WorldWideScience

Sample records for improve population forecasts

  1. Forecaster priorities for improving probabilistic flood forecasts

    Science.gov (United States)

    Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta

    2014-05-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  2. Improving Garch Volatility Forecasts

    NARCIS (Netherlands)

    Klaassen, F.J.G.M.

    1998-01-01

    Many researchers use GARCH models to generate volatility forecasts. We show, however, that such forecasts are too variable. To correct for this, we extend the GARCH model by distinguishing two regimes with different volatility levels. GARCH effects are allowed within each regime, so that our model

  3. HESS Opinions "Forecaster priorities for improving probabilistic flood forecasts"

    Science.gov (United States)

    Wetterhall, F.; Pappenberger, F.; Alfieri, L.; Cloke, H. L.; Thielen-del Pozo, J.; Balabanova, S.; Daňhelka, J.; Vogelbacher, A.; Salamon, P.; Carrasco, I.; Cabrera-Tordera, A. J.; Corzo-Toscano, M.; Garcia-Padilla, M.; Garcia-Sanchez, R. J.; Ardilouze, C.; Jurela, S.; Terek, B.; Csik, A.; Casey, J.; Stankūnavičius, G.; Ceres, V.; Sprokkereef, E.; Stam, J.; Anghel, E.; Vladikovic, D.; Alionte Eklund, C.; Hjerdt, N.; Djerv, H.; Holmberg, F.; Nilsson, J.; Nyström, K.; Sušnik, M.; Hazlinger, M.; Holubecka, M.

    2013-11-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  4. Influenza Forecasting in Human Populations: A Scoping Review

    Science.gov (United States)

    Chretien, Jean-Paul; George, Dylan; Shaman, Jeffrey; Chitale, Rohit A.; McKenzie, F. Ellis

    2014-01-01

    Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials. PMID:24714027

  5. How to Improve the SPF Forecasts?

    National Research Council Canada - National Science Library

    Bratu (Simionescu) Mihaela

    2013-01-01

    .... This implies the improvement of predictions accuracy. In this study, many types of forecasts of the annual rate of change for the HICP for EU were developed, their accuracy was evaluated and compared with the accuracy of SPF predictions...

  6. An Approach to Improve the Performance of PM Forecasters.

    Directory of Open Access Journals (Sweden)

    Paulo S G de Mattos Neto

    Full Text Available The particulate matter (PM concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth's atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governments to alert the population. Forecasting systems based on Artificial Neural Networks (ANNs have been highlighted in the literature due to their performances. In general, three ANN-based approaches have been found for this task: ANN trained via learning algorithms, hybrid systems that combine search algorithms with ANNs, and hybrid systems that combine ANN with other forecasters. Independent of the approach, it is common to suppose that the residuals (error series, obtained from the difference between actual series and forecasting, have a white noise behavior. However, it is possible that this assumption is infringed due to: misspecification of the forecasting model, complexity of the time series or temporal patterns of the phenomenon not captured by the forecaster. This paper proposes an approach to improve the performance of PM forecasters from residuals modeling. The approach analyzes the remaining residuals recursively in search of temporal patterns. At each iteration, if there are temporal patterns in the residuals, the approach generates the forecasting of the residuals in order to improve the forecasting of the PM time series. The proposed approach can be used with either only one forecaster or by combining two or more forecasting models. In this study, the approach is used to improve the performance of a hybrid system (HS composed by genetic algorithm (GA and ANN from residuals modeling performed by two methods, namely, ANN and own hybrid system. Experiments were performed for PM2.5 and PM10 concentration series in Kallio and Vallila stations in

  7. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  8. Improving of local ozone forecasting by integrated models.

    Science.gov (United States)

    Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

    2016-09-01

    This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

  9. Forecast Value Added (FVA Analysis as a Means to Improve the Efficiency of a Forecasting Process

    Directory of Open Access Journals (Sweden)

    Filip Chybalski

    2017-01-01

    Full Text Available A praxeological approach has been proposed in order to improve a forecasting process through the employment of the forecast value added (FVA analysis. This may be interpreted as a manifestation of lean management in forecasting. The author discusses the concepts of the effectiveness and efficiency of forecasting. The former, defined in the praxeology as the degree to which goals are achieved, refers to the accuracy of forecasts. The latter reflects the relation between the benefits accruing from the results of forecasting and the costs incurred in this process. Since measuring the benefits accruing from a forecasting is very difficult, a simplification according to which this benefit is a function of the forecast accuracy is proposed. This enables evaluating the efficiency of the forecasting process. Since improving this process may consist of either reducing forecast error or decreasing costs, FVA analysis, which expresses the concept of lean management, may be applied to reduce the waste accompanying forecasting. (original abstract

  10. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  11. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

    Science.gov (United States)

    Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

    2017-02-01

    Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

  12. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  13. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  14. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

    Science.gov (United States)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2016-09-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping

  15. Bayesian Population Forecasting: Extending the Lee-Carter Method.

    Science.gov (United States)

    Wiśniowski, Arkadiusz; Smith, Peter W F; Bijak, Jakub; Raymer, James; Forster, Jonathan J

    2015-06-01

    In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

  16. Study on Population Forecast Model in Planning of Land Use

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    On the basis of describing characteristics and condition of application of natural growth model of population,weighted average growth model,regression forecast model and GM(1,1) forecast model,taking Gushi County in Henan Province as an example,according to the statistics of population in Gushi County Statistical Yearbook from 1991 to 2007,we establish four models to conduct fitting on population change respectively,and meanwhile,we predict population size from 2008 to 2009 and conduct preciseness test on the population size.The test results show that the preciseness of forecast results of natural growth model is not high,and the preciseness of forecast results of weighted average growth model is not scientific when the total size of population is unstable.The results of GM(1,1) forecast model and regression forecast model largely conform to the actual data,so we can take the mean of the two as the final forecast result.

  17. Requirements and benefits of flow forecasting for improving hydropower generation

    OpenAIRE

    Dong, Xiaohua; Vrijling, J. K.; Dohmen-Janssen, Catarine M.; Ruigh, E.; Booij, Martijn J.; Stalenberg, B.; Hulscher, Suzanne J.M.H.; Van Gelder, P.H.A.J.M.; Verlaan, M.; Zijderveld, A; Waarts, P.

    2005-01-01

    This paper presents a methodology to identify the required lead time and accuracy of flow forecasting for improving hydropower generation of a reservoir, by simulating the benefits (in terms of electricity generated) obtained from the forecasting with varying lead times and accuracies. The benefit-lead time relationship was investigated only for perfect inflow forecasts, with a few selected forecasting lead times: 4, 10 days and 1 year. The water level and the release from the reservoir were ...

  18. Improved Forecasting Methods for Naval Manpower Studies

    Science.gov (United States)

    2015-03-25

    accuracy. Exogenous events or structural breaks in time - series data can result in large forecasting errors. Using the Bai-Perron (BP) test, we...accuracy. Exogenous events or structural breaks in time - series data can result in large forecasting errors. Using the Bai-Perron (BP) test, we...structural changes on forecast accuracy. Exogenous events or structural breaks in time - series data can result in large forecasting errors. Using

  19. Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

    Science.gov (United States)

    Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

    2016-12-01

    Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

  20. Improving Air Quality Forecasts with AURA Observations

    Science.gov (United States)

    Newchurch, M. J.; Biazer, A.; Khan, M.; Koshak, W. J.; Nair, U.; Fuller, K.; Wang, L.; Parker, Y.; Williams, R.; Liu, X.

    2008-01-01

    Past studies have identified model initial and boundary conditions as sources of reducible errors in air-quality simulations. In particular, improving the initial condition improves the accuracy of short-term forecasts as it allows for the impact of local emissions to be realized by the model and improving boundary conditions improves long range transport through the model domain, especially in recirculating anticyclones. During the August 2006 period, we use AURA/OMI ozone measurements along with MODIS and CALIPSO aerosol observations to improve the initial and boundary conditions of ozone and Particulate Matter. Assessment of the model by comparison of the control run and satellite assimilation run to the IONS06 network of ozonesonde observations, which comprise the densest ozone sounding campaign ever conducted in North America, to AURA/TES ozone profile measurements, and to the EPA ground network of ozone and PM measurements will show significant improvement in the CMAQ calculations that use AURA initial and boundary conditions. Further analyses of lightning occurrences from ground and satellite observations and AURA/OMI NO2 column abundances will identify the lightning NOx signal evident in OMI measurements and suggest pathways for incorporating the lightning and NO2 data into the CMAQ simulations.

  1. Improving Air Quality Forecasts with AURA Observations

    Science.gov (United States)

    Newchurch, M. J.; Biazer, A.; Khan, M.; Koshak, W. J.; Nair, U.; Fuller, K.; Wang, L.; Parker, Y.; Williams, R.; Liu, X.

    2008-01-01

    Past studies have identified model initial and boundary conditions as sources of reducible errors in air-quality simulations. In particular, improving the initial condition improves the accuracy of short-term forecasts as it allows for the impact of local emissions to be realized by the model and improving boundary conditions improves long range transport through the model domain, especially in recirculating anticyclones. During the August 2006 period, we use AURA/OMI ozone measurements along with MODIS and CALIPSO aerosol observations to improve the initial and boundary conditions of ozone and Particulate Matter. Assessment of the model by comparison of the control run and satellite assimilation run to the IONS06 network of ozonesonde observations, which comprise the densest ozone sounding campaign ever conducted in North America, to AURA/TES ozone profile measurements, and to the EPA ground network of ozone and PM measurements will show significant improvement in the CMAQ calculations that use AURA initial and boundary conditions. Further analyses of lightning occurrences from ground and satellite observations and AURA/OMI NO2 column abundances will identify the lightning NOx signal evident in OMI measurements and suggest pathways for incorporating the lightning and NO2 data into the CMAQ simulations.

  2. Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-05-01

    The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.

  3. Improving the Model for Energy Consumption Load Demand Forecasting

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.

  4. Bridging the Micro-Macro Gap in Population Forecasting

    NARCIS (Netherlands)

    NIDI, .

    2011-01-01

    MicMac - Bridging the micro-macro gap in population forecasting: a study funded by the European Commission under the 6th Framework Programme "Integrating and strengthening the European Research Area". In an ageing population, the demand for adequate health care services, pension systems and other

  5. Bridging the Micro-Macro Gap in Population Forecasting

    NARCIS (Netherlands)

    NIDI, .

    2011-01-01

    MicMac - Bridging the micro-macro gap in population forecasting: a study funded by the European Commission under the 6th Framework Programme "Integrating and strengthening the European Research Area". In an ageing population, the demand for adequate health care services, pension systems and other so

  6. School Science Inspired by Improving Weather Forecasts

    Science.gov (United States)

    Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint

    2014-01-01

    High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…

  7. How MAG4 Improves Space Weather Forecasting

    Science.gov (United States)

    Falconer, David; Khazanov, Igor; Barghouty, Nasser

    2013-01-01

    Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.

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

    Energy Technology Data Exchange (ETDEWEB)

    Wilczak, James M. [NOAA, Boulder, CO (United States); Finley, Cathy [WindLogics, Inc., St. Paul, MN (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States); Cline, Joel [USDOE Office of Energy Efficiency and Renewable Energy, Washington, DC (United States); Bianco, L. [Univ. of Colorado, Boulder, CO (United States); Olson, J. [Univ. of Colorado, Boulder, CO (United States); Djalaova, I. [Univ. of Colorado, Boulder, CO (United States); Sheridan, L. [WindLogics, Inc., St. Paul, MN (United States); Ahlstrom, M. [WindLogics, Inc., St. Paul, MN (United States); Manobianco, J. [Meso, Inc., Troy, NY (United States); Zack, J. [Meso, Inc., Troy, NY (United States); Carley, J. [National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States); Benjamin, S. [NOAA, Boulder, CO (United States); Coulter, R. L. [Argonne National Lab. (ANL), Lemont, IL (United States); Berg, Larry K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Mirocha, Jeff D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Clawson, K. [National Oceanic and Atmospheric Administration (NOAA), Idaho Falls, ID (United States); Natenberg, E. [Meso, Inc., Troy, NY (United States); Marquis, M. [NOAA, Boulder, CO (United States)

    2015-10-30

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

  9. Requirements and benefits of flow forecasting for improving hydropower generation

    NARCIS (Netherlands)

    Dong, Xiaohua; Vrijling, J.K.; Dohmen-Janssen, Catarine M.; Ruigh, E.; Booij, Martijn J.; Stalenberg, B.; Hulscher, Suzanne J.M.H.; van Gelder, P.H.A.J.M.; Verlaan, M.; Zijderveld, A.; Waarts, P.

    2005-01-01

    This paper presents a methodology to identify the required lead time and accuracy of flow forecasting for improving hydropower generation of a reservoir, by simulating the benefits (in terms of electricity generated) obtained from the forecasting with varying lead times and accuracies. The

  10. Requirements and benefits of flow forecasting for improving hydropower generation

    NARCIS (Netherlands)

    Dong, X.; Dohmen-Janssen, C.M.; Booij, M.J.; Hulscher, S.J.M.H.

    2005-01-01

    This paper presents a methodology to identify the required lead time and accuracy of flow forecasting for improving hydropower generation of a reservoir, by simulating the benefits (in terms of electricity generated) obtained from the forecasting with varying lead times and accuracies. The benefit-l

  11. Disaggregating residential water demand for improved forecasts and decision making

    Science.gov (United States)

    Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

    2003-04-01

    Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water

  12. Stochastic forecast of the population of Poland, 2005-2050

    NARCIS (Netherlands)

    Matysiak, Anna; Nowok, Beata

    2007-01-01

    Forecasting the population of Poland is very challenging. Firstly, the country has been undergoing rapid demographic changes. In the 1990s, they were influenced by the political, economic, and social consequences of the collapse of the communist regime. Since 2004 they have been shaped by Poland's e

  13. Blending forest fire smoke forecasts with observed data can improve their utility for public health applications

    Science.gov (United States)

    Yuchi, Weiran; Yao, Jiayun; McLean, Kathleen E.; Stull, Roland; Pavlovic, Radenko; Davignon, Didier; Moran, Michael D.; Henderson, Sarah B.

    2016-11-01

    Fine particulate matter (PM2.5) generated by forest fires has been associated with a wide range of adverse health outcomes, including exacerbation of respiratory diseases and increased risk of mortality. Due to the unpredictable nature of forest fires, it is challenging for public health authorities to reliably evaluate the magnitude and duration of potential exposures before they occur. Smoke forecasting tools are a promising development from the public health perspective, but their widespread adoption is limited by their inherent uncertainties. Observed measurements from air quality monitoring networks and remote sensing platforms are more reliable, but they are inherently retrospective. It would be ideal to reduce the uncertainty in smoke forecasts by integrating any available observations. This study takes spatially resolved PM2.5 estimates from an empirical model that integrates air quality measurements with satellite data, and averages them with PM2.5 predictions from two smoke forecasting systems. Two different indicators of population respiratory health are then used to evaluate whether the blending improved the utility of the smoke forecasts. Among a total of six models, including two single forecasts and four blended forecasts, the blended estimates always performed better than the forecast values alone. Integrating measured observations into smoke forecasts could improve public health preparedness for smoke events, which are becoming more frequent and intense as the climate changes.

  14. Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

    Science.gov (United States)

    Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

    2014-01-01

    Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

  15. Integrating uncertainty in time series population forecasts: An illustration using a simple projection model

    Directory of Open Access Journals (Sweden)

    Guy J. Abel

    2013-12-01

    Full Text Available Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model. Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts. Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated. Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.

  16. Approaches in Health Human Resource Forecasting: A Roadmap for Improvement.

    Science.gov (United States)

    Rafiei, Sima; Mohebbifar, Rafat; Hashemi, Fariba; Ezzatabadi, Mohammad Ranjbar; Farzianpour, Fereshteh

    2016-09-01

    Forecasting the demand and supply of health manpower in an accurate manner makes appropriate planning possible. The aim of this paper was to review approaches and methods for health manpower forecasting and consequently propose the features that improve the effectiveness of this important process of health manpower planning. A literature review was conducted for studies published in English from 1990-2014 using Pub Med, Science Direct, Pro Quest, and Google Scholar databases. Review articles, qualitative studies, retrospective and prospective studies describing or applying various types of forecasting approaches and methods in health manpower forecasting were included in the review. The authors designed an extraction data sheet based on study questions to collect data on studies' references, designs, and types of forecasting approaches, whether discussed or applied, with their strengths and weaknesses. Forty studies were included in the review. As a result, two main categories of approaches (conceptual and analytical) for health manpower forecasting were identified. Each approach had several strengths and weaknesses. As a whole, most of them were faced with some challenges, such as being static and unable to capture dynamic variables in manpower forecasting and causal relationships. They also lacked the capacity to benefit from scenario making to assist policy makers in effective decision making. An effective forecasting approach is supposed to resolve all the deficits that exist in current approaches and meet the key features found in the literature in order to develop an open system and a dynamic and comprehensive method necessary for today complex health care systems.

  17. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-05

    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.

  19. Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Patton, Andrew J.; Quaedvlieg, Rogier

    with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P500 equity index and the individual constituents of the Dow Jones Industrial Average......, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors....

  20. Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

    Science.gov (United States)

    Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

    2013-12-01

    Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail

  1. Improvements in medium range weather forecasting system of India

    Indian Academy of Sciences (India)

    V S Prasad; Saji Mohandas; Surya Kanti Dutta; M Das Gupta; G R Iyengar; E N Rajagopal; Swati Basu

    2014-03-01

    Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day.

  2. A new method for improved global mapping forecast

    Science.gov (United States)

    Alves, P. R. L.; Duarte, L. G. S.; da Mota, L. A. C. P.

    2016-10-01

    The Maple package TimeS for time series analysis has a new feature and an improvement in forecasting by phase space reconstruction. An optional argument in the computational routines that allows the researcher to choose the different number of steps ahead to forecast. This update extends the running of the package with this new feature for the current versions of the Maple software too.

  3. Why population forecasts should be probabilistic - illustrated by the case of Norway

    Directory of Open Access Journals (Sweden)

    2002-05-01

    Full Text Available Deterministic population forecasts do not give an appropriate indication of forecast uncertainty. Forecasts should be probabilistic, rather than deterministic, so that their expected accuracy can be assessed. We review three main methods to compute probabilistic forecasts, namely time series extrapolation, analysis of historical forecast errors, and expert judgement. We illustrate, by the case of Norway up to 2050, how elements of these three methods can be combined when computing prediction intervals for a population's future size and age-sex composition. We show the relative importance for prediction intervals of various sources of variance, and compare our results with those of the official population forecast computed by Statistics Norway.

  4. Sales Growth Rate Forecasting Using Improved PSO and SVM

    Directory of Open Access Journals (Sweden)

    Xibin Wang

    2014-01-01

    Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

  5. Improved El Nino forecasting by cooperativity detection.

    Science.gov (United States)

    Ludescher, Josef; Gozolchiani, Avi; Bogachev, Mikhail I; Bunde, Armin; Havlin, Shlomo; Schellnhuber, Hans Joachim

    2013-07-16

    Although anomalous episodic warming of the eastern equatorial Pacific, dubbed El Niño by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead. A significant extension of the prewarning time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a unique avenue toward El Niño prediction based on network methods, inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode--linking the El Niño basin (equatorial Pacific corridor) and the rest of the ocean--builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data available since 1950 and yields hit rates above 0.5, whereas false-alarm rates are below 0.1.

  6. Development and testing of improved statistical wind power forecasting methods.

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

  7. 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.

  8. Approaches in Health Human Resource Forecasting: A Roadmap for Improvement

    Science.gov (United States)

    Rafiei, Sima; Mohebbifar, Rafat; Hashemi, Fariba; Ezzatabadi, Mohammad Ranjbar; Farzianpour, Fereshteh

    2016-01-01

    Introduction Forecasting the demand and supply of health manpower in an accurate manner makes appropriate planning possible. The aim of this paper was to review approaches and methods for health manpower forecasting and consequently propose the features that improve the effectiveness of this important process of health manpower planning. Methods A literature review was conducted for studies published in English from 1990–2014 using Pub Med, Science Direct, Pro Quest, and Google Scholar databases. Review articles, qualitative studies, retrospective and prospective studies describing or applying various types of forecasting approaches and methods in health manpower forecasting were included in the review. The authors designed an extraction data sheet based on study questions to collect data on studies’ references, designs, and types of forecasting approaches, whether discussed or applied, with their strengths and weaknesses Results Forty studies were included in the review. As a result, two main categories of approaches (conceptual and analytical) for health manpower forecasting were identified. Each approach had several strengths and weaknesses. As a whole, most of them were faced with some challenges, such as being static and unable to capture dynamic variables in manpower forecasting and causal relationships. They also lacked the capacity to benefit from scenario making to assist policy makers in effective decision making. Conclusions An effective forecasting approach is supposed to resolve all the deficits that exist in current approaches and meet the key features found in the literature in order to develop an open system and a dynamic and comprehensive method necessary for today complex health care systems. PMID:27790343

  9. Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias; Florita, Anthony; Freedman, Jeffrey

    2017-03-01

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

  10. Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts

    Science.gov (United States)

    Yang, Tsun-Hua; Hwang, Gong-Do; Tsai, Chin-Cheng; Ho, Jui-Yi

    2016-11-01

    Urban inundation forecasting with extended lead times is useful in saving lives and property. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, predicting peak flows and to allow response agencies to take necessary preparatory measures. However, ensemble precipitation forecasting is time- and resource-intensive. Using rainfall thresholds to estimate urban areas' inundation risk can decrease this complexity and save computation time. This study evaluated the performance of this system using 352 townships in Taiwan and seven typhoons during the period 2013-2015. The levels of forecast probability needed to issue inundation alerts were addressed because ensemble forecasts are probability based. This study applied six levels of forecast probability and evaluated their performance using five measures. The results showed that this forecasting system performed better before a typhoon made landfall. Geography had a strong impact at the start of the numerical weather modeling, resulting in the underestimation of rainfall forecasts. Regardless of this finding, the inundation forecast performance was highly contingent on the rainfall forecast skill. This study then tested a hybrid approach of on-site observations and rainfall forecasts to decrease the influence of numerical weather predictions and improve the forecast performance. The results of this combined system showed that forecasts with a 24 h lead time improved significantly. These findings and the hybrid approach can be applied to other hydrometeorological early warning systems to improve hazard-related forecasts.

  11. Application of Improved Grey Prediction Model to Petroleum Cost Forecasting

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. In this paper, an improved grey model using step function was proposed.Petroleum cost forecast of the Henan oil field was used as the case study to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed method obviously could improve the prediction accuracy of the original grey model.

  12. Improving urban streamflow forecasting using a high-resolution large scale modeling framework

    Science.gov (United States)

    Read, Laura; Hogue, Terri; Gochis, David; Salas, Fernando

    2016-04-01

    Urban flood forecasting is a critical component in effective water management, emergency response, regional planning, and disaster mitigation. As populations across the world continue to move to cities (~1.8% growth per year), and studies indicate that significant flood damages are occurring outside the floodplain in urban areas, the ability to model and forecast flow over the urban landscape becomes critical to maintaining infrastructure and society. In this work, we use the Weather Research and Forecasting- Hydrological (WRF-Hydro) modeling framework as a platform for testing improvements to representation of urban land cover, impervious surfaces, and urban infrastructure. The three improvements we evaluate include: updating the land cover to the latest 30-meter National Land Cover Dataset, routing flow over a high-resolution 30-meter grid, and testing a methodology for integrating an urban drainage network into the routing regime. We evaluate performance of these improvements in the WRF-Hydro model for specific flood events in the Denver-Metro Colorado domain, comparing to historic gaged streamflow for retrospective forecasts. Denver-Metro provides an interesting case study as it is a rapidly growing urban/peri-urban region with an active history of flooding events that have caused significant loss of life and property. Considering that the WRF-Hydro model will soon be implemented nationally in the U.S. to provide flow forecasts on the National Hydrography Dataset Plus river reaches - increasing capability from 3,600 forecast points to 2.7 million, we anticipate that this work will support validation of this service in urban areas for operational forecasting. Broadly, this research aims to provide guidance for integrating complex urban infrastructure with a large-scale, high resolution coupled land-surface and distributed hydrologic model.

  13. Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling

    Science.gov (United States)

    Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal

    2014-05-01

    Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is

  14. Integrated systems for forecasting urban meteorology, air pollution and population exposure

    Directory of Open Access Journals (Sweden)

    A. Baklanov

    2007-01-01

    Full Text Available Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

  15. An intercomparison of approaches for improving operational seasonal streamflow forecasts

    Directory of Open Access Journals (Sweden)

    P. A. Mendoza

    2017-07-01

    Full Text Available For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1 objective approaches supporting

  16. An intercomparison of approaches for improving operational seasonal streamflow forecasts

    Science.gov (United States)

    Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

    2017-07-01

    For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically

  17. Skill improvement of dynamical seasonal Arctic sea ice forecasts

    Science.gov (United States)

    Krikken, Folmer; Schmeits, Maurice; Vlot, Willem; Guemas, Virginie; Hazeleger, Wilco

    2016-05-01

    We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic sea ice area, mainly due to a differently simulated seasonal cycle and long term trend compared to observations. This translates very quickly (1-3 months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the forecast skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic sea ice shows that the Northeast Passage and the Kara and Barents Sea are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of seasonal forecasts of Arctic sea ice.

  18. Agricultural Productivity Forecasts for Improved Drought Monitoring

    Science.gov (United States)

    Limaye, Ashutosh; McNider, Richard; Moss, Donald; Alhamdan, Mohammad

    2010-01-01

    Water stresses on agricultural crops during critical phases of crop phenology (such as grain filling) has higher impact on the eventual yield than at other times of crop growth. Therefore farmers are more concerned about water stresses in the context of crop phenology than the meteorological droughts. However the drought estimates currently produced do not account for the crop phenology. US Department of Agriculture (USDA) and National Oceanic and Atmospheric Administration (NOAA) have developed a drought monitoring decision support tool: The U.S. Drought Monitor, which currently uses meteorological droughts to delineate and categorize drought severity. Output from the Drought Monitor is used by the States to make disaster declarations. More importantly, USDA uses the Drought Monitor to make estimates of crop yield to help the commodities market. Accurate estimation of corn yield is especially critical given the recent trend towards diversion of corn to produce ethanol. Ethanol is fast becoming a standard 10% ethanol additive to petroleum products, the largest traded commodity. Thus the impact of large-scale drought will have dramatic impact on the petroleum prices as well as on food prices. USDA's World Agricultural Outlook Board (WAOB) serves as a focal point for economic intelligence and the commodity outlook for U.S. WAOB depends on Drought Monitor and has emphatically stated that accurate and timely data are needed in operational agrometeorological services to generate reliable projections for agricultural decision makers. Thus, improvements in the prediction of drought will reflect in early and accurate assessment of crop yields, which in turn will improve commodity projections. We have developed a drought assessment tool, which accounts for the water stress in the context of crop phenology. The crop modeling component is done using various crop modules within Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT is an agricultural crop

  19. Improving the accuracy of macroeconomic forecasts made by National Commission of Prognosis and Institute of Economic Forecasting for Romania

    Directory of Open Access Journals (Sweden)

    Mihaela Bratu (Simionescu

    2012-01-01

    Full Text Available In this article, the accuracy of forecasts for inflation rate, unemployment, exchange rate and GDP index provided by Institute of Economic Forecasting (IEF and National Commission of Prognosis (NCP was assessed for the forecasting horizon 2004-2011. The hypothesis that combined forecasts is a suitable strategy of improving the predictions accuracy was tested. Only for the unemployment rate the combined forecasts based on IEF and NCP evaluations performed better than the initial forecasts. For inflation and exchange rate Dobrescu model of IEF provided better predictions, but the combined ones were more accurate than NCP expectations. The Dobrescu model predictions combined with ARMA static respectively dynamic forecasts and NCP estimations combined with ARMA static prognosis, respectively Dobrescu forecasts using EQ scheme for unemployment on a horizon of 2 years (2010-2011 improved the accuracy of forecasts made by both institutions, the combined predictions based on Dobrescu predictions and ARMA static ones using OPT scheme being the most accurate, according to U1 Theils’ statistic.

  20. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1995-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  1. The circadian profile of epilepsy improves seizure forecasting.

    Science.gov (United States)

    Karoly, Philippa J; Ung, Hoameng; Grayden, David B; Kuhlmann, Levin; Leyde, Kent; Cook, Mark J; Freestone, Dean R

    2017-08-01

    It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For

  2. Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Yu Zhao

    2013-01-01

    Full Text Available In the study, we discussed the generalized autoregressive conditional heteroskedasticity model and enhanced it with wavelet transform to evaluate the daily returns for 1/4/2002-30/12/2011 period in Brent oil market. We proposed discrete wavelet transform generalized autoregressive conditional heteroskedasticity model to increase the forecasting performance of the generalized autoregressive conditional heteroskedasticity model. Our new approach can overcome the defect of generalized autoregressive conditional heteroskedasticity family models which can’t describe the detail and partial features of times series and retain the advantages of them at the same time. Comparing with the generalized autoregressive conditional heteroskedasticity model, the new approach significantly improved forecast results and greatly reduces conditional variances.

  3. Modeling Growth Trend and Forecasting Techniques for Vehicular Population in India

    Directory of Open Access Journals (Sweden)

    Kartikeya Jha

    2013-06-01

    Full Text Available Forecasting and estimation of growth in vehicular population is a sine qua non of any major transportation engineering development, requires capturing the past trend and using it to predict the future trend based on qualified assumptions, simulations and models created using explanatory variables. This work attempts to review the in vogue approaches and investigate a more contemporary approach, the Time Series (TS Analysis. Three fundamentally different methods were explored and results from each of these analyses were collated to check for respective levels of accuracy in predicting vehicular population for the same target year. Within the scope of this study and estimation, results obtained from TS Analysis were found to be considerably more accurate than those from Trend Line Analysis and observably better than those from Econometric Analysis. To reinforce these observations and inferences drawn, a second set of analysis was done on more recent input by using AADT data from PeMS, California. Inter alia this was carried out to contrast any statistical improvement observed when doing TS analysis with rich and accurate data. With all the data sets used and locations analyzed for forecasting, the Time Series analysis technique was invariably found to be a potent tool for forecasting.

  4. Improved grey-based approach for power demand forecasting

    Institute of Scientific and Technical Information of China (English)

    LIN Jia-mu; LIU Dan

    2006-01-01

    Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.

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

    Science.gov (United States)

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

    2017-04-01

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

  6. Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

    Science.gov (United States)

    Susskind, Joel; Reale, Oreste

    2010-01-01

    AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best

  7. Improving Weather Forecasts Through Reduced Precision Data Assimilation

    Science.gov (United States)

    Hatfield, Samuel; Düben, Peter; Palmer, Tim

    2017-04-01

    We present a new approach for improving the efficiency of data assimilation, by trading numerical precision for computational speed. Future supercomputers will allow a greater choice of precision, so that models can use a level of precision that is commensurate with the model uncertainty. Previous studies have already indicated that the quality of climate and weather forecasts is not significantly degraded when using a precision less than double precision [1,2], but so far these studies have not considered data assimilation. Data assimilation is inherently uncertain due to the use of relatively long assimilation windows, noisy observations and imperfect models. Thus, the larger rounding errors incurred from reducing precision may be within the tolerance of the system. Lower precision arithmetic is cheaper, and so by reducing precision in ensemble data assimilation, we can redistribute computational resources towards, for example, a larger ensemble size. Because larger ensembles provide a better estimate of the underlying distribution and are less reliant on covariance inflation and localisation, lowering precision could actually allow us to improve the accuracy of weather forecasts. We will present results on how lowering numerical precision affects the performance of an ensemble data assimilation system, consisting of the Lorenz '96 toy atmospheric model and the ensemble square root filter. We run the system at half precision (using an emulation tool), and compare the results with simulations at single and double precision. We estimate that half precision assimilation with a larger ensemble can reduce assimilation error by 30%, with respect to double precision assimilation with a smaller ensemble, for no extra computational cost. This results in around half a day extra of skillful weather forecasts, if the error-doubling characteristics of the Lorenz '96 model are mapped to those of the real atmosphere. Additionally, we investigate the sensitivity of these results

  8. THE ACCURACY AND BIAS EVALUATION OF THE USA UNEMPLOYMENT RATE FORECASTS. METHODS TO IMPROVE THE FORECASTS ACCURACY

    Directory of Open Access Journals (Sweden)

    MIHAELA BRATU (SIMIONESCU

    2012-12-01

    Full Text Available In this study some alternative forecasts for the unemployment rate of USA made by four institutions (International Monetary Fund (IMF, Organization for Economic Co-operation and Development (OECD, Congressional Budget Office (CBO and Blue Chips (BC are evaluated regarding the accuracy and the biasness. The most accurate predictions on the forecasting horizon 201-2011 were provided by IMF, followed by OECD, CBO and BC.. These results were gotten using U1 Theil’s statistic and a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the institutions regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. The IMF, OECD and CBO predictions are unbiased. The combined forecasts of institutions’ predictions are a suitable strategy to improve the forecasts accuracy of IMF and OECD forecasts when all combination schemes are used, but INV one is the best. The filtered and smoothed original predictions based on Hodrick-Prescott filter, respectively Holt-Winters technique are a good strategy of improving only the BC expectations. The proposed strategies to improve the accuracy do not solve the problem of biasness. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decisional process.

  9. The importance of the reference populations for coherent mortality forecasting models

    DEFF Research Database (Denmark)

    Kjærgaard, Søren; Canudas-Romo, Vladimir; Vaupel, James W.

    -population mortality models aiming to find the optimal of the set of countries to use as reference population and analyse the importance of the selection of countries. The two multi-population mortality models used are the Li-Lee model and the Double-Gap life expectancy forecasting model. The reference populations...... is calculated taking into account all the possible combinations of a set of 20 industrialized countries. The different reference populations possibilities are compared by their forecast performance. The results show that the selection of countries for multi-population mortality models has a significant effect...

  10. Using new satellite data would improve hurricane forecasts

    National Research Council Canada - National Science Library

    Schultz, Colin

    2013-01-01

    To track and forecast the development of dangerous tropical cyclones, the National Weather Service's National Centers for Environmental Prediction uses a model known as the Hurricane Weather Research and Forecasting (HWRF) system...

  11. Improved forecasting with leading indicators: the principal covariate index

    NARCIS (Netherlands)

    C. Heij (Christiaan)

    2007-01-01

    textabstractWe propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite Coincident Index. The forecast performance is compared

  12. Improving probabilistic forecast skill by calibrating site-specific and gridded ensemble forecasts

    Science.gov (United States)

    Schuhen, Nina; Evans, Gavin; Jackson, Simon; Wright, Bruce

    2016-04-01

    While forecast ensembles allow for the design and usage of novel probabilistic forecast products, they still cannot capture all sources of uncertainty inherent to NWP forecasting. In particular they are often not calibrated, resulting in the fact that the probabilistic forecasts derived from ensembles are not statistically consistent with the corresponding observations. A number of statistical post-processing methods for the purpose of calibrating ensemble forecasts have been proposed over the last decade, with Bayesian Model Averaging and Ensemble Model Output Statistics (or Non-homogeneous Gaussian Regression) being among the most successful, as they can be applied to a variety of weather parameters. At the Met Office, the calibration of probabilistic forecasts has received more and more attention over the last few years and several calibration techniques based on BMA and EMOS are being trialled and assessed for their benefit over the raw ensemble forecasts. Challenges arise when addressing weather parameters which by nature don't exhibit a normal distribution. We present results for the calibration of site-specific and gridded forecasts, in the short- to medium-range, while highlighting the need for preserving the multivariate dependency structure inherent to the ensemble forecasts. We will also draw conclusions on the practicality of operational implementation and discuss the performance at individual sites.

  13. Constraining the Ensemble Kalman Filter for improved streamflow forecasting

    Science.gov (United States)

    Maxwell, Deborah; Jackson, Bethanna; McGregor, James

    2016-04-01

    Data assimilation techniques such as the Kalman Filter and its variants are often applied to hydrological models with minimal state volume/capacity constraints. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this presentation, we investigate the effect of constraining the Ensemble Kalman Filter (EnKF) on forecast performance. An EnKF implementation with no constraints is compared to model output with no assimilation, followed by a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then a more tightly constrained implementation where flux as well as mass constraints are imposed to limit the rate of water movement within a state. A three year period (2008-2010) with no significant data gaps and representative of the range of flows observed over the fuller 1976-2010 record was selected for analysis. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Overall, neither the unconstrained nor the "typically" mass-constrained forecasts were significantly better than the non-filtered forecasts; in fact several were significantly degraded. Flux constraints (in conjunction with mass constraints) significantly improved the forecast performance of six events relative to all other implementations, while the remaining two events showed no significant difference in performance. We conclude that placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state updating and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also experiment with the observation error, and find that this

  14. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    Science.gov (United States)

    Wanders, Niko; Wood, Eric F.

    2016-09-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

  15. Fusion of Hurricane Models and Observations: Developing the Technology to Improve the Forecasts Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Develop the technology to provide the fusion of observations and operational model simulations to help improve the understanding and forecasting of hurricane...

  16. Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error

    Science.gov (United States)

    Joslyn, Susan L.; LeClerc, Jared E.

    2012-01-01

    Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather…

  17. Improving the Seasonal Forecast of Summer Precipitation in China Using a Dynamical-Statistical Approach

    Institute of Scientific and Technical Information of China (English)

    JIA Xiao-Jing; ZHU Pei-Jun

    2010-01-01

    A dynamical-statistical post-processing approach is applied to seasonal precipitation forecasts in China during the summer.The data are ensemble-mean seasonal forecasts in summer(June-August)from four atmospheric general circulation models(GCMs)in the second phase of the Canadian Historical Forecasting Project(HFP2)from 1969 to 2001.This dynamical-statistical approach is designed based on the relationship between the 500 geopotential height(Z500)forecast and the observed sea surface temperature(SST)to calibrate the precipitation forecasts.The results show that the post-processing can improve summer precipitation forecasts for many areas in China.Further examination shows that this post-processing approach is very effective in reducing the model-dependent part of the errors,which are associated with GCMs.The possible mechanisms behind the forecast's improvements are investigated.

  18. Ageing of a giant: a stochastic population forecast for China, 2006-2060

    NARCIS (Netherlands)

    Li, Q.; Reuser, M.; Kraus, C.; Alho, J.S.

    2009-01-01

    This paper presents a stochastic population forecast for China with a special emphasis on population ageing. The so-called scaled model for error was used to quantify the uncertainty attached to the population predictions. Data scarcity was a major problem in the specification of the expected error

  19. Ageing of a giant: a stochastic population forecast for China, 2006-2060

    NARCIS (Netherlands)

    Li, Q.; Reuser, M.; Kraus, C.; Alho, J.S.

    2009-01-01

    This paper presents a stochastic population forecast for China with a special emphasis on population ageing. The so-called scaled model for error was used to quantify the uncertainty attached to the population predictions. Data scarcity was a major problem in the specification of the expected error

  20. Improved higher lead time river flow forecasts using sequential neural network with error updating

    Directory of Open Access Journals (Sweden)

    Prakash Om

    2014-03-01

    Full Text Available This paper presents a novel framework to use artificial neural network (ANN for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN, is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (>94% efficiency was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead. The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.

  1. Improving multimodel medium range forecasts over the Greater Horn of Africa using the FSU superensemble

    Science.gov (United States)

    Kipkogei, O.; Bhardwaj, A.; Kumar, V.; Ogallo, L. A.; Opijah, F. J.; Mutemi, J. N.; Krishnamurti, T. N.

    2016-08-01

    This study makes use of the WMO's multimodel data set called THORPEX integrated grand global ensemble (TIGGE) towards the construction of multimodel superensemble forecasts covering a period of 10 days. The goal of this study is to explore the forecast skill for precipitation forecasts over the Greater Horn of Africa (this is a consortium of 11 countries). The multimodels include forecast data set from a suite of models that include: The European Centre for Medium Range Weather Forecasts (ECMWF), the National Centre for Environmental Prediction (NCEP), the Center for Weather Forecast and Climatic Studies (CPTEC) and the United Kingdom Meteorological Office (UKMO). After performing a training phase for the superensemble weights covering the previous 450 days of October, November and December months of 2008-2012, forecasts of precipitation were prepared for the multimodel superensemble. These covered day 1 to day 10 of forecasts over the region. Various skill metrics were prepared to validate the forecast rainfall against the tropical rainfall measuring mission (TRMM) observed rainfall data. This study shows that the construction of the multimodel superensemble was a worthwhile effort since it provided the best overall skills for the RMS errors, the spatial correlations and the equitable threat scores and their bias errors for precipitation forecasts from day 1 to day 10 over all of the countries covered by the Greater Horn of Africa. The best among the member model was the UKMO model. This study strongly suggests the usefulness of a product such as the multimodel superensemble for improved precipitation forecasts over East Africa.

  2. Improving statistical forecasts of seasonal streamflows using hydrological model output

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-02-01

    Full Text Available Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1 when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2 when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3 when the initial catchment condition is near saturation intermittently throughout the historical record.

  3. Improving statistical forecasts of seasonal streamflows using hydrological model output

    Science.gov (United States)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2013-02-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  4. Case studies on forecasting for innovative technologies: frequent revisions improve accuracy.

    Science.gov (United States)

    Lerner, Jeffrey C; Robertson, Diane C; Goldstein, Sara M

    2015-02-01

    Health technology forecasting is designed to provide reliable predictions about costs, utilization, diffusion, and other market realities before the technologies enter routine clinical use. In this article we address three questions central to forecasting's usefulness: Are early forecasts sufficiently accurate to help providers acquire the most promising technology and payers to set effective coverage policies? What variables contribute to inaccurate forecasts? How can forecasters manage the variables to improve accuracy? We analyzed forecasts published between 2007 and 2010 by the ECRI Institute on four technologies: single-room proton beam radiation therapy for various cancers; digital breast tomosynthesis imaging technology for breast cancer screening; transcatheter aortic valve replacement for serious heart valve disease; and minimally invasive robot-assisted surgery for various cancers. We then examined revised ECRI forecasts published in 2013 (digital breast tomosynthesis) and 2014 (the other three topics) to identify inaccuracies in the earlier forecasts and explore why they occurred. We found that five of twenty early predictions were inaccurate when compared with the updated forecasts. The inaccuracies pertained to two technologies that had more time-sensitive variables to consider. The case studies suggest that frequent revision of forecasts could improve accuracy, especially for complex technologies whose eventual use is governed by multiple interactive factors. Project HOPE—The People-to-People Health Foundation, Inc.

  5. Improved Water and Energy Management Utilizing Seasonal to Interannual Hydroclimatic Forecasts

    Science.gov (United States)

    Arumugam, S.; Lall, U.

    2014-12-01

    Seasonal to interannual climate forecasts provide valuable information for improving water and energy management. Given that the climatic attributes over these time periods are typically expressed as probabilistic information, we propose an adaptive water and energy management framework that uses probabilistic inflow forecasts to allocate water for uses with pre-specified reliabilities. To ensure that the system needs are not compromised due to forecast uncertainty, we propose uncertainty reduction using model combination and based on a probabilistic constraint in meeting the target storage. The talk will present findings from recent studies from various basins that include (a) role of multimodel combination in reducing the uncertainty in allocation (b) relevant system characteristics that improve the utility of forecasts, (c) significance of streamflow forecasts in promoting interbasin transfers and (d) scope for developing power demand forecasts utilizing temperature forecasts. Potential for developing seasonal nutrient forecasts using climate forecasts for supporting water quality trading will also be presented. Findings and synthesis from the panel discussion from the recently concluded AGU chapman conference on "Seaonal to Interannual Hydroclimatic Forecasts and Water Management" will also be summarized.

  6. Improvement in global forecast for chaotic time series

    Science.gov (United States)

    Alves, P. R. L.; Duarte, L. G. S.; da Mota, L. A. C. P.

    2016-10-01

    In the Polynomial Global Approach to Time Series Analysis, the most costly (computationally speaking) step is the finding of the fitting polynomial. Here we present two routines that improve the forecasting. In the first, an algorithm that greatly improves this situation is introduced and implemented. The heart of this procedure is implemented on the specific routine which performs a mapping with great efficiency. In comparison with the similar procedure of the TimeS package developed by Carli et al. (2014), an enormous gain in efficiency and an increasing in accuracy are obtained. Another development in this work is the establishment of a level of confidence in global prediction with a statistical test for evaluating if the minimization performed is suitable or not. The other program presented in this article applies the Shapiro-Wilk test for checking the normality of the distribution of errors and calculates the expected deviation. The development is employed in observed and simulated time series to illustrate the performance obtained.

  7. Skill improvement of seasonal Arctic sea ice forecasts using bias-correction and ensemble calibration

    Science.gov (United States)

    Krikken, Folmer; Hazeleger, Wilco; Vlot, Willem; Schmeits, Maurice; Guemas, Virginie

    2016-04-01

    We explore the standard error and skill of dynamical seasonal sea ice forecasts of the Arctic using different bias-correction and ensemble calibration methods. The latter is often used in weather forecasting, but so far has not been applied to Arctic sea ice forecasts. We use seasonal predictions of Arctic sea ice of a 5-member ensemble forecast using the fully coupled GCM EC-Earth, with model initial states obtained by nudging towards ORAS4 and ERA-Interim. The raw model forecasts contain large biases in total sea ice area, especially during the summer months. This is mainly caused by a difference in average seasonal cycle between EC-Earth and observations, which translates directly into the forecasts yielding large biases. Further errors are introduced by the differences in long term trend between the observed sea ice, and the uninitialised EC-earth simulation. We find that extended logistic regression (ELR) and heteroscedastic extended logistic regression (HELR) both prove viable ensemble calibration methods, and improve the forecasts substantially compared to standard bias correction techniques. No clear distinction between ELR and HELR is found. Forecasts starting in May have higher skill (CRPSS > 0 up to 5 months lead time) than forecasts starting in August (2-3 months) and November (2-3 months), with trend-corrected climatology as reference. Analysis of regional skill in the Arctic shows distinct differences, where mainly the Arctic ocean and the Kara and Barents sea prove to be one of the more predictable regions with skilful forecasts starting in May up to 5-6 months lead time. Again, forecasts starting in August and November show much lower regional skill. Overall, it is still difficult to beat relative simple statistical forecasts, but by using ELR and HELR we are getting reasonably close to skilful seasonal forecasts up to 12 months lead time. These results show there is large potential, and need, for using ensemble calibration in seasonal forecasts of

  8. To improve their predictions, election forecasters should look to other disciplines like meteorology

    OpenAIRE

    Michael S. Lewis-Beck; Stegmaier, Mary

    2014-01-01

    The recent surge in public attention to election predictions has generated much discussion about how to improve forecasting model accuracy. Michael S. Lewis-Beck and Mary Stegmaier argue that advances in weather forecasting hold lessons for election forecasting. First, like weather models, election models should be based on sound theory. Second, more intensive data gathering, especially at the state level with repeated measurements over time, will capture the dynamics of the campaign and ulti...

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

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions so as to significantly reduce the cost for frost and freeze protection and crop losses. The design and implementation of the first phase of an economic experiment which will monitor citrus growers decisions, actions, costs and losses, and meteorological forecasts and actual weather events was carried out. The economic experiment was designed to measure the change in annual protection costs and crop losses which are the direct result of improved temperature forecasts. To estimate the benefits that may result from improved temperature forecasting capability, control and test groups were established with effective separation being accomplished temporally. The control group, utilizing current forecasting capability, was observed during the 1976-77 frost season and the results are reported. A brief overview is given of the economic experiment, the results obtained to date, and the work which still remains to be done.

  10. AN EVALUATION OF USA UNEMPLOYMENT RATE FORECASTS IN TERMS OF ACCURACY AND BIAS. EMPIRICAL METHODS TO IMPROVE THE FORECASTS ACCURACY

    Directory of Open Access Journals (Sweden)

    BRATU (SIMIONESCU MIHAELA

    2013-02-01

    Full Text Available The most accurate forecasts for USA unemployment rate on the horizon 2001-2012, according to U1 Theil’s coefficient and to multi-criteria ranking methods, were provided by International Monetary Fund (IMF, being followed by other institutions as: Organization for Economic Co-operation and Development (OECD, Congressional Budget Office (CBO and Blue Chips (BC. The multi-criteria ranking methods were applied to solve the divergence in assessing the accuracy, differences observed by computing five chosen measures of accuracy: U1 and U2 statistics of Theil, mean error, mean squared error, root mean squared error. Some strategies of improving the accuracy of the predictions provided by the four institutions, which are biased in all cases, excepting BC, were proposed. However, these methods did not generate unbiased forecasts. The predictions made by IMF and OECD for 2001-2012 can be improved by constructing combined forecasts, the INV approach and the scheme proposed by author providing the most accurate expections. The BC forecasts can be improved by smoothing the predictions using Holt-Winters method and Hodrick - Prescott filter.

  11. Improvement of ECMWF monthly forecasts of precipitation over France with an analog method

    Science.gov (United States)

    Berthelot, M.; Dubus, L.; Gailhard, J.

    2010-09-01

    Optimal operation of hydro-power plants requires accurate forecasts of precipitation which are then integrated into hydrological models to forecast river flows and water volumes in reservoirs. Precipitation is a difficult parameter to forecast, especially at long lead times (monthly and over) one of the reasons being the too coarse resolution of numerical weather prediction systems. In this study, we evaluate ECMWF's monthly forecasts of precipitation over 9 important basins in France. The deterministic approach shows that forecasts are useless over week 1. Using the probabilistic approach allows to get useful information for some events (lower and upper terciles for instance), up to week 3, but the overall scores are quite low, and hardly better than climatological scores. In a second step, EDF's analog method, currently used in operations for D+7 forecasts, has been adapted to ECWMF's monthly forecasts. It uses Z700 and Z1000 fields over North Atlantic and Europe to get local precipitations. For the nine catchments studied here and for the four weeks, results show an overall improvement of analog precipitation forecasts compared to raw forecasts. Improvement is also identified with respect to climatology in more than half of the catchments. The prediction skill is mostly pronounced for extreme events (low and heavy precipitations). The analog method thus presents significant performance, suited for operational use. Improvements can also be expected with some optimization of the method (mix of predictors, new similarity criterion…)

  12. Translating the potential of hydrological forecasts into improved decision making in African regions

    Science.gov (United States)

    Sheffield, J.; He, X.; Wanders, N.; Wood, E. F.; Ali, A.; Olang, L.; Estes, L. D.; Caylor, K. K.; Evans, T. P.

    2015-12-01

    Hydrological forecasts at local scale and seasonal time scales have the potential to inform decision-making by individuals and institutions to improve management of water resources and enhance food security. Much progress has been made in recent years in understanding climate variability and its predictability over African regions. However, there remain many challenges in translating large-scale evaluations and forecasts into locally relevant information. This is hampered by lack of on the ground data of hydrological and agricultural states, and the generally low skill of climate forecasts at time scales beyond one or two weeks. Additionally, the uptake of forecasts is not prevalent because of lack of capacity, and institutional and cultural barriers to using new and uncertain information. New technologies for monitoring and forecasting relevant hydrological variables, and novel approaches to understanding how this information may be used within decision making processes, have the potential to make substantial progress in addressing these challenges. We present a quasi-operational drought and flood monitoring and forecasting system and its use in understanding the potential of hydrological forecasts for improved decision-making. The system monitors in near real-time the terrestrial water cycle for the African continent based on remote sensing data and land surface hydrological modeling. The monitoring forms initial conditions for hydrological forecasts at short time scale, aimed at flood forecasting, and seasonal scale aimed at drought and crop yield forecasts. The flood forecasts are driven by precipitation and temperature forecasts from the Global Forecast System (GFS). The drought forecasts are driven by climate forecasts from the North American Multi-Model Ensemble (NMME). The seasonal forecast skill is modest and seasonally/regionally dependent with part of the skill coming from persistence in initial land surface conditions. We discuss the use of the system

  13. Biographic forecasting: bridging the micro-macro gap in population forecasting

    NARCIS (Netherlands)

    Willekens, F.J.

    2005-01-01

    The paper outlines a new model for demographic projections by detailed population categories that are required in the development of sustainable (elderly) health care systems and pension systems. The methodology consists of a macro-model (MAC) that models demographic changes at the population level

  14. Population forecasting with endogenous migration: an application to trans-Tasman migration.

    Science.gov (United States)

    Gorbey, S; James, D; Poot, J

    1999-04-01

    "This article focuses on forecasting migration between Australia and New Zealand (trans-Tasman migration), which is largely visa-free and therefore resembles internal migration. Net trans-Tasman migration is a major component of New Zealand population change and is embedded in this article in a Bayesian or unrestricted vector autoregression (VAR) model, which includes foreign and domestic economic variables. When time series of net migration are available, this approach provides a useful input into forecasting population growth in the short run in the absence of major policy changes. This conclusion applies equally to interregional migration and to unrestricted international migration between economically integrated nations."

  15. Historic and forecasted population and land-cover change in eastern North Carolina, 1992-2030

    Science.gov (United States)

    Claggett, Peter R.; Hearn,, Paul P.; Donato, David I.

    2015-01-01

    The Southeast Regional Partnership for Planning and Sustainability (SERPPAS) was formed in 2005 as a partnership between the Department of Defense (DOD) and State and Federal agencies to promote better collaboration in making resource-use decisions. In support of this goal, the U.S. Geological Survey (USGS) conducted a study to evaluate historic population growth and land-cover change, and to model future change, for the 13-county SERPPAS study area in southeastern North Carolina (fig. 1). Improved understanding of trends in land-cover change and the ability to forecast land-cover change that is consistent with these trends will be a key component of efforts to accommodate local military-mission imperatives while also promoting sustainable economic growth throughout the 13-county study area. The study had three principal objectives:    1.  Evaluate historic changes in population and land cover for the period 1992–2006 using both previously existing as well as newly generated land-cover data.    2.  Develop models to forecast future change in land cover using the data gathered in objective 1 in conjunction with ancillary data on the suitability of the various sub-areas within the study area for low- and high-intensity urban development.    3.  Deliver these results—including an executive-level briefing and a USGS technical report—to DOD, other project cooperators, and local counties in hard-copy and digital formats and via the Web through a map-based data viewer. This report provides a general overview of the study and is intended for general distribution to non-technical audiences.

  16. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

    2016-04-01

    This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  17. Improved NN-GM(1,1 for Postgraduates’ Employment Confidence Index Forecasting

    Directory of Open Access Journals (Sweden)

    Lu Wang

    2014-01-01

    Full Text Available Postgraduates’ employment confidence index (ECI forecasting can help the university to predict the future trend of postgraduates’ employment. However, the common forecast method based on the grey model (GM has unsatisfactory performance to a certain extent. In order to forecast postgraduates’ ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples. Different from previous work, the residual modified GM(1,1 model is combined with the improved neural network (NN in this work. In particullar, the hybrid model reduces the residue of the standard GM(1,1 model as well as accelerating the convergence rate of the standard NN. After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model. In addition, some strategies for improving the postgraduates’ employment confidence have been discussed.

  18. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

  19. Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Pagowski, M O; Grell, G A; Devenyi, D; Peckham, S E; McKeen, S A; Gong, W; Monache, L D; McHenry, J N; McQueen, J; Lee, P

    2006-02-02

    Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost.

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

    Science.gov (United States)

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

    2014-01-01

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

  1. Improving Global Flood Forecasting using Satellite Detected Flood Extent

    NARCIS (Netherlands)

    Revilla Romero, B.

    2016-01-01

    Flooding is a natural global phenomenon but in many cases is exacerbated by human activity. Although flooding generally affects humans in a negative way, bringing death, suffering, and economic impacts, it also has potentially beneficial effects. Early flood warning and forecasting systems, as well

  2. Improved forecasting of thermospheric densities using multi-model ensembles

    Science.gov (United States)

    Elvidge, Sean; Godinez, Humberto C.; Angling, Matthew J.

    2016-07-01

    This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere-Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the "standard" runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.

  3. Doppler Lidar in the Wind Forecast Improvement Projects

    Directory of Open Access Journals (Sweden)

    Pichugina Yelena

    2016-01-01

    Full Text Available This paper will provide an overview of some projects in support of Wind Energy development involving Doppler lidar measurement of wind flow profiles. The high temporal and vertical resolution of these profiles allows the uncertainty of Numerical Weather Prediction models to be evaluated in forecasting dynamic processes and wind flow phenomena in the layer of rotor-blade operation.

  4. Doppler Lidar in the Wind Forecast Improvement Projects

    Science.gov (United States)

    Pichugina, Yelena; Banta, Robert; Brewer, Alan; Choukulkar, Aditya; Marquis, Melinda; Olson, Joe; Hardesty, Mike

    2016-06-01

    This paper will provide an overview of some projects in support of Wind Energy development involving Doppler lidar measurement of wind flow profiles. The high temporal and vertical resolution of these profiles allows the uncertainty of Numerical Weather Prediction models to be evaluated in forecasting dynamic processes and wind flow phenomena in the layer of rotor-blade operation.

  5. Improving Global Flood Forecasting using Satellite Detected Flood Extent

    NARCIS (Netherlands)

    Revilla Romero, B.

    2016-01-01

    Flooding is a natural global phenomenon but in many cases is exacerbated by human activity. Although flooding generally affects humans in a negative way, bringing death, suffering, and economic impacts, it also has potentially beneficial effects. Early flood warning and forecasting systems, as well

  6. Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

    Science.gov (United States)

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

    2014-10-01

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

  7. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

    Science.gov (United States)

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

    2015-08-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

  8. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    Energy Technology Data Exchange (ETDEWEB)

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  9. Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

    Science.gov (United States)

    Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

    2017-08-01

    Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

  10. Forecasting the mortality rates of Malaysian population using Heligman-Pollard model

    Science.gov (United States)

    Ibrahim, Rose Irnawaty; Mohd, Razak; Ngataman, Nuraini; Abrisam, Wan Nur Azifah Wan Mohd

    2017-08-01

    Actuaries, demographers and other professionals have always been aware of the critical importance of mortality forecasting due to declining trend of mortality and continuous increases in life expectancy. Heligman-Pollard model was introduced in 1980 and has been widely used by researchers in modelling and forecasting future mortality. This paper aims to estimate an eight-parameter model based on Heligman and Pollard's law of mortality. Since the model involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 7.0 (MATLAB 7.0) software will be used in order to estimate the parameters. Statistical Package for the Social Sciences (SPSS) will be applied to forecast all the parameters according to Autoregressive Integrated Moving Average (ARIMA). The empirical data sets of Malaysian population for period of 1981 to 2015 for both genders will be considered, which the period of 1981 to 2010 will be used as "training set" and the period of 2011 to 2015 as "testing set". In order to investigate the accuracy of the estimation, the forecast results will be compared against actual data of mortality rates. The result shows that Heligman-Pollard model fit well for male population at all ages while the model seems to underestimate the mortality rates for female population at the older ages.

  11. The United Nations Probabilistic Population Projections: An Introduction to Demographic Forecasting with Uncertainty.

    Science.gov (United States)

    Alkema, Leontine; Gerland, Patrick; Raftery, Adrian; Wilmoth, John

    2015-01-01

    The United Nations publishes projections of populations around the world and breaks these down by age and sex. Traditionally, they are produced with standard demographic methods based on assumptions about future fertility rates, survival probabilities, and migration counts. Such projections, however, were not accompanied by formal statements of uncertainty expressed in probabilistic terms. In July 2014 the UN for the first time issued official probabilistic population projections for all countries to 2100. These projections quantify uncertainty associated with future fertility and mortality trends worldwide. This review article summarizes the probabilistic population projection methods and presents forecasts for population growth over the rest of this century.

  12. Operational data assimilation for improving hydrologic, hydrodynamic, and water quality forecasting using open tools

    Science.gov (United States)

    Weerts, Albrecht; Kockx, Arno; Sumihar, Julius; Verlaan, Martin; Hummel, Stef; Kramer, Werner; de Klaermaker, Simone

    2014-05-01

    Data assimilation holds considerable potential for improving water quantity (hydrologic/ hydraulic) and water quality predictions. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. In contrast to most operational weather (related) forecast centers operational hydrologic forecast centers often are unable to support & maintain or lack the required computing support to implement such intensive DA calculations. Moreover, it remains difficult to achieve coupling of models, data, DA techniques and exploitation of high performance computing solutions in the operational forecasting process. Several potential components of a future solution have been or are being developed, one of those being the open source project OpenDA (www.openda.org). The objective of this poster is to highlight the development of OpenDA for operational forecasting and its integration with Delft-FEWS that is being used by more than 40 operational forecast centres around the world. Several applications of OpenDA using open source (and available) model codes from various fields will be highlighted.

  13. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  14. Ratio-based lengths of intervals to improve fuzzy time series forecasting.

    Science.gov (United States)

    Huarng, Kunhuang; Yu, Tiffany Hui-Kuang

    2006-04-01

    The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

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

    Science.gov (United States)

    Bhattacharyya, R.; Greenberg, J.

    1975-01-01

    Benefit-cost relationships for the development of meteorological satellites are outlined. The weather forecast capabilities of the various weather satellites (Tiros, SEOS, Nimbus) are discussed, and the development of additional satellite systems is examined. A rational approach is development that leads to the establishment of the economic benefits which may result from the utilization of meteorological satellite data. The economic and social impacts of improved weather forecasting for industries and resources management are discussed, and significant weather sensitive industries are listed.

  16. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  17. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  18. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    Energy Technology Data Exchange (ETDEWEB)

    Finley, Cathy [WindLogics, St. Paul, MN (United States)

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  19. Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

    KAUST Repository

    Zhu, Xinxin

    2014-09-01

    Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.

  20. Improved El Ni\\~no-Forecasting by Cooperativity Detection

    CERN Document Server

    Ludescher, Josef; Bogachev, Mikhail I; Bunde, Armin; Havlin, Shlomo; Schellnhuber, Hans Joachim

    2013-01-01

    Although anomalous episodical warming of the eastern equatorial Pacific, dubbed El Ni\\~no by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about six months ahead. A significant extension of the pre-warming time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a novel avenue towards El Ni\\~no-prediction based on network methods inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode - linking the El Ni\\~no-basin (equatorial Pacific corridor) and the rest of the ocean - builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12 months-forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data as available since 1950 and yields hit rates above 0.5, while false-alarm rates are below 0.1.

  1. On using numerical sea-ice prediction and indigenous observations to improve operational sea-ice forecasts during spring in the bering sea

    Science.gov (United States)

    Deemer, Gregory Joseph

    Impacts of a rapidly changing climate are amplified in the Arctic. The most notorious change has come in the form of record-breaking summertime sea-ice retreat. Larger areas of open water and a prolonged ice-free season create opportunity for some industries, but bring new challenges to indigenous populations that rely on sea-ice cover for subsistence. Observed and projected increases in maritime activities require accurate sea-ice forecasts on the weather timescale, which are currently lacking. Motivated by this need, this study explores how new modeling developments and local-scale observations can contribute to improving sea-ice forecasts. The Arctic Cap Nowcast/Forecast System, a research sea-ice forecast model developed by the U.S. Navy, is evaluated for forecast skill. Forecasts of ice concentration, thickness, and drift speed produced by the model from April through June 2011 in the Bering Sea were investigated to determine how the model performs relative to persistence and climatology. Results show that model forecasts can outperform forecasts based on climatology or persistence. However, predictive skill is less consistent during powerful, synoptic-scale events and near the Bering Slope. Forecast case studies in Western Alaska were presented. Community-based observations from recognized indigenous sea-ice experts have been analyzed to gauge the prospect of using local observations in the operational sea-ice monitoring and prediction process. Local observations were discussed in the context of cross-validating model guidance, data sources used in operational ice monitoring, and public sea-ice information products issued by the U.S. National Weather Service. Instrumentation for observing sea-ice and weather at the local scale was supplied to key observers. The instrumentation shows utility in the field and may help translate the context of indigenous observations and provide ground-truth data for use by forecasters.

  2. 3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The vertical structure of water vapor in atmosphere is one of the initial information of numerical weather forecast model. Because of the strong variation of water vapor in atmosphere and limited spatio-temporal solutions of traditional observation technique, the initial water vapor field of numerical weather forecast model can not accurately be described. At present, using GPS slant observations to study water vapor profile is very popular in the world. Using slant water vapor(SWV) observations from Shanghai GPS network,we diagnose the three-dimensional(3D) water vapor structure over Shanghai area firstly in China. In water vapor tomography, Gauss weighted function is used as horizontal constraint, the output of numerical forecast is used as apriori information, and boundary condition is also considered. For the problem without exact apriori weights for observations, estimation of variance components is introduced firstly in water vapor tomography to determine posteriori weights. Robust estimation is chosen for reducing the effect of blunders on solutions. For the descending characteristic of water vapor with height increasing, non-equal weights are used along vertical direction. Comparisons between tomography results and the profile provided by numerical model (MM5) show that the forecasted moisture fields of MM5 can be improved obviously by GPS slant water vapor. Using GPS slant observations to study 3D structure of atmosphere in near real-time is very important for improving initial water vapor field of short-term weather forecast and enhancing the accuracy of numerical weather forecast.

  3. Stochastic population forecasting based on combinations of expert evaluations within the Bayesian paradigm.

    Science.gov (United States)

    Billari, Francesco C; Graziani, Rebecca; Melilli, Eugenio

    2014-10-01

    This article suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution: that is, fertility, mortality, and migration. Here, two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but rather is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach, according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution, and a Markov chain Monte Carlo algorithm is designed to approximate this posterior. This article provides the questionnaire designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian population from 2010 to 2065 is proposed.

  4. Preliminary forecasts of Pacific bigeye tuna population trends under the A2 IPCC scenario

    Science.gov (United States)

    Lehodey, P.; Senina, I.; Sibert, J.; Bopp, L.; Calmettes, B.; Hampton, J.; Murtugudde, R.

    2010-07-01

    An improved version of the spatial ecosystem and population dynamics model SEAPODYM was used to investigate the potential impacts of global warming on tuna populations. The model included an enhanced definition of habitat indices, movements, and accessibility of tuna predators to different vertically migrant and non-migrant micronekton functional groups. The simulations covered the Pacific basin (model domain) at a 2° × 2° geographic resolution. The structure of the model allows an evaluation from multiple data sources, and parameterization can be optimized by adjoint techniques and maximum likelihood using fishing data. A first such optimized parameterization was obtained for bigeye tuna ( Thunnus obesus) in the Pacific Ocean using historical catch data for the last 50 years and a hindcast from a coupled physical-biogeochemical model driven by the NCEP atmospheric reanalysis. The parameterization provided very plausible biological parameter values and a good fit to fishing data from the different fisheries, both within and outside the time period used for optimization. We then employed this model to forecast the future of bigeye tuna populations in the Pacific Ocean. The simulation was driven by the physical-biogeochemical fields predicted from a global marine biogeochemistry - climate simulation. This global simulation was performed with the IPSL climate model version 4 (IPSL-CM4) coupled to the oceanic biogeochemical model PISCES and forced by atmospheric CO 2, from historical records over 1860-2000, and under the SRES A2 IPCC scenario for the 21st century (i.e. atmospheric CO 2 concentration reaching 850 ppm in the year 2100). Potential future changes in distribution and abundance under the IPCC scenario are presented but without taking into account any fishing effort. The simulation showed an improvement in bigeye tuna spawning habitat both in subtropical latitudes and in the eastern tropical Pacific (ETP) where the surface temperature becomes optimal for

  5. WOVOdat as a worldwide resource to improve eruption forecasts

    Science.gov (United States)

    Widiwijayanti, Christina; Costa, Fidel; Zar Win Nang, Thin; Tan, Karine; Newhall, Chris; Ratdomopurbo, Antonius

    2015-04-01

    During periods of volcanic unrest, volcanologists need to interpret signs of unrest to be able to forecast whether an eruption is likely to occur. Some volcanic eruptions display signs of impending eruption such as seismic activity, surface deformation, or gas emissions; but not all will give signs and not all signs are necessarily followed by an eruption. Volcanoes behave differently. Precursory signs of an eruption are sometimes very short, less than an hour, but can be also weeks, months, or even years. Some volcanoes are regularly active and closely monitored, while other aren't. Often, the record of precursors to historical eruptions of a volcano isn't enough to allow a forecast of its future activity. Therefore, volcanologists must refer to monitoring data of unrest and eruptions at similar volcanoes. WOVOdat is the World Organization of Volcano Observatories' Database of volcanic unrest - an international effort to develop common standards for compiling and storing data on volcanic unrests in a centralized database and freely web-accessible for reference during volcanic crises, comparative studies, and basic research on pre-eruption processes. WOVOdat will be to volcanology as an epidemiological database is to medicine. We have up to now incorporated about 15% of worldwide unrest data into WOVOdat, covering more than 100 eruption episodes, which includes: volcanic background data, eruptive histories, monitoring data (seismic, deformation, gas, hydrology, thermal, fields, and meteorology), monitoring metadata, and supporting data such as reports, images, maps and videos. Nearly all data in WOVOdat are time-stamped and geo-referenced. Along with creating a database on volcanic unrest, WOVOdat also developing web-tools to help users to query, visualize, and compare data, which further can be used for probabilistic eruption forecasting. Reference to WOVOdat will be especially helpful at volcanoes that have not erupted in historical or 'instrumental' time and

  6. Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

    Science.gov (United States)

    Khajehei, Sepideh; Moradkhani, Hamid

    2017-03-01

    Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

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

    Science.gov (United States)

    1977-01-01

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

  8. Using OMI data to improve air quality forecast - does it work?

    Science.gov (United States)

    Hvidberg, M.; Brandt, J.

    2009-04-01

    What benefits can we obtain from using data assimilation of remotely sensed air quality parameters into the CTM? This work presented aims to compare an air quality forecast with and without the use of satellite data, and to quantify the improvement gained from satellite data. The air quality forecast used is the Danish O3 operational warning system. Forecast are generated for each hour, for a 50km grid over Europe. O3 can irritate lungs and airways and can cause inflammation in the respiratory system. It can also trigger other diseases like asthma or bronchitis. O3 is a very important parameter in the CTM as it is highly reactive. The forecast is based on DEHM "Danish Eulerian Hemispheric Model" that is a CTM "Chemical Transport Model" designed to forecast air pollution. DEHM is part of the Thor model system. The satellite data used is the OMI NO2, Near Real Time data stream (DOMINO) from KNMI. The model was run for a reference year 2005, both with and without the use of Data Assimilation of OMI data. The results were each compared to reference measurements from ground stations in the European EMEP network. Many stations do not report hourly, but daily values. The validation uses the highest available resolution, temporal ans well as spatial. The present project is not entirely completed. However, expectations are that data assimilation of remotely sensed air quality parameters will increase the accuracy of the air pollution forecasts.

  9. Use of AWS, GPS, and Radiosonde observations to improve the 0-36h streamflow forecasts

    Science.gov (United States)

    Wang, Lei; Koike, Toshio; Liu, Jianyu; Wang, Man; Sun, Jihua; Lu, Hui; Tsutsui, Hiroyuki; Tamagawa, Katsunori; Li, Juan; Xu, Xiangde

    2010-05-01

    The real-time streamflow forecasting is largely determined by the quantitative precipitation forecasting (QPF), but convective weather remains a significant challenge for numerical weather prediction systems. The unreliable accuracies of the streamflow forecasts largely contributed to the historical flood disasters all over the world, including the southwest of China. In this study, the 36-h real-time streamflow forecasts with the Advanced Research Weather Research and Forecasting Model (WRF-ARW) and a distributed biosphere hydrological model (WEB-DHM) on 01 July and 15 July 2008 are presented. Results in the upper Nanpan River Basin of China demonstrated that the assimilation of the new and integrated meteorological observations into the WRF-ARW model has led to significant improvements on the 0-36h real-time QPF, and thus more reliable short-term streamflow forecasting. The newly-assimilated data includes the observations obtained from the newly-built Automatic Weather Stations (AWS), Global Positioning System (GPS), and Radiosonde in southwest China, funded by the cooperative JICA project between Japan and China.

  10. Improving medium-range ensemble streamflow forecasts through statistical post-processing

    Science.gov (United States)

    Mendoza, Pablo; Wood, Andy; Clark, Elizabeth; Nijssen, Bart; Clark, Martyn; Ramos, Maria-Helena; Nowak, Kenneth; Arnold, Jeffrey

    2017-04-01

    Probabilistic hydrologic forecasts are a powerful source of information for decision-making in water resources operations. A common approach is the hydrologic model-based generation of streamflow forecast ensembles, which can be implemented to account for different sources of uncertainties - e.g., from initial hydrologic conditions (IHCs), weather forecasts, and hydrologic model structure and parameters. In practice, hydrologic ensemble forecasts typically have biases and spread errors stemming from errors in the aforementioned elements, resulting in a degradation of probabilistic properties. In this work, we compare several statistical post-processing techniques applied to medium-range ensemble streamflow forecasts obtained with the System for Hydromet Applications, Research and Prediction (SHARP). SHARP is a fully automated prediction system for the assessment and demonstration of short-term to seasonal streamflow forecasting applications, developed by the National Center for Atmospheric Research, University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. The suite of post-processing techniques includes linear blending, quantile mapping, extended logistic regression, quantile regression, ensemble analogs, and the generalized linear model post-processor (GLMPP). We assess and compare these techniques using multi-year hindcasts in several river basins in the western US. This presentation discusses preliminary findings about the effectiveness of the techniques for improving probabilistic skill, reliability, discrimination, sharpness and resolution.

  11. Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

    Directory of Open Access Journals (Sweden)

    L. Mediero

    2012-12-01

    Full Text Available Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

  12. Improved wind and precipitation forecasts over South China using a modified orographic drag parameterization scheme

    Science.gov (United States)

    Zhong, Shuixin; Chen, Zitong

    2015-02-01

    To improve the wind and precipitation forecasts over South China, a modified orographic drag parameterization (OP) scheme that considers both the gravity wave drag (GWD) and the mountain blocking drag (MBD) effects was implemented in the Global/Regional Assimilation and Prediction System Tropical Mesoscale Model (GRAPES_TMM). Simulations were performed over one month starting from 1200 UTC 19 June 2013. The initial and lateral boundary conditions were obtained from the NCEP global forecast system output. The simulation results were compared among a control (CTL) experiment without the OP scheme, a GWDO experiment with the OP scheme that considers only the GWD effect, and an MBD experiment with the modified OP scheme (including both GWD and MBD). The simulation with the modified OP scheme successfully captured the main features of precipitation, including its distribution and intensity, and improved the wind circulation forecast in the lower troposphere. The modified OP scheme appears to improve the wind forecast by accelerating the ascending air motion and reinforcing the convergence in the rainfall area. Overall, the modified OP scheme exerts positive impacts on the forecast of large-scale atmospheric fields in South China.

  13. Improved Wind and Precipitation Forecasts over South China Using a Modified Orographic Drag Parameterization Scheme

    Institute of Scientific and Technical Information of China (English)

    钟水新; 陈子通

    2015-01-01

    To improve the wind and precipitation forecasts over South China, a modifi ed orographic drag param-eterization (OP) scheme that considers both the gravity wave drag (GWD) and the mountain blocking drag (MBD) eff ects was implemented in the Global/Regional Assimilation and Prediction System Tropical Mesoscale Model (GRAPES−TMM). Simulations were performed over one month starting from 1200 UTC 19 June 2013. The initial and lateral boundary conditions were obtained from the NCEP global forecast system output. The simulation results were compared among a control (CTL) experiment without the OP scheme, a GWDO experiment with the OP scheme that considers only the GWD eff ect, and an MBD ex-periment with the modifi ed OP scheme (including both GWD and MBD). The simulation with the modifi ed OP scheme successfully captured the main features of precipitation, including its distribution and intensity, and improved the wind circulation forecast in the lower troposphere. The modifi ed OP scheme appears to improve the wind forecast by accelerating the ascending air motion and reinforcing the convergence in the rainfall area. Overall, the modifi ed OP scheme exerts positive impacts on the forecast of large-scale atmospheric fi elds in South China.

  14. Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

    Science.gov (United States)

    Federico, Stefano; Petracca, Marco; Panegrossi, Giulia; Dietrich, Stefano

    2017-01-01

    This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature, and graupel mixing ratio. The methodology is set up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to 20 cases that occurred in fall 2012, which were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of rain gauges over Italy, which is the target area of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy). Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of convection by lightning data assimilation improves the precipitation forecast at fine scales (meso-β). The application of the methodology to 20 cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the bias. The probability of detection (POD) increases by 3-5 % for the 3 h forecast and by more than 5

  15. The evolutionary time machine: forecasting how populations can adapt to changing environments using dormant propagules

    Science.gov (United States)

    Orsini, Luisa; Schwenk, Klaus; De Meester, Luc; Colbourne, John K.; Pfrender, Michael E.; Weider, Lawrence J.

    2013-01-01

    Evolutionary changes are determined by a complex assortment of ecological, demographic and adaptive histories. Predicting how evolution will shape the genetic structures of populations coping with current (and future) environmental challenges has principally relied on investigations through space, in lieu of time, because long-term phenotypic and molecular data are scarce. Yet, dormant propagules in sediments, soils and permafrost are convenient natural archives of population-histories from which to trace adaptive trajectories along extended time periods. DNA sequence data obtained from these natural archives, combined with pioneering methods for analyzing both ecological and population genomic time-series data, are likely to provide predictive models to forecast evolutionary responses of natural populations to environmental changes resulting from natural and anthropogenic stressors, including climate change. PMID:23395434

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

    Science.gov (United States)

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

    2014-01-01

    In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

  17. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    Directory of Open Access Journals (Sweden)

    Jingmin Wang

    2016-01-01

    Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.

  18. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey M. [AWS Truepower, LLC, Albany, NY (United States); Manobianco, John [MESO, Inc., Troy, NY (United States); Schroeder, John [Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.; Ancell, Brian [Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group; Brewster, Keith [Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms; Basu, Sukanta [North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Banunarayanan, Venkat [ICF International (United States); Hodge, Bri-Mathias [National Renewable Energy Lab. (NREL), Golden, CO (United States); Flores, Isabel [Electricity Reliability Council of Texas (United States)

    2014-04-30

    This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

  19. The suitability of remotely sensed soil moisture for improving operational flood forecasting

    NARCIS (Netherlands)

    Wanders, N.|info:eu-repo/dai/nl/364253940; Karssenberg, D.|info:eu-repo/dai/nl/241557119; De Roo, A.|info:eu-repo/dai/nl/343686104; De Jong, S. M.|info:eu-repo/dai/nl/120221306; Bierkens, M. F P|info:eu-repo/dai/nl/125022794

    2014-01-01

    We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a

  20. Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route.

    Science.gov (United States)

    Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime

    2015-01-01

    During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR.

  1. Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

    Directory of Open Access Journals (Sweden)

    Kun Lang

    Full Text Available An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW. The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.

  2. Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

    Science.gov (United States)

    Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

    2015-01-01

    An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.

  3. Improving the Army’s Next Effort in Technology Forecasting

    Science.gov (United States)

    2010-09-01

    Cortisol Effects on Body Mass, Blood Pressure, and Cholesterol in the General Population,” Hypertension, 33 (1999), 1364–1368. 13 V. Technological...of HPA (hypothalamus-pituitary-adrenal) axis stress response hormones, notably cortisol and b-endorphin.62 These authors suggest that high hardy...stressors. In short, hardy individuals are more likely to remain emotionally stable during stressful situations. In support of this interpretation is

  4. Targeted observations to improve tropical cyclone track forecasts in the Atlantic and eastern Pacific basins

    Science.gov (United States)

    Aberson, Sim David

    In 1997, the National Hurricane Center and the Hurricane Research Division began conducting operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve operational forecast models. During the first two years, twenty-four missions were conducted around tropical cyclones threatening the continental United States, Puerto Rico, and the Virgin Islands. Global Positioning System dropwindsondes were released from the aircraft at 150--200 km intervals along the flight track in the tropical cyclone environment to obtain wind, temperature, and humidity profiles from flight level (around 150 hPa) to the surface. The observations were processed and formatted aboard the aircraft and transmitted to the National Centers for Environmental Prediction (NCEP). There, they were ingested into the Global Data Assimilation System that subsequently provides initial and time-dependent boundary conditions for numerical models that forecast tropical cyclone track and intensity. Three dynamical models were employed in testing the targeting and sampling strategies. With the assimilation into the numerical guidance of all the observations gathered during the surveillance missions, only the 12-h Geophysical Fluid Dynamics Laboratory Hurricane Model forecast showed statistically significant improvement. Neither the forecasts from the Aviation run of the Global Spectral Model nor the shallow-water VICBAR model were improved with the assimilation of the dropwindsonde data. This mediocre result is found to be due mainly to the difficulty in operationally quantifying the storm-motion vector used to create accurate synthetic data to represent the tropical cyclone vortex in the models. A secondary limit on forecast improvements from the surveillance missions is the limited amount of data provided by the one surveillance aircraft in regular missions. The inability of some surveillance missions to surround the tropical cyclone with dropwindsonde observations is a possible

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

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu; Florita, Anthony R.; Hodge, Bri-Mathias

    2016-10-01

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

  6. Measuring populations to improve vaccination coverage

    Science.gov (United States)

    Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.

    2016-10-01

    In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.

  7. Measuring populations to improve vaccination coverage

    Science.gov (United States)

    Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.

    2016-01-01

    In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes. PMID:27703191

  8. Skill improvement of dynamical seasonal Arctic sea ice forecasts

    NARCIS (Netherlands)

    Krikken, Folmer; Schmeits, Maurice; Vlot, Willem; Guemas, Virginie; Hazeleger, Wilco

    2016-01-01

    We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The

  9. Skill improvement of dynamical seasonal Arctic sea ice forecasts

    NARCIS (Netherlands)

    Krikken, Folmer; Schmeits, Maurice; Vlot, Willem; Guemas, Virginie; Hazeleger, Wilco

    2016-01-01

    We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The ra

  10. Payette River Basin Project: Improving Operational Forecasting in Complex Terrain through Chemistry

    Science.gov (United States)

    Blestrud, D.; Kunkel, M. L.; Parkinson, S.; Holbrook, V. P.; Benner, S. G.; Fisher, J.

    2015-12-01

    Idaho Power Company (IPC) is an investor owned hydroelectric based utility, serving customers throughout southern Idaho and eastern Oregon. The University of Arizona (UA) runs an operational 1.8-km resolution Weather and Research Forecast (WRF) model for IPC, which is incorporated into IPC near and real-time forecasts for hydro, solar and wind generation, load servicing and a large-scale wintertime cloud seeding operation to increase winter snowpack. Winter snowpack is critical to IPC, as hydropower provides ~50% of the company's generation needs. In efforts to improve IPC's near-term forecasts and operational guidance to its cloud seeding program, IPC is working extensively with UA and the National Center for Atmospheric Research (NCAR) to improve WRF performance in the complex terrain of central Idaho. As part of this project, NCAR has developed a WRF based cloud seeding module (WRF CS) to deliver high-resolution, tailored forecasts to provide accurate guidance for IPC's operations. Working with Boise State University (BSU), IPC is conducting a multiyear campaign to validate the WRF CS's ability to account for and disperse the cloud seeding agent (AgI) within the boundary layer. This improved understanding of how WRF handles the AgI dispersion and fate will improve the understanding and ultimately the performance of WRF to forecast other parameters. As part of this campaign, IPC has developed an extensive ground based monitoring network including a Remote Area Snow Sampling Device (RASSD) that provides spatially and temporally discrete snow samples during active cloud seeding periods. To quantify AgI dispersion in the complex terrain, BSU conducts trace element analysis using LA-ICP-MS on the RASSD sampled snow to provide measurements (at the 10-12 level) of incorporated AgI, measurements are compare directly with WRF CS's estimates of distributed AgI. Modeling and analysis results from previous year's research and plans for coming seasons will be presented.

  11. Demand forecasting

    OpenAIRE

    Gregor, Belčec

    2011-01-01

    Companies operate in an increasingly challenging environment that requires them to continuously improve all areas of the business process. Demand forecasting is one area in manufacturing companies where we can hope to gain great advantages. Improvements in forecasting can result in cost savings throughout the supply chain, improve the reliability of information and the quality of the service for our customers. In the company Danfoss Trata, d. o. o. we did not have a system for demand forecast...

  12. The potential economic benefits of improvements in weather forecasting

    Science.gov (United States)

    Thompson, J. C.

    1972-01-01

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

  13. SEASAT economic assessment. Volume 9: Ports and harbors case study and generalization. [economic benefits of SEASAT satellites to harbors and shipping industries through improved weather forecasting

    Science.gov (United States)

    1975-01-01

    This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.

  14. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

    Science.gov (United States)

    Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

    2013-10-01

    Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

  15. Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

    Science.gov (United States)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2016-08-01

    The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

  16. Mortality forecast from gastroduodenal ulcer disease for different gender and age population groups in Ukraine

    Directory of Open Access Journals (Sweden)

    Duzhiy I.D.

    2016-03-01

    Full Text Available Until 2030 the ulcer mortality will have a growing trend as estimated by the World Health Organization. Detection of countries and population groups with high risks for the ulcer mortality is possible using forecast method. The authors made a forecast of mortality rate from complicated ulcer disease in males and females and their age groups (15-24, 25-34, 35-54, 55-74, over 75, 15 - over 75 in our country. The study included data of the World Health Organization Database from 1991 to 2012. The work analyzed absolute all-Ukrainian numbers of persons of both genders died from the ulcer causes (К25-К27 coded by the 10th International Diseases Classification. The relative mortality per 100 000 of alive persons of the same age was calculated de novo. The analysis of distribution laws and their estimation presents a trend of growth of the relative mortality. A remarkable increase of deaths from the ulcer disease is observed in males and females of the age after 55 years old. After the age of 75 years this trend is more expressed.

  17. Recent advances in improvement of forecast skill and understanding climate processes using AIRS Version-5 products

    Science.gov (United States)

    Susskind, Joel; Molnar, Gyula; Iredell, Lena; Rosenberg, Robert

    2012-10-01

    The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) generates products derived from AIRS/AMSU-A observations, starting from September 2002 when the AIRS instrument became stable, using the AIRS Science Team Version-5 retrieval algorithm. This paper shows results of some of our research using Version-5 products from the points of view of improving forecast skill as well as aiding in the understanding of climate processes.

  18. An improved method of support vector machine and its applications to financial time series forecasting

    Institute of Scientific and Technical Information of China (English)

    LIANG Yanchun; SUN Yanfeng

    2003-01-01

    A novel method for kernel function of support vector machine is presented based on the information geometry theory. The kernel function is modified using a conformal mapping to make the kernel data-dependent so as to increase the ability of predicting high noise data of the method. Numerical simulations demonstrate the effectiveness of the method. Simulated results on the prediction of the stock price show that the improved approach possesses better forecasting precision and ability of generalization than the conventional models.

  19. Improving Aerosol and Visibility Forecasting Capabilities Using Current and Future Generations of Satellite Observations

    Science.gov (United States)

    2015-08-27

    indicate that the assimilation of satellite observations significantly improves NAAPS aerosol forecasting capability and reliability. To fully utilize...method derives a semi-quantitative indicator of nighttime x using artificial light sources. Nighttime x retrievals from the newly-developed method are...Kemper, T. Craig, I. Ginis , Evaluation of Maine aerosol production simulated using the WaveWatchlll prognostic Wave Model coupled to the Community

  20. Improving forecast skill by assimilation of quality-controlled AIRS temperature retrievals under partially cloudy conditions

    Science.gov (United States)

    Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Terry, J.; Jusem, J. C.

    2008-04-01

    The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite is now recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

  1. Improving Forecast Skill by Assimilation of Quality-controlled AIRS Temperature Retrievals under Partially Cloudy Conditions

    Science.gov (United States)

    Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Riishojgaard, L.; Liu, E.; Terry, J.; Jusem, J. C.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite has been long recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

  2. Valuing year-to-go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

    Science.gov (United States)

    Rheinheimer, David E.; Bales, Roger C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.

    2016-05-01

    We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by 0.14 to 1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.

  3. Improving solar wind persistence forecasts: Removing transient space weather events, and using observations away from the Sun-Earth line

    Science.gov (United States)

    Kohutova, Petra; Bocquet, François-Xavier; Henley, Edmund M.; Owens, Matthew J.

    2016-10-01

    This study demonstrates two significant ways of improving persistence forecasts of the solar wind, which exploit the relatively unchanging nature of the ambient solar wind to provide 27 day forecasts, when using data from the Lagrangian L1 point. Such forecasts are useful as a prediction tool for the ambient wind, and for benchmarking of solar wind models. We show that solar wind persistence forecasts can be improved by removing transient solar wind features such as coronal mass ejections (CMEs). Using CME indicators to automatically identify CME-contaminated periods in ACE data from 1998 to 2011, and replacing these with solar wind from a previous synodic rotation, persistence forecasts improve (relative to a baseline): skill scores for Bz, a crucial parameter for determining solar wind geoeffectiveness, improve by 7.7 percentage points when using a proton temperature-based indicator with good operational potential. We also show that persistence forecasts can be improved by using measurements away from L1, to reduce the requirement on coronal stability for an entire synodic period, at the cost of reduced lead time. Using STEREO-B data from 2007 to 2013 to create such a reduced lead time persistence forecast, we show that Bz skill scores improve by 17.1 percentage points relative to ACE. Finally, we report on implications for persistence forecasts from any future missions to the L5 Lagrangian point and on the successful operational implementation (in spring 2015) of the normal (ACE-based) and reduced lead time (STEREO-based) persistence forecasts in the Met Office's Space Weather Operations Centre, as well as plans for future improvements.

  4. L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

    Science.gov (United States)

    Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

    2017-06-01

    Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total streamflow divided by total rainfall accumulation in depth units) and prestorm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting streamflow response to future rainfall events.type="synopsis">type="main">Plain Language SummaryForecasting streamflow conditions is important for minimizing loss of life and property during flooding and adequately planning for low streamflow conditions accompanying drought. One way to improve these forecasts is measuring the amount of water in the soil—since soil moisture conditions determine what fraction of rainfall will run off horizontally into stream channels (versus vertically infiltrate into the soil column). Within the past 5 years, there have been important advances in our ability to monitor soil moisture over large scales using both satellite-based sensors and the application of new land data assimilation techniques. This paper illustrates that these advances have significantly improved our capacity to forecast how much streamflow will be generated by future precipitation events. These results may eventually be used by operational forecasters to improve flash flood forecasting and agricultural water use management.

  5. Improving the evaluation of hydrological multi-model forecast performance in the Upper Danube Catchment

    NARCIS (Netherlands)

    Bogner, K.; Cloke, H.; Pappenberger, F.; Roo, A.P.J. de; Thielen, J.

    2011-01-01

    Medium range flood forecasting activities, driven by various meteorological forecasts ranging from high resolution deterministic forecasts to low spatial resolution ensemble prediction systems, share a major challenge in the appropriateness and design of performance measures. In this paper

  6. Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts

    Science.gov (United States)

    Jones, Thomas A.; Koch, Steven; Li, Zhenglong

    2017-04-01

    Assimilation of hyperspectral sounder data into numerical weather prediction (NWP) models has proven vital to generating accurate model analyses of tropospheric temperature and humidity where few conventional observations exist. Applications to storm-scale models are limited since the low temporal resolution provided by polar orbiting sensors cannot adequately sample rapidly changing environments associated with high impact weather events. To address this limitation, hyperspectral sounders have been proposed for geostationary orbiting satellites, but these have yet to be built and launched in part due to much higher engineering costs and a lack of a definite requirement for the data. This study uses an Observation System Simulation Experiment (OSSE) approach to simulate temperature and humidity profiles from a hypothetical geostationary-based sounder from a nature run of a high impact weather event on 20 May 2013. The simulated observations are then assimilated using an ensemble adjustment Kalman filter approach, testing both hourly and 15 minute cycling to determine their relative effectiveness at improving the near storm environment. Results indicate that assimilating both temperature and humidity profiles reduced mid-tropospheric both mean and standard deviation of analysis and forecast errors compared to assimilating conventional observations alone. The 15 minute cycling generally produced the lowest errors while also generating the best 2-4 hour updraft helicity forecasts of ongoing convection. This study indicates the potential for significant improvement in short-term forecasting of severe storms from the assimilation of hyperspectral geostationary satellite data. However, more studies are required using improved OSSE designs encompassing multiple storm environments and additional observation types such as radar reflectivity to fully define the effectiveness of assimilating geostationary hyperspectral observations for high impact weather forecasting

  7. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

    Science.gov (United States)

    Case. Jonathan; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS

  8. Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

    Institute of Scientific and Technical Information of China (English)

    Seung-Woo LEE; Dong-Kyou LEE

    2011-01-01

    Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data.In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors.The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.

  9. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags

    Science.gov (United States)

    Meng, Shanshan; Xie, Xianhong; Liang, Shunlin

    2017-07-01

    Assimilation of either soil moisture or streamflow has been well demonstrated to improve flood forecasting. However, it is difficult to assimilate two different types of observations into a rainfall-runoff model simultaneously because there is a time lag between soil moisture and streamflow owing to the runoff routing process. In this study, we developed an effective data assimilation scheme based on the ensemble Kalman filter and smoother (named as EnKF-S) to exploit the benefits of the two observation types while accounting for the runoff routing lag. To prove the importance of accounting for the time lag, a scheme named Dual-EnKF was used to compare. To demonstrate the schemes, we designed synthetic cases regarding two typical flood patterns, i.e., flash flood and gradual flood. The results show that EnKF-S can effectively improve flood forecasting compared with Dual-EnKF, particularly when the runoff routing has distinct time lags. For the synthetic cases, EnKF-S reduced root-mean-square error (RMSE) by more than 70% relative to the data assimilation scheme without considering runoff routing lags. Therefore, this effective data assimilation scheme holds great potential for short-term flood forecasting by merging observations from ground measurement and remote sensing retrievals.

  10. A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

    Directory of Open Access Journals (Sweden)

    Hongzhuan Zhao

    2016-04-01

    Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

  11. Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Tansu Filik

    2016-03-01

    Full Text Available In this paper, the spatio-temporal (multi-channel linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR and multi-channel/vector autoregressive (VAR models. It is also shown that the model can predict wind speed very fast (in milliseconds which is suitable for the immediate short-term forecasting.

  12. Improving Forecasts of Cumulus: An Intersection of the Renewable Energy and Climate Science Communities

    Science.gov (United States)

    Berg, L. K.; Gustafson, W. I., Jr.; Kassianov, E.; Long, C. N.

    2015-12-01

    Accurate forecasts of broken cloud fields and their associated impact on the downwelling solar irradiance has remained a challenge to the renewable energy industry. Likewise, shallow cumulus play an important role in the Earth's radiation budget and hydrologic cycle and are of interest to the weather forecasting and climate science communities. The main challenge associated with predicting these clouds are their relatively small size (on the order of a kilometer or less) relative to the model grid spacing. Recently, however, there have been significant efforts put into improving forecasts of shallow clouds and the associated temporal and spatial variability of the solar irradiance that they induce. As an example of these efforts, we will describe recent modifications to the standard Kain-Fritsch parameterization as applied within the Weather Research and Forecasting (WRF) model that are designed to improve predictions of the macroscale and microscale structure of shallow cumulus. These modifications are shown to lead to a realistic increase in the simulated cloud fraction and associated decrease in the solar irradiance. We will evaluate our results using data collected at the Department of Energy's Atmospheric Radiation Measurement (ARM) Southern Great Plains site, which is located in north-central Oklahoma. Our team has analyzed over 5 years of data collected at this site to document the macroscale structure of the clouds (including cloud fraction, cloud-base and cloud-top height) as well as their impact on the downwelling shortwave and longwave irradiance. One particularly interesting impact of shallow cumuli is the enhancement of the diffuse radiation, such that during periods in which the sun is not blocked, the observed irradiance can be significantly larger than the corresponding clear sky case. To date, this feature is not accurately represented by models that apply the plane-parallel assumption applied in the standard radiation parameterizations.

  13. Efficient training schemes that improve the forecast quality of a supermodel

    Science.gov (United States)

    Schevenhoven, Francine; Selten, Frank; Duane, Gregory; Keenlyside, Noel

    2017-04-01

    Weather and climate models have improved steadily over time as witnessed by objective skill scores, although they remain imperfect. Given these imperfect models, predictions might be improved by combining them dynamically into a so-called "supermodel". In contrast to the standard multi-model ensemble approach, the models exchange information during the simulation, which leads to new solutions. In this study we explore different techniques to create such a supermodel. The techniques are applied to global climate models. The results indicate that the techniques are computationally efficient and lead to supermodels with superior forecast quality and climatology compared to the individual models or the standard multi-model ensemble approach.

  14. Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.

    Science.gov (United States)

    LaDeau, Shannon L; Glass, Gregory E; Hobbs, N Thompson; Latimer, Andrew; Ostfeld, Richard S

    2011-07-01

    Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector-borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data-model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state-space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.

  15. Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available An improved Bayesian fusion algorithm (BFA is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of only a few previous intervals. With this assumption, the weights of the component predictors in the improved BFA are calculated according to their prediction performance only from a few intervals rather than from all intervals. Therefore, compared with the conventional BFA, the improved BFA is more sensitive to the disturbed condition of the component predictors for adjusting their weights more rapidly. To determine the most relevant intervals, the grey relation entropy-based analysis (GREBA method is proposed, which can be used analyze the relevancy between the historical data flows of blink number and the data flow at the current interval. Three single predictors, that is, the autoregressive integrated moving average (ARIMA, radial basis function neural network (RBFNN, and Kalman filter (KF, are designed and incorporated linearly into the BFA. Experimental results demonstrate that the improved BFA obviously outperforms the conventional BFA in both accuracy and stability; also fatigue driving can be accurately warned against in advance based on the blink number forecasted by the improved BFA.

  16. Application of MODIS GPP to Forecast Risk of Hantavirus Pulmonary Syndrome Based on Fluctuations in Reservoir Population Density

    Science.gov (United States)

    Loehman, R.; Heinsch, F. A.; Mills, J. N.; Wagoner, K.; Running, S.

    2003-12-01

    Recent predictive models for hantavirus pulmonary syndrome (HPS) have used remotely sensed spectral reflectance data to characterize risk areas with limited success. We present an alternative method using gross primary production (GPP) from the MODIS sensor to estimate the effects of biomass accumulation on population density of Peromyscus maniculatus (deer mouse), the principal reservoir species for Sin Nombre virus (SNV). The majority of diagnosed HPS cases in North America are attributed to SNV, which is transmitted to humans through inhalation of excretions and secretions from infected rodents. A logistic model framework is used to evaluate MODIS GPP, temperature, and precipitation as predictors of P. maniculatus density at established trapping sites across the western United States. Rodent populations are estimated using monthly minimum number alive (MNA) data for 2000 through 2002. Both local meteorological data from nearby weather stations and 1.25 degree x 1 degree gridded data from the NASA DAO were used in the regression model to determine the spatial sensitivity of the response. MODIS eight-day GPP data (1-km resolution) were acquired and binned to monthly average and monthly sum GPP for 3km x 3km grids surrounding each rodent trapping site. The use of MODIS GPP to forecast HPS risk may result in a marked improvement over past reflectance-based risk area characterizations. The MODIS GPP product provides a vegetation dynamics estimate that is unique to disease models, and targets the fundamental ecological processes responsible for increased rodent density and amplified disease risk.

  17. Predicting and Mitigating Socioeconomic Impacts of Extreme Space Weather: Benefits of Improved Forecasts (Invited)

    Science.gov (United States)

    Kanekal, S. G.; Baker, D. N.

    2013-12-01

    Vulnerability of society to severe space weather is an issue of increasing worldwide concern. A notable example is that electric power networks connecting widely separated geographic areas may incur debilitating damage induced by geomagnetic storms. The conclusion of a recent National Research Council report was that harsh space weather events can cause tens of millions to many billions of dollars of damage to space and ground-based assets during major solar storms. The most extreme events could cause months-long power outages and could cost in excess of one trillion dollars. In this presentation, we discuss broad socioeconomic impacts of space weather and also discuss the immense potential benefits of improved space weather forecasts. Such forecasts would be based on continuous observations of disturbances on the Sun and would take advantage of our increased understanding of the Earth's space environmental conditions and the causative solar drivers. We consider scenarios of how such observation-based forecasts could be used most effectively by policy makers and technology management officials.

  18. A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

    Science.gov (United States)

    Barba, Lida; Rodríguez, Nibaldo

    2017-01-01

    Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

  19. Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

    Directory of Open Access Journals (Sweden)

    Lida Barba

    2014-01-01

    Full Text Available Two smoothing strategies combined with autoregressive integrated moving average (ARIMA and autoregressive neural networks (ANNs models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD. In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%.

  20. Towards a More Accurate Solar Power Forecast By Improving NWP Model Physics

    Science.gov (United States)

    Köhler, C.; Lee, D.; Steiner, A.; Ritter, B.

    2014-12-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the uncertainties associated with the large share of weather-dependent power sources. Precise power forecast, well-timed energy trading on the stock market, and electrical grid stability can be maintained. The research project EWeLiNE is a collaboration of the German Weather Service (DWD), the Fraunhofer Institute (IWES) and three German transmission system operators (TSOs). Together, wind and photovoltaic (PV) power forecasts shall be improved by combining optimized NWP and enhanced power forecast models. The conducted work focuses on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. Not only the representation of the model cloud characteristics, but also special events like Sahara dust over Germany and the solar eclipse in 2015 are treated and their effect on solar power accounted for. An overview of the EWeLiNE project and results of the ongoing research will be presented.

  1. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  2. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  3. A Novel Hydro-information System for Improving National Weather Service River Forecast System

    Science.gov (United States)

    Nan, Z.; Wang, S.; Liang, X.; Adams, T. E.; Teng, W. L.; Liang, Y.

    2009-12-01

    A novel hydro-information system has been developed to improve the forecast accuracy of the NOAA National Weather Service River Forecast System (NWSRFS). An MKF-based (Multiscale Kalman Filter) spatial data assimilation framework, together with the NOAH land surface model, is employed in our system to assimilate satellite surface soil moisture data to yield improved evapotranspiration. The latter are then integrated into the distributed version of the NWSRFS to improve its forecasting skills, especially for droughts, but also for disaster management in general. Our system supports an automated flow into the NWSRFS of daily satellite surface soil moisture data, derived from the TRMM Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and the forcing information of the North American Land Data Assimilation System (NLDAS). All data are custom processed, archived, and supported by the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). An optional data fusing component is available in our system, which fuses NEXRAD Stage III precipitation data with the NLDAS precipitation data, using the MKF-based framework, to provide improved precipitation inputs. Our system employs a plug-in, structured framework and has a user-friendly, graphical interface, which can display, in real-time, the spatial distributions of assimilated state variables and other model-simulated information, as well as their behaviors in time series. The interface can also display watershed maps, as a result of the integration of the QGIS library into our system. Extendibility and flexibility of our system are achieved through the plug-in design and by an extensive use of XML-based configuration files. Furthermore, our system can be extended to support multiple land surface models and multiple data assimilation schemes, which would further increase its capabilities. Testing of the integration of the current system into the NWSRFS is

  4. Forecasting the Optimal Factors of Formation of the Population Savings as the Basis for Investment Resources of the Regional Economy

    Directory of Open Access Journals (Sweden)

    Odintsova Tetiana M.

    2017-04-01

    Full Text Available The article is aimed at studying the optimal factors of formation of the population savings as the basis for investment resources of the regional economy. A factorial (nonlinear correlative-regression analysis of the formation of savings of the population of Ukraine was completed. On its basis a forecast of the optimal structure and volumes of formation of the population incomes was carried out taking into consideration impact of fundamental factors on these incomes. Such approach provides to identify the marginal volumes of tax burden, population savings, and capital investments, directed to economic growth.

  5. Public policy frameworks for improving population health.

    Science.gov (United States)

    Tarlov, A R

    1999-01-01

    Four conceptual frameworks provide bases for constructing comprehensive public policy strategies for improving population health within wealthy (OECD) nations. (1) Determinants of population health. There are five broad categories: genes and biology, medical care, health behaviors, the ecology of all living things, and social/societal characteristics. (2) Complex systems: Linear effects models and multiple independent effects models fail to yield results that explain satisfactorily the dynamics of population health production. A different method (complex systems modeling) is needed to select the most effective interventions to improve population health. (3) An intervention framework for population health improvement. A two-by-five grid seems useful. Most intervention strategies are either ameliorative or fundamentally corrective. The other dimension of the grid captures five general categories of interventions: child development, community development, adult self-actualization, socioeconomic well-being, and modulated hierarchical structuring. (4) Public policy development process: the process has two phases. The initial phase, in which public consensus builds and an authorizing environment evolves, progresses from values and culture to identification of the problem, knowledge development from research and experience, the unfolding of public awareness, and the setting of a national agenda. The later phase, taking policy action, begins with political engagement and progresses to interest group activation, public policy deliberation and adoption, and ultimately regulation and revision. These frameworks will be applied to help understand the 39 recommendations of the Independent Inquiry into Inequalities in Health, the Sir Donald Acheson Report from the United Kingdom, which is the most ambitious attempt to date to develop a comprehensive plan to improve population health.

  6. Medical weather forecast as the risk management facilities of meteopathia with population

    Science.gov (United States)

    Efimenko, Natalya; Chalaya, Elena; Povolotskaia, Nina; Senik, Irina; Topuriya, David

    2013-04-01

    surface atmosphere. The correlation of the results of the research of external respiration function, cardiovascular and central nervous systems with people suffering from DA (187 people) made in days with favorable weathers, but different in natural anion quantity in the surface atmosphere, allowed us to develop similar physiological processes at the phenomena of natural deionization. When the anions amount reduces from 1255±38 ion/cm3 to 190±13 ion/cm3, we have detected the increase of tension of vegetative index (from 458±24 to 802±44), the decrease in efficiency of neurohumoral regulation (from 0,25±0,08 to 0,06±0,02), the increase of spectrum excitability of cortical activity in the wave range of delta 0 0.4 Hz by 29%, the decrease in cortical activity in the wave range of theta 4 … 8 Hz, alpha 8 … 13 Hz beta 13 … 19 Hz, gamma 19 … 25Hz by 4-10%; the decrease in organism adaptation layer by 14% and integrated health indicator by 18%. We have also detected similar processes in cardiovascular and respiratory systems. So the problem of creation of high-quality system of medical weather forecast for the population demands the performance of interdisciplinary researches in the field of medicine, biology, meteorology and the development of DMR risk management programs at various natural and anthropogenic stressors. The studies were performed by support of the Program "Basic Sciences for Medicine" and RFBR project No.10-05-01014_a.

  7. WFIP2 - The Second Wind Forecast Improvement Project: Observing Systems And Case Studies

    Science.gov (United States)

    Wilczak, J. M.; Cline, J.; Banta, R. M.; Benjamin, L.; Benjamin, S.; Berg, L. K.; Bianco, L.; Bickford, J.; Brewer, A.; Choukulkar, A.; Clawson, K.; Clifton, A.; Cook, D. R.; Djalalova, I.; Fernando, H.; Friedrich, K.; Kenyon, J.; Kosovic, B.; King, C. W.; Marquis, M.; McCaa, J. R.; McCaffrey, K.; Olson, J. B.; Pichugina, Y. L.; Sharp, J.; Shaw, W. J.; Wade, K.; Wharton, S.; Lundquist, J. K.; Lantz, K. O.; Long, C. N.

    2015-12-01

    The second Wind Forecast Improvement Project (WFIP2) is a DOE and NOAA funded public-private partnership whose goal is to improve NWP model forecast skill for turbine-height winds in regions with complex terrain. WFIP2 partners include DOE National Laboratories (PNNL, ANL, NREL, LLNL), NOAA Laboratories (ESRL, ARL), Vaisala Inc., NCAR, the University of Notre Dame and University of Colorado, and the Bonneville Power Administration. A core element of WFIP2 is an 18 month field program located in the Pacific Northwest, focusing on the Columbia River Gorge and Basin in eastern Oregon and Washington states, with instrument deployment occurring in the summer and autumn of 2015. The approach taken is to collect an extensive set of new meteorological observations, especially within the atmospheric boundary layer, use these to observe and understand relevant atmospheric processes, develop and test new model physical parameterization schemes, and ultimately transfer these improved models to NOAA/NWS operations and to the wider meteorological community. Observing systems that will be deployed for WFIP2 include: 11 wind profiling radars 17 sodars 5 wind profiling lidars 4 scanning lidars 4 radiometers 10 microbarographs ceilometer 28 sonic anemometers Numerical models that are being used for WFIP2 are WRF-based models including the NOAA RAP (Rapid Refresh) and High Resolution Rapid Refresh (HRRR), as well as the NAM and GFS. Science issues that are being addressed include gap flow, mountain waves, mountain wakes, convective storm outflows, the mix-out of stable cold pools, and boundary layer turbulence profiling. An overview of WFIP2 will be given with an emphasis on the suite of instrumentation deployed and their observational capabilities. Several case studies of interesting meteorological events from the first several months of the field program will be presented, including comparisons with model forecasts.

  8. Analysis of PG&E`s residential end-use metered data to improve electricity demand forecasts -- final report

    Energy Technology Data Exchange (ETDEWEB)

    Eto, J.H.; Moezzi, M.M.

    1993-12-01

    This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.

  9. Impact of Targeted Ocean Observations for Improving Ocean Model Initialization for Coupled Hurricane Forecasting

    Science.gov (United States)

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

    2012-12-01

    The accuracy of hurricane intensity forecasts produced by coupled forecast models is influenced by errors and biases in SST forecasts produced by the ocean model component and the resulting impact on the enthalpy flux from ocean to atmosphere that powers the storm. Errors and biases in fields used to initialize the ocean model seriously degrade SST forecast accuracy. One strategy for improving ocean model initialization is to design a targeted observing program using airplanes and in-situ devices such as floats and drifters so that assimilation of the additional data substantially reduces errors in the ocean analysis system that provides the initial fields. Given the complexity and expense of obtaining these additional observations, observing system design methods such as OSSEs are attractive for designing efficient observing strategies. A new fraternal-twin ocean OSSE system based on the HYbrid Coordinate Ocean Model (HYCOM) is used to assess the impact of targeted ocean profiles observed by hurricane research aircraft, and also by in-situ float and drifter deployments, on reducing errors in initial ocean fields. A 0.04-degree HYCOM simulation of the Gulf of Mexico is evaluated as the nature run by determining that important ocean circulation features such as the Loop Current and synoptic cyclones and anticyclones are realistically simulated. The data-assimilation system is run on a 0.08-degree HYCOM mesh with substantially different model configuration than the nature run, and it uses a new ENsemble Kalman Filter (ENKF) algorithm optimized for the ocean model's hybrid vertical coordinates. The OSSE system is evaluated and calibrated by first running Observing System Experiments (OSEs) to evaluate existing observing systems, specifically quantifying the impact of assimilating more than one satellite altimeter, and also the impact of assimilating targeted ocean profiles taken by the NOAA WP-3D hurricane research aircraft in the Gulf of Mexico during the Deepwater

  10. Dynamical Downscaling of GCM Simulations: Toward the Improvement of Forecast Bias over California

    Energy Technology Data Exchange (ETDEWEB)

    Chin, H S

    2008-09-24

    The effects of climate change will mostly be felt on local to regional scales. However, global climate models (GCMs) are unable to produce reliable climate information on the scale needed to assess regional climate-change impacts and variability as a result of coarse grid resolution and inadequate model physics though their capability is improving. Therefore, dynamical and statistical downscaling (SD) methods have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these downscaling techniques show that both downscaling methods have similar skill in simulating the mean and variability of present climate conditions while they show significant differences for future climate conditions (Leung et al., 2003). One difficulty with the SD method is that it relies on predictor-predict and relationships, which may not hold in future climate conditions. In addition, it is now commonly accepted that the dynamical downscaling with the regional climate model (RCM) is more skillful at the resolving orographic climate effect than the driving coarser-grid GCM simulations. To assess the possible societal impacts of climate changes, many RCMs have been developed and used to provide a better projection of future regional-scale climates for guiding policies in economy, ecosystem, water supply, agriculture, human health, and air quality (Giorgi et al., 1994; Leung and Ghan, 1999; Leung et al., 2003; Liang et al., 2004; Kim, 2004; Duffy et al., 2006). Although many regional climate features, such as seasonal mean and extreme precipitation have been successfully captured in these RCMs, obvious biases of simulated precipitation remain, particularly the winter wet bias commonly seen in mountain regions of the Western United States. The importance of regional climate research over California is not only because California has the largest population in the nation, but California has one of the most

  11. How to improve an un-alterable model forecast? A sequential data assimilation based error updating approach

    Science.gov (United States)

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

    2012-12-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing value of water resources and influences operation of hydropower reservoirs significantly. Improving hourly reservoir inflow forecasts over a 24 hours lead-time is considered with the day-ahead (Elspot) market of the Nordic exchange market in perspectives. The procedure presented comprises of an error model added on top of an un-alterable constant parameter conceptual model, and a sequential data assimilation routine. The structure of the error model was investigated using freely available software for detecting mathematical relationships in a given dataset (EUREQA) and adopted to contain minimum complexity for computational reasons. As new streamflow data become available the extra information manifested in the discrepancies between measurements and conceptual model outputs are extracted and assimilated into the forecasting system recursively using Sequential Monte Carlo technique. Besides improving forecast skills significantly, the probabilistic inflow forecasts provided by the present approach entrains suitable information for reducing uncertainty in decision making processes related to hydropower systems operation. The potential of the current procedure for improving accuracy of inflow forecasts at lead-times unto 24 hours and its reliability in different seasons of the year will be illustrated and discussed thoroughly.

  12. The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin; Wu, Hongyu; Florita, Anthony R.; Brancucci Martinez-Anido, Carlo; Hodge, Bri-Mathias

    2016-12-01

    The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Finally, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.

  13. Behavioral forecasts do not improve the prediction of future behavior: a prospective study of self-injury.

    Science.gov (United States)

    Janis, Irene Belle; Nock, Matthew K

    2008-10-01

    Clinicians are routinely encouraged to use multimodal assessments incorporating information from multiple sources when determining an individual's risk of dangerous or self-injurious behavior; however, some sources of information may not improve prediction models and so should not be relied on in such assessments. The authors examined whether individuals' prediction of their own future behavior improves prediction over using history of self-injurious thoughts and behaviors (SITB) alone. Sixty-four adolescents with a history of SITB were interviewed regarding their past year history of SITB, asked about the likelihood that they would engage in future SITB, and followed over a 6-month period. Individuals' forecasts of their future behavior were related to subsequent SITB, but did not improve prediction beyond the use of SITB history. In contrast, history of SITB improved prediction of subsequent SITB beyond individuals' behavioral forecasts. Clinicians should rely more on past history of a behavior than individuals' forecasts of future behavior in predicting SITB.

  14. Options to Improve the Quality of Wind Generation Output Forecasting with the Use of Available Information as Explanatory Variables

    Directory of Open Access Journals (Sweden)

    Rafał Magulski

    2015-06-01

    Full Text Available Development of wind generation, besides its positive aspects related to the use of renewable energy, is a challenge from the point of view of power systems’ operational security and economy. The uncertain and variable nature of wind generation sources entails the need for the for the TSO to provide adequate reserves of power, necessary to maintain the grid’s stable operation, and the actors involved in the trading of energy from these sources incur additional of balancing unplanned output deviations. The paper presents the results of analyses concerning the options to forecast a selected wind farm’s output exercised by means of different methods of prediction, using a different range of measurement and forecasting data available on the farm and its surroundings. The analyses focused on the evaluation of forecast errors, and selection of input data for forecasting models and assessment of their impact on prediction quality improvement.

  15. Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation

    CSIR Research Space (South Africa)

    Landman, WA

    2012-11-01

    Full Text Available -forecasts) have been generated by a statistical model and by state-of-the-art fully coupled ocean-atmosphere general circulation models. Since forecast users generally require well-calibrated probability forecasts we employ a model output statistics approach...

  16. Multi-initial-conditions and Multi-physics Ensembles in the Weather Research and Forecasting Model to Improve Coastal Stratocumulus Forecasts for Solar Power Integration

    Science.gov (United States)

    Yang, H.

    2015-12-01

    In coastal Southern California, variation in solar energy production is predominantly due to the presence of stratocumulus clouds (Sc), as they greatly attenuate surface solar irradiance and cover most distributed photovoltaic systems on summer mornings. Correct prediction of the spatial coverage and lifetime of coastal Sc is therefore vital to the accuracy of solar energy forecasts in California. In Weather Research and Forecasting (WRF) model simulations, underprediction of Sc inherent in the initial conditions directly leads to an underprediction of Sc in the resulting forecasts. Hence, preprocessing methods were developed to create initial conditions more consistent with observational data and reduce spin-up time requirements. Mathiesen et al. (2014) previously developed a cloud data assimilation system to force WRF initial conditions to contain cloud liquid water based on CIMSS GOES Sounder cloud cover. The Well-mixed Preprocessor and Cloud Data Assimilation (WEMPPDA) package merges an initial guess of cloud liquid water content obtained from mixed-layer theory with assimilated CIMSS GOES Sounder cloud cover to more accurately represent the spatial coverage of Sc at initialization. The extent of Sc inland penetration is often constrained topographically; therefore, the low inversion base height (IBH) bias in NAM initial conditions decreases Sc inland penetration. The Inversion Base Height (IBH) package perturbs the initial IBH by the difference between model IBH and the 12Z radiosonde measurement. The performance of these multi-initial-condition configurations was evaluated over June, 2013 against SolarAnywhere satellite-derived surface irradiance data. Four configurations were run: 1) NAM initial conditions, 2) RAP initial conditions, 3) WEMPPDA applied to NAM, and 4) IBH applied to NAM. Both preprocessing methods showed significant improvement in the prediction of both spatial coverage and lifetime of coastal Sc. The best performing configuration was then

  17. Improving Seasonal Crop Monitoring and Forecasting for Soybean and Corn in Iowa

    Science.gov (United States)

    Togliatti, K.; Archontoulis, S.; Dietzel, R.; VanLoocke, A.

    2016-12-01

    Accurately forecasting crop yield in advance of harvest could greatly benefit farmers, however few evaluations have been conducted to determine the effectiveness of forecasting methods. We tested one such method that used a combination of short-term weather forecasting from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation and radiation at 4 different forecast lengths (2 weeks, 1 week, 3 days, and 0 days). This forecasted weather data along with the current and historic (previous 35 years) data from the Iowa Environmental Mesonet was combined to drive Agricultural Production Systems sIMulator (APSIM) simulations to forecast soybean and corn yields in 2015 and 2016. The goal of this study is to find the forecast length that reduces the variability of simulated yield predictions while also increasing the accuracy of those predictions. APSIM simulations of crop variables were evaluated against bi-weekly field measurements of phenology, biomass, and leaf area index from early and late planted soybean plots located at the Agricultural Engineering and Agronomy Research Farm in central Iowa as well as the Northwest Research Farm in northwestern Iowa. WRF model predictions were evaluated against observed weather data collected at the experimental fields. Maximum temperature was the most accurately predicted variable, followed by minimum temperature and radiation, and precipitation was least accurate according to RMSE values and the number of days that were forecasted within a 20% error of the observed weather. Our analysis indicated that for the majority of months in the growing season the 3 day forecast performed the best. The 1 week forecast came in second and the 2 week forecast was the least accurate for the majority of months. Preliminary results for yield indicate that the 2 week forecast is the least variable of the forecast lengths, however it also is the least accurate

  18. State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter

    Science.gov (United States)

    Mat Daud, Auni Aslah

    In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.

  19. Use of a Sodar to Improve the Forecast of Fogs and Low Clouds on Airports

    Science.gov (United States)

    Dabas, Alain; Remy, Samuel; Bergot, Thierry

    2012-05-01

    A sodar was deployed at Roissy-Charles de Gaulle airport near Paris, France, in 2008 with the aim of improving the forecast of low visibility conditions there. During the winter of 2008-2009, an experiment was conducted that showed that the sodar can effectively detect and locate the top of fog layers which is signaled by a strong peak of acoustic reflectivity. The peak is generated by turbulence activity in the inversion layer that contrasts sharply with the low reflectivity recorded in the fog layer below. A specific version of the 1D-forecast model deployed at Roissy for low visibility conditions (COBEL-ISBA) was developed in which fogs' thicknesses are initialized by the sodar measurements rather than the information derived from the down-welling IR fluxes observed on the site. It was tested on data archived during the winters of 2008-2009 and 2009-2010 and compared to the version of the model presently operational. The results show a significant improvement—dissipation times of fogs are better predicted.

  20. Looking forward by looking back: using historical calibration to improve forecasts of human disease vector distributions.

    Science.gov (United States)

    Acheson, Emily Sohanna; Kerr, Jeremy Thomas

    2015-03-01

    Arthropod disease vectors, most notably mosquitoes, ticks, tsetse flies, and sandflies, are strongly influenced by environmental conditions and responsible for the vast majority of global vector-borne human diseases. The most widely used statistical models to predict future vector distributions model species niches and project the models forward under future climate scenarios. Although these methods address variations in vector distributions through space, their capacity to predict changing distributions through time is far less certain. Here, we review modeling methods used to validate and forecast future distributions of arthropod vectors under the effects of climate change and outline the uses or limitations of these techniques. We then suggest a validation approach specific to temporal extrapolation models that is gaining momentum in macroecological modeling and has great potential for epidemiological modeling of disease vectors. We performed systematic searches in the Web of Science, ScienceDirect, and Google Scholar to identify peer-reviewed English journal articles that model arthropod disease vector distributions under future environment scenarios. We included studies published up to and including June, 2014. We identified 29 relevant articles for our review. The majority of these studies predicted current species niches and projected the models forward under future climate scenarios without temporal validation. Historically calibrated forecast models improve predictions of changing vector distributions by tracking known shifts through recently observed time periods. With accelerating climate change, accurate predictions of shifts in disease vectors are crucial to target vector control interventions where needs are greatest.

  1. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

    Science.gov (United States)

    Wang, Wen-chuan; Chau, Kwok-wing; Qiu, Lin; Chen, Yang-bo

    2015-05-01

    Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.

  2. Daily quantitative precipitation forecasts based on the analogue method: Improvements and application to a French large river basin

    Science.gov (United States)

    Ben Daoud, Aurélien; Sauquet, Eric; Bontron, Guillaume; Obled, Charles; Lang, Michel

    2016-03-01

    This paper presents some improvements of a probabilistic quantitative precipitation forecasting method based on analogues, formerly developed on small basins located in South-Eastern France. The scope is extended to large scale basins mainly influenced by frontal systems, considering a case study area related to the Saône river, a large basin in eastern France. For a given target situation, this method consists in searching for the most similar situations observed in a historical meteorological archive. Precipitation amounts observed during analogous situations are then collected to derive an empirical predictive distribution function, i.e. the probabilistic estimation of the precipitation amount expected for the target day. The former version of this forecasting method (Bontron, 2004) has been improved by introducing two innovative variables: temperature, that allows taking seasonal effects into account and vertical velocity, which enables a better characterization of the vertical atmospheric motion. The new algorithm is first applied in a perfect prognosis context (target situations come from a meteorological reanalysis) and then in an operational forecasting context (target situations come from weather forecasts) for a three years period. Results show that this approach yields useful forecasts, with a lower false alarm rate and improved performances from the present day D to day D + 2.

  3. Basic tasks for improving spectral-acoustic forecasting of dynamic phenomena in coal mines

    Science.gov (United States)

    Shadrin, A. V.; Kontrimas, A. A.

    2017-09-01

    A number of tasks for improving the spectral-acoustic method for forecasting dynamic phenomena and controlling stress condition in coalmines is considered. They are: considering the influence of a gas factor on the danger indicator, dependence of a relative pressure coefficient on the distance between the source and the receiver of the probing acoustic signal, correct selection of operating frequencies, the importance of developing the techniques for defining the critical value of the outburst danger index The influence of the rock mass stress condition ahead of the preliminary opening face on the relative pressure coefficient defined for installing the sound receiver in the wall of the opening behind the opening face is also justified in the article.

  4. Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component

    Directory of Open Access Journals (Sweden)

    Manish Kumar

    2010-06-01

    Full Text Available In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the realized volatility. We also tried to investigate whether dividing volatility into simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The results provide the evidence that inclusion of realized bipower variance in the HAR models helps in predicting future volatility.

  5. Improved Ceiling and Visibility Forecasts from the US NOAA HRRR/RAP - Hydrometeor Assimilation and Modeling

    Science.gov (United States)

    Benjamin, Stan; Alexander, Curtis; Weygandt, Stephen; Brown, John; Smirnova, Tatiana; Hu, Ming; Kenyon, Jaymes; Olson, Joseph; James, Eric; Thompson, Greg

    2017-04-01

    Cloud microphysics fields in the US hourly updated weather models, the 13km Rapid Refresh (covering North America) and the 3km High-Resolution Rapid Refresh (covering the lower 48 United States) are used for explicitly derived guidance of ceiling and visibility. Recent changes to the RAP and HRRR models at NOAA's NCEP with improvements for ceiling/visibility fields occurred in August 2016. This coordinated upgrade (RAP version 3 and HRRR version 2, RAPv3/HRRRv2) includes enhancements to the data assimilation, model, and post-processing formulations. Key assimilation/modeling changes relevant to ceiling/visibility forecasts will be described toward the next NCEP operational implementation (RAPv4/HRRRv3), planned for early 2018. These include further enhancements to the model physics components (aerosol-aware Thompson microphysics, MYNN PBL scheme, Smirnova land-surface model), application of a new vertical coordinate), and possible merger with prognostic smoke/aerosol prediction.

  6. Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

    Science.gov (United States)

    Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

    2015-01-01

    The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

  7. Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2015-01-01

    Full Text Available The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

  8. Improvement of Solar and Wind forecasting in southern Italy through a multi-model approach: preliminary results

    Science.gov (United States)

    Avolio, Elenio; Torcasio, Rosa Claudia; Lo Feudo, Teresa; Calidonna, Claudia Roberta; Contini, Daniele; Federico, Stefano

    2016-04-01

    The improvement of the Solar and Wind short-term forecasting represents a critical goal for the weather prediction community and is of great importance for a better estimation of power production from solar and wind farms. In this work we analyze the performance of two deterministic models operational at ISAC-CNR for the prediction of short-wave irradiance and wind speed, at two experimental sites in southern Italy. A post-processing technique, i.e the multi-model, is adopted to improve the performance of the two mesoscale models. The results show that the multi-model approach produces a significant error reduction with respect to the forecast of each model. The error is reduced up to 20 % of the model errors, depending on the parameter and forecasting time.

  9. Improving PM2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

    Science.gov (United States)

    Peng, Zhen; Liu, Zhiquan; Chen, Dan; Ban, Junmei

    2017-04-01

    In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2. 5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the Beijing-Tianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both

  10. Advances in Business and Management Forecasting

    CERN Document Server

    Lawrence, Kenneth D

    2011-01-01

    The topics within Advances in Business and Management Forecasting will normally include sales and marketing, forecasting, new product forecasting, judgmentally-based forecasting, the application of surveys to forecasting, forecasting for strategic business decisions, improvements in forecasting accuracy, and sales response models.

  11. Theoretical basis for expert system to forecast and assess economic impact of anthropogenic pollution on population disease level

    Directory of Open Access Journals (Sweden)

    M.I. Bublyk

    2014-09-01

    Full Text Available The aim of the article. Theoretical basis of mathematical apparatus of fuzzy sets to evaluate and account the man-made (anthropogenic losses is improved in the article in order to take effective administrative decisions of their reduction and prevention. Theoretical basis for building an expert system for forecasting the economic effects of man-made (anthropogenic pollution on population levels of disease is analyzed. Practically these investigations will give the opportunity to control measures of orientation of the national economy and its individual industries on sustainable development. The results of the analysis. The theoretical foundations and applied problems of predicting man-made damage to the national economy and methods of management at the state level allowed for the following conclusions. 1. To justify the application of theoretical principles of fuzzy sets as an effective mathematical tool in conditions of incomplete information and uncertainty in future work the advantages of fuzzy expert systems, including the possibility of approximate descriptions such complex phenomena that can not be described in conventional quantitative terms, and the ability to receive, store and adjust the knowledge possessed by experts in this subject area in the process of dialogue with them in order to get real results. 2. The model of fuzzy expert system for establishing interdependencies between the amount of pollution (emissions, effluents, waste and deterioration of health in Ukraine has been proposed. 3. The model in predicting the technogenic load (discharges (drained polluted waters without treatment and emissions of sulfur dioxide and nitric oxide due to economic activity and its effects on the number of newly registered tumors in 1000 people of the population in Ukraine has been investigated. 4. During the investigation it was established as a rising idea to use the claim that the impact of emissions and discharges of pollutants to the number

  12. Improvement of inventory control and forecast according to activity-based classifications: T company as an example

    Science.gov (United States)

    Huang, Jui-Chan; Wu, Tzu-Jung; Chiu, Yen-Chun; Lu, Chunwei

    2017-06-01

    Inventory management is a major issue for all the industries. The supply of products to customers requires the readiness of the inventory. This allows rapid delivery and reduces waiting time for customers so that companies can profit from it. Any stock out or insufficiency will lead to loss of customers because their needs cannot be met. This will hurt firm profitability and market competitiveness. Inventory control is critical to retain liquidity and avoid overstocking. This is also the key to firm's survival and sustainability. To ensure an appropriate level of inventory, it is necessary to manage the inventory levels with sales forecast on an on-going basis. This paper seeks to assist Company T to improve its inventory control. Firstly, the products offered by Company T are classified into groups. The R programming language is used to stimulate and forecast future sales of different products. Different techniques are applied to manage the inventory levels according to the results of categorizations and forecasts that are consolidation of all the product items and grouping them into activity-based classifications, simulation and forecasting of future sales according to the categorization results, and formulation of different control techniques based on the simulations and forecasts. The results and the inventory management can be used to enhance the inventory control as well.

  13. Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport

    NARCIS (Netherlands)

    Fu, G.; Lin, H.X.; Heemink, A.W.; Segers, A.J.; Lu, S.; Palsson, T.

    2015-01-01

    The 2010 Eyjafjallajökull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash transport forecast in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs

  14. Using synchronization to improve the forecasting of large relaxations in a cellular-automaton model

    DEFF Research Database (Denmark)

    González, Á.; Gómez, J.B.; Vázquez-Prada, M.

    2004-01-01

    A new forecasting strategy for stochastic systems is introduced. It is inspired by the concept of synchronization, developed in the area of Dynamical Systems, and by the earthquake forecasting algorithms in which different pattern recognition functions are used for identifying seismic premonitory...

  15. Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport

    NARCIS (Netherlands)

    Fu, G.; Lin, H.X.; Heemink, A.W.; Segers, A.J.; Lu, S.; Palsson, T.

    2015-01-01

    The 2010 Eyjafjallajokull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash transport forecast in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs

  16. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    NARCIS (Netherlands)

    Wanders, Niko|info:eu-repo/dai/nl/364253940; Wood, Eric F.

    2016-01-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts

  17. The Forecast Scenarios of Development of the National Economy in the Context of the Need to Improve the «Cost of Living»

    Directory of Open Access Journals (Sweden)

    Kulakov Gennady T.

    2017-04-01

    Full Text Available The article is aimed at elaborating and materialization of the forecast scenarios of development of the national economy in the context of substantiating the feasibility of improving the «cost of living» being the equivalent of the liability of public authorities for the value of human life. The article researches the phenomenon of the «cost of living» in the context of sustainable innovative development of a socially oriented development of economy as an axis for developing its forecast scenarios. Focus has been set on complementarity of the terms of «cost of living» and «sustainable development» in the context of satisfying vital interests of the population of Ukraine. It has been suggested that wages accounting as an equivalent to the «cost of living» should not be included with costs but with the value added, however, the growth rate of wages must not outpace the growth rate of labor productivity. For the first time on the basis of the interdisciplinary and intersectoral approach, as well as the index method, have been elaborated baseline scenarios of development of the national economy on the basis of the upgraded human development index: pessimistic, realistic, and optimistic forecasts.

  18. 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.

  19. Long forecast horizon to improve Real Time Control of urban drainage systems

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen

    2014-01-01

    on DORA’s approach, this study investigated the implementation of long forecast horizon using an ensemble forecast from a Numerical Weather Prediction (NWP) model. The uncertainty of the prediction is characterized by an ensemble of 25 forecast scenarios. According to the status of the UDS......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...

  20. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  1. Improving Multimodel Weather Forecast of Monsoon Rain Over China Using FSU Superensemble

    Institute of Scientific and Technical Information of China (English)

    T.N.KRISHNAMURTI; A.D.SAGADEVAN; A.CHAKRABORTY; A.K.MISHRA; A.SIMON

    2009-01-01

    In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts.Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program.These are:ECMWF,UKMO,JMA,NCEP,CMA,CMC,BOM,MF,KMA and the CPTEC models.The superensemble strategy includes a training and a forecasts phase,for these the periods chosen for this study include the months February through September for the years 2007 and 2008.This paper addresses precipitation forecasts for the mcdium range i.e.Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models.For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product.We make use of standard metrics for forecast validations that include the RMS errors,spatial correlations and the equitable threat scores.The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite.Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation.Those skills are shown for a global belt and especially over China.Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon,the life cycle of the mci-yu rains and post typhoon landfall heavy rains and flood events.The higher skills of the multimodel superensemble make it a very useful product for such real time events.

  2. An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

    Directory of Open Access Journals (Sweden)

    Nathaniel K. Newlands

    2014-06-01

    Full Text Available We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011 for spring wheat (Triticum aestivum L.. The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.

  3. Calibration of seasonal forecasts over Euro-Mediterranean region: improve climate information for the applications in the energy sector

    Science.gov (United States)

    De Felice, Matteo; Alessandri, Andrea; Catalano, Franco

    2013-04-01

    Accurate and reliable climate information, calibrated for the specific geographic domain, are critical for an effective planning of operations in industrial sectors, and more in general, for all the human activities. The connection between climate and energy sector became particularly evident in the last decade, due to the diffusion of renewable energy sources and the consequent attention on the socio-economical effects of extreme climate events .The energy sector needs reliable climate information in order to plan effectively power plants operations and forecast energy demand and renewable output. On time-scales longer than two weeks (seasonal), it is of critical importance the optimization of global climate information on the local domains needed by specific applications. An application that is distinctly linked with climate is electricity demand forecast, in fact, especially during cold/hot periods, the electricity usage patterns are influenced by the use of electric heating/cooling equipments which diffusion is steadily increasing worldwide [McNeil & Letschert, 2007]. Following an approach similar to [Navarra & Tribbia, 2005], we find a linear relationship between seasonal forecasts main modes of temperature anomaly and the main modes of reanalysis on Euro-Mediterranean domain. Then, seasonal forecasts are calibrated by means of a cross-validation procedure with the aim of optimize climate information over Italy. Calibrated seasonal forecasts are used as predictor for electricity demand forecast on Italy during the summer (JJA) in the period 1990-2009. Finally, a comparison with the results obtained with not calibrated climate forecasts is performed. The proposed calibration procedure led to an improvements of electricity demand forecast performance with more evident effects on the North of Italy, reducing the overall RMSE of 10% (from 1.09 to 0.98). Furthermore, main principal components are visualized and put in relation with electricity demand patterns in

  4. Norway and Cuba Continue Collaborating to Build Capacity to Improve Weather Forecasting

    Science.gov (United States)

    Antuña, Juan Carlos; Kalnay, Eugenia; Mesquita, Michel D. S.

    2014-06-01

    The Future of Climate Extremes in the Caribbean Extreme Cuban Climate (XCUBE) project, which is funded by the Norwegian Directorate for Civil Protection as part of an assignment for the Norwegian Ministry of Foreign Affairs to support scientific cooperation between Norway and Cuba, carried out a training workshop on seasonal forecasting, reanalysis data, and weather research and forecasting (WRF). The workshop was a follow-up to the XCUBE workshop conducted in Havana in 2013 and provided Cuban scientists with access to expertise on seasonal forecasting, the WRF model developed by the National Center for Atmospheric Research (NCAR) and the community, data assimilation, and reanalysis.

  5. Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar; Marianno, Fernando J.; Shao, Xiaoyan; Zhang, Jie; Hodge, Bri-Mathias; Hamann, Hendrik F.

    2015-07-15

    With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

  6. Use of JPSS ATMS, CrIS, and VIIRS data to Improve Tropical Cyclone Track and Intensity Forecasting

    Science.gov (United States)

    Chirokova, G.; Demaria, M.; DeMaria, R.; Knaff, J. A.; Dostalek, J.; Musgrave, K. D.; Beven, J. L.

    2015-12-01

    JPSS data provide unique information that could be critical for the forecasting of tropical cyclone (TC) track and intensity and is currently underutilized. Preliminary results from several TC applications using data from the Advanced Technology Microwave Sounder (ATMS), the Cross-Track Infrared Sounder (CrIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-Orbiting Partnership satellite (SNPP), will be discussed. The first group of applications, which includes applications for moisture flux and for eye-detection, aims to improve rapid intensification (RI) forecasts, which is one of the highest priorities within NOAA. The applications could be used by forecasters directly and will also provide additional input to the Rapid Intensification Index (RII), the statistical-dynamical tool for forecasting RI events that is operational at the National Hurricane Center. The moisture flux application uses bias-corrected ATMS-MIRS (Microwave Integrated Retrieval System) and NUCAPS (NOAA Unique CrIS ATMS Processing System), retrievals that provide very accurate temperature and humidity soundings in the TC environment to detect dry air intrusions. The objective automated eye-detection application uses geostationary and VIIRS data in combination with machine learning and computer vision techniques for determining the onset of eye formation which is very important for TC intensity forecast but is usually determined by subjective methods. First version of the algorithm showed very promising results with a 75% success rate. The second group of applications develops tools to better utilize VIIRS data, including day-night band (DNB) imagery, for tropical cyclone forecasting. Disclaimer: The views, opinions, and findings contained in this article are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.

  7. Forecasting effects of climate change on Great Lakes fisheries: models that link habitat supply to population dynamics can help

    Science.gov (United States)

    Jones, Michael L.; Shuter, Brian J.; Zhao, Yingming; Stockwell, Jason D.

    2006-01-01

    Future changes to climate in the Great Lakes may have important consequences for fisheries. Evidence suggests that Great Lakes air and water temperatures have risen and the duration of ice cover has lessened during the past century. Global circulation models (GCMs) suggest future warming and increases in precipitation in the region. We present new evidence that water temperatures have risen in Lake Erie, particularly during summer and winter in the period 1965–2000. GCM forecasts coupled with physical models suggest lower annual runoff, less ice cover, and lower lake levels in the future, but the certainty of these forecasts is low. Assessment of the likely effects of climate change on fish stocks will require an integrative approach that considers several components of habitat rather than water temperature alone. We recommend using mechanistic models that couple habitat conditions to population demographics to explore integrated effects of climate-caused habitat change and illustrate this approach with a model for Lake Erie walleye (Sander vitreum). We show that the combined effect on walleye populations of plausible changes in temperature, river hydrology, lake levels, and light penetration can be quite different from that which would be expected based on consideration of only a single factor.

  8. Reflections on Improving the Accuracy of Weather Forecasts%关于提高天气预报准确率的思考

    Institute of Scientific and Technical Information of China (English)

    李学欣

    2014-01-01

    The weather forecast meteorological services in the most basic work. Analyzes the importance of weather forecast accuracy and factors affecting the accuracy of weather forecasts, made several on improving the accuracy of weather forecasts measures for reference.%天气预报是气象服务业中最基础的工作。分析了天气预报准确率的重要性和影响天气预报准确率的因素,提出了几点关于提高天气预报准确率的措施,以供参考。

  9. Improving photovoltaics grid integration through short time forecasting and self-consumption

    OpenAIRE

    Masa Bote, Daniel; Castillo Cagigal, Manuel; Matallanas de Avila, Eduardo; Caamaño Martín, Estefanía; Gutierrez Martín, Alvaro; Monasterio-Huelin Maciá, Felix; Jiménez Leube, Francisco Javier

    2014-01-01

    The uncertainty associated to the forecast of photovoltaic generation is a major drawback for the widespread introduction of this technology into electricity grids. This uncertainty is a challenge in the design and operation of electrical systems that include photovoltaic generation. Demand-Side Management (DSM) techniques are widely used to modify energy consumption. If local photovoltaic generation is available, DSM techniques can use generation forecast to schedule the local consumption. O...

  10. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    OpenAIRE

    Jingmin Wang; Jian Zhang; Jing Nie

    2016-01-01

    Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity con...

  11. Improving Weather Research and Forecasting Model Initial Conditions via Surface Pressure Analysis

    Science.gov (United States)

    2015-09-01

    Obsgrid) that creates input data for the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) is modified to perform a...Configuration  The Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) V3.6.1 (Skamarock et al. 2008) is applied with 56 vertical...those with more benign weather. On 7 February a trough moved onshore and led to widespread precipitation in the region . More quiescent weather was in

  12. 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.

  13. Improved Orbit Determination and Forecasts with an Assimilative Tool for Satellite Drag Specification

    Science.gov (United States)

    Pilinski, M.; Crowley, G.; Sutton, E.; Codrescu, M.

    2016-09-01

    Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. As the population of satellites in Earth orbit grows, higher space-weather prediction accuracy is required for critical missions, such as accurate catalog maintenance, collision avoidance for manned and unmanned space flight, reentry prediction, satellite lifetime prediction, defining on-board fuel requirements, and satellite attitude dynamics. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. In this paper, we will review the driving requirements for our model, summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models. As part of the analysis, we compare the drag observed by

  14. Gulf of Mexico Ecological Forecasting - Atlantic Bluefin Tuna Population Assessment and Management using Synthetic Aperture Radar (SAR) Data

    Science.gov (United States)

    Laygo, K.; Jones, I.; Huerta, J.; Holt, B.

    2010-12-01

    Atlantic Bluefin Tuna (Thunnus thynnus) is one of the largest vertebrates in the world and is in high demand in sushi markets. It is a highly political species and is managed internationally by the International Commission for the Conservation of Atlantic Tuna. The Gulf of Mexico and the Mediterranean Sea are the only two known spawning sites in the world. However, there is a large variance in estimates of adult Atlantic Tuna spawning. This research focuses on extending Earth science research results to existing decision-making systems, National Oceanic and Atmospheric Administration (NOAA) and the National Marine Fisheries Service (NMFS)for population assessment and management of Atlantic Bluefin Tuna. The research team is a multi-sector and multi-disciplinary team composed of government (NOAA_NMFS), academic (University of South Florida Institute for Marine Remote Sensing) and commercial (Roffer’s Ocean Fishing Forecasting Service, Inc.) institutions. Their goal is to reduce the variance in the estimates of adult Bluefin Tuna spawning stock abundance in the Gulf of Mexico (GOM). Therefore, this paper will be derived from the innovative use of several earth orbiting satellites focusing on the use of synthetic aperture radar (SAR) data to identify Sargassum, which is a floating marine algae that may be relevant to the presence of Bluefin Tuna aggregations. The SAR imagery will be examined in combination with MODIS and MERIS Chlorophyll-a products to detect fine-scale surface current shear, eddy and frontal features, as well as biological slicks due to the presence of Sargassum. In addition, wind records from NOAA buoy data will be studied to analyze wind patterns in the Gulf of Mexico. The fine-resolution, all-weather capabilities of SAR provide a valuable complement to optical/IR sensors, which are often impacted by cloud cover. This study will provide an assessment of whether or not SAR can contribute to decision support efforts relevant to commercial fisheries

  15. Densified GPS Estimates of Integrated Precipitable Water Vapor Improve Weather Forecasting during the North American Monsoon

    Science.gov (United States)

    Moore, A. W.; Small, I.; Gutman, S. I.; Bock, Y.; Dumas, J.; Haase, J. S.; Laber, J. L.

    2013-12-01

    Continuous GPS (CGPS) stations for observing crustal motion in the western U.S. now number more than 1200, with over 500 of them operating in real time. Tropospheric wet delay from real-time processing of the GPS data, along with co-located or nearby surface and temperature measurements, are being operationally converted to Integrated Precipitable Water Vapor (IPW) for evaluation as a forecasting tool (Gutman, 2011). The available density of real-time GPS in southern California now allows us to explore usage of densified GPS IPW in operational weather forecasting during weather conditions involving moisture extremes. Under a NASA Advanced Information Systems Technology (AIST) project, 27 southern California stations have been added to the NOAA GPS-Met observing network providing 30-minute estimates of IPW for ingestion into operational NOAA weather models, as well as for direct use by National Weather Service forecasters in monitoring developing weather conditions. The densified network proved advantageous in the 2013 North American Monsoon season, allowing forecasters to visualize rapid moisture increases at intervals between model runs and radiosonde observations and assisting in flood watch/warning decisions. We discuss the observed relationship between IPW and onset of precipitation in monsoon events in southern California and possibilities for additional decision support tools for forecasters.

  16. Improving the degree-day model for forecasting Locusta migratoria manilensis (Meyen (Orthoptera: Acridoidea.

    Directory of Open Access Journals (Sweden)

    Xiongbing Tu

    Full Text Available The degree-day (DD model is an important tool for forecasting pest phenology and voltinism. Unfortunately, the DD model is inaccurate, as is the case for the Oriental migratory locust. To improve the existing DD model for this pest, we first studied locust development in seven growth chambers, each of which simulated the complete growing-season climate of a specific region in China (Baiquan, Chengde, Tumotezuoqi, Wenan, Rongan, Qiongzhong, or Qiongshan. In these seven treatments, locusts completed 0.95, 1, 1.1, 2.2, 2.95, 3.95, and 4.95 generations, respectively. Hence, in the Baiquan (700, Rongan (2400, Qiongzhong (3200, and Qiongshan (2400 treatments, the final generation were unable to lay eggs. In a second experiment, we reared locusts for a full generation in growth chambers, at different constant temperatures. This experiment provided two important findings. First, temperatures between 32 and 42°C did not influence locust development rate. Hence, the additional heat provided by temperatures above 32°C did not add to the total heat units acquired by the insects, according to the traditional DD model. Instead, temperatures above 32°C represent overflow heat, and can not be included when calculating total heat acquired during development. We also noted that females raised at constant 21°C failed to oviposit. Hence, temperatures lower than 21°C should be deducted when calculating total heat acquired during adult development. Using our experimental findings, we next micmiked 24-h temperature curve and constructed a new DD model based on a 24-h temperature integral calculation. We then compared our new model with the traditional DD model, results showed the DD deviation was 166 heat units in Langfang during 2011. At last we recalculated the heat by our new DD model, which better predicted the results from our first growth chamber experiment.

  17. Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

    Directory of Open Access Journals (Sweden)

    Jingwei Song

    2014-01-01

    Full Text Available A simulated annealing (SA based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN, and partial least square support vector machine (PLS-SVM to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model, 12.93% (ANN, and 12.94% (PLS-SVM to 9.38%. Five-week average has been raised from 13.02% (chaotic model, 15.69% (ANN, and 15.92% (PLS-SVM to 11.27%.

  18. Improving River Flow Predictions from the NOAA NCRFC Forecasting Model by Incorporating Satellite Observations

    Science.gov (United States)

    Tuttle, S. E.; Jacobs, J. M.; Restrepo, P. J.; Deweese, M. M.; Connelly, B.; Buan, S.

    2016-12-01

    The NOAA National Weather Service North Central River Forecast Center (NCRFC) is responsible for issuing river flow forecasts for parts of the Upper Mississippi, Great Lakes, and Hudson Bay drainages, including the Red River of the North basin (RRB). The NCRFC uses an operational hydrologic modeling infrastructure called the Community Hydrologic Prediction System (CHPS) for its operational forecasts, which currently links the SNOW-17 snow accumulation and ablation model, to the Sacramento-Soil Moisture Accounting (SAC-SMA) rainfall-runoff model, to a number of hydrologic and hydraulic flow routing models. The operational model is lumped and requires only area-averaged precipitation and air temperature as inputs. NCRFC forecasters use observational data of hydrological state variables as a source of supplemental information during forecasting, and can use professional judgment to modify the model states in real time. In a few recent years (e.g. 2009, 2013), the RRB exhibited unexpected anomalous hydrologic behavior, resulting in overestimation of peak flood discharge by up to 70% and highlighting the need for observations with high temporal and spatial coverage. Unfortunately, observations of hydrological states (e.g. soil moisture, snow water equivalent (SWE)) are relatively scarce in the RRB. Satellite remote sensing can fill this need. We use Minnesota's Buffalo River watershed within the RRB as a test case and update the operational CHPS model using modifications based on satellite observations, including AMSR-E SWE and SMOS soil moisture estimates. We evaluate the added forecasting skill of the satellite-enhanced model compared to measured streamflow using hindcasts from 2010-2013.

  19. Problem and Improvement of R-values Applied to Assessment of Earthquake Forecast

    Institute of Scientific and Technical Information of China (English)

    Wang Xiaoqing

    2001-01-01

    The researches on the assessment of earthquake forecast are reviewed, then the R-value assessment is further developed theoretically in the paper. The results include the arithmetic of the R-values of earthquake occurrence under the condition that "anomaly" occurred or no "anomaly" occurred respectively, and the relation between the values. The distribution of Rvalue of a forecast method, corresponding to multi-status anomalies being independent each other, is also developed in the paper. The appropriate methods to estimate the R-values and extrapolate the occurrence probability of future earthquakes are also given in the paper.

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

    Science.gov (United States)

    High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

  1. SIGNIFICANCE OF СD4+ Т-LYMPHOCYTE POPULATIONS MONITORING FOR DIAGNOSING AND FORECASTING OF ORGANISM REACTION ON TRANSPLANT

    Directory of Open Access Journals (Sweden)

    N. A. Onishchenko

    2013-01-01

    Full Text Available In this review article the necessity of adaptation and introduction into clinical practice of simultaneous monitoring of immune blood cells and cytokines in patients with grafted organs for a choice of individual tactic of immuno- suppressive therapy, determination of its efficiency and forecasting is proved. It is emphasized, that with the spe- cial attention it ought to concern to characteristic of CD4 + T-lymphocytes and to definition of an interrelation of their separate populations in peripheral blood (Treg, Th17, Tact memory cells – CD4+CD25hiCD127hiCD45RO since they are the basic participants of immune system reaction on grafts. 

  2. 1. The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System

    Science.gov (United States)

    Pappenberger, Florian; Thielen, Jutta; Del Medico, Mauro

    2010-05-01

    The European Flood Alert System (EFAS) provides early flood alerts on a pre-operational basis to National hydrological services. EFAS river discharge forecasts are based on probabilistic techniques, using ensemble system and deterministic numerical weather prediction data. The performance of EFAS is regularly analysed with regard to individual flood events and case studies. Although this analysis provides important insight into the strengths and weaknesses of the forecast system, it lacks statistical and independent measures of its long-term performance. In this paper an assessment of EFAS results based on ECMWF weather forecasts over a period of 10 years is presented. EFAS river discharge forecasts have been rerun every week for a period of 10 years using the weather forecast available at the time. These are evaluated for a total of 500 river gauging stations distributed across Europe.. The selected stations are sufficiently separated in space to avoid autocorrelation of station time series. Also, analysis is performed with a gap of 3 days between each forecast which reduces the temporal correlation of the time series of the same station. The data are analysed with regard to skill, bias and quality of river discharge forecast. The 10 year simulations clearly show that the skill of the river discharge forecasts have undergone an evolution linked to the quality of the operational meteorological forecast. Overall, over the period of 10 years, the skill of the EFAS forecasts has steadily increased. Important hydrological extreme events cannot be clearly identified with the skill score analysis, highlighting the necessity for event based analysis in addition to statistical long-term assessments for a better understanding of the EFAS system and large scale river discharge predictions in general. he predictability is shown to depend on catchment size and geographical location.

  3. Improving Aerosol and Visibility Forecasting Capabilities Using Current and Future Generations of Satellite Observations

    Science.gov (United States)

    2015-08-27

    retrievals . 15. SUBJECT TERMS ’ Aerosol, data assimilation, satellite remote sensing, visibility forecast, electro-optical propagation 16. SECURITY...innovative methods for retrieving aerosol optical depth at nighttime using Visible Infrared Imaging Radiometer Suite (VIIRS) data (Johnson et al...Orthogonal Polarization (CALIOP) aerosol and cloud layer products, as well as collocated Ozone Monitoring Instrument (OMI) Aerosol Index (Al) data and

  4. Improving Students' Data Analysis and Presentation Skills: The Ocean State Circuits, Inc. Forecasting Project

    Science.gov (United States)

    Kroes, James R.; Chen, Yuwen; Mangiameli, Paul

    2013-01-01

    Many potential employers expect that newly hired students will arrive on-the-job with the ability to analyze data, utilize spreadsheets, and communicate findings and recommendations. We designed the Ocean State Circuits, Inc. Forecasting Project to address these gaps in our students' knowledge of analytical tools (such as the "vlookup()"…

  5. Improved forecasting of global vegetation conditions using remotely-sensed surface soil moisture

    Science.gov (United States)

    Timely and accurate monitoring of anomalies in root-zone soil water availability is essential for assessing global agricultural crop conditions. Root-zone soil moisture estimates are particularly important for obtaining forecasts of end-of-season crop yield fluctuations provided by the United States...

  6. Improving short-range ensemble Kalman storm surge forecasting using robust adaptive inflation

    NARCIS (Netherlands)

    Altaf, M.U.; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, I.

    2013-01-01

    This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense t

  7. Improving Students' Data Analysis and Presentation Skills: The Ocean State Circuits, Inc. Forecasting Project

    Science.gov (United States)

    Kroes, James R.; Chen, Yuwen; Mangiameli, Paul

    2013-01-01

    Many potential employers expect that newly hired students will arrive on-the-job with the ability to analyze data, utilize spreadsheets, and communicate findings and recommendations. We designed the Ocean State Circuits, Inc. Forecasting Project to address these gaps in our students' knowledge of analytical tools (such as the "vlookup()"…

  8. Improving short-range ensemble Kalman storm surge forecasting using robust adaptive inflation

    NARCIS (Netherlands)

    Altaf, M.U.; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, I.

    2013-01-01

    This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense t

  9. On noice in data assimilation schemes for improved flood forecasting using distributed hydrological models

    NARCIS (Netherlands)

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

    2014-01-01

    We investigate the effects of noise specification on the quality of hydrological forecasts via an advanced data assimilation (DA) procedure using a distributed hydrological model driven by numerical weather predictions. The sequential DA procedure is based on (1) a multivariate rainfall ensemble

  10. Improved Satellite Techniques for Monitoring and Forecasting the Transition of Hurricanes to Extratropical Storms

    Science.gov (United States)

    Folmer, Michael; Halverson, Jeffrey; Berndt, Emily; Dunion, Jason; Goodman, Steve; Goldberg, Mitch

    2014-01-01

    The Geostationary Operational Environmental Satellites R-Series (GOES-R) and Joint Polar Satellite System (JPSS) Satellite Proving Grounds have introduced multiple proxy and operational products into operations over the last few years. Some of these products have proven to be useful in current operations at various National Weather Service (NWS) offices and national centers as a first look at future satellite capabilities. Forecasters at the National Hurricane Center (NHC), Ocean Prediction Center (OPC), NESDIS Satellite Analysis Branch (SAB) and the NASA Hurricane and Severe Storms Sentinel (HS3) field campaign have had access to a few of these products to assist in monitoring extratropical transitions of hurricanes. The red, green, blue (RGB) Air Mass product provides forecasters with an enhanced view of various air masses in one complete image to help differentiate between possible stratospheric/tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. As a compliment to this product, a new Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) Ozone product was introduced in the past year to assist in diagnosing the dry air intrusions seen in the RGB Air Mass product. Finally, a lightning density product was introduced to forecasters as a precursor to the new Geostationary Lightning Mapper (GLM) that will be housed on GOES-R, to monitor the most active regions of convection, which might indicate a disruption in the tropical environment and even signal the onset of extratropical transition. This presentation will focus on a few case studies that exhibit extratropical transition and point out the usefulness of these new satellite techniques in aiding forecasters forecast these challenging events.

  11. IMPROVEMEnTS In ASSESSInG THE FORECASTS ACCURACY - A CASE STUDY FOR ROMAnIAn MACROECOnOMIC FORECASTS

    Directory of Open Access Journals (Sweden)

    Mihaela (Simionescu Bratu

    2013-05-01

    Full Text Available The objective of this study is to introduce new forecasts’ accuracy measures for two types of predictions: point forecasts (radical of order n of the mean of squared errors, mean for the differencebetween each predicted value and the mean of the effective values, ratio of radicals of sum of squared errors (RRSSE- for forecasts comparisons, different versions of U2 Theil’s statistic and forforecast intervals (number of intervals including the realization, difference between the realization and the lower limit, the upper one, respectively the interval centre. Comparisons are made to presentthe differences in results determined by the application of the classical measures of predictions accuracy for the inflation and unemployment rate forecasts provided for Romania by Institute forEconomic Forecasting (IEF and National Commission of Prognosis (NCP on the horizon 2010- 2012 and the values of new point forecasts accuracy measures. The hierarchy of predictions provided by the classical indicators and by the new ones are different. A novelty in literature is also brought by the methods of building the forecasts intervals. In addition to the classical interval basedon historical error method, some new techniques of building forecasts are used: intervals based on the standard deviation and those constructed using bootstrap technique bias-corrected-accelerated(BCA bootstrap method.

  12. Grand Canyon Humpback Chub Population Improving

    Science.gov (United States)

    Andersen, Matthew E.

    2007-01-01

    The humpback chub (Gila cypha) is a long-lived, freshwater fish found only in the Colorado River Basin. Physical adaptations-large adult body size, large predorsal hump, and small eyes-appear to have helped humpback chub evolve in the historically turbulent Colorado River. A variety of factors, including habitat alterations and the introduction of nonnative fishes, likely prompted the decline of native Colorado River fishes. Declining numbers propelled the humpback chub onto the Federal list of endangered species in 1967, and the species is today protected under the Endangered Species Act of 1973. Only six populations of humpback chub are currently known to exist, five in the Colorado River Basin above Lees Ferry, Ariz., and one in Grand Canyon, Ariz. The U.S. Geological Survey's Grand Canyon Monitoring and Research Center oversees monitoring and research activities for the Grand Canyon population under the auspices of the Glen Canyon Dam Adaptive Management Program (GCDAMP). Analysis of data collected through 2006 suggests that the number of adult (age 4+ years) humpback chub in Grand Canyon increased to approximately 6,000 fish in 2006, following an approximate 40-50 percent decline between 1989 and 2001. Increasing numbers of adult fish appear to be the result of steadily increasing numbers of juvenile fish reaching adulthood beginning in the mid- to late-1990s and continuing through at least 2002.

  13. Improving real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using Particle Filter

    Science.gov (United States)

    Xu, X.

    2016-12-01

    The uncertainty associated with inflow boundary forcing data has been recognized as an important and dominant source of uncertainties in hydraulic model. Here, we develop a real-time probabilistic channel flood forecasting model with a novel function to incorporate the uncertainty of forcing inflow. This new approach couples a hydraulic model with the Sequential Monte Carlo (SMC), Particle filter (PF), data assimilation algorithm. Stage observations at hydrological stations along the channel are assimilated at each time step to update the model states in order to improve next time step's forecasting. We test this new approach for a real flood event occurred during June 27, 2009 and July 9, 2009 in the river reach from upstream Cuntan station to downstream Zhongxian station of the Middle Yangtze River, China. As compared with open loop model simulations, model evaluations with several quantitative deterministic and probabilistic metrics indicate that accuracy of the ensemble mean prediction and reliability of the uncertainty quantification are improved pronouncedly as a result of the PF assimilation. Further assessment of forecasting performance at different lead times shows that the degree of model improvement weakens with the increase of lead time due to the gradual diminishing of updating effect on initial conditions. The examination of different number of particles shows that the optimal number of particles can be chosen as a tradeoff between model performance and computation burden. The analysis of different assimilation frequency indicates that higher assimilation frequency can help improve the model performance by incorporating more observation information and updating model states to better represent instantaneous flood conditions.

  14. Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation.

    Science.gov (United States)

    Recknagel, Friedrich; Orr, Philip T; Cao, Hongqing

    2014-01-01

    Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 and the hybrid evolutionary algorithm HEA. Resulting models using all measured variables as inputs as well as models using electronically measurable variables only as inputs forecasted accurately timing of overgrowth of C. raciborskii and matched well high and low magnitudes of observed bloom events with 0.45≤r(2)>0.61 and 0.4≤r(2)>0.57, respectively. The models also revealed relationships and thresholds triggering bloom events that provide valuable information on synergism between water quality conditions and population dynamics of C. raciborskii. Best performing models based on using all measured variables as inputs indicated electrical conductivity (EC) within the range of 206-280mSm(-1) as threshold above which fast growth and high abundances of C. raciborskii have been observed for the three lakes. Best models based on electronically measurable variables for the Lakes Wivenhoe and Somerset indicated a water temperature (WT) range of 25.5-32.7°C within which fast growth and high abundances of C. raciborskii can be expected. By contrast the model for Lake Samsonvale highlighted a turbidity (TURB) level of 4.8 NTU as indicator for mass developments of C. raciborskii. Experiments with online measured water quality data of the Lake Wivenhoe from 2007 to 2010 resulted in predictive models with 0.61≤r(2)>0.65 whereby again similar levels of EC and WT have been discovered as thresholds for outgrowth of C. raciborskii. The highest validity of r(2)=0.75 for an in situ data-based model has been achieved after considering time lags for EC by 7 days and dissolved oxygen by 1 day. These time lags have been discovered by a systematic screening of all possible combinations of time lags between 0 and 10 days for all electronically measurable variables. The so

  15. Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

    KAUST Repository

    Altaf, Muhammad

    2013-08-01

    This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

  16. Survey-based indicators vs. hard data: What improves export forecasts in Europe?

    OpenAIRE

    2015-01-01

    In this study, we evaluate whether survey-based indicators produce lower forecast errorsfor export growth than indicators obtained from hard data such as price and costcompetitiveness measures. Our pseudo out-of-sample analyses and forecastencompassingtests reveal that survey-based indicators outperform the benchmarkmodel as well as the indicators from hard data for most of the twenty European statesfocused on in our study and the aggregates EA-18 and EU-28. The most accurate forecastsare on ...

  17. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

    Directory of Open Access Journals (Sweden)

    Yi Liang

    2016-10-01

    Full Text Available Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT and least squares support vector machine (LSSVM, which is optimized by an improved cuckoo search (CS. To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

  18. Improved Analyses and Forecasts of Snowpack, Runoff and Drought through Remote Sensing and Land Surface Modeling in Southeastern Europe

    Science.gov (United States)

    Matthews, D.; Brilly, M.; Gregoric, G.; Polajnar, J.; Kobold, M.; Zagar, M.; Knoblauch, H.; Staudinger, M.; Mecklenburg, S.; Lehning, M.; Schweizer, J.; Balint, G.; Cacic, I.; Houser, P.; Pozzi, W.

    2008-12-01

    European hydrometeorological services and research centers are faced with increasing challenges from extremes of weather and climate that require significant investments in new technology and better utilization of existing human and natural resources to provide improved forecasts. Major advances in remote sensing, observation networks, data assimilation, numerical modeling, and communications continue to improve our ability to disseminate information to decision-makers and stake holders. This paper identifies gaps in current technologies, key research and decision-maker teams, and recommends means for moving forward through focused applied research and integration of results into decision support tools. This paper reports on the WaterNet - NASA Water Cycle Solutions Network contacts in Europe and summarizes progress in improving water cycle related decision-making using NASA research results. Products from the Hydrologic Sciences Branch, Goddard Space Flight Center, NASA, Land Information System's (LIS) Land Surface Models (LSM), the SPoRT, CREW , and European Space Agency (ESA), and Joint Research Center's (JRC) natural hazards products, and Swiss Federal Institute for Snow and Avalanche Research's (SLF), and others are discussed. They will be used in collaboration with the ESA and the European Commission to provide solutions for improved prediction of water supplies and stream flow, and droughts and floods, and snow avalanches in the major river basins serviced by EARS, ZAMG, SLF, Vituki Consult, and other European forecast centers. This region of Europe includes the Alps and Carpathian Mountains and is an area of extreme topography with abrupt 2000 m mountains adjacent to the Adriatic Sea. These extremes result in the highest precipitation ( > 5000 mm) in Europe in Montenegro and low precipitation of 300-400 mm at the mouth of the Danube during droughts. The current flood and drought forecasting systems have a spatial resolution of 9 km, which is currently being

  19. Mathematical Modeling and Forecasting Population for Muslim of Rural Region in Bangladesh

    Directory of Open Access Journals (Sweden)

    Rafiqul Islam

    2010-03-01

    Full Text Available In this research the population for Muslim of Rural region in Bangladesh is predicted by using theexponential growth rate method. For this link, the information of data for the Rural M uslim population for maleand female of Bangladesh is obtained from 1991 and 2001 censuses. The predictions are computed in threephases. In the first phase, the predictions are computed using negative exponential growth model estimated bythe Quasi-Newton method using STATISTICA for the years 1991 and 2001. Using the Cross ValidationPredictive Power (CVPP criterion and R2, the shrinkage coefficient (8 is constructed. The shrinkagecoefficient determines the adequacy of the first phase prediction. In the second phase, these predicted valuesare used to estimate the growth rate, for different age groups, by using the exponential growth rate m ethod. Inthe third phase, that is, finally considering the observed population for Muslim of Rural region in Bangladeshfor the Census year 2001 as the base population and using the estimated exponential growth rate, at differentage groups, of the second phase estimation, the predictions of the population of M uslim of Rural region areobtained for the years 2002 through to 2021 employing exponential growth rate method successively 20 times.

  20. Improving precipitation forecast with hybrid 3DVar and time-lagged ensembles in a heavy rainfall event

    Science.gov (United States)

    Wang, Yuanbing; Min, Jinzhong; Chen, Yaodeng; Huang, Xiang-Yu; Zeng, Mingjian; Li, Xin

    2017-01-01

    This study evaluates the performance of three-dimensional variational (3DVar) and a hybrid data assimilation system using time-lagged ensembles in a heavy rainfall event. The time-lagged ensembles are constructed by sampling from a moving time window of 3 h along a model trajectory, which is economical and easy to implement. The proposed hybrid data assimilation system introduces flow-dependent error covariance derived from time-lagged ensemble into variational cost function without significantly increasing computational cost. Single observation tests are performed to document characteristic of the hybrid system. The sensitivity of precipitation forecasts to ensemble covariance weight and localization scale is investigated. Additionally, the TLEn-Var is evaluated and compared to the ETKF(ensemble transformed Kalman filter)-based hybrid assimilation within a continuously cycling framework, through which new hybrid analyses are produced every 3 h over 10 days. The 24 h accumulated precipitation, moisture, wind are analyzed between 3DVar and the hybrid assimilation using time-lagged ensembles. Results show that model states and precipitation forecast skill are improved by the hybrid assimilation using time-lagged ensembles compared with 3DVar. Simulation of the precipitable water and structure of the wind are also improved. Cyclonic wind increments are generated near the rainfall center, leading to an improved precipitation forecast. This study indicates that the hybrid data assimilation using time-lagged ensembles seems like a viable alternative or supplement in the complex models for some weather service agencies that have limited computing resources to conduct large size of ensembles.

  1. Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean.

    Science.gov (United States)

    Marcos, Raül; Llasat, Ma Carmen; Quintana-Seguí, Pere; Turco, Marco

    2017-08-09

    In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Forecasting the Relative and Cumulative Effects of Multiple Stressors on At-risk Populations

    Science.gov (United States)

    2011-08-01

    and Lennon 1999) and changes in phenology (Beebee 1995, Crick and Sparks 1999). Furthermore, climate change has been clearly implicated in species...addressing the issue of spatial scale and variation in modeling approaches is to create ensembles of models or model predictions. Predictions resulting...the understanding of URTD population-level effects and has led to speculation that environmental factors may be partly responsible for the variation

  3. Confidence in Coastal Forecasts

    NARCIS (Netherlands)

    Baart, F.

    2013-01-01

    This thesis answers the question "How can we show and improve our confidence in coastal forecasts?", by providing four examples of common coastal forecasts. The first example shows how to improve the estimate of the one in ten thousand year storm-surge level. The three dimensional reconstruction,

  4. Methods of improvement of forecasting of development of mineral deposits' power supply

    Directory of Open Access Journals (Sweden)

    Alexander V. Putilov

    2015-03-01

    Full Text Available Mineral deposits (among which non-ferrous metals take a leading place are situated on the territory of our planet rather unevenly, and often in out-of-the-way places. Nuclear power (particularly, transportable nuclear power plants provides the new possibilities of power supply, which is very important for deposits' development. This article shares the economic aspects of forecasting in the field of power development (in particular, nuclear power on the basis of transportable nuclear power plants. Economic barriers of development of innovative nuclear technologies are considered on the example of transportable nuclear power plants. At the same time, there are given the ways of elimination of such barrier to development of this technology as methodical absence of investigation of a question of distribution of added cost between producers of innovative equipment and final product. Addition of new analytical tool (“business diagonal” is offered for a method of definition of economically efficient distribution of added cost (received as a result of introduction of innovative technologies between participants of production and consumption of atomic energy within the “economic cross” model. There is offered the order of use of method of cash flows discounting at calculations between nuclear market participants. Economic methods, offered in this article, may be used in forecasting of development of other energy technologies and introduction of prospective energy equipment.

  5. Improvement of machining accuracy in precision micro-boring system by forecasting compensatory control technique

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Presents the design of a micro-boring servo system. A piezoelectric actuator is employed to compensate the deflection errors of the cutter in the radial direction to reduce the force-induced errors in the workpiece. In order to bore small and deep holes, the boring bar is designed with a new structure consisting of two concentric bars, one being used for error measuring and the other for error compensation. As a result, the size of the micro-boring bar is not af fected even after the piezoelectric actuator and strain gauges have been incorporated. The outer diameter of the boring bar used is 16 mm and the length to diameter ratio is greater than 9. A Forecasting Compensatory Control (FCC) technique is adopted in this system for error prediction and error compensation. The off-line forecasting compensatory control simulation and on-line cutting results have verified that the roundness form errors in the workpiece can be re duced up to 60 percent with the developed micro-boring servo system.

  6. Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products

    Science.gov (United States)

    Uysal, Gökçen; Şensoy, Aynur; Şorman, A. Arda

    2016-12-01

    This paper investigates the contribution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Snow Cover Area (SCA) product and in-situ snow depth measurements to Artificial Neural Network model (ANN) based daily streamflow forecasting in a mountainous river basin. In order to represent non-linear structure of the snowmelt process, Multi-Layer Perceptron (MLP) Feed-Forward Backpropagation (FFBP) architecture is developed and applied in Upper Euphrates River Basin (10,275 km2) of Turkey where snowmelt constitutes approximately 2/3 of total annual volume of runoff during spring and early summer months. Snowmelt season is evaluated between March and July; 7 years (2002-2008) seasonal daily data are used during training while 3 years (2009-2011) seasonal daily data are split for forecasting. One of the fastest ANN training algorithms, the Levenberg-Marquardt, is used for optimization of the network weights and biases. The consistency of the network is checked with four performance criteria: coefficient of determination (R2), Nash-Sutcliffe model efficiency (ME), root mean square error (RMSE) and mean absolute error (MAE). According to the results, SCA observations provide useful information for developing of a neural network model to predict snowmelt runoff, whereas snow depth data alone are not sufficient. The highest performance is experienced when total daily precipitation, average air temperature data are combined with satellite snow cover data. The data preprocessing technique of Discrete Wavelet Analysis (DWA) is coupled with MLP modeling to further improve the runoff peak estimates. As a result, Nash-Sutcliffe model efficiency is increased from 0.52 to 0.81 for training and from 0.51 to 0.75 for forecasting. Moreover, the results are compared with that of a conceptual model, Snowmelt Runoff Model (SRM), application using SCA as an input. The importance and the main contribution of this study is to use of satellite snow products and data

  7. Forecasting Helicoverpa populations in Australia: A comparison of regression based models and a bioclimatic based modelling approach

    Institute of Scientific and Technical Information of China (English)

    MYRONP.ZALUCKI; MICHAELJ.FURLONG

    2005-01-01

    Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallenglen) and H. armigera (Hübner)(Lepidoptera:Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.

  8. 改进的灰色模型在船闸通过量预测中的应用%Application of Improved Gray Forecasting Model in Forecasting Lock Freight Volume

    Institute of Scientific and Technical Information of China (English)

    王馨; 陶桂兰; 杨正

    2012-01-01

    The accuracy of gray forecasting model was increased by improving the smooth degree and the applicable scope was expanded by improving the algorithm of z. Based on the calendar total cargo of the locks in northern section of Beijing-Hangzhou Grand Canal, we used the gray model method and an improved gray model to forecast future freight volumes. Comparing the two forecasting results, we tested and verified that the improved gray forecasting model has higher accuracy and more extensive applicable scope, and it's appropriate to be used in forecasting lock freight volume.%对灰色GM(1,1)模型进行了改进,通过函数变换改变序列的光滑度,以积分逼近值代替均值作为相邻时间间隔增长量,以提高发展系数精度,从而得到了比原GM(1,1)模型模拟精度高和适用法范围更广的新模型.并以苏北运河船闸历年累计货物通过量为实例,运用原始模型与改进模型对船闸通过量进行预测,预测值与真实值相比较后,证实了文中改进的灰色模型精度较高,适用范围更广.

  9. Changing Arctic Ecosystems: Updated forecast: Reducing carbon dioxide (CO2) emissions required to improve polar bear outlook

    Science.gov (United States)

    Oakley, Karen L.; Atwood, Todd C.; Mugel, Douglas N.; Rode, Karyn D.; Whalen, Mary E.

    2015-01-01

    The Arctic is warming faster than other regions of the world due to the loss of snow and ice, which increases the amount of solar energy absorbed by the region. The most visible consequence has been the rapid decline in sea ice over the last 3 decades-a decline projected to bring long ice-free summers if greenhouse gas (GHG) emissions are not significantly reduced. The polar bear (Ursus maritimus) depends on sea ice over the biologically productive continental shelves of the Arctic Ocean as a platform for hunting seals. In 2008, the U.S. Fish and Wildlife Service listed the polar bear as threatened under the Endangered Species Act (ESA) due to the threat posed by sea ice loss. The polar bear was the first species to be listed due to forecasted population declines from climate change.

  10. Forecasting future oil production in Norway and the UK: a general improved methodology

    CERN Document Server

    Fievet, Lucas; Cauwels, Peter; Sornette, Didier

    2014-01-01

    We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the standard methodology that tends to underestimate remaining oil reserves, our method gives a better description of future oil production, as validated by our back-tests starting in 2008. Specifically, we predict remaining reserves extractable until 2030 to be 188 +/- 10 million barrels for Norway and 98 +/- 10 million barrels for the UK, which are respectively 45% and 66% above the predictions using the standard methodology.

  11. Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Computer Science; Simmhan, Yogesh [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Electrical Engineering; Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Electrical Engineering

    2011-12-11

    The rising global demand for energy is best addressed by adopting and promoting sustainable methods of power consumption. We employ an informatics approach towards forecasting the energy consumption patterns in a university campus micro-grid which can be used for energy use planning and conservation. We use novel indirect indicators of energy that are commonly available to train regression tree models that can predict campus and building energy use for coarse (daily) and fine (15-min) time intervals, utilizing 3 years of sensor data collected at 15min intervals from 170 smart power meters. We analyze the impact of individual features used in the models to identify the ones best suited for the application. Our models show a high degree of accuracy with CV-RMSE errors ranging from 7.45% to 19.32%, and a reduction in error from baseline models by up to 53%.

  12. Dynamics and forecast in a simple model of sustainable development for rural populations.

    Science.gov (United States)

    Angulo, David; Angulo, Fabiola; Olivar, Gerard

    2015-02-01

    Society is becoming more conscious on the need to preserve the environment. Sustainable development schemes have grown rapidly as a tool for managing, predicting and improving the growth path in different regions and economy sectors. We introduce a novel and simple mathematical model of ordinary differential equations (ODEs) in order to obtain a dynamical description for each one of the sustainability components (economy, social development and environment conservation), together with their dependence with demographic dynamics. The main part in the modeling task is inspired by the works by Cobb, Douglas, Brander and Taylor. This is completed through some new insights by the authors. A model application is presented for three specific geographical rural regions in Caldas (Colombia).

  13. Intelligent energy demand forecasting

    CERN Document Server

    Hong, Wei-Chiang

    2013-01-01

    This book offers approaches and methods to calculate optimal electric energy allocation, using evolutionary algorithms and intelligent analytical tools to improve the accuracy of demand forecasting. Focuses on improving the drawbacks of existing algorithms.

  14. Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

    Science.gov (United States)

    Zheng, Fei; Zhu, Jiang

    2016-12-01

    How to design a reliable ensemble prediction strategy with considering the major uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In this study, a new stochastic perturbation technique is developed to improve the prediction skills of El Niño-Southern Oscillation (ENSO) through using an intermediate coupled model. We first estimate and analyze the model uncertainties from the ensemble Kalman filter analysis results through assimilating the observed sea surface temperatures. Then, based on the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error model to characterize the model uncertainties mainly induced by the missed physical processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble forecast at each step by the developed stochastic model-error model during the 12-month forecasting process, and add the zero-mean perturbations into the physical fields to mimic the presence of missing processes and high-frequency stochastic noises. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-year hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations. The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting process. This improvement occurs mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.

  15. A Forecasting Decision Support System

    OpenAIRE

    Sayed, Hanaa E.; Hossam A. Gabbar; Fouad, Soheir A.; Ahmed, Khalil M.; Miyazaki, Shigeji

    2008-01-01

    Nowadays forecasting is needed in many fields such as weather forecasting, population estimation, industry demand forecasting, and many others. As complexity and factors increase, it becomes impossible for a human being to do the prediction operation without support of computer system. A Decision support system is needed to model all demand factors and combine with expert opinions to enhance forecasting accuracy. In this research work, we present a decision support system using winters', simp...

  16. Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting

    OpenAIRE

    Tao, P.; Tie-Yuan, S.; Zhi-Yuan, Y.; Jun-Chao, W.

    2015-01-01

    The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period....

  17. Improved Orbit Determination and Forecasts with an Assimilative Tool for Atmospheric Density and Satellite Drag Specification

    Science.gov (United States)

    Crowley, G.; Pilinski, M.; Sutton, E. K.; Codrescu, M.; Fuller-Rowell, T. J.; Matsuo, T.; Fedrizzi, M.; Solomon, S. C.; Qian, L.; Thayer, J. P.

    2016-12-01

    Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. Features of this technique include: • Satellite drag specifications with errors lower than current models • Altitude coverage up to 1000km • Background state representation using both first principles and empirical models • Assimilation of satellite drag and other datatypes • Real time capability • Ability to produce 72-hour forecasts of the atmospheric state In this paper, we will summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models including the High Accuracy Satellite Drag Model, which is currently used

  18. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical significance...... have improved over time, as often claimed by forecasters, this does not show in the data. For nine out of ten rail projects, passenger forecasts are overestimated; average overestimation is 106%. For 72% of rail projects, forecasts are overestimated by more than two-thirds. For 50% of road projects...... forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data...

  19. Revised cloud processes to improve the mean and intraseasonal variability of Indian summer monsoon in climate forecast system: Part 1

    Science.gov (United States)

    Abhik, S.; Krishna, R. P. M.; Mahakur, M.; Ganai, Malay; Mukhopadhyay, P.; Dudhia, J.

    2017-06-01

    The National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFS) is being used for operational monsoon prediction over the Indian region. Recent studies indicate that the moist convective process in CFS is one of the major sources of uncertainty in monsoon predictions. In this study, the existing simple cloud microphysics of CFS is replaced by the six-class Weather Research Forecasting (WRF) single moment (WSM6) microphysical scheme. Additionally, a revised convective parameterization is employed to improve the performance of the model in simulating the boreal summer mean climate and intraseasonal variability over the Indian summer monsoon (ISM) region. The revised version of the model (CFSCR) exhibits a potential to improve shortcomings in the seasonal mean precipitation distribution relative to the standard CFS (CTRL), especially over the ISM region. Consistently, notable improvements are also evident in other observed ISM characteristics. These improvements are found to be associated with a better simulation of spatial and vertical distributions of cloud hydrometeors in CFSCR. A reasonable representation of the subgrid-scale convective parameterization along with cloud hydrometeors helps to improve the convective and large-scale precipitation distribution in the model. As a consequence, the simulated low-frequency boreal summer intraseasonal oscillation (BSISO) exhibits realistic propagation and the observed northwest-southeast rainband is well reproduced in CFSCR. Additionally, both the high and low-frequency BSISOs are better captured in CFSCR. The improvement of low and high-frequency BSISOs in CFSCR is shown to be related to a realistic phase relationship of clouds.type="synopsis">type="main">Plain Language SummaryThis study attempts to demonstrate the impact of better representation of cloud processes on simulating the mean and intraseasonal variability of Indian summer monsoon in a revised version of CFSv2 called CFSCR. The CFSCR shows

  20. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  1. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    Wang Ting

    2009-01-01

    @@ The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  2. Using Moving North Pacific Index to Improve Rainy Season Rainfall Forecast over the Yangtze River Basin by Analog Error Correction

    Institute of Scientific and Technical Information of China (English)

    2015-01-01

    A new analog error correction (AEC) scheme based on the moving North Pacifi c index (MNPI) is designed in this study. This scheme shows obvious improvement in the prediction skill of the operational coupled general circulation model (CGCM) of the National Climate Center of China for the rainy season rainfall (RSR) anomaly pattern correlation coeffi cient (ACC) over the mid-to-lower reaches of the Yangtze River (MLRYR). A comparative analysis indicates that the eff ectiveness of the new scheme using the MNPI is better than the system error correction scheme using the North Pacifi c index (NPI). A Euclidean distance-weighted mean rather than a traditional arithmetic mean, is applied to the integration of the analog year’s prediction error fi elds. By using the MNPI AEC scheme, independent sample hindcasts of RSR during the period 2003–2009 are then evaluated. The results show that the new scheme exhibited a higher forecast skill during 2003–2009, with an average ACC of 0.47;while the ACC for the NPI case was only 0.19. Furthermore, the forecast skill of the RSR over the MLRYR is examined. In the MNPI case, empirical orthogonal function (EOF) was used in the degree compression of the prediction error fi elds from the CGCM, whereas the AEC scheme was applied only to its fi rst several EOF components for which the accumulative explained variance accounted for 80%of the total variance. This further improved the ACC of the independent sample hindcasts to 0.55 during the 7-yr period.

  3. Forecasting hysteresis behaviours of magnetorheological elastomer base isolator utilizing a hybrid model based on support vector regression and improved particle swarm optimization

    Science.gov (United States)

    Yu, Yang; Li, Yancheng; Li, Jianchun

    2015-03-01

    Due to its inherent hysteretic characteristics, the main challenge for the application of a magnetorheological elastomer- (MRE) based isolator is the exploitation of the accurate model, which could fully describe its unique behaviour. This paper proposes a nonparametric model for a MRE-based isolator based on support vector regression (SVR). The trained identification model is to forecast the shear force of the MRE-based isolator online; thus, the dynamic response from the MRE-based isolator can be well captured. In order to improve the forecast capacity of the model, a type of improved particle swarm optimization (IPSO) is employed to optimize the parameters in SVR. Eventually, the trained model is applied to the MRE-based isolator modelling with testing data. The results indicate that the proposed hybrid model has a better generalization capacity and better recognition accuracy than other conventional models, and it is an effective and suitable approach for forecasting the behaviours of a MRE-based isolator.

  4. Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms

    Institute of Scientific and Technical Information of China (English)

    Lean YU; Shouyang WANG; Kin Keung LAI

    2009-01-01

    The slow convergence of back-propagation neu-ral network (BPNN) has become a challenge in data-mining and knowledge discovery applications due to the drawbacks of the gradient descent (GD) optimization method, which is widely adopted in BPNN learning. To solve this problem,some standard Optimization techniques such as conjugate-gradient and Newton method have been proposed to improve the convergence rate of BP learning algorithm. This paper presents a heuristic method that adds an adaptive smooth-ing momentum term to original BP learning algorithm to speedup the convergence. In this improved BP learning al-gorithm, adaptive smoothing technique is used to adjust the momentums of weight updating formula automatically in terms of "3 σ limits theory." Using the adaptive smoothing momentum terms, the improved BP learning algorithm can make the network training and convergence process faster,and the network's generalization performance stronger than the standard BP learning algorithm can do. In order to ver-ify the effectiveness of the proposed BP learning algorithm,three typical foreign exchange rates, British pound (GBP),Euro (EUR), and Japanese yen (JPY), are chosen as the fore-casting targets for illustration purpose. Experimental results from homogeneous algorithm comparisons reveal that the proposed BP learning algorithm outperforms the other com-parable BP algorithms in performance and convergence rate.Furthermore, empirical results from heterogeneous model comparisons also show the effectiveness of the proposed BP learning algorithm.

  5. L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

    Science.gov (United States)

    Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i...

  6. Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter–based steady state Kalman filter

    NARCIS (Netherlands)

    El Serafy, G.Y.H.; Mynett, A.E.

    2008-01-01

    Numerical models of a water system are always based on assumptions and simplifications that may result in errors in the model's predictions. Such errors can be reduced through the use of data assimilation and thus can significantly improve the success rate of the predictions and operational forecast

  7. OSSE impact analysis of airborne ocean surveys for improving upper-ocean dynamical and thermodynamical forecasts in the Gulf of Mexico

    Science.gov (United States)

    Halliwell, George R.; Kourafalou, Vassiliki; Le Hénaff, Matthieu; Shay, Lynn K.; Atlas, Robert

    2015-01-01

    A prototype, rigorously validated ocean Observing System Simulation Experiment (OSSE) system is used to evaluate the impact of different sampling strategies for rapid-response airborne ocean profile surveys in the eastern interior Gulf of Mexico. Impacts are assessed with respect to improving ocean analyses, and forecasts initialized from those analyses, for two applications: improving oil spill forecasts and improving the ocean model response to tropical cyclone (TC) forcing. Rapid model error growth in this region requires that repeat surveys be conducted frequently in time, with separation of less than 4 days required to approach maximum error reduction in model analyses. Substantial additional error reduction in model dynamical fields is achieved by deploying deep (1000 m) AXCTDs instead of shallow (400 m) AXBTs. Shallow AXBTs constrain the ocean thermal field over the upper 400 m nearly as well as deep AXCTDs. However, in addition to constraining ocean fields over a greater depth range, AXCTDs also measure salinity profiles and more accurately constrain upper-ocean density than AXBTs, leading to a more accurate representation of upper ocean pressure and velocity fields. Sampling AXCTD profiles over a one-half degree array compared to one degree leads to substantial additional error reduction by constraining variability with horizontal scales too small to be corrected by satellite altimetry assimilation. A 2-day lag in availability of airborne profiles does not increase errors in dynamical ocean fields, but it does increase errors in upper-ocean thermal field including Tropical Cyclone Heat Potential (TCHP), demonstrating that these profiles must be rapidly made available for assimilation to improve TC forecasts. The additional error reduction in ocean analyses achieved by assimilation of airborne surveys translates into significantly improved forecasts persisting over time intervals ranging between 1 and 2 weeks for most model variables but several weeks for

  8. Forecasting natural gas consumption in China by Bayesian Model Averaging

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2015-11-01

    Full Text Available With rapid growth of natural gas consumption in China, it is in urgent need of more accurate and reliable models to make a reasonable forecast. Considering the limitations of the single model and the model uncertainty, this paper presents a combinative method to forecast natural gas consumption by Bayesian Model Averaging (BMA. It can effectively handle the uncertainty associated with model structure and parameters, and thus improves the forecasting accuracy. This paper chooses six variables for forecasting the natural gas consumption, including GDP, urban population, energy consumption structure, industrial structure, energy efficiency and exports of goods and services. The results show that comparing to Gray prediction model, Linear regression model and Artificial neural networks, the BMA method provides a flexible tool to forecast natural gas consumption that will have a rapid growth in the future. This study can provide insightful information on natural gas consumption in the future.

  9. A BP Neural Network Based on Improved Particle Swarm Optimization and its Application in Reliability Forecasting

    Directory of Open Access Journals (Sweden)

    Heqing Li

    2013-07-01

    Full Text Available The basic Particle Swarm Optimization (PSO algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow full play to the approximation capability of the function of BP neural network and overcome the main shortcomings of its liability to fall into local extreme value and the study proposed a concept of applying improved PSO algorithm and BP network jointly to optimize the original weight and threshold value of network and incorporating the improved PSO algorithm into BP network to establish a improved PSO-BP network system. This method improves convergence speed and the ability to search optimal value. We apply the improved particle swarm algorithm to reliability prediction. Compared with the traditional BP method, this kind of algorithm can minimize errors and improve convergence speed at the same time.

  10. Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections.

    Science.gov (United States)

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E

    2015-01-01

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010-2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(± 2)% to 36(± 5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(± 5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(± 1) to 40(± 2) with a corresponding increase from 59(± 2)% to 80(± 6)% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

  11. Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections.

    Directory of Open Access Journals (Sweden)

    Alexander Gutfraind

    Full Text Available People who inject drugs (PWID are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV. HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK. APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available and shown to agree with network and epidemiological surveys to within 1%. For the period 2010-2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(± 2% to 36(± 5%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(± 5%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years. APK also forecasts an increase in PWID mean age from 35(± 1 to 40(± 2 with a corresponding increase from 59(± 2% to 80(± 6% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

  12. Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

    Science.gov (United States)

    Gallego, C.; Costa, A.; Cuerva, A.

    2010-09-01

    -ANN model (without regime classification) is adopted as a reference model. Both models are evaluated in terms of Improvement over Persistence on the Mean Square Error basis (IoP%) when predicting horizons form 1 time-step to 5. The case of a wind farm located in the complex terrain of Alaiz (north of Spain) has been considered. Three years of available power output data with a hourly resolution have been employed: two years for training and validation of the model and the last year for assessing the accuracy. Results showed that the RS-ANN overcame the single-ANN model for one step-ahead forecasts: the overall IoP% was up to 8.66% for the RS-ANN model (depending on the gradient criterion selected to consider the ramp regime triggered) and 6.16% for the single-ANN. However, both models showed similar accuracy for larger horizons. A locally-weighted evaluation during ramp events for one-step ahead was also performed. It was found that the IoP% during ramps-up increased from 17.60% (case of single-ANN) to 22.25% (case of RS-ANN); however, during the ramps-down events this improvement increased from 18.55% to 19.55%. Three main conclusions are derived from this case study: It highlights the importance of considering statistical models capable of differentiate several regimes showed by the output power time series in order to improve the forecasting during extreme events like ramps. On-line regime classification based on available power output data didn't seem to contribute to improve forecasts for horizons beyond one-step ahead. Tacking into account other explanatory variables (local wind measurements, NWP outputs) could lead to a better understanding of ramp events, improving the regime assessment also for further horizons. The RS-ANN model slightly overcame the single-ANN during ramp-down events. If further research reinforce this effect, special attention should be addressed to understand the underlying processes during ramp-down events.

  13. Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting

    Directory of Open Access Journals (Sweden)

    P. Tao

    2015-05-01

    Full Text Available The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.

  14. Improvement of Monsoon Depressions Forecast with Assimilation of Indian DWR Data Using WRF-3DVAR Analysis System

    Science.gov (United States)

    Routray, Ashish; Mohanty, U. C.; Osuri, Krishna K.; Kiran Prasad, S.

    2013-12-01

    An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the

  15. Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

    Science.gov (United States)

    Zhang, Banglin; Lindzen, Richard S.; Tallapragada, Vijay; Weng, Fuzhong; Liu, Qingfu; Sippel, Jason A.; Ma, Zaizhong; Bender, Morris A.

    2016-10-01

    The atmosphere-ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels.

  16. Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example.

    Science.gov (United States)

    Zhang, Banglin; Lindzen, Richard S; Tallapragada, Vijay; Weng, Fuzhong; Liu, Qingfu; Sippel, Jason A; Ma, Zaizhong; Bender, Morris A

    2016-10-18

    The atmosphere-ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels.

  17. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  18. Forecasting the number of human immunodeficiency virus infections in the korean population using the autoregressive integrated moving average model.

    Science.gov (United States)

    Yu, Hye-Kyung; Kim, Na-Young; Kim, Sung Soon; Chu, Chaeshin; Kee, Mee-Kyung

    2013-12-01

    From the introduction of HIV into the Republic of Korea in 1985 through 2012, 9,410 HIV-infected Koreans have been identified. Since 2000, there has been a sharp increase in newly diagnosed HIV-infected Koreans. It is necessary to estimate the changes in HIV infection to plan budgets and to modify HIV/AIDS prevention policy. We constructed autoregressive integrated moving average (ARIMA) models to forecast the number of HIV infections from 2013 to 2017. HIV infection data from 1985 to 2012 were used to fit ARIMA models. Akaike Information Criterion and Schwartz Bayesian Criterion statistics were used to evaluate the constructed models. Estimation was via the maximum likelihood method. To assess the validity of the proposed models, the mean absolute percentage error (MAPE) between the number of observed and fitted HIV infections from 1985 to 2012 was calculated. Finally, the fitted ARIMA models were used to forecast the number of HIV infections from 2013 to 2017. The fitted number of HIV infections was calculated by optimum ARIMA (2,2,1) model from 1985-2012. The fitted number was similar to the observed number of HIV infections, with a MAPE of 13.7%. The forecasted number of new HIV infections in 2013 was 962 (95% confidence interval (CI): 889-1,036) and in 2017 was 1,111 (95% CI: 805-1,418). The forecasted cumulative number of HIV infections in 2013 was 10,372 (95% CI: 10,308-10,437) and in 2017 was14,724 (95% CI: 13,893-15,555) by ARIMA (1,2,3). Based on the forecast of the number of newly diagnosed HIV infections and the current cumulative number of HIV infections, the cumulative number of HIV-infected Koreans in 2017 would reach about 15,000.

  19. Evaluation of different operational strategies for lithium ion battery systems connected to a wind turbine for primary frequency regulation and wind power forecast accuracy improvement

    DEFF Research Database (Denmark)

    Swierczynski, Maciej Jozef; Stroe, Daniel Ioan; Stan, Ana-Irina

    2012-01-01

    High penetration levels of variable wind energy sources can cause problems with their grid integration. Energy storage systems connected to wind turbine/wind power plants can improve predictability of the wind power production and provide ancillary services to the grid. This paper investigates ec...... economics of different operational strategies for Li-ion systems connected to wind turbines for wind power forecast accuracy improvement and primary frequency regulation....

  20. Rooms for genetic improvement in Indonesian Bali cattle population

    Science.gov (United States)

    Widyas, N.; Nugroho, T.; Prastowo, S.

    2017-04-01

    Bali cattle is a species of Bos javanicus d’Alton, a local cattle in Indonesia. They are loaded with potential as meat producer and well adapted to tropical climate and limited feed resources. Studies have been made to characterize the species. This paper presents a rough estimate of the opportunity to improve the Bali cattle population genetically. Our aim is to endorse that the Bali cattle could be both superior and efficient as tropical meat producer cattle. Results shows that Bali cattle population size is decreasing for the last years with a possibility to be accompanied by genetic quality decline. However, there is hope in improving Bali cattle genetically by a proper breeding strategy. This could also be an answer to the challenge of climate change which leads to global warming; where species adaptable to such environment is more beneficial in the future.

  1. Improved hurricane forecasting from a variational bogus and ozone data assimilation (BODA) scheme: case study

    Science.gov (United States)

    Liu, Yin; Zhang, Wei

    2016-12-01

    This study develops a proper way to incorporate Atmospheric Infrared Sounder (AIRS) ozone data into the bogus data assimilation (BDA) initialization scheme for improving hurricane prediction. First, the observation operator at some model levels with the highest correlation coefficients is established to assimilate AIRS ozone data based on the correlation between total column ozone and potential vorticity (PV) ranging from 400 to 50 hPa level. Second, AIRS ozone data act as an augmentation to a BDA procedure using a four-dimensional variational (4D-Var) data assimilation system. Case studies of several hurricanes are performed to demonstrate the effectiveness of the bogus and ozone data assimilation (BODA) scheme. The statistical result indicates that assimilating AIRS ozone data at 4, 5, or 6 model levels can produce a significant improvement in hurricane track and intensity prediction, with reasonable computation time for the hurricane initialization. Moreover, a detailed analysis of how BODA scheme affects hurricane prediction is conducted for Hurricane Earl (2010). It is found that the new scheme developed in this study generates significant adjustments in the initial conditions (ICs) from the lower levels to the upper levels, compared with the BDA scheme. With the BODA scheme, hurricane development is found to be much more sensitive to the number of ozone data assimilation levels. In particular, the experiment with the assimilation of AIRS ozone data at proper number of model levels shows great capabilities in reproducing the intensity and intensity changes of Hurricane Earl, as well as improve the track prediction. These results suggest that AIRS ozone data convey valuable meteorological information in the upper troposphere, which can be assimilated into a numerical model to improve hurricane initialization when the low-level bogus data are included.

  2. Improving the assessment of instream flow needs for fish populations

    Energy Technology Data Exchange (ETDEWEB)

    Sale, M.J. (Oak Ridge National Lab., TN (USA)); Otto, R.G. (Otto (R.G.) and Associates, Arlington, VA (USA))

    1991-01-01

    Instream flow requirements are one of the most frequent and most costly environmental issues that must be addressed in developing hydroelectric projects. Existing assessment methods for determining instream flow requirements have been criticized for not including all the biological response mechanisms that regulate fishery resources. A new project has been initiated to study the biological responses of fish populations to altered stream flows and to develop improved ways of managing instream flows. 21 refs., 3 figs.

  3. Improving health literacy in community populations: a review of progress.

    Science.gov (United States)

    Nutbeam, Don; McGill, Bronwyn; Premkumar, Pav

    2017-03-28

    Governments around the world have adopted national policies and programs to improve health literacy. This paper examines progress in the development of evidence to support these policies from interventions to improve health literacy among community populations. Our review found only a limited number of studies (n=7) that met the criteria for inclusion, with many more influenced by the concept of health literacy but not using it in the design and evaluation. Those included were diverse in setting, population and intended outcomes. All included educational strategies to develop functional health literacy, and a majority designed to improve interactive or critical health literacy skills. Several papers were excluded because they described a protocol for an intervention, but not results, indicating that our review may be early in a cycle of activity in community intervention research. The review methodology may not have captured all relevant studies, but it provides a clear message that the academic interest and attractive rhetoric surrounding health literacy needs to be tested more systematically through intervention experimentation in a wide range of populations using valid and reliable measurement tools. The distinctive influence of the concept of health literacy on the purpose and methodologies of health education and communication is not reflected in many reported interventions at present. Evidence to support the implementation of national policies and programs, and the intervention tools required by community practitioners are not emerging as quickly as needed. This should be addressed as a matter of priority by research funding agencies.

  4. Application of a Distributed, Physically Based, Hydrologic Model to Improve Streamflow Forecasts in the Upper Rio Grande Basin

    Science.gov (United States)

    Gorham, T. A.; Boyle, D. P.; McConnell, J. R.; Lamorey, G. W.; Markstrom, S.; Viger, R.; Leavesley, G.

    2001-12-01

    Approximately two-thirds of the runoff in the Rio Grande begins as seasonal snowpack in the headwaters above the USGS stream gaging stations at several points (nodes) above Albuquerque, New Mexico. Resource managers in the Rio Grande Basin rely on accurate short and long term forecasts of water availability and flow at these nodes to make important decisions aimed at achieving a balance among many different and competing water uses such as municipal, fish and wildlife, agricultural, and water quality. In this study, a distributed, physically based hydrologic model is used to investigate the degree of spatial and temporal distribution of snow and the processes that control snowmelt necessary to accurately simulate streamflow at seven of these nodes. Specifically, snow distribution and surface runoff are estimated using a combination of the USGS Modular Modeling System (MMS), GIS Weasel, Precipitation-Runoff Modeling System (PRMS), and XYZ snow distribution model. This highly collaborative work between researchers at the Desert Research Institute and the USGS is an important part of SAHRA (Sustainability of semi-Arid Hydrology and Riparian Areas) efforts aimed at improving models of snow distribution and snowmelt processes.

  5. Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: application to ETEX tracer experiment.

    Science.gov (United States)

    Zhang, X L; Li, Q B; Su, G F; Yuan, M Q

    2015-04-01

    The accidental release of radioactive materials from nuclear power plant leads to radioactive pollution. We apply an augmented ensemble Kalman filter (EnKF) with a chemical transport model to jointly estimate the emissions of Perfluoromethylcyclohexane (PMCH), a tracer substitute for radionuclides, from a point source during the European Tracer Experiment, and to improve the forecast of its dispersion downwind. We perturb wind fields to account for meteorological uncertainties. We expand the state vector of PMCH concentrations through continuously adding an a priori emission rate for each succeeding assimilation cycle. We adopt a time-correlated red noise to simulate the temporal emission fluctuation. The improved EnKF system rapidly updates (and reduces) the excessively large initial first-guess emissions, thereby significantly improves subsequent forecasts (r = 0.83, p 80% average reduction of the normalized mean square error).

  6. Improving the health forecasting alert system for cold weather and heat-waves in England: a case-study approach using temperature-mortality relationships

    Science.gov (United States)

    Masato, Giacomo; Cavany, Sean; Charlton-Perez, Andrew; Dacre, Helen; Bone, Angie; Carmicheal, Katie; Murray, Virginia; Danker, Rutger; Neal, Rob; Sarran, Christophe

    2015-04-01

    The health forecasting alert system for cold weather and heatwaves currently in use in the Cold Weather and Heatwave plans for England is based on 5 alert levels, with levels 2 and 3 dependent on a forecast or actual single temperature action trigger. Epidemiological evidence indicates that for both heat and cold, the impact on human health is gradual, with worsening impact for more extreme temperatures. The 60% risk of heat and cold forecasts used by the alerts is a rather crude probabilistic measure, which could be substantially improved thanks to the state-of-the-art forecast techniques. In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater

  7. Short term wave forecasting, using digital filters, for improved control of Wave Energy Converters

    Energy Technology Data Exchange (ETDEWEB)

    Tedd, J.; Frigaard, P. [Department of Civil Engineering, Aalborg University, Aalborg (Denmark)

    2007-07-01

    This paper presents a Digital Filter method for real time prediction of waves incident upon a Wave Energy device. The method transforms waves measured at a point ahead of the device, to expected waves incident on the device. The relationship between these incident waves and power capture is derived experimentally. Results are shown form measurements taken on the Wave Dragon prototype device, a floating overtopping device situated in Northern Denmark. In this case the method is able to accurately predict the surface elevation at the device 11.2 seconds before the measurement is made. This is sufficient to allow advanced control systems to be developed using this knowledge to significantly improve power capture.

  8. Translating evidence into population health improvement: strategies and barriers.

    Science.gov (United States)

    Woolf, Steven H; Purnell, Jason Q; Simon, Sarah M; Zimmerman, Emily B; Camberos, Gabriela J; Haley, Amber; Fields, Robert P

    2015-03-18

    Among the challenges facing research translation-the effort to move evidence into policy and practice-is that key questions chosen by investigators and funders may not always align with the information priorities of decision makers, nor are the findings always presented in a form that is useful for or relevant to the decisions at hand. This disconnect is a problem particularly for population health, where the change agents who can make the biggest difference in improving health behaviors and social and environmental conditions are generally nonscientists outside of the health professions. To persuade an audience that does not read scientific journals, strong science may not be enough to elicit change. Achieving influence in population health often requires four ingredients for success: research that is responsive to user needs, an understanding of the decision-making environment, effective stakeholder engagement, and strategic communication. This article reviews the principles and provides examples from a national and local initiative.

  9. Judgmental forecasting from graphs and from experience

    OpenAIRE

    Theochari, Z.

    2014-01-01

    Research in the field of forecasting suggests that judgmental forecasts are typically subject to a number of biases. These biases may be related to the statistical characteristics of the data series, or to the characteristics of the forecasting task. Here, a number of understudied forecasting paradigms have been investigated and these revealed interesting ways of improving forecasting performance. In a series of experiments, by controlling parameters such as the horizon and direction of the f...

  10. Analysis of thickness fields retrieved from NOAA-7 observations through the 3I (Improved Initialization Inversion) method. Interest for weather forecasting

    Science.gov (United States)

    Chedin, A.; Scott, N. A.; Flobert, J.; Husson, N.; Levy, C.; Rochard, G.; Quere, J.; Bellec, B.; Simeon, J.

    1987-08-01

    The improved initialization inversion method for the 3 dimensional analysis of the atmospheric structure from satellite obsevations (TIROS-N series) was applied to NOAA-7 data over Europe. The scenes selected correspond to complex meteorological situations and resulted in substantial errors in forecasting. One of the situations is presented. Comparisons between retrieved and operational (conventional) thickness charts show that the method is ready for operational use.

  11. Three Ingredients for Improved Global Aftershock Forecasts: Tectonic Region, Time-Dependent Catalog Incompleteness, and Inter-Sequence Variability

    Science.gov (United States)

    Page, M. T.; Hardebeck, J.; Felzer, K. R.; Michael, A. J.; van der Elst, N.

    2015-12-01

    Following a large earthquake, seismic hazard can be orders of magnitude higher than the long-term average as a result of aftershock triggering. Due to this heightened hazard, there is a demand from emergency managers and the public for rapid, authoritative, and reliable aftershock forecasts. In the past, USGS aftershock forecasts following large, global earthquakes have been released on an ad-hoc basis with inconsistent methods, and in some cases, aftershock parameters adapted from California. To remedy this, we are currently developing an automated aftershock product that will generate more accurate forecasts based on the Reasenberg and Jones (Science, 1989) method. To better capture spatial variations in aftershock productivity and decay, we estimate regional aftershock parameters for sequences within the Garcia et al. (BSSA, 2012) tectonic regions. We find that regional variations for mean aftershock productivity exceed a factor of 10. The Reasenberg and Jones method combines modified-Omori aftershock decay, Utsu productivity scaling, and the Gutenberg-Richter magnitude distribution. We additionally account for a time-dependent magnitude of completeness following large events in the catalog. We generalize the Helmstetter et al. (2005) equation for short-term aftershock incompleteness and solve for incompleteness levels in the global NEIC catalog following large mainshocks. In addition to estimating average sequence parameters within regions, we quantify the inter-sequence parameter variability. This allows for a more complete quantification of the forecast uncertainties and Bayesian updating of the forecast as sequence-specific information becomes available.

  12. Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

    Directory of Open Access Journals (Sweden)

    Jaime Lloret

    2013-08-01

    Full Text Available Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.

  13. Airborne Snow Observatory: measuring basin-wide seasonal snowpack with LiDAR and an imaging spectrometer to improve runoff forecasting and reservoir operation (Invited)

    Science.gov (United States)

    McGurk, B. J.; Painter, T. H.

    2013-12-01

    The Airborne Snow Observatory (ASO) NASA-JPL demonstration mission collected detailed snow information for portions of the Tuolumne Basin in California and the Uncompahgre Basin in Colorado in spring of 2013. The ASO uses an imaging spectrometer and LiDAR sensors mounted in an aircraft to collect snow depth and extent data, and snow albedo. By combining ground and modeled density fields, the ~weekly flights over the Tuolumne produced both basin-wide and detailed sub-basin snow water equivalent (SWE) estimates that were used in a hydrologic simulation model to improve the accuracy and timing of runoff forecasting tools used to manage Hetch Hetchy Reservoir, the source of 85% of the water supply for 2.5 million people on the San Francisco Peninsula. The USGS PRMS simulation model was calibrated to the 459 square mile basin and was updated with both weather forecast data and distributed snow information from ASO flights to inform the reservoir operators of predicted inflow volumes and timing. Information produced by the ASO data collection was used to update distributed SWE and albedo state variables in the PRMS model and improved inflow forecasts for Hetch Hetchy. Data from operational ASO programs is expected to improve the ability of reservoir operators to more efficiently allocate the last half of the recession limb of snowmelt inflow and be more assured of meeting operational mandates. This presentation will provide results from the project after its first year.

  14. Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

    Science.gov (United States)

    Chen, Xingchao; Zhao, Kun; Sun, Juanzhen; Zhou, Bowen; Lee, Wen-Chau

    2016-10-01

    This paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System (VDRAS). Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational (4DVAR) data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments—assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function—are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line—including the surface warm inflow, cold pool, gust front, and low-level wind—are much closer to the observations after assimilating the surface data in VDRAS.

  15. Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy

    Directory of Open Access Journals (Sweden)

    Wolfgang Wagner

    2012-05-01

    Full Text Available Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational issue due to the high threat that they pose to human life and property. This study investigates the relationship between rainfall, soil moisture conditions and landslide movement by using recorded movements of a rock slope located in central Italy, the Torgiovannetto landslide. This landslide is a very large rock slide, threatening county and state roads. Data acquired by a network of extensometers and a meteorological station clearly indicate that the movements of the unstable wedge, first detected in 2003, are still proceeding and the alternate phases of quiescence and reactivation are associated with rainfall patterns. By using a multiple linear regression approach, the opening of the tension cracks (as recorded by the extensometers as a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted for the period 2007–2009. Specifically, soil moisture indicators are obtained through the Soil Water Index, SWI, a product derived by the Advanced SCATterometer (ASCAT on board the MetOp (Meteorological Operational satellite and by an Antecedent Precipitation Index, API. Results indicate that the regression performance (in terms of correlation coefficient, r significantly enhances if an indicator of the soil moisture conditions is included. Specifically, r is equal to 0.40 when only rainfall is used as a predictor variable and increases to r = 0.68 and r = 0.85 if the API and the SWI are used respectively. Therefore, the coarse spatial resolution (25 km of satellite data notwithstanding, the ASCAT SWI is found to be very useful for the prediction of landslide movements on a local scale. These findings, although valid for a specific area, present new opportunities for the effective use of satellite-derived soil moisture estimates to improve landslide forecasting.

  16. Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

    Science.gov (United States)

    Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

    2016-05-01

    events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

  17. Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

    Science.gov (United States)

    Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

    2017-02-01

    events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

  18. Moving beyond the cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

    Science.gov (United States)

    Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier Filion, Thomas-Charles

    2017-06-01

    A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed

  19. Moving beyond the cost–loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

    Directory of Open Access Journals (Sweden)

    S. Matte

    2017-06-01

    Full Text Available A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost–loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s. In this study, three concurrent ensemble streamflow forecasting systems are compared: simple

  20. Improving of an Artificial Neural Networks Forecasting Model for Determining of the Number of Calls in 112 Emergency Call Center

    Directory of Open Access Journals (Sweden)

    Erdal Aydemir

    2014-05-01

    Full Text Available Forecasting studies are extremely important in the technical, social and economic research. Generally, we know it is very difficult to forecast with higher accurate about a system by using recent values. In the scientific literature, the forecasting studies of energy, personnel planning, production planning, climate changes, sales and marketing and economics etc. are frequently found. In this paper, for an emergency calls center in Isparta province of Turkey an artificial neural network (ANN forecasting model was developed to determine the number of calls for as health, fire and security services on a pilot implementation of the emergency calls center on a single number 112. In the developed model, the gradient descent with adaptive learning and momentum (GDX algorithm is selected as the training algorithm with feed-forward back-propagation by using 80% of input data and the 20% of input data is used for testing set data from last month. After the testing, the mean absolute percentage error (MAPE rate is obtained as 4.5% and it is useful to test. In addition, the forecasting results of the next month are shown that the MAPE values are 2.65%, 6.40% and 5.24% with ANN, trend analysis and ARIMA (1 1 1 models respectively and, the number of calls are found separately on the types of calls in daily. Consequently, the developed model by using ANN to forecast the number of calls in an emergency call center is more accurate than the trend analysis and ARIMA models.

  1. Development of a System to Generate Near Real Time Tropospheric Delay and Precipitable Water Vapor in situ at Geodetic GPS Stations, to Improve Forecasting of Severe Weather Events

    Science.gov (United States)

    Moore, A. W.; Bock, Y.; Geng, J.; Gutman, S. I.; Laber, J. L.; Morris, T.; Offield, D. G.; Small, I.; Squibb, M. B.

    2012-12-01

    We describe a system under development for generating ultra-low latency tropospheric delay and precipitable water vapor (PWV) estimates in situ at a prototype network of geodetic GPS sites in southern California, and demonstrating their utility in forecasting severe storms commonly associated with flooding and debris flow events along the west coast of North America through infusion of this meteorological data at NOAA National Weather Service (NWS) Forecast Offices and the NOAA Earth System Research Laboratory (ESRL). The first continuous geodetic GPS network was established in southern California in the early 1990s and much of it was converted to real-time (latency tropospheric zenith delays, which can be converted into mm-accuracy PWV using collocated pressure and temperature measurements, the basis for GPS meteorology (Bevis et al. 1992, 1994; Duan et al. 1996) as implemented by NOAA with a nationwide distribution of about 300 GPS-Met stations providing PW estimates at subhourly resolution currently used in operational weather forecasting in the U.S. We improve upon the current paradigm of transmitting large quantities of raw data back to a central facility for processing into higher-order products. By operating semi-autonomously, each station will provide low-latency, high-fidelity and compact data products within the constraints of the narrow communications bandwidth that often occurs in the aftermath of natural disasters. The onsite ambiguity-resolved precise point positioning solutions are enabled by a power-efficient, low-cost, plug-in Geodetic Module for fusion of data from in situ sensors including GPS and a low-cost MEMS meteorological sensor package. The decreased latency (~5 minutes) PW estimates will provide the detailed knowledge of the distribution and magnitude of PW that NWS forecasters require to monitor and predict severe winter storms, landfalling atmospheric rivers, and summer thunderstorms associated with the North American monsoon. On the

  2. Seasonal Forecasts of Climate Indices: Impact of Definition and Spatial Aggregation on Predictive Skill

    Science.gov (United States)

    Bhend, Jonas; Mahlstein, Irina; Liniger, Mark

    2016-04-01

    Seasonal forecasting models are increasingly being used to forecast application-relevant aspects. A simple way to make such user-oriented predictions are application-specific climate indices. Little is known, however, on how the predictive skill of forecasts of such climate indices relates to the predictive skill in forecasting seasonal mean conditions. Here we analyse forecasts of two types of indices derived from daily precipitation and temperature: counts of events such as the number of dry days and accumulated threshold exceedances such as degree days. We find that the predictive skill of forecasts of heating and cooling degree days and of consecutive dry days is generally lower than the skill of seasonal mean temperature and rainfall forecasts respectively. By use of a toy model we demonstrate that this reduction in skill is more pronounced for skilful forecasts and climate indices with a threshold in the tail of the statistical distribution. We further analyse the impact of spatial aggregation and find that aggregation generally improves the predictive skill. Using appropriate covariates for weighting - for example population density to derive a proxy for the national energy demand for heating - the usefulness of forecasts of climate indices can be further enhanced while retaining predictive skill. We conclude that processing of direct model output to derive climate indices in combination with spatial aggregation can be used to render still skilful and even more useful seasonal forecasts of user-relevant quantities.

  3. Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

    Science.gov (United States)

    Gochis, D. J.; Busto, J.; Howard, K.; Mickey, J.; Deems, J. S.; Painter, T. H.; Richardson, M.; Dugger, A. L.; Karsten, L. R.; Tang, L.

    2015-12-01

    Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitationsin observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases

  4. Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

    Science.gov (United States)

    Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

    2008-12-01

    The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously

  5. An experimental system for flood risk forecasting at global scale

    Science.gov (United States)

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

    2016-12-01

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

  6. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    Science.gov (United States)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2015-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  7. Development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia

    Directory of Open Access Journals (Sweden)

    S. Lee

    2015-07-01

    Full Text Available To improve short-term particulate matter (PM forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD data retrieved from a geostationary equatorial orbit (GEO satellite sensor, GOCI (Geostationary Ocean Color Imager which covers Northeast Asia (113–146° E; 25–47° N, were used. Although GOCI can provide a higher number of AOD data in a semi-continuous manner than low Earth orbit (LEO satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatio-temporal (ST kriging method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages to using the ST-kriging method in this study is that more observed AOD data can be used to prepare the best initial AOD fields. It is demonstrated in this study that the short-term PM forecast system developed with the application of the ST-kriging method can greatly improve PM10 predictions in Seoul Metropolitan Area (SMA, when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ~ 60 and ~ 70 %, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors (such as choices of observation operators and control variables on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM

  8. Innovation Forecasting

    Science.gov (United States)

    1997-11-01

    relating to “ injectors ”) to develop a map of the related technologies [33.] Another approach is to develop a “tree” showing a system branching into its...additional terms such as “trend,” “forecast,” “ delphi ,” “assessment,” and so forth may call up other forecasts and assessments relating to the topic...present and future engine technologies. A preliminary search (Step 1, Table 5) located prior forecasts, in particular, a Delphi study [36]. The Delphi

  9. Grey-Markov Model for Road Accidents Forecasting

    Institute of Scientific and Technical Information of China (English)

    李相勇; 严余松; 蒋葛夫

    2003-01-01

    In order to improve the forecasting precision of road accidents, by introducing Markov chains forecasting method, a grey-Markov model for forecasting road accidents is established based on grey forecasting method. The model combines the advantages of both grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. An application example is conducted to evaluate the grey-Markov model, which shows that the precision of the grey-Markov model is better than that of grey model in forecasting road accidents.

  10. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2016-01-01

    Full Text Available Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD, which reduces the effect of noise on the wind speed data; (II artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM model are optimized by the cuckoo search (CS algorithm; (III parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

  11. Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada

    Science.gov (United States)

    A Beneficial Use Impairment (BUI) common at Great Lakes Areas of Concern (AOCs) is loss of fish and wildlife populations. Consequently, recovery of populations after stressor mitigation serves as a basis for evaluating remediation success. We describe a framework that can be a...

  12. 基于改进的 LMD 和 GRNN 组合风速预测%Composite wind speed forecasting model based on improved LMD and GRNN

    Institute of Scientific and Technical Information of China (English)

    雷庆坤; 李生虎; 陈曦鸣; 王艳艳; 华玉婷

    2015-01-01

    Traditional local mean decomposition(LMD) algorithm employs the moving average method to obtain the local mean function and local envelope function ,which causes the over-smoothness easily and affects the precision of decomposition .Therefore ,Akima interpolation method is proposed to calculate the envelop lines of upper and down extreme point sets and then calculate the local mean function and local envelope function to improve LMD .Considering the non-linearity and non-stationary characteristics of wind speed ,the combina-tion forecasting model based on the improved LMD and generalized regression neural network(GRNN) is built up .The wind speed time series are decomposed firstly by the improved LMD ,then each component is predic-ted separately by GRNN ,and the final forecasting value is obtained by adding forecasting results of each com-ponent up .The simulation results show that the decomposition of wind speed can improve the prediction accu-racy ,the decomposition results of improved LMD are more accurate than those of traditional LMD ,and the improved LMD-GRNN combination forecasting model has higher accuracy than GRNN model and LMD-GRNN model .%传统局域均值分解(LMD)算法采用滑动平均法计算局域均值函数和局域包络函数,易造成过平滑,影响分解精度。文章提出采用Akima插值法分别计算上下极值点包络线,进而求出局域均值函数和局域包络函数,对LMD方法进行改进;针对风速的非线性和非平稳性,提出基于改进 LMD 和广义神经网络(GRNN)的组合预测模型,用改进LMD算法分解风速,然后用GRNN对各分量分别建模预测,最后将预测结果叠加得出最终预测值。算例结果表明,LMD分解预处理可以提高预测精度,相对于现有LMD算法,改进算法分解结果更为精确,相对于GRNN及LMD-GRNN模型,改进后LMD-GRNN组合模型预测精度更高。

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

    Science.gov (United States)

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

    2017-04-01

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

  14. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  15. IEA Wind Task 36 Forecasting

    Science.gov (United States)

    Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

    2017-04-01

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts

  16. Statistical earthquake focal mechanism forecasts

    CERN Document Server

    Kagan, Yan Y

    2013-01-01

    Forecasts of the focal mechanisms of future earthquakes are important for seismic hazard estimates and Coulomb stress and other models of earthquake occurrence. Here we report on a high-resolution global forecast of earthquake rate density as a function of location, magnitude, and focal mechanism. In previous publications we reported forecasts of 0.5 degree spatial resolution, covering the latitude range magnitude, and focal mechanism. In previous publications we reported forecasts of 0.5 degree spatial resolution, covering the latitude range from -75 to +75 degrees, based on the Global Central Moment Tensor earthquake catalog. In the new forecasts we've improved the spatial resolution to 0.1 degree and the latitude range from pole to pole. Our focal mechanism estimates require distance-weighted combinations of observed focal mechanisms within 1000 km of each grid point. Simultaneously we calculate an average rotation angle between the forecasted mechanism and all the surrounding mechanisms, using the method ...

  17. Drought risk management in southern Africa. The potential of long lead climate forecasts for improved drought management

    OpenAIRE

    Gibberd, V.; Rook, J.; Sear, C. B.; Williams, J. B.

    1995-01-01

    Although climate variability is the single most important factor affecting the livelihood of the people of southern Africa, there is no country in which drought risk is managed well. This mission set out to determine whether the social and economic benefits from making use of long lead climate forecast techniques for managing drought risk in southern Africa would justify investment directed towards bringing forward the techniques into operational usage. The four person mission consulted a wid...

  18. Assimilation of satellite information in a snowpack model to improve characterization of snow cover for runoff simulation and forecasting

    OpenAIRE

    2009-01-01

    A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with...

  19. Improving the Health Forecasting Alert System for Cold Weather and Heat-Waves In England: A Proof-of-Concept Using Temperature-Mortality Relationships

    OpenAIRE

    Masato, Giacomo; Bone, Angie; Charlton-Perez, Andrew; Cavany, Sean; Neal, Robert; Dankers, Rutger; Dacre, Helen; Carmichael, Katie; Murray, Virginia

    2015-01-01

    Objectives\\ud \\ud In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office’s (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold...

  20. Bayesian flood forecasting methods: A review

    Science.gov (United States)

    Han, Shasha; Coulibaly, Paulin

    2017-08-01

    developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

  1. 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...

  2. Ensemble Forecast: A New Approach to Uncertainty and Predictability

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecast uncertainty (forecast error) by applying either the initial perturbation method or the multi-model/multiphysics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic (or control) forecast after a short lead time (3 5 days) for global modelling applications. There is about a 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecast for longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extended to 8 days (or longer) by present-day ensemble forecast systems. Furthermore, ensemble forecasts can deliver a probabilistic forecast to the users, which is based on the probability density function (PDF)instead of a single-value forecast from a traditional deterministic system. It has long been recognized that the ensemble forecast not only improves our weather forecast predictability but also offers a remarkable forecast for the future uncertainty, such as the relative measure of predictability (RMOP) and probabilistic quantitative precipitation forecast (PQPF). Not surprisingly, the success of the ensemble forecast and its wide application greatly increase the confidence of model developers and research communities.

  3. Improving the Health Forecasting Alert System for Cold Weather and Heat-Waves In England: A Proof-of-Concept Using Temperature-Mortality Relationships.

    Directory of Open Access Journals (Sweden)

    Giacomo Masato

    Full Text Available In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO National Severe Weather Warning Service (NSWWS. This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings.The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013, and compared to the performance of the current alert system.The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.

  4. Improving the Health Forecasting Alert System for Cold Weather and Heat-Waves In England: A Proof-of-Concept Using Temperature-Mortality Relationships.

    Science.gov (United States)

    Masato, Giacomo; Bone, Angie; Charlton-Perez, Andrew; Cavany, Sean; Neal, Robert; Dankers, Rutger; Dacre, Helen; Carmichael, Katie; Murray, Virginia

    2015-01-01

    In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.

  5. Improved viability of populations with diverse life-history portfolios.

    Science.gov (United States)

    Greene, Correigh M; Hall, Jason E; Guilbault, Kimberly R; Quinn, Thomas P

    2010-06-23

    A principle shared by both economists and ecologists is that a diversified portfolio spreads risk, but this idea has little empirical support in the field of population biology. We found that population growth rates (recruits per spawner) and life-history diversity as measured by variation in freshwater and ocean residency were negatively correlated across short time periods (one to two generations), but positively correlated at longer time periods, in nine Bristol Bay sockeye salmon populations. Further, the relationship between variation in growth rate and life-history diversity was consistently negative. These findings strongly suggest that life-history diversity can both increase production and buffer population fluctuations, particularly over long time periods. Our findings provide new insights into the importance of biocomplexity beyond spatio-temporal aspects of populations, and suggest that maintaining diverse life-history portfolios of populations may be crucial for their resilience to unfavourable conditions like habitat loss and climate change.

  6. Improved viability of populations with diverse life-history portfolios

    Science.gov (United States)

    Greene, Correigh M.; Hall, Jason E.; Guilbault, Kimberly R.; Quinn, Thomas P.

    2010-01-01

    A principle shared by both economists and ecologists is that a diversified portfolio spreads risk, but this idea has little empirical support in the field of population biology. We found that population growth rates (recruits per spawner) and life-history diversity as measured by variation in freshwater and ocean residency were negatively correlated across short time periods (one to two generations), but positively correlated at longer time periods, in nine Bristol Bay sockeye salmon populations. Further, the relationship between variation in growth rate and life-history diversity was consistently negative. These findings strongly suggest that life-history diversity can both increase production and buffer population fluctuations, particularly over long time periods. Our findings provide new insights into the importance of biocomplexity beyond spatio-temporal aspects of populations, and suggest that maintaining diverse life-history portfolios of populations may be crucial for their resilience to unfavourable conditions like habitat loss and climate change. PMID:20007162

  7. Artificial Neural Network forecasting of storm surge water levels at major estuarine ports to supplement national tide-surge models and improve port resilience planning

    Science.gov (United States)

    French, Jon; Mawdsley, Robert; Fujiyama, Taku; Achuthan, Kamal

    2017-04-01

    Effective prediction of tidal storm surge is of considerable importance for operators of major ports, since much of their infrastructure is necessarily located close to sea level. Storm surge inundation can damage critical elements of this infrastructure and significantly disrupt port operations and downstream supply chains. The risk of surge inundation is typically approached using extreme value analysis, while short-term forecasting generally relies on coastal shelf-scale tide and surge models. However, extreme value analysis does not provide information on the duration of a surge event and can be sensitive to the assumptions made and the historic data available. Also, whilst regional tide and surge models perform well along open coasts, their fairly coarse spatial resolution means that they do not always provide accurate predictions for estuarine ports. As part of a NERC Environmental Risks to Infrastructure Innovation Programme project, we have developed a tool that is specifically designed to forecast the North Sea storm surges on major ports along the east coast of the UK. Of particular interest is the Port of Immingham, Humber estuary, which handles the largest volume of bulk cargo in the UK including major flows of coal and biomass for power generation. A tidal surge in December 2013, with an estimated return period of 760 years, partly flooded the port, damaged infrastructure and disrupted operations for several weeks. This and other recent surge events highlight the need for additional tools to supplement the national UK Storm Tide Warning Service. Port operators are also keen to have access to less computationally expensive forecasting tools for scenario planning and to improve their resilience to actual events. In this paper, we demonstrate the potential of machine learning methods based on Artificial Neural Networks (ANNs) to generate accurate short-term forecasts of extreme water levels at estuarine North Sea ports such as Immingham. An ANN is

  8. A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

    Directory of Open Access Journals (Sweden)

    Lianhui Li

    2015-12-01

    Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

  9. Forecasting Japanese encephalitis incidence from historical morbidity patterns: Statistical analysis with 27 years of observation in Assam, India.

    Science.gov (United States)

    Handique, Bijoy K; Khan, Siraj A; Mahanta, J; Sudhakar, S

    2014-09-01

    Japanese encephalitis (JE) is one of the dreaded mosquito-borne viral diseases mostly prevalent in south Asian countries including India. Early warning of the disease in terms of disease intensity is crucial for taking adequate and appropriate intervention measures. The present study was carried out in Dibrugarh district in the state of Assam located in the northeastern region of India to assess the accuracy of selected forecasting methods based on historical morbidity patterns of JE incidence during the past 22 years (1985-2006). Four selected forecasting methods, viz. seasonal average (SA), seasonal adjustment with last three observations (SAT), modified method adjusting long-term and cyclic trend (MSAT), and autoregressive integrated moving average (ARIMA) have been employed to assess the accuracy of each of the forecasting methods. The forecasting methods were validated for five consecutive years from 2007-2012 and accuracy of each method has been assessed. The forecasting method utilising seasonal adjustment with long-term and cyclic trend emerged as best forecasting method among the four selected forecasting methods and outperformed the even statistically more advanced ARIMA method. Peak of the disease incidence could effectively be predicted with all the methods, but there are significant variations in magnitude of forecast errors among the selected methods. As expected, variation in forecasts at primary health centre (PHC) level is wide as compared to that of district level forecasts. The study showed that adopted forecasting techniques could reasonably forecast the intensity of JE cases at PHC level without considering the external variables. The results indicate that the understanding of long-term and cyclic trend of the disease intensity will improve the accuracy of the forecasts, but there is a need for making the forecast models more robust to explain sudden variation in the disease intensity with detail analysis of parasite and host population

  10. Improvement of the Surface Pressure Operator in GRAPES and Its Application in Precipitation Forecasting in South China

    Institute of Scientific and Technical Information of China (English)

    HUANG Yanyan; XUE Jishan; WAN Qilin; CHEN Zitong; DING Weiyu; ZHANG Chengzhong

    2013-01-01

    In this study we investigated the problems involved in assimilating surface pressure in the current global and regional assimilation and prediction system,GRAPES.A new scheme of assimilating surface pressure was proposed,including a new interpolation scheme and a refreshed background covariance.The new scheme takes account of the differences between station elevation and model topography,and it especially deals with stations located at elevations below that of the first model level.Contrast experiments were conducted using both the original and the new assimilation schemes.The influence of the new interpolation scheme and the updated background covariance were investigated.Our results show that the new interpolation scheme utilized more observations and immproved the quality of the mass analysis.The background covariance was refreshed using statistics resulting from the technique proposed by Parrish and Derber in 1992.Experiments show that the updated vertical covariance may have a positive influence on the analysis at higher levels of the atmosphere when assimilating surface pressure.This influence may be more significant if the quality of the background field at high levels is poor.A series of assimilation experiments were performed to test the validity of the new scheme.The corresponding simulation experiments were conducted using the analysis of both schemes as initial conditions.The results indicated that the new scheme leads to better forecasting of sea level pressure and precipitation in South China,especially the forecast of moderate and heavy rain.

  11. Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City.

    Science.gov (United States)

    Yang, Wan; Olson, Donald R; Shaman, Jeffrey

    2016-11-01

    The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast.

  12. A Bayesian Combination Forecasting Model for Retail Supply Chain Coordination

    Directory of Open Access Journals (Sweden)

    W.J. Wang

    2014-04-01

    Full Text Available Retailing plays an important part in modern economic development, and supply chain coordination is the research focus in retail operations management. This paper reviews the collaborative forecasting process within the framework of the collaborative planning, forecasting and replenishment of retail supply chain. A Bayesian combination forecasting model is proposed to integrate multiple forecasting resources and coordinate forecasting processes among partners in the retail supply chain. Based on simulation results for retail sales, the effectiveness of this combination forecasting model is demonstrated for coordinating the collaborative forecasting processes, resulting in an improvement of demand forecasting accuracy in the retail supply chain.

  13. Tropical cyclones-Pacific Asian Research Campaign for Improvement of Intensity estimations/forecasts (T-PARCII): A research plan of typhoon aircraft observations in Japan

    Science.gov (United States)

    Tsuboki, Kazuhisa

    2017-04-01

    Typhoons are the most devastating weather system occurring in the western North Pacific and the South China Sea. Violent wind and heavy rainfall associated with a typhoon cause huge disaster in East Asia including Japan. In 2013, Supertyphoon Haiyan struck the Philippines caused a very high storm surge and more than 7000 people were killed. In 2015, two typhoons approached the main islands of Japan and severe flood occurred in the northern Kanto region. Typhoons are still the largest cause of natural disaster in East Asia. Moreover, many researches have projected increase of typhoon intensity with the climate change. This suggests that a typhoon risk is increasing in East Asia. However, the historical data of typhoon include large uncertainty. In particular, intensity data of the most intense typhoon category have larger error after the US aircraft reconnaissance of typhoon was terminated in 1987.The main objective of the present study is improvements of typhoon intensity estimations and of forecasts of intensity and track. We will perform aircraft observation of typhoon and the observed data are assimilated to numerical models to improve intensity estimation. Using radars and balloons, observations of thermodynamical and cloud-microphysical processes of typhoons will be also performed to improve physical processes of numerical model. In typhoon seasons (mostly in August and September), we will perform aircraft observations of typhoons. Using dropsondes from the aircraft, temperature, humidity, pressure, and wind are measured in surroundings of the typhoon inner core region. The dropsonde data are assimilated to a cloud-resolving model which has been developed in Nagoya University and named the Cloud Resolving Storm Simulator (CReSS). Then, more accurate estimations and forecasts of the typhoon intensity will be made as well as typhoon tracks. Furthermore, we will utilize a ground-based balloon with microscope camera, X-band precipitation radar, Ka-band cloud radar

  14. Load forecasting method considering temperature effect for distribution network

    Directory of Open Access Journals (Sweden)

    Meng Xiao Fang

    2016-01-01

    Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.

  15. General forecasting correcting formula

    OpenAIRE

    Harin, Alexander

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  16. General forecasting correcting formula

    OpenAIRE

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  17. Information Forecasting.

    Science.gov (United States)

    Hanneman, Gerhard J.

    Information forecasting provides a means of anticipating future message needs of a society or predicting the necessary types of information that will allow smooth social functioning. Periods of unrest and uncertainty in societies contribute to "societal information overload," whereby an abundance of information channels can create communication…

  18. Study of Differential Equation Application in the Forecast of Population Growth%差分方程在人口增长预测中的应用研究

    Institute of Scientific and Technical Information of China (English)

    曾维

    2011-01-01

    In the study of population growth forecast, the accuracy of common prediction model is somewhat low and the results lack of guidance. However, the hyperbolic model and the computational complexity of differential equation model are both complex in calculating and also difficult to solve. It is not convenient for use in the area with low technology. To solve these problems, based on the gray prediction model, a differential equation model of population growth has been built. The model simulates different ages, different types of demographic changes. It can accurately predict the structural changes, changes in sex ratio, urbanization, and so on. The model was applied to predict China's population. The result is consistent of the population development prediction in the study of national population development strategy. The calculation of residuals is less than 0.2 and the model predicts good results.%在人口增长预测的研究中,关于人口总数、性别、年龄结构等预测,一般预测模型准确性较低,预测结果缺乏指导意义;而双曲模型和微分方程模型计算复杂,很难求解,不便于科技水平较低地区使用.为解决上述问题,基于灰色预测模型,提出差分方程构建人口增长预测模型.模型能仿真出不同年龄、不同类型人口变化情况,能准确预测结构变化、性别比例变化、城镇化情况等.应用模型对中国人口进行仿真预测,得到结果与国家人口发展战略研究人口发展预测相符合,为人口增长提供了较好的预测模型.

  19. The impact of different sea-surface temperature prediction scenarios on Southern African seasonal climate forecast skill

    CSIR Research Space (South Africa)

    Landman, WA

    2009-12-01

    Full Text Available ) and reliability (whether the confidence communicated in the forecasts is appropriate). Rainfall forecasts produced by forcing the AGCM with dynamically predicted SSTs produce the higher skill, and ensemble mean SST forecasts lead to improved skill over forecasts...

  20. Improving aquatic warbler population assessments by accounting for imperfect detection.

    Directory of Open Access Journals (Sweden)

    Steffen Oppel

    Full Text Available Monitoring programs designed to assess changes in population size over time need to account for imperfect detection and provide estimates of precision around annual abundance estimates. Especially for species dependent on conservation management, robust monitoring is essential to evaluate the effectiveness of management. Many bird species of temperate grasslands depend on specific conservation management to maintain suitable breeding habitat. One such species is the Aquatic Warbler (Acrocephalus paludicola, which breeds in open fen mires in Central Europe. Aquatic Warbler populations have so far been assessed using a complete survey that aims to enumerate all singing males over a large area. Because this approach provides no estimate of precision and does not account for observation error, detecting moderate population changes is challenging. From 2011 to 2013 we trialled a new line transect sampling monitoring design in the Biebrza valley, Poland, to estimate abundance of singing male Aquatic Warblers. We surveyed Aquatic Warblers repeatedly along 50 randomly placed 1-km transects, and used binomial mixture models to estimate abundances per transect. The repeated line transect sampling required 150 observer days, and thus less effort than the traditional 'full count' approach (175 observer days. Aquatic Warbler abundance was highest at intermediate water levels, and detection probability varied between years and was influenced by vegetation height. A power analysis indicated that our line transect sampling design had a power of 68% to detect a 20% population change over 10 years, whereas raw count data had a 9% power to detect the same trend. Thus, by accounting for imperfect detection we increased the power to detect population changes. We recommend to adopt the repeated line transect sampling approach for monitoring Aquatic Warblers in Poland and in other important breeding areas to monitor changes in population size and the effects of

  1. Improving aquatic warbler population assessments by accounting for imperfect detection.

    Science.gov (United States)

    Oppel, Steffen; Marczakiewicz, Piotr; Lachmann, Lars; Grzywaczewski, Grzegorz

    2014-01-01

    Monitoring programs designed to assess changes in population size over time need to account for imperfect detection and provide estimates of precision around annual abundance estimates. Especially for species dependent on conservation management, robust monitoring is essential to evaluate the effectiveness of management. Many bird species of temperate grasslands depend on specific conservation management to maintain suitable breeding habitat. One such species is the Aquatic Warbler (Acrocephalus paludicola), which breeds in open fen mires in Central Europe. Aquatic Warbler populations have so far been assessed using a complete survey that aims to enumerate all singing males over a large area. Because this approach provides no estimate of precision and does not account for observation error, detecting moderate population changes is challenging. From 2011 to 2013 we trialled a new line transect sampling monitoring design in the Biebrza valley, Poland, to estimate abundance of singing male Aquatic Warblers. We surveyed Aquatic Warblers repeatedly along 50 randomly placed 1-km transects, and used binomial mixture models to estimate abundances per transect. The repeated line transect sampling required 150 observer days, and thus less effort than the traditional 'full count' approach (175 observer days). Aquatic Warbler abundance was highest at intermediate water levels, and detection probability varied between years and was influenced by vegetation height. A power analysis indicated that our line transect sampling design had a power of 68% to detect a 20% population change over 10 years, whereas raw count data had a 9% power to detect the same trend. Thus, by accounting for imperfect detection we increased the power to detect population changes. We recommend to adopt the repeated line transect sampling approach for monitoring Aquatic Warblers in Poland and in other important breeding areas to monitor changes in population size and the effects of habitat management.

  2. A Review of Demand Forecast for Charging Facilities of Electric Vehicles

    Science.gov (United States)

    Jiming, Han; Lingyu, Kong; Yaqi, Shen; Ying, Li; Wenting, Xiong; Hao, Wang

    2017-05-01

    The demand forecasting of charging facilities is the basis of its planning and locating, which has important role in promoting the development of electric vehicles and alleviating the energy crisis. Firstly, this paper analyzes the influence of the charging mode, the electric vehicle population and the user’s charging habits on the demand of charging facilities; Secondly, considering these factors, the recent analysis on charging and switching equipment demand forecast is divided into two methods—forecast based on electric vehicle population and user traveling behavior. Then, the article analyzes the two methods and puts forward the advantages and disadvantages. Finally, in view of the defects of current research, combined with the current situation of the development of the city and comprehensive consideration of economic, political, environmental and other factors, this paper proposes an improved demand forecasting method which has great practicability and pertinence and lays the foundation for the plan of city electric facilities.

  3. Improvements in the percent range. Machine learning improves accuracy of forecasting for wind power generation; Verbesserungen im Prozentbereich. Maschinelles Lernen steigert Prognosegenauigkeit bei Windkrafterzeugung

    Energy Technology Data Exchange (ETDEWEB)

    Anon.

    2011-07-01

    The more accurate the availability of wind power can be predicted, the more better electricity can be placed on the market and the less control energy is required. Machine learning methods allow a further increase in the accuracy of forecasting. However, this requires enormous computational resources. Researchers at the Centre on Research of Solar Energy and Hydrogen (Stuttgart, Federal Republic of Germany) use powerful graphics processors. Project partner EWC Weather Consult GmbH (Karlsruhe, Federal Republic of Germany) combines various weather models to the best possible prediction by means of machine learning. This new offer takes particular interest to producers who want to go into the direct marketing of wind power.

  4. Measuring inaccuracy in travel demand forecasting

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent

    2005-01-01

    Project promoters, forecasters, and managers sometimes object to two things in measuring inaccuracy in travel demand forecasting: (1)using the forecast made at the time of making the decision to build as the basis for measuring inaccuracy and (2)using traffic during the first year of operations...... as the basis for measurement. This paper presents the case against both objections. First, if one is interested in learning whether decisions about building transport infrastructure are based on reliable information, then it is exactly the traffic forecasted at the time of making the decision to build...... in travel demand forecasts are likely to be conservatively biased, i.e., accuracy in travel demand forecasts estimated from such samples would likely be higher than accuracy in travel demand forecasts in the project population. This bias must be taken into account when interpreting the results from...

  5. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    Science.gov (United States)

    Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.

    2012-12-01

    Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.

  6. Atmospheric composition forecasting in Europe

    Directory of Open Access Journals (Sweden)

    L. Menut

    2010-01-01

    Full Text Available The atmospheric composition is a societal issue and, following new European directives, its forecast is now recommended to quantify the air quality. It concerns both gaseous and particles species, identified as potential problems for health. In Europe, numerical systems providing daily air quality forecasts are numerous and, mostly, operated by universities. Following recent European research projects (GEMS, PROMOTE, an organization of the air quality forecast is currently under development. But for the moment, many platforms exist, each of them with strengths and weaknesses. This overview paper presents all existing systems in Europe and try to identify the main remaining gaps in the air quality forecast knowledge. As modeling systems are now able to reasonably forecast gaseous species, and in a lesser extent aerosols, the future directions would concern the use of these systems with ensemble approaches and satellite data assimilation. If numerous improvements were recently done on emissions and chemistry knowledge, improvements are still needed especially concerning meteorology, which remains a weak point of forecast systems. Future directions will also concern the use of these forecast tools to better understand and quantify the air pollution impact on health.

  7. The practical method of improve earthquake forecast accuracy by MSDP software%MSDP软件提高地震速报质量

    Institute of Scientific and Technical Information of China (English)

    苏莉华; 赵晖; 李源; 魏玉霞

    2012-01-01

    Select the records of Henan digital seismic network within the network and outside the network (the sidelines within 100 km) of seismic events from 2008 to 2011. Analysis and comparison those records by MSDP software, and coordinate with the daily experience, generalize the practical method of improve earthquake forecast accuracy.%选取2008-2011年河南数字地震台网记录的网内和网外(边线外100 km以内)的地震事件,运用MSDP软件对这些震例进行实际分析对比,再结合日常的工作经验,从而归纳出提高地震速报质量的实用方法.

  8. Tide forecasting method based on dynamic weight distribution for operational evaluation

    Institute of Scientific and Technical Information of China (English)

    Shao-wei QIU; Zeng-chuan DONG; Fen XU; Li SUN; Sheng CHEN

    2009-01-01

    Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one's forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.

  9. Comparative population genomics of maize domestication and improvement.

    Science.gov (United States)

    Hufford, Matthew B; Xu, Xun; van Heerwaarden, Joost; Pyhäjärvi, Tanja; Chia, Jer-Ming; Cartwright, Reed A; Elshire, Robert J; Glaubitz, Jeffrey C; Guill, Kate E; Kaeppler, Shawn M; Lai, Jinsheng; Morrell, Peter L; Shannon, Laura M; Song, Chi; Springer, Nathan M; Swanson-Wagner, Ruth A; Tiffin, Peter; Wang, Jun; Zhang, Gengyun; Doebley, John; McMullen, Michael D; Ware, Doreen; Buckler, Edward S; Yang, Shuang; Ross-Ibarra, Jeffrey

    2012-06-03

    Domestication and plant breeding are ongoing 10,000-year-old evolutionary experiments that have radically altered wild species to meet human needs. Maize has undergone a particularly striking transformation. Researchers have sought for decades to identify the genes underlying maize evolution, but these efforts have been limited in scope. Here, we report a comprehensive assessment of the evolution of modern maize based on the genome-wide resequencing of 75 wild, landrace and improved maize lines. We find evidence of recovery of diversity after domestication, likely introgression from wild relatives, and evidence for stronger selection during domestication than improvement. We identify a number of genes with stronger signals of selection than those previously shown to underlie major morphological changes. Finally, through transcriptome-wide analysis of gene expression, we find evidence both consistent with removal of cis-acting variation during maize domestication and improvement and suggestive of modern breeding having increased dominance in expression while targeting highly expressed genes.

  10. Toward improving hurricane forecasts using the JPL Tropical Cyclone Information System (TCIS): A framework to address the issues of Big Data

    Science.gov (United States)

    Hristova-Veleva, S. M.; Boothe, M.; Gopalakrishnan, S.; Haddad, Z. S.; Knosp, B.; Lambrigtsen, B.; Li, P.; montgomery, M. T.; Niamsuwan, N.; Tallapragada, V. S.; Tanelli, S.; Turk, J.; Vukicevic, T.

    2013-12-01

    Accurate forecasting of extreme weather requires the use of both regional models as well as global General Circulation Models (GCMs). The regional models have higher resolution and more accurate physics - two critical components needed for properly representing the key convective processes. GCMs, on the other hand, have better depiction of the large-scale environment and, thus, are necessary for properly capturing the important scale interactions. But how to evaluate the models, understand their shortcomings and improve them? Satellite observations can provide invaluable information. And this is where the issues of Big Data come: satellite observations are very complex and have large variety while model forecast are very voluminous. We are developing a system - TCIS - that addresses the issues of model evaluation and process understanding with the goal of improving the accuracy of hurricane forecasts. This NASA/ESTO/AIST-funded project aims at bringing satellite/airborne observations and model forecasts into a common system and developing on-line tools for joint analysis. To properly evaluate the models we go beyond the comparison of the geophysical fields. We input the model fields into instrument simulators (NEOS3, CRTM, etc.) and compute synthetic observations for a more direct comparison to the observed parameters. In this presentation we will start by describing the scientific questions. We will then outline our current framework to provide fusion of models and observations. Next, we will illustrate how the system can be used to evaluate several models (HWRF, GFS, ECMWF) by applying a couple of our analysis tools to several hurricanes observed during the 2013 season. Finally, we will outline our future plans. Our goal is to go beyond the image comparison and point-by-point statistics, by focusing instead on understanding multi-parameter correlations and providing robust statistics. By developing on-line analysis tools, our framework will allow for consistent

  11. Forecasting carbon dioxide emissions.

    Science.gov (United States)

    Zhao, Xiaobing; Du, Ding

    2015-09-01

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

  12. Improved near real-time data management procedures for the Mediterranean ocean Forecasting System-Voluntary Observing Ship program

    Directory of Open Access Journals (Sweden)

    G. M. R. Manzella

    Full Text Available A "ship of opportunity" program was launched as part of the Mediterranean Forecasting System Pilot Project. During the operational period (September 1999 to May 2000, six tracks covered the Mediterranean from the northern to southern boundaries approximately every 15 days, while a long eastwest track from Haifa to Gibraltar was covered approximately every month. XBT data were collected, sub-sampled at 15 inflection points and transmitted through a satellite communication system to a regional data centre. It was found that this data transmission system has limitations in terms of quality of the temperature profiles and quantity of data successfully transmitted. At the end of the MFSPP operational period, a new strategy for data transmission and management was developed. First of all, VOS-XBT data are transmitted with full resolution. Secondly, a new data management system, called Near Real Time Quality Control for XBT (NRT.QC.XBT, was defined to produce a parallel stream of high quality XBT data for further scientific analysis. The procedure includes: (1 Position control; (2 Elimination of spikes; (3 Re-sampling at a 1 metre vertical interval; (4 Filtering; (5 General malfunctioning check; (6 Comparison with climatology (and distance from this in terms of standard deviations; (7 Visual check; and (8 Data consistency check. The first six steps of the new procedure are completely automated; they are also performed using a new climatology developed as part of the project. The visual checks are finally done with a free-market software that allows NRT final data assessment.

    Key words. Oceanography: physical (instruments and techniques; general circulation; hydrography

  13. Assimilation of satellite information in a snowpack model to improve characterization of snow cover for runoff simulation and forecasting

    Directory of Open Access Journals (Sweden)

    L. S. Kuchment

    2009-08-01

    Full Text Available A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of temporal changes of the snow depth, density and water equivalent (SWE, accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The model was first calibrated against available ground-based snow measurements and then was applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The remote sensing data used in the model consist of products derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites. They include daily maps of snow cover, snow water equivalent (SWE, land surface temperature, and weekly maps of surface albedo. Maps of land cover classes and tree cover fraction derived from NOAA AVHRR were used to characterize the vegetation cover. The developed technique was tested over a study area of approximately 200 000 km2 located in the European part of Russia (56° N to 60° N, and 48° E to 54° E. The study area comprises the Vyatka River basin with the catchment area of 124 000 km2. The spatial distributions of SWE, obtained with the coupled model, as well as solely from satellite data were used as the inputs in a physically-based model of runoff generation to simulate runoff hydrographs on the Vyatka river for spring seasons of 2003, 2005. The comparison of simulated hydrographs with the observed ones has shown that suggested procedure gives a higher accuracy of snow cover spatial distribution representation and hydrograph simulations than the direct use of satellite SWE data.

  14. Assimilation of satellite information in a snowpack model to improve characterization of snow cover for runoff simulation and forecasting

    Science.gov (United States)

    Kuchment, L. S.; Romanov, P.; Gelfan, A. N.; Demidov, V. N.

    2009-08-01

    A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The model was first calibrated against available ground-based snow measurements and then was applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The remote sensing data used in the model consist of products derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites. They include daily maps of snow cover, snow water equivalent (SWE), land surface temperature, and weekly maps of surface albedo. Maps of land cover classes and tree cover fraction derived from NOAA AVHRR were used to characterize the vegetation cover. The developed technique was tested over a study area of approximately 200 000 km2 located in the European part of Russia (56° N to 60° N, and 48° E to 54° E). The study area comprises the Vyatka River basin with the catchment area of 124 000 km2. The spatial distributions of SWE, obtained with the coupled model, as well as solely from satellite data were used as the inputs in a physically-based model of runoff generation to simulate runoff hydrographs on the Vyatka river for spring seasons of 2003, 2005. The comparison of simulated hydrographs with the observed ones has shown that suggested procedure gives a higher accuracy of snow cover spatial distribution representation and hydrograph simulations than the direct use of satellite SWE data.

  15. Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae.

    Science.gov (United States)

    Martínez, Brezo; Arenas, Francisco; Trilla, Alba; Viejo, Rosa M; Carreño, Francisco

    2015-04-01

    Species distribution models (SDM) are a useful tool for predicting species range shifts in response to global warming. However, they do not explore the mechanisms underlying biological processes, making it difficult to predict shifts outside the environmental gradient where the model was trained. In this study, we combine correlative SDMs and knowledge on physiological limits to provide more robust predictions. The thermal thresholds obtained in growth and survival experiments were used as proxies of the fundamental niches of two foundational marine macrophytes. The geographic projections of these species' distributions obtained using these thresholds and existing SDMs were similar in areas where the species are either absent-rare or frequent and where their potential and realized niches match, reaching consensus predictions. The cold-temperate foundational seaweed Himanthalia elongata was predicted to become extinct at its southern limit in northern Spain in response to global warming, whereas the occupancy of southern-lusitanic Bifurcaria bifurcata was expected to increase. Combined approaches such as this one may also highlight geographic areas where models disagree potentially due to biotic factors. Physiological thresholds alone tended to over-predict species prevalence, as they cannot identify absences in climatic conditions within the species' range of physiological tolerance or at the optima. Although SDMs tended to have higher sensitivity than threshold models, they may include regressions that do not reflect causal mechanisms, constraining their predictive power. We present a simple example of how combining correlative and mechanistic knowledge provides a rapid way to gain insight into a species' niche resulting in consistent predictions and highlighting potential sources of uncertainty in forecasted responses to climate change. © 2014 John Wiley & Sons Ltd.

  16. An improved combination forecasting method based on IOWA operator and application%一种改进的基于IOWA算子的组合预测方法及应用

    Institute of Scientific and Technical Information of China (English)

    敖培; 牟龙华

    2011-01-01

    The combination forecasting model based on induced ordered weighted averaging (IOWA) operators, which is built according to the criterion of error sum of squares, failes to reflect the influence of errors arising from observation points in various periods on the predictive values. Moreover, this method can not be used to predict directly because future data are unknown in actual forecasting. In order to overcome the above flaws, an improved method is proposed. First, individual forecasting model that has higher forecasting accuracy is chosen as a criterion. Then, the deviation of predictive values between other models and standard model is computed. The weights are given according to the mean value size of the absolute value sum of deviation in every individual forecasting model in every period. Finally, a new forecasting model is built in accordance with the weighted error sum of squares. And genetic algorithm is used to solve the optimal weights. Verified by an example, the improved combination forecasting method is better than the original combination forecasting method based on IOWA operator. Forecasting accuracy is improved effectively.%按误差平方和准则建立的基于IOWA算子的组合预测模型并不能正确反映出各个时期观测点所引起误差对预测值的影响程度,在实际预测时预测期数据是未知的,无法直接利用该方法进行预测.针对以上缺陷,提出以单项预测模型中精度较高者的预测值为标准,计算其余模型的预测值与其偏差,再按各个时期各单项偏差绝对值和的平均值大小赋予权系数,建立按照加权误差平方和准则新的预测模型,并利用遗传算法求解最优权系数.通过实例验证,改进后的组合预测方法优于原来的基于IOWA算子的组合预测方法,有效地提高了预测精度.

  17. Uses and Applications of Climate Forecasts for Power Utilities.

    Science.gov (United States)

    Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

    1995-05-01

    The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.

  18. The eSurge-Venice project: altimeter and scatterometer satellite data to improve the storm surge forecasting in the city of Venice

    Science.gov (United States)

    Zecchetto, Stefano; De Biasio, Francesco; Umgiesser, Georg; Bajo, Marco; Vignudelli, Stefano; Papa, Alvise; Donlon, Craig; Bellafiore, Debora

    2013-04-01

    On the framework of the Data User Element (DUE) program, the European Space Agency is funding a project to use altimeter Total Water Level Envelope (TWLE) and scatterometer wind data to improve the storm surge forecasting in the Adriatic Sea and in the city of Venice. The project will: a) Select a number of Storm Surge Events occurred in the Venice lagoon in the period 1999-present day b) Provide the available satellite Earth Observation (EO) data related to the Storm Surge Events, mainly satellite winds and altimeter data, as well as all the available in-situ data and model forecasts c) Provide a demonstration Near Real Time service of EO data products and services in support of operational and experimental forecasting and warning services d) Run a number of re-analysis cases, both for historical and contemporary storm surge events, to demonstrate the usefulness of EO data The re-analysis experiments, based on hindcasts performed by the finite element 2-D oceanographic model SHYFEM (https://sites.google.com/site/shyfem/), will 1. use different forcing wind fields (calibrated and not calibrated with satellite wind data) 2. use Storm Surge Model initial conditions determined from altimeter TWLE data. The experience gained working with scatterometer and Numerical Weather Prediction (NWP) winds in the Adriatic Sea tells us that the bias NWP-Scatt wind is negative and spatially and temporally not uniform. In particular, a well established point is that the bias is higher close to coasts then offshore. Therefore, NWP wind speed calibration will be carried out on each single grid point in the Adriatic Sea domain over the period of a Storm Surge Event, taking into account of existing published methods. Point #2 considers two different methodologies to be used in re-analysis tests. One is based on the use of the TWLE values from altimeter data in the Storm Surge Model (SSM), applying data assimilation methodologies and trying to optimize the initial conditions of the

  19. Novel grey forecast model and its application

    Institute of Scientific and Technical Information of China (English)

    丁洪发; 舒双焰; 段献忠

    2003-01-01

    The advancement of grey system theory provides an effective analytic tool for power system load fore-cast. All kinds of presently available grey forecast models can be well used to deal with the short-term load fore-cast. However, they make big errors for medium or long-term load forecasts, and the load that does not satisfythe approximate exponential increasing law in particular. A novel grey forecast model that is capable of distin-guishing the increasing law of load is adopted to forecast electric power consumption (EPC) of Shanghai. Theresults show that this model can be used to greatly improve the forecast precision of EPC for a secondary industryor the whole society.

  20. Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

    Science.gov (United States)

    Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

    2013-12-01

    The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that

  1. Forecasting global atmospheric CO2

    Directory of Open Access Journals (Sweden)

    A. Agustí-Panareda

    2014-05-01

    become available in near-real time. In this way, the accumulation of errors in the atmospheric CO2 forecast will be reduced. Improvements in the CO2 forecast are also expected with the continuous developments in the operational IFS.

  2. HYDROLOGICAL FORECASTS OF DANUBE FLOOD 2013 BY THE HUNGARIAN HYDROLOGICAL FORECASTING SERVICE

    Directory of Open Access Journals (Sweden)

    A. CSÍK

    2014-10-01

    Full Text Available The significant lead time resulting from the use of the OLSER system of the Hungarian Hydrological Forecasting Service is of key importance in making timely preparations for flood defence. Due to continuous improvements to the quantitative meteorological forecast models (primarily the generally used ECMWF model and the OLSER system over the past years, we have by now reached a point where the previously separately managed flood peak forecasting and continuous forecasting can no longer be interpreted independently. Continuous forecasting taking into account precipitation forecasts and monitoring spatial changes of the complex physics-based concentration process also offers a level of accuracy suitable to identify peak values. The flood wave of June 2013 along the Hungarian Danube section exceeded the ever observed highest high water levels everywhere (except for gauge Mohács. The forecasts prepared by HHFS played a crucial role both in terms of lead time and the forecasted water levels.

  3. Hurricane Modeling and Supercomputing: Can a global mesoscale model be useful in improving forecasts of tropical cyclogenesis?

    Science.gov (United States)

    Shen, B.; Tao, W.; Atlas, R.

    2007-12-01

    Hurricane modeling, along with guidance from observations, has been used to help construct hurricane theories since the 1960s. CISK (conditional instability of the second kind, Charney and Eliassen 1964; Ooyama 1964,1969) and WISHE (wind-induced surface heat exchange, Emanuel 1986) are among the well-known theories being used to understand hurricane intensification. For hurricane genesis, observations have indicated the importance of large-scale flows (e.g., the Madden-Julian Oscillation or MJO, Maloney and Hartmann, 2000) on the modulation of hurricane activity. Recent modeling studies have focused on the role of the MJO and Rossby waves (e.g., Ferreira and Schubert, 1996; Aivyer and Molinari, 2003) and/or the interaction of small-scale vortices (e.g., Holland 1995; Simpson et al. 1997; Hendrick et al. 2004), of which determinism could be also built by large-scale flows. The aforementioned studies suggest a unified view on hurricane formation, consisting of multiscale processes such as scale transition (e.g., from the MJO to Equatorial Rossby Waves and from waves to vortices), and scale interactions among vortices, convection, and surface heat and moisture fluxes. To depict the processes in the unified view, a high-resolution global model is needed. During the past several years, supercomputers have enabled the deployment of ultra-high resolution global models, obtaining remarkable forecasts of hurricane track and intensity (Atlas et al. 2005; Shen et al. 2006). In this work, hurricane genesis is investigated with the aid of a global mesoscale model on the NASA Columbia supercomputer by conducting numerical experiments on the genesis of six consecutive tropical cyclones (TCs) in May 2002. These TCs include two pairs of twin TCs in the Indian Ocean, Supertyphoon Hagibis in the West Pacific Ocean and Hurricane Alma in the East Pacific Ocean. It is found that the model is capable of predicting the genesis of five of these TCs about two to three days in advance. Our

  4. Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

    Science.gov (United States)

    Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd

    2017-09-01

    Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.

  5. kwmc Terminal Aerodrome Forecast

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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