalent (SWE) observations and products from the MODIS Snow Covered-Area and Grain size retrieval algorithm (when available). We have generated April-July water supply reforecasts for a 20-year period (1991-2010) for several headwater catchments in the UCRB using HL-RDHM and snow data assimilation in the Ensemble Streamflow Prediction (ESP) framework. The existing CBRFC ESP reforecasts will provide a baseline for comparison to determine whether the data assimilation process adds skill to the water supply forecasts. Preliminary results from one headwater basin show improved skill in water supply forecasting when HL-RDHM is run with the data assimilation step compared to HL-RDHM run without the data assimilation step, particularly in years when MODSCAG data were available (2000-2010). The final

75 FR 13345 - Pricing for Certain 2010 United States Mint Products

Science.gov (United States)

2010-03-19

... DEPARTMENT OF THE TREASURY United States Mint Pricing for Certain 2010 United States Mint Products AGENCY: United States Mint, Department of the Treasury. ACTION: Notice. SUMMARY: The United States Mint is announcing the price of the 2010 United States Mint Presidential $1 Coin and First Spouse Medal...

Intergenerational educational mobility in Denmark and the United States

DEFF Research Database (Denmark)

Andrade, Stefan Bastholm; Thomsen, Jens-Peter

2018-01-01

An overall finding in comparative mobility studies is that intergenerational mobility is greater in Scandinavia than in liberal welfare-state countries like the United States and United Kingdom. However, in a recent study, Landersø and Heckman (L & H) (2017) argue that intergenerational educational...... mobility in Denmark and the United States is remarkably similar. L & H’s findings run contrary to widespread beliefs and have been echoed in academia and mass media on both sides of the Atlantic Ocean. In this article, we reanalyze educational mobility in Denmark and the United States using the same data...... sources as L & H. We apply several different methodological approaches from economics and sociology, and we consistently find that educational mobility is higher in Denmark than in the United States....

Toll Facilities in the United States

Data.gov (United States)

Department of Transportation — Biennial report containing selected information on toll facilities in the United States that has been provided to FHWA by the States and/or various toll authorities...

Improving seasonal forecasts of hydroclimatic variables through the state of multiple large-scale climate signals

Science.gov (United States)

Castelletti, A.; Giuliani, M.; Block, P. J.

2017-12-01

Increasingly uncertain hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite modern forecasts are skillful over short lead time (from hours to days), predictability generally tends to decrease on longer lead times. Global climate teleconnection, such as El Niño Southern Oscillation (ENSO), may contribute in extending forecast lead times. However, ENSO teleconnection is well defined in some locations, such as Western USA and Australia, while there is no consensus on how it can be detected and used in other regions, particularly in Europe, Africa, and Asia. In this work, we generalize the Niño Index Phase Analysis (NIPA) framework by contributing the Multi Variate Niño Index Phase Analysis (MV-NIPA), which allows capturing the state of multiple large-scale climate signals (i.e. ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multi-decadal Oscillation, Indian Ocean Dipole) to forecast hydroclimatic variables on a seasonal time scale. Specifically, our approach distinguishes the different phases of the considered climate signals and, for each phase, identifies relevant anomalies in Sea Surface Temperature (SST) that influence the local hydrologic conditions. The potential of the MV-NIPA framework is demonstrated through an application to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Numerical results show high correlations between seasonal SST values and one season-ahead precipitation in the Lake Como basin. The skill of the resulting MV-NIPA forecast outperforms the one of ECMWF products. This information represents a valuable contribution to partially anticipate the summer water availability, especially during drought events, ultimately supporting the improvement of the Lake Como

State nuclear initiatives in the United States

International Nuclear Information System (INIS)

Strauss, P.L.; Stoiber, C.R.

1977-01-01

The paper deals with State nuclear initiatives regarding the role of nuclear power in the energy future of the United States. The question of whether and under what circumstances nuclear facilities should be used to generate electricity was put to the popular vote in several States in 1976. Some general principles of Federal-State relations are discussed with specific reference to nuclear regulations. The initiative mechanism itself is described as well as its legal form and background. The parallel developments in the State and Federal legislative consideration of nuclear issues is reviewed and the suggested reasons for the defeat of the proposals in the seven States concerned are discussed. Finally, the author draws some conclusions on the effects of the 1976 initiatives on future decision-making in the US on energy policy in general and nuclear power in particular. (NEA) [fr

Forecasting daily political opinion polls using the fractionally cointegrated VAR model

DEFF Research Database (Denmark)

Nielsen, Morten Ørregaard; Shibaev, Sergei S.

We examine forecasting performance of the recent fractionally cointegrated vector autoregressive (FCVAR) model. We use daily polling data of political support in the United Kingdom for 2010-2015 and compare with popular competing models at several forecast horizons. Our findings show that the four...... trend from the model follows the vote share of the UKIP very closely, and we thus interpret it as a measure of Euro-skepticism in public opinion rather than an indicator of the more traditional left-right political spectrum. In terms of prediction of vote shares in the election, forecasts generated...... variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated VAR (CVAR) model at all forecast horizons. The relative forecast improvement...

27 CFR 479.89 - Transfers to the United States.

Science.gov (United States)

2010-04-01

... Transfers to the United States. A firearm may be transferred to the United States or any department... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Transfers to the United States. 479.89 Section 479.89 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO...

Forecasting of cash flow from an enterprise’s principal activities

OpenAIRE

Kanapickienė, Rasa; Šlekienė, Vaida

2008-01-01

The article deals with the forecasting of a cash flow from primary activities of an enterprise. Different mathematical methods are applied to forecast cash flow. Cash flow forecasts are often introduced in the project of the enterprise budget. Some scientific sources state that it is possible to forecast cash flow from primary activities of an enterprise according to its historical financial data by the means of regression analysis. These sources suggest various models of regression analysis ...

32 CFR 516.54 - Witnesses for the United States.

Science.gov (United States)

2010-07-01

... 32 National Defense 3 2010-07-01 2010-07-01 true Witnesses for the United States. 516.54 Section..., Travel, and Expenses of Witnesses § 516.54 Witnesses for the United States. (a) Status of witness. A military member authorized to appear as a witness for the United States, including those authorized to...

32 CFR 150.21 - Appeals by the United States.

Science.gov (United States)

2010-07-01

... 32 National Defense 1 2010-07-01 2010-07-01 false Appeals by the United States. 150.21 Section 150... the United States. (a) Restricted filing. Only a representative of the government designated by the Judge Advocate General of the respective service may file an appeal by the United States under Article...

United States housing, first quarter 2013

Science.gov (United States)

Delton Alderman

2014-01-01

Provides current and historical information on housing market in the United States. Information includes trends for housing permits and starts, housing under construction, and housing completions for single and multifamily units, and sales and construction. This report will be updated regularly.

Wind forecasting for grid code compliance

Energy Technology Data Exchange (ETDEWEB)

Vanitha, V.; Kishore, S.R.N. [Amrita Vishwa Vidyapeetham Univ.. Dept. of Electrical and Electronics Engineering, Coimbatore (India)

2012-07-01

This work explores Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the average hourly wind speed. To determine the characteristics of ANFIS that best suited the target wind speed forecasting system, several ANFIS models were trained, tested and compared. Different types and number of inputs, training and checking sizes, type and number of membership functions and techniques to generate the initial (FIS) were analyzed. Comparisons with other forecasting methods were analyzed the models were given wind speed, direction and air pressure as inputs having the best forecasting accuracy. SCADA system is utilized to obtain the wind speed to the forecasting system in the host computer where ANFIS is present. The SCADA is located in the central room, the substation of the wind farm, or even at a remote off site point. The data obtained from the site is plotted at every instant and the predicted wind speed is displayed and also exported to the excel sheet which will be sent/e-mailed in the form of Graphs and excel sheets to the operator, State load dispatch centre (SLDC) and to the customer. (Author)

Modeling and forecasting petroleum futures volatility

International Nuclear Information System (INIS)

Sadorsky, Perry

2006-01-01

Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)

Forecast Mekong 2012: Building scientific capacity

Science.gov (United States)

Stefanov, James E.

2012-01-01

In 2009, U.S. Secretary of State Hillary R. Clinton joined the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam in launching the Lower Mekong Initiative to enhance U.S. engagement with the countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey Forecast Mekong supports the Lower Mekong Initiative through a variety of activities. The principal objectives of Forecast Mekong include the following: * Build scientific capacity in the Lower Mekong Basin and promote cooperation and collaboration among scientists working in the region. * Provide data, information, and scientific models to help resource managers there make informed decisions. * Produce forecasting and visualization tools to support basin planning, including climate change adaptation. The focus of this product is Forecast Mekong accomplishments and current activities related to the development of scientific capacity at organizations and institutions in the region. Building on accomplishments in 2010 and 2011, Forecast Mekong continues to enhance scientific capacity in the Lower Mekong Basin with a suite of activities in 2012.

State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

Directory of Open Access Journals (Sweden)

O. Rakovec

2012-09-01

Full Text Available This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property.

Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km², a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1 various sets of the spatially distributed discharge gauges and (2 the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.

On the episodic nature of derecho-producing convective systems in the United States

Science.gov (United States)

Ashley, Walker S.; Mote, Thomas L.; Bentley, Mace L.

2005-11-01

Convectively generated windstorms occur over broad temporal and spatial scales; however, one of the larger-scale and most intense of these windstorms has been given the name derecho. This study illustrates the tendency for derecho-producing mesoscale convective systems to group together across the United States - forming a derecho series. The derecho series is recognized as any succession of derechos that develop within a similar synoptic environment with no more than 72 h separating individual events. A derecho dataset for the period 1994-2003 was assembled to investigate the groupings of these extremely damaging convective wind events. Results indicate that over 62% of the derechos in the dataset were members of a derecho series. On average, nearly six series affected the United States annually. Most derecho series consisted of two or three events; though, 14 series during the period of record contained four or more events. Two separate series involved nine derechos within a period of nine days. Analyses reveal that derecho series largely frequent regions of the Midwest, Ohio Valley, and the south-central Great Plains during May, June, and July. Results suggest that once a derecho occurred during May, June, or July, there was a 58% chance that this event was the first of a series of two or more, and about a 46% chance that this was the first of a derecho series consisting of three or more events. The derecho series climatology reveals that forecasters in regions frequented by derechos should be prepared for the probable regeneration of a derecho-producing convective system after an initial event occurs. Copyright

A state-dependent model for inflation forecasting

OpenAIRE

Andrea Stella; James H. Stock

2012-01-01

We develop a parsimonious bivariate model of inflation and unemployment that allows for persistent variation in trend inflation and the NAIRU. The model, which consists of five unobserved components (including the trends) with stochastic volatility, implies a time-varying VAR for changes in the rates of inflation and unemployment. The implied backwards-looking Phillips curve has a time-varying slope that is steeper in the 1970s than in the 1990s. Pseudo out-of-sample forecasting experiments i...

Multi-parametric variational data assimilation for hydrological forecasting

Science.gov (United States)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

Arsenic in Ground Water of the United States

Science.gov (United States)

... Team More Information Arsenic in groundwater of the United States Arsenic in groundwater is largely the result of ... Gronberg (2011) for updated arsenic map. Featured publications United States Effects of human-induced alteration of groundwater flow ...

Forecast Combinations

OpenAIRE

Timmermann, Allan G

2005-01-01

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

Forecaster Behaviour and Bias in Macroeconomic Forecasts

OpenAIRE

Roy Batchelor

2007-01-01

This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

Enrichment situation outside the United States

International Nuclear Information System (INIS)

Anon.

1979-01-01

Different enrichment technologies are briefly characterized which include gaseous diffusion, which is presently the production mainstay of the United States and France; the gaseous centrifuge which is the production plant for Urenco and the technology for future United States enrichment expansion; the aero-dynamic processes which include the jet nozzle (also known as the Becker process) and the fixed-wall centrifuge (also known as the Helikon process); chemical processes; laser isotope separation processes (also referred to in the literature as LIS); and plasma technology

Leading Causes of Death in Females United States

Science.gov (United States)

... and Health Issues at Work Health Equity Leading Causes of Death in Females, United States Recommend on Facebook Tweet ... to current and previous listings for the leading causes of death in females in the United States. Please note ...

Forecast of the installed capacity for renewable energy installations and its influence on the grid extensions in the State of Brandenburg

Energy Technology Data Exchange (ETDEWEB)

Schwarz, Harald; Pfeiffer, Klaus [Brandenburgische Technische Univ. Cottbus (Germany); Zeidler, Jens [MITNETZ Strom, Halle/Saale (Germany); Schulz, Stephan [50Hertz Transmission, Berlin (Germany); Dorendorf, Stefan [E.ON edis, Fuerstenwalde (Germany)

2012-07-01

Development of installations for renewable electrical generation forced by the Federal Government of Germany causes an increased expansion of wind, photovoltaic and biomass installations, especially in the plain states, such as Brandenburg in Germany. Therefore, a study on grid integration of renewable energy in the state of Brandenburg was commissioned on behalf of the Brandenburg Ministry of Economics and European Affairs. The work tasks of the study, on the one hand, consisted of comprehensive forecast for the renewable energy sources such as wind, biomass and photovoltaic. On the other hand, grid calculations for the evaluation of plausibility of the existing grid extension concepts of network operators in Brandenburg were conducted based on this forecast. The results of this study are to be presented to general public in this contribution. (orig.)

Forecast combinations

OpenAIRE

Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

2010-01-01

We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

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

Science.gov (United States)

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.

33 CFR 2.38 - Waters subject to the jurisdiction of the United States; waters over which the United States has...

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Waters subject to the jurisdiction of the United States; waters over which the United States has jurisdiction. 2.38 Section 2.38 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY GENERAL JURISDICTION...

Unites States and the oil of the Middle-East

International Nuclear Information System (INIS)

Noel, P.

2005-08-01

The author discusses different aspects of the United States intervention and behavior in the Middle-East petroleum management. The Iraq and Iran potentials are largely under used. The Saudi Arabia defines its own oil policy, but benefits of the Unites States military help. The United States intervention is in the domain of the security of flux on the world market. (A.L.B.)

Technology data characterizing refrigeration in commercial buildings: Application to end-use forecasting with COMMEND 4.0

Energy Technology Data Exchange (ETDEWEB)

Sezgen, O.; Koomey, J.G.

1995-12-01

In the United States, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of the refrigeration end use in terms of specific technologies, however, is complicated by several factors. First, the number of configurations of refrigeration cases and systems is quite large. Also, energy use is a complex function of the refrigeration-case properties and the refrigeration-system properties. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. Expanding end-use forecasting models so that they address individual technology options requires characterization of the present floorstock in terms of service requirements, energy technologies used, and cost-efficiency attributes of the energy technologies that consumers may choose for new buildings and retrofits. This report describes the process by which we collected refrigeration technology data. The data were generated for COMMEND 4.0 but are also generally applicable to other end-use forecasting frameworks for the commercial sector.

AREVA in the United States

Energy Technology Data Exchange (ETDEWEB)

NONE

2007-07-01

In 2005, the United States had 297 million inhabitants (the 3. most populous country in the world) and a land area of 9.4 million km{sup 2} (17 times larger than France). With a GDP of 10,996 billion dollars (under the economic conditions of the year 2000), the U.S. is the largest economic power in the world. It is also the largest consumer of energy, with primary energy consumption of 2,329 million metric tons, meaning that 25% of the world's energy is consumed by just 4% of its population. Although it has large domestic energy supplies, the U.S. is very far from achieving energy self-sufficiency. A decline of nearly 50% in oil production over a period of more than 30 years and the simultaneous stagnation of gas production have further weakened the U.S. energy balance. On a more general level, the increasing depletion of hydrocarbon resources (gas and oil), the concentration of the world's main resources in geo-politically unstable areas and the forecasted increase in the consumption and price of hydrocarbons, especially since 2005, mean that energy independence and supply security have become 2 of the top priorities of U.S. commercial and international policy. In 2007, the U.S. accounted for 22% of global CO{sub 2} emissions, equaling those of China. In relation to population, the U.S. emits 8 metric tons/inhabitant compared to a world average of 4.2 metric tons/inhabitant. Although global warming is seen as a reality by the American public, it has only recently become a major argument in favor of a nuclear energy revival in the U.S. The context is, however, changing significantly. This is evidenced by America's adoption, in recent years, of measures to reduce greenhouse gases, particularly through the development of new, more environmentally friendly technologies. Since 2001, nearly 23 billion dollars in public funds have been devoted to climate research and the development of clean energy sources, notably renewable energies such as wind and solar

Food irradiation in the United States

International Nuclear Information System (INIS)

Pauli, G.H.

1991-01-01

Since 1963, some irradiated foods have been permitted for sale in the United States. Yet, at this time, commercial application has been limited to irradiation of a relatively small fraction of the spices and seasonings used as ingredients in other foods. The current situation regarding irradiated foods in the United States and how it developed is discussed. The author writes from experience gained as a Government regulator concerned primarily with ensuring safety of food and therefore this is stressed together with the crucial role played by consumers and industry. (author)

System of the Wind Wave Operational Forecast by the Black Sea Marine Forecast Center

Directory of Open Access Journals (Sweden)

Yu.B. Ratner

2017-10-01

Full Text Available System of the wind wave operational forecast in the Black Sea based on the SWAN (Simulating Waves Nearshore numerical spectral model is represented. In the course of the system development the SWAN model was adapted to take into account the features of its operation at the Black Sea Marine Forecast Center. The model input-output is agreed with the applied nomenclature and the data representation formats. The user interface for rapid access to simulation results was developed. The model adapted to wave forecast in the Black Sea in a quasi-operational mode, is validated for 2012–2015. Validation of the calculation results was carried out for all five forecasting terms based on the analysis of two-dimensional graphs of the wave height distribution derived from the data of prognostic calculations and remote measurements obtained with the altimeter installed on the Jason-2 satellite. Calculation of the statistical characteristics of the deviations between the wave height prognostic values and the data of their measurements from the Jason-2 satellite, as well as a regression analysis of the relationship between the forecasted and measured wave heights was additionally carried out. A comparison of the results obtained with the similar results reported in the works of other authors published in 2009–2016 showed their satisfactory compliance with each other. The forecasts carried out by the authors for the Black Sea as well as those obtained for the other World Ocean regions show that the current level of numerical methods for sea wave forecasting is in full compliance with the requirements of specialists engaged in studying and modeling the state of the ocean and the atmosphere, as well as the experts using these results for solving applied problems.

The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States

Science.gov (United States)

Horton, John D.; San Juan, Carma A.; Stoeser, Douglas B.

2017-06-30

The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (https://doi. org/10.5066/F7WH2N65) represents a seamless, spatial database of 48 State geologic maps that range from 1:50,000 to 1:1,000,000 scale. A national digital geologic map database is essential in interpreting other datasets that support numerous types of national-scale studies and assessments, such as those that provide geochemistry, remote sensing, or geophysical data. The SGMC is a compilation of the individual U.S. Geological Survey releases of the Preliminary Integrated Geologic Map Databases for the United States. The SGMC geodatabase also contains updated data for seven States and seven entirely new State geologic maps that have been added since the preliminary databases were published. Numerous errors have been corrected and enhancements added to the preliminary datasets using thorough quality assurance/quality control procedures. The SGMC is not a truly integrated geologic map database because geologic units have not been reconciled across State boundaries. However, the geologic data contained in each State geologic map have been standardized to allow spatial analyses of lithology, age, and stratigraphy at a national scale.

A hybrid wind power forecasting model based on data mining and wavelets analysis

International Nuclear Information System (INIS)

Azimi, R.; Ghofrani, M.; Ghayekhloo, M.

2016-01-01

Highlights: • An improved version of K-means algorithm is proposed for clustering wind data. • A persistence based method is applied to select the best cluster for NN training. • A combination of DWT and HANTS methods is used to provide a deep learning for NN. • A hybrid of T.S.B K-means, DWT and HANTS and NN is developed for wind forecasting. - Abstract: Accurate forecasting of wind power plays a key role in energy balancing and wind power integration into the grid. This paper proposes a novel time-series based K-means clustering method, named T.S.B K-means, and a cluster selection algorithm to better extract features of wind time-series data. A hybrid of T.S.B K-means, discrete wavelet transform (DWT) and harmonic analysis time series (HANTS) methods, and a multilayer perceptron neural network (MLPNN) is developed for wind power forecasting. The proposed T.S.B K-means classifies data into separate groups and leads to more appropriate learning for neural networks by identifying anomalies and irregular patterns. This improves the accuracy of the forecast results. A cluster selection method is developed to determine the cluster that provides the best training for the MLPNN. This significantly accelerates the forecast process as the most appropriate portion of the data rather than the whole data is used for the NN training. The wind power data is decomposed by the Daubechies D4 wavelet transform, filtered by the HANTS, and pre-processed to provide the most appropriate inputs for the MLPNN. Time-series analysis is used to pre-process the historical wind-power generation data and structure it into input-output series. Wind power datasets with diverse characteristics, from different wind farms located in the United States, are used to evaluate the accuracy of the hybrid forecasting method through various performance measures and different experiments. A comparative analysis with well-established forecasting models shows the superior performance of the proposed

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

Science.gov (United States)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate

Responses to task 1 questionnaire of INFCE Working Group 6 supplied by participating states

International Nuclear Information System (INIS)

Responses to Task 1 Questionnaire of INFCE Working Group 6 supplied by participating states (Argentina, Australia, Austria, Belgium, Canada, Finland, France, Federal Republic of Germany, Italy, Japan, Netherlands, Portugal, Spain, Sweden, Switzerland, Turkey, USSR, United Kingdom, United States, Yugoslavia). Data and information are given on nuclear power forecast, spent fuel requirements for AR and AFR storage, current programmes for storage, future spent fuel disposition plans and transport

Deep Neural Network Based Demand Side Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Seunghyoung Ryu

2016-12-01

Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

Wind power forecasting accuracy and uncertainty in Finland

Energy Technology Data Exchange (ETDEWEB)

Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

2013-04-15

Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

Short-term Inundation Forecasting for Tsunamis Version 4.0 Brings Forecasting Speed, Accuracy, and Capability Improvements to NOAA's Tsunami Warning Centers

Science.gov (United States)

Sterling, K.; Denbo, D. W.; Eble, M. C.

2016-12-01

Short-term Inundation Forecasting for Tsunamis (SIFT) software was developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) for use in tsunami forecasting and has been used by both U.S. Tsunami Warning Centers (TWCs) since 2012, when SIFTv3.1 was operationally accepted. Since then, advancements in research and modeling have resulted in several new features being incorporated into SIFT forecasting. Following the priorities and needs of the TWCs, upgrades to SIFT forecasting were implemented into SIFTv4.0, scheduled to become operational in October 2016. Because every minute counts in the early warning process, two major time saving features were implemented in SIFT 4.0. To increase processing speeds and generate high-resolution flooding forecasts more quickly, the tsunami propagation and inundation codes were modified to run on Graphics Processing Units (GPUs). To reduce time demand on duty scientists during an event, an automated DART inversion (or fitting) process was implemented. To increase forecasting accuracy, the forecasted amplitudes and inundations were adjusted to include dynamic tidal oscillations, thereby reducing the over-estimates of flooding common in SIFTv3.1 due to the static tide stage conservatively set at Mean High Water. Further improvements to forecasts were gained through the assimilation of additional real-time observations. Cabled array measurements from Bottom Pressure Recorders (BPRs) in the Oceans Canada NEPTUNE network are now available to SIFT for use in the inversion process. To better meet the needs of harbor masters and emergency managers, SIFTv4.0 adds a tsunami currents graphical product to the suite of disseminated forecast results. When delivered, these new features in SIFTv4.0 will improve the operational tsunami forecasting speed, accuracy, and capabilities at NOAA's Tsunami Warning Centers.

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge

Exporting Rambutan to United States: One Reality?

International Nuclear Information System (INIS)

Ahmad Zainuri Mohd Dzomir; Zainon Othman; Mohd Sidek Othman

2011-01-01

Rambutan is a one of commodity that are passed by United States of America authority to be market in that states. The main condition for the approval is the exporter must use irradiation technology as quarantine treatment to monitor the insects in there. United States of America's Agriculture Department (USDA-APHIS) has make early survey to the facilities involved in exporting process chain to overview Malaysia preparedness for this purpose. This paper work will discussed the possibility of this exporting implemented based on conditions rule by the USDA. (author)

Multiple-Decrement Compositional Forecasting with the Lee-Carter Model

OpenAIRE

Guan, Tianyu

2014-01-01

Changes in cause of death patterns have a great impact on health and social care costs paid by government and insurance companies. Unfortunately an overwhelming majority of methods for mortality projections is based on overall mortality with only very few studies focusing on forecasting cause-specific mortality. In this project, our aim is to forecast cause-specific death density with a coherent model. Since cause-specific death density obeys a unit sum constraint, it can be considered as com...

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

Directory of Open Access Journals (Sweden)

Erick López

2018-02-01

Full Text Available Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM and Echo State Network (ESN, have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i the hidden layer is trained with a descending gradient method online using one epoch; (ii the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii, a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used.

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

Science.gov (United States)

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.

Hybrid empirical mode decomposition- ARIMA for forecasting exchange rates

Science.gov (United States)

Abadan, Siti Sarah; Shabri, Ani; Ismail, Shuhaida

2015-02-01

This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian financial crisis causing the exchange rates are fixed to 3.800 from 2nd of September 1998 until 21st of July 2005. Thus, the chosen data in this paper is the post-July 2005 data, starting from August 2005 to July 2010. The comparative study using root mean square error (RMSE) and mean absolute error (MAE) showed that the EMD-ARIMA outperformed the single-ARIMA and the random walk benchmark model.

Wheat rusts in the United States in 2016

Science.gov (United States)

In 2016, wheat stripe rust caused by Puccinia striiformis f. sp. graminis was widespread throughout the United States. Cool temperatures and abundant rainfall in the southern Great Plains allowed stripe rust to become widely established and spread throughout the Great Plains and eastern United State...

Satellite provided customer promises services, a forecast of potential domestic demand through the year 2000. Volume 4: Sensitivity analysis

Science.gov (United States)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1984-03-01

The overall purpose was to forecast the potential United States domestic telecommunications demand for satellite provided customer promises voice, data and video services through the year 2000, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: (1) development of a forecast of the total domestic telecommunications demand; (2) identification of that portion of the telecommunications demand suitable for transmission by satellite systems; (3) identification of that portion of the satellite market addressable by consumer promises service (CPS) systems; (4) identification of that portion of the satellite market addressable by Ka-band CPS system; and (5) postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Forecasting Western U.S. Snowpack

Science.gov (United States)

Kapnick, S. B.; Yang, X.; Vecchi, G. A.; Delworth, T. L.; Gudgel, R.; Malyshev, S.; Milly, C.; Shevliakova, E.; Underwood, S.; Margulis, S. A.

2017-12-01

Cold season mountain snow accumulation in the western United States plays a critical role in regional hydroclimate and water supply. While climate projections provide estimates of future snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions and hazards out to two weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), particularly beyond 6 months. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate our dynamical system's feasibility of seasonal snowpack predictions and quantify the limits of predictive skill more than 2 seasons in advance for snowpack—snow that accumulates on the ground in the mountains. Our ability to predict snowpack is reliant on both temperature and precipitation prediction skill modulating both the amount of frozen precipitation that falls and how much snow accumulates and stays on the ground throughout the season. We will quantify prediction skill and outline areas necessary for the future advancement of seasonal hydroclimate prediction.

Short-term wind power combined forecasting based on error forecast correction

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

United States National Seismographic Network

International Nuclear Information System (INIS)

Buland, R.

1993-09-01

The concept of a United States National Seismograph Network (USNSN) dates back nearly 30 years. The idea was revived several times over the decades. but never funded. For, example, a national network was proposed and discussed at great length in the so called Bolt Report (U. S. Earthquake Observatories: Recommendations for a New National Network, National Academy Press, Washington, D.C., 1980, 122 pp). From the beginning, a national network was viewed as augmenting and complementing the relatively dense, predominantly short-period vertical coverage of selected areas provided by the Regional Seismograph Networks (RSN's) with a sparse, well-distributed network of three-component, observatory quality, permanent stations. The opportunity finally to begin developing a national network arose in 1986 with discussions between the US Geological Survey (USGS) and the Nuclear Regulatory Commission (NRC). Under the agreement signed in 1987, the NRC has provided $5 M in new funding for capital equipment (over the period 1987-1992) and the USGS has provided personnel and facilities to develop. deploy, and operate the network. Because the NRC funding was earmarked for the eastern United States, new USNSN station deployments are mostly east of 105 degree W longitude while the network in the western United States is mostly made up of cooperating stations (stations meeting USNSN design goals, but deployed and operated by other institutions which provide a logical extension to the USNSN)

37 CFR 1.413 - The United States International Searching Authority.

Science.gov (United States)

2010-07-01

... Processing Provisions General Information § 1.413 The United States International Searching Authority. (a... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false The United States International Searching Authority. 1.413 Section 1.413 Patents, Trademarks, and Copyrights UNITED STATES PATENT...

United States Attorney Prosecutions

Science.gov (United States)

1993-10-01

property of CocaCola Bottling Company, Fayetteville, North Carolina, of a value in excess of $100.00, in violation of Title 18 United States Code, Section...another, to-wit: a Cocacola soft drink machine, the amount of damage to said personal property being more than $200.00, in violation of North Carolina

77 FR 48542 - United States

Science.gov (United States)

2012-08-14

... litigation.'' United States v. Armour and Co., 402 U.S. 673, 681 (1971). Section 5 of the Clayton Act... relief in consent judgment that contained recitals in which defendants asserted their innocence); Armour...

United States Strategy for Mexico

National Research Council Canada - National Science Library

Centner, Robert C

2005-01-01

The security and stability of Mexico is of national interest to the United States, and a strong, effective alliance between the two countries is pivotal to our national defense strategy and economic prosperity...

Fuel cycle forecasting - there are forecasts and there are forecasts

International Nuclear Information System (INIS)

Puechl, K.H.

1975-01-01

The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis. (author)

Fuel cycle forecasting - there are forecasts and there are forecasts

Energy Technology Data Exchange (ETDEWEB)

Puechl, K H

1975-12-01

The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis.

NCHS - Injury Mortality: United States

Data.gov (United States)

U.S. Department of Health & Human Services — This dataset describes injury mortality in the United States beginning in 1999. Two concepts are included in the circumstances of an injury death: intent of injury...

State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy

Science.gov (United States)

Rakovec, O.; Weerts, A.; Hazenberg, P.; Torfs, P.; Uijlenhoet, R.

2012-12-01

This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model (Rakovec et al., 2012a). The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km2), a relatively quickly responding catchment in the Belgian Ardennes. The uncertain precipitation model forcings were obtained using a time-dependent multivariate spatial conditional simulation method (Rakovec et al., 2012b), which is further made conditional on preceding simulations. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty. Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., and Uijlenhoet, R.: State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy, Hydrol. Earth Syst. Sci. Discuss., 9, 3961-3999, doi:10.5194/hessd-9-3961-2012, 2012a. Rakovec, O., Hazenberg, P., Torfs, P. J. J. F., Weerts, A. H., and Uijlenhoet, R.: Generating spatial precipitation ensembles: impact of

Both Europe's and the United States' electrification

International Nuclear Information System (INIS)

Matly, M.

2006-01-01

While the United States quickly had the largest electrical indus in the world, electrification in rural areas ended about thirty years after most European countries. Public intervention is a deciding factor in completing electrification, and the late involvement by the American authorities explains the gap. However it would be wrong to oppose in Europe and in the United States a motivated public sector and little involved private companies. In both continents indeed, major private and public urban distributors were almost not involved in rural electrification processes, where local players prevailed: local communities around Europe, small and medium size business in some European countries such as France, co-operative companies in the United States. Additionally, there is an essential difference between electrification in Europe and in the United States. The former does not provide much more than lighting and its success leaves few traces in popular memories; the latter includes many facilities and services, changes the lives of rural populations and is celebrated a such. Whereas the colonial venture keep European economies away from their domestic markets, while in the United States the urban market growth contents large companies, the American co-operative movement is right to believe in the existence of a large electrical equipment market among farmers then considered poor and behind. It even uses the market to complete a more profitable and less costly electrification. Electricity stories that offer food for the thoughts of Third World decision makers and power companies, when they entrust most rural electrification to their large urban companies and deny the existence of a real equipment market in their own rural world. (author)

Asian Immigration: The View from the United States.

Science.gov (United States)

Gardner, Robert W.

1992-01-01

Examines contemporary Asian immigration to the United States from a U.S. perspective. Analyzes immigration policies and data on recent immigration from Asia. Discusses impacts concerning the United States and the immigrants themselves and speculates on future immigration. The composition of Asian immigration might change, and the number might…

Short-term load forecasting of power system

Science.gov (United States)

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.

Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

Directory of Open Access Journals (Sweden)

Jinyin Ye

2016-01-01

Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

26 CFR 1.956-2 - Definition of United States property.

Science.gov (United States)

2010-04-01

..., which is— (i) Tangible property (real or personal) located in the United States; (ii) Stock of a... year ending June 30, 1964, R Corporation's increase in earnings invested in United States property for... United States property during its taxable year 1965, S Corporation's increase in earnings invested in...

Licensed pertussis vaccines in the United States: History and current state

OpenAIRE

Klein, Nicola P

2014-01-01

The United States switched from whole cell to acellular pertussis vaccines in the 1990s following global concerns with the safety of the whole cell vaccines. Despite high levels of acellular pertussis vaccine coverage, the United States and other countries are experiencing large pertussis outbreaks. The aim of this article is to describe the historical context which led to acellular pertussis vaccine development, focusing on vaccines currently licensed in the US, and to review evidence that w...

Nations United: The United Nations, the United States, and the Global Campaign Against Terrorism. A Curriculum Unit & Video for Secondary Schools.

Science.gov (United States)

Houlihan, Christina; McLeod, Shannon

This curriculum unit and 1-hour videotape are designed to help students understand the purpose and functions of the United Nations (UN) and explore the relationship between the United Nations and the United States. The UN's role in the global counterterrorism campaign serves as a case study for the unit. The students are asked to develop a basic…

Enhanced short-term wind power forecasting and value to grid operations. The wind forecasting improvement project

Energy Technology Data Exchange (ETDEWEB)

Orwig, Kirsten D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States). Transmission Grid Integration; Benjamin, Stan; Wilczak, James; Marquis, Melinda [National Oceanic and Atmospheric Administration, Boulder, CO (United States). Earth System Research Lab.; Stern, Andrew [National Oceanic and Atmospheric Administration, Silver Spring, MD (United States); Clark, Charlton; Cline, Joel [U.S. Department of Energy, Washington, DC (United States). Wind and Water Power Program; Finley, Catherine [WindLogics, Grand Rapids, MN (United States); Freedman, Jeffrey [AWS Truepower, Albany, NY (United States)

2012-07-01

The current state-of-the-art wind power forecasting in the 0- to 6-h timeframe has levels of uncertainty that are adding increased costs and risks to the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: (1) a one-year field measurement campaign within two regions; (2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and (3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provide an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis. (orig.)

Forecasting Housing Approvals in Australia: Do Forecasters Herd?

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Rülke

2012-01-01

Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

Estimated United States Transportation Energy Use 2005

Energy Technology Data Exchange (ETDEWEB)

Smith, C A; Simon, A J; Belles, R D

2011-11-09

A flow chart depicting energy flow in the transportation sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 31,000 trillion British Thermal Units (trBTUs) of energy were used throughout the United States in transportation activities. Vehicles used in these activities include automobiles, motorcycles, trucks, buses, airplanes, rail, and ships. The transportation sector is powered primarily by petroleum-derived fuels (gasoline, diesel and jet fuel). Biomass-derived fuels, electricity and natural gas-derived fuels are also used. The flow patterns represent a comprehensive systems view of energy used within the transportation sector.

United States of America National Report

International Nuclear Information System (INIS)

1992-01-01

The United States has produced this report as part of the preparations for the United Nations Conference on Environment and Development (UNCED) to be held in Brazil in June 1992. It summarizes this nation's efforts to protect and enhance the quality of the human environment in concert with its efforts to provide economic well-being during the two decades since the United Nations Conference on the Human Environment was held in Stockholm. The information presented in this report is primarily and deliberately retrospective. It is an attempt to portray the many human, economic and natural resources of the United States, to describe resource use and the principal national laws and programs established to protect these resources, and to analyze key issues on the agenda of UNCED. This analysis is presented in terms of past and present conditions and trends, measures of progress made in responding to the key issues, and a summary of government activities, underway or pending, to address ongoing or newly emerging national environmental and resource management problems

African Journals Online: United States Minor Outlying Islands

African Journals Online (AJOL)

African Journals Online: United States Minor Outlying Islands. Home > African Journals Online: United States Minor Outlying Islands. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register · Browse By Category · Browse Alphabetically · Browse By Country · List All Titles ...

Forecasting freight flows

DEFF Research Database (Denmark)

Lyk-Jensen, Stéphanie

2011-01-01

Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...... constitute a valuable input to freight models for forecasting future capacity problems.......Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...

Refugee Status Required for Resettlement in the United States

Science.gov (United States)

2017-06-09

STATES REFUGEE ADMISSIONS PROGRAM FLOWCHART ...the American public’s concerns. 50 APPENDIX A UNITED STATES REFUGEE ADMISSIONS PROGRAM FLOWCHART Source: US Citizenship and Immigration...TITLE AND SUBTITLE Refugee Status Required for Resettlement in the United States 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

Next-generation probabilistic seismicity forecasting

Energy Technology Data Exchange (ETDEWEB)

Hiemer, S.

2014-07-01

novel automated method to investigate the significance of spatial b-value variations. The method incorporates an objective data-driven partitioning scheme, which is based on penalized likelihood estimates. These well-defined criteria avoid the difficult choice of commonly applied spatial mapping parameters, such as grid spacing or size of mapping radii. We construct a seismicity forecast that includes spatial b-value variations and demonstrate our model’s skill and reliability when applied to data from California. All proposed probabilistic seismicity forecasts were subjected to evaluation methods using state of the art algorithms provided by the 'Collaboratory for the Study of Earthquake Predictability' infrastructure. First, we evaluated the statistical agreement between the forecasted and observed rates of target events in terms of number, space and magnitude. Secondly, we assessed the performance of one forecast relative to another. We find that the forecasts presented in this thesis are reliable and show significant skills with respect to established classical forecasts. These next-generation probabilistic seismicity forecasts can thus provide hazard information that are potentially useful in reducing earthquake losses and enhancing community preparedness and resilience. (author)

Next-generation probabilistic seismicity forecasting

International Nuclear Information System (INIS)

Hiemer, S.

2014-01-01

novel automated method to investigate the significance of spatial b-value variations. The method incorporates an objective data-driven partitioning scheme, which is based on penalized likelihood estimates. These well-defined criteria avoid the difficult choice of commonly applied spatial mapping parameters, such as grid spacing or size of mapping radii. We construct a seismicity forecast that includes spatial b-value variations and demonstrate our model’s skill and reliability when applied to data from California. All proposed probabilistic seismicity forecasts were subjected to evaluation methods using state of the art algorithms provided by the 'Collaboratory for the Study of Earthquake Predictability' infrastructure. First, we evaluated the statistical agreement between the forecasted and observed rates of target events in terms of number, space and magnitude. Secondly, we assessed the performance of one forecast relative to another. We find that the forecasts presented in this thesis are reliable and show significant skills with respect to established classical forecasts. These next-generation probabilistic seismicity forecasts can thus provide hazard information that are potentially useful in reducing earthquake losses and enhancing community preparedness and resilience. (author)

A Wind Forecasting System for Energy Application

Science.gov (United States)

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

Latin America and the United States: What Do United States History Textbooks Tell Us?

Science.gov (United States)

Fleming, Dan B.

1982-01-01

Evaluates how U.S.-Latin American relations are presented in high school U.S. history textbooks. An examination of 10 textbooks published between 1977-81 revealed inadequate coverage of Latin American cultural diversity and United States foreign policy from the Latin American perspective. (AM)

Robust forecast comparison

OpenAIRE

Jin, Sainan; Corradi, Valentina; Swanson, Norman

2015-01-01

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

Preparation of School Psychologists in the United States

Science.gov (United States)

Joyce-Beaulieu, Diana; Rossen, Eric

2014-01-01

School psychology in the United States continues to evolve in response to shifts in the country's demographic characteristics, an increasing focus on the importance of child mental health, together with health and education reforms. The landscape of school psychological services in the United States also is shaped through the changing roles and…

United States position on severe accidents

International Nuclear Information System (INIS)

Ross, D.F.

1988-01-01

The United States policy on severe accidents was published in 1985 for both new plant applications and for existing plants. Implementation of this policy is in progress. This policy, aided by a related safety goal policy and by analysis capabilities emerging from improved understanding of accident phenomenology, is viewed as a logical development from the pioneering work in the WASH-1400 Reactor Safety Study published by the United States Nuclear Regulatory Commission (NRC) in 1975. This work provided an estimate of the probability and consequences of severe accidents which, prior to that time, had been mostly evaluated by somewhat arbitrary assumptions dating back 30 years. The early history of severe accident evaluation is briefly summarized for the period 1957-1979. Then, the galvanizing action of Three Mile Island Unit 2 (TMI-2) on severe accident analysis, experimentation and regulation is reviewed. Expressions of US policy in the form of rulemaking, severe accident policy, safety research, safety goal policy and court decisions (on adequacy of safety) are discussed. Finally, the NRC policy as of March 1988 is stated, along with a prospective look at the next few years. (author). 19 refs

Understanding human trafficking in the United States.

Science.gov (United States)

Logan, T K; Walker, Robert; Hunt, Gretchen

2009-01-01

The topic of modern-day slavery or human trafficking has received increased media and national attention. However, to date there has been limited research on the nature and scope of human trafficking in the United States. This article describes and synthesizes nine reports that assess the U.S. service organizations' legal representative knowledge of, and experience with, human trafficking cases, as well as information from actual cases and media reports. This article has five main goals: (a) to define what human trafficking is, and is not; (b) to describe factors identified as contributing to vulnerability to being trafficked and keeping a person entrapped in the situation; (c) to examine how the crime of human trafficking differs from other kinds of crimes in the United States; (d) to explore how human trafficking victims are identified; and, (e) to provide recommendations to better address human trafficking in the United States.

Sharing wind power forecasts in electricity markets: A numerical analysis

DEFF Research Database (Denmark)

Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

2016-01-01

In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day......-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding...... flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making...

42 CFR 410.175 - Alien absent from the United States.

Science.gov (United States)

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false Alien absent from the United States. 410.175... Alien absent from the United States. (a) Medicare does not pay Part B benefits for services furnished to... during the first full calendar month the alien is back in the United States. [53 FR 6634, Mar. 2, 1988] ...

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Directory of Open Access Journals (Sweden)

Tadesse Kassahun Birhanu

2017-12-01

Full Text Available Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI and Hannan–Quinn (HQ criteria, SARIMA (3, 0, 2 x (3, 1, 312 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

Satellite based Ocean Forecasting, the SOFT project

Science.gov (United States)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

Inventory of power plants in the United States. [By state within standard Federal Regions, using county codes

Energy Technology Data Exchange (ETDEWEB)

None

1977-12-01

The purpose of this inventory of power plants is to provide a ready reference for planners whose focus is on the state, standard Federal region, and/or national level. Thus the inventory is compiled alphabetically by state within standard Federal regions. The units are listed alphabetically within electric utility systems which in turn are listed alphabetically within states. The locations are identified to county level according to the Federal Information Processing Standards Publication Counties and County Equivalents of the States of the United States. Data compiled include existing and projected electrical generation units, jointly owned units, and projected construction units.

Norovirus in the United States

Centers for Disease Control (CDC) Podcasts

2013-09-09

Dr. Aron Hall, a CDC epidemiologist specializing in norovirus, discusses the impact of norovirus in the United States.  Created: 9/9/2013 by National Center for Emerging and Zoonotic Infectious Diseases (NCEZID).   Date Released: 9/17/2013.

Forecasting Skill

Science.gov (United States)

1981-01-01

for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

Fragmentation of Continental United States Forests

Science.gov (United States)

Kurt H. Riitters; James D. Wickham; Robert V. O' Neill; K. Bruce Jones; Elizabeth R. Smith; John W. Coulston; Timothy G. Wade; Jonathan H. Smith

2002-01-01

We report a multiple-scale analysis of forest fragmentation based on 30-m (0.09 ha pixel-1) land- cover maps for the conterminous United States. Each 0.09-ha unit of forest was classified according to fragmentation indexes measured within the surrounding landscape, for five landscape sizes including 2.25, 7.29, 65.61, 590.49, and 5314.41 ha....

Trial by jury in the United States

Directory of Open Access Journals (Sweden)

Lochhead Robert

2015-10-01

Full Text Available Th e Republic of Moldova is considering the adoption of trial by jury in select criminal cases. Th e following article is intended to contribute to the discussion of that proposal. Th e article will briefl y describe the history of juries under the English common law and as adopted by the United States. It will then outline some of the basic procedures in trials by jury as currently practiced in the United States federal court system.

CEDAW in the Eyes of the United States

Directory of Open Access Journals (Sweden)

Al Shraideh Saleh

2017-12-01

Full Text Available Despite the large number of reservations registered by Member countries, making it one of the, if not the, most heavily reserved human rights treaties; the Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW has managed to achieve a very high rate of states’ membership [1]. Currently, 187 countries out of the 193 United Nations Members are parties to CEDAW [2]. What is strange to digest, however, is the fact that the United States is one of the seven countries that are yet to ratify the Convention [3]. This article provides an insight into the position of the United States from the ratification of CEDAW. It examines the merits of arguments made for and against the ratification and their rationale to provide a better understanding that explains what is considered by many as a buzzling stand of the United States from the Convention.