WorldWideScience

Sample records for regional forecast model

  1. NAVO NCOM Relocatable Model: Fukushima Regional Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary NCOM Relocatable 1km forecast model for Fukushima Region. USERS ARE REMINDED TO USE THE FUKUSHIMA 1KM NCOM DATA WITH CAUTION. THE MODEL WAS INITIATED ON...

  2. Operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Balk, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2011-01-01

    Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed

  3. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

  4. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

  5. Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

  6. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

  7. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  8. A review of operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.-H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2012-01-01

    Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed

  9. Nearest neighbour models for local and regional avalanche forecasting

    Directory of Open Access Journals (Sweden)

    M. Gassner

    2002-01-01

    Full Text Available This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF. Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

  10. Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

    Science.gov (United States)

    Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

    2016-10-01

    Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

  11. Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

    Science.gov (United States)

    Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

    2017-08-01

    Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

  12. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...

  13. Regional probabilistic fertility forecasting by modeling between-country correlations

    Directory of Open Access Journals (Sweden)

    Bailey Fosdick

    2014-04-01

    Full Text Available Background: The United Nations (UN Population Division constructs probabilistic projections for the total fertility rate (TFR using the Bayesian hierarchical model of Alkema et al. (2011, which produces predictive distributions of the TFR for individual countries. The UN is interested in publishing probabilistic projections for aggregates of countries, such as regions and trading blocs. This requires joint probabilistic projections of future countryspecific TFRs, taking account of the correlations between them. Objective: We propose an extension of the Bayesian hierarchical model that allows for probabilistic projection of aggregate TFR for any set of countries. Methods: We model the correlation between country forecast errors as a linear function of time invariant covariates, namely whether the countries are contiguous, whether they had a common colonizer after 1945, and whether they are in the same UN region. The resulting correlation model is incorporated into the Bayesian hierarchical model's error distribution. Results: We produce predictive distributions of TFR for 1990-2010 for each of the UN's primary regions. We find that the proportions of the observed values that fall within the prediction intervals from our method are closer to their nominal levels than those produced by the current model. Conclusions: Our results suggest that a substantial proportion of the correlation between forecast errors for TFR in different countries is due to the countries' geographic proximity to one another, and that if this correlation is accounted for, the quality of probabilistic projections of TFR for regions and other aggregates is improved.

  14. Regressional modeling and forecasting of economic growth for arkhangelsk region

    Directory of Open Access Journals (Sweden)

    Robert Mikhailovich Nizhegorodtsev

    2012-12-01

    Full Text Available The regression models of GRP, considering the impact of three main factors: investment in fixed assets, wages amount, and, importantly, the innovation factor –the expenditures for research and development, are constructed in this paper on the empirical data for Arkhangelsk region. That approach permits to evaluate explicitly the contribution of innovation to economic growth. Regression analysis is the main research instrument, all calculations areperformedin the Microsoft Excel. There were made meaningful conclusions regarding the potential of the region's GRP growth by various factors, including impacts of positive and negative time lags. Adequate and relevant models are the base for estimation and forecasting values of the dependent variable (GRP and evaluating their confidence intervals. The invented method of research can be used in factor assessment and prediction of regional economic growth, including growth by expectations.

  15. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

  16. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

  17. Middle Atlantic Bight Marine Ecosystem: A Regional Forecast Model Study

    Science.gov (United States)

    Kim, H.; Coles, V. J.; Garraffo, Z. D.

    2011-12-01

    Changes in basin scale climate patterns can drive changes in mesoscale physical oceanographic processes and subsequent alterations of ecosystem states. Climatic variability can be induced in the northeastern shelfbreak large marine ecosystem by climate oscillations, such as North Atlantic Oscillation, Atlantic Multidecadal Oscillation; and long-term trends, such as a warming pattern. Short term variability can be induced by changes in the water masses in the northern and southern boundaries, by Gulf Stream path and transport variations, and by local mesoscale and submesoscale features. A coupled bio-physical model (HYbrid Coordinate Ocean Model) is being used to forecast the evolution of the frontal and current systems of the shelf and Gulf Stream, and subsequent changes in thermal conditions and ecosystem structure over the Middle Atlantic Bight (MAB). This study aims to forecast the ocean state and nutrients in the MAB, and to investigate how cross-shelf exchanges of different water masses could affect nutrient budgets, primary and secondary production, and fish populations in coastal and shelf marine ecosystems. Preliminary results are shown for a regional MAB model nested to the global 1/12o HYCOM run at NOAA/NCEP/EMC using Naval Oceanographic Office (NAVO) daily initialization. Elements of this simulation are nutrient influx condition at the northern and southern boundaries through regression to ocean thermodynamic variables, and nutrient input at the river mouths.

  18. 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.

  19. Retrospective forecasting test of a statistical physics model for earthquakes in Sichuan-Yunnan region

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Pattern informatics (PI) model is one of the recently developed predictive models of earthquake phys- ics based on the statistical mechanics of complex systems. In this paper, retrospective forecast test of the PI model was conducted for the earthquakes in Sichuan-Yunnan region since 1988, exploring the possibility to apply this model to the estimation of time-dependent seismic hazard in continental China. Regional earthquake catalogue down to ML3.0 from 1970 to 2007 was used. The ‘target magnitude’ for the forecast test was MS5.5. Fifteen-year long ‘sliding time window’ was used in the PI calculation, with ‘anomaly training time window’ being 5 years and ‘forecast time window’ being 5 years, respectively. Receiver operating characteristic (ROC) test was conducted for the evaluation of the forecast result, showing that the PI forecast outperforms not only random guess but also the simple number counting approach based on the clustering hypothesis of earthquakes (the RI forecast). If the ‘forecast time window’ was shortened to 3 years and 1 year, respectively, the forecast capability of the PI model de- creased significantly, albeit outperformed random forecast. For the one year ‘forecast time window’, the PI result was almost comparable to the RI result, indicating that clustering properties play a more important role at this time scale.

  20. Experimental weekly to seasonal U.S. forecasts with the Regional Spectral Model

    Science.gov (United States)

    J. Roads

    2004-01-01

    As described previously Roads et al. 2001a, hereafter RCF), the Scripps Experimental Climate Prediction Center (ECPC) has been making routine, near-real-time, long-range experimental global and regional dynamical forecasts since 27 September 1997. The global spectral model (GSM) used for these forecasts is that of National Centers for Environmental Prediction’s (NCEP;...

  1. Regional landslide forecasting model using interferometric SAR images

    Institute of Scientific and Technical Information of China (English)

    董育烦; 张发明; 高正夏; 蒯志要

    2008-01-01

    Method of obtaining landslide evaluating information by using Interferometric Synthetic Aperture Radar (InSAR) technique was discussed. More precision landslide surface deformation data extracted from InSAR image need take suitable SAR interferometric data selecting, path tracking, phase unwrapping processes. Then, the DEM model of scope and surface shape of the landslide was built. Combining with geological property of landslide and sliding displacements obtained from InSAR/D-InSAR images, a new landslide forecasting model called equal central angle slice method for those not obviously deformed landslides was put forward. This model breaks the limits of traditional research methods of geology. In this model, the landslide safety factor was calculated by equal central angle slice method, then considering the persistence ratio of the sliding surface based on plastic theory, the minimum safety factor was the phase when plastic area were complete persistence. This new model makes the application of InSAR/D-InSAR technology become more practical in geology hazard research.

  2. Model complex of forecasting of interdependent development of migration processes and region labour market

    Directory of Open Access Journals (Sweden)

    Valery Aleksandrovich Chereshnev

    2013-09-01

    Full Text Available The essential problems of current international labor migration raising the need to forecast interdependent labor market and migration processes in a region for improving the effectiveness of regional migration policy in Russia are considered. A model for the prediction of migration flows as determined by wage differentials, distances between populations of the regions as well as wages and unemployment, which come from the impact of migration on the availability of jobs at the labor market with search-matching frictions for source and host regions is presented in the framework of search and matching theory. Applying the model to statistical data, the forecast for labor migration flows to regions of Russia from CIS countries, as well as its effects on regional labor markets for 2012-2021 is maid. Recommendations for improving the effectiveness of regional migration policy are given on the basis of the forecast.

  3. Probability Forecast of Regional Landslide Based on Numerical Weather Forecast

    Institute of Scientific and Technical Information of China (English)

    GAO Kechang; WEI Fangqiang; CUI Peng; HU Kaiheng; XU Jing; ZHANG Guoping; BI Baogui

    2006-01-01

    The regional forecast of landslide is one of the key points of hazard mitigation. It is also a hot and difficult point in research field. To solve this problem has become urgent task along with Chinese economy fast development. This paper analyzes the principle of regional landslide forecast and the factors for forecasting. The method of a combination of Information Value Model and Extension Model has been put forward to be as the forecast model. Using new result of Numerical Weather Forecast Research and that combination model, we discuss the implementation feasibility of regional landslide forecast. Finally, with the help of Geographic Information System, an operation system for southwest of China landslide forecast has been developed. It can carry out regional landslide forecast daily and has been pilot run in NMC. Since this is the first time linking theoretical research with meteorological service, further works are needed to enhance it.

  4. A review of operational, regional-scale, chemical weather forecasting models in Europe

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2012-01-01

    Full Text Available Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

  5. Implications of the Regional Earthquake Likelihood Models test of earthquake forecasts in California

    Directory of Open Access Journals (Sweden)

    Michael Karl Sachs

    2012-09-01

    Full Text Available The Regional Earthquake Likelihood Models (RELM test was the first competitive comparison of prospective earthquake forecasts. The test was carried out over 5 years from 1 January 2006 to 31 December 2010 over a region that included all of California. The test area was divided into 7682 0.1°x0.1° spatial cells. Each submitted forecast gave the predicted numbers of earthquakes Nemi larger than M=4.95 in 0.1 magnitude bins for each cell. In this paper we present a method that separates the forecast of the number of test earthquakes from the forecast of their locations. We first obtain the number Nem of forecast earthquakes in magnitude bin m. We then determine the conditional probability λemi=Nemi/Nem that an earthquake in magnitude bin m will occur in cell i. The summation of λemi over all 7682 cells is unity. A random (no skill forecast gives equal values of λemi for all spatial cells and magnitude bins. The skill of a forecast, in terms of the location of the earthquakes, is measured by the success in assigning large values of λemi to the cells in which earthquakes occur and low values of λemi to the cells where earthquakes do not occur. Thirty-one test earthquakes occurred in 27 different combinations of spatial cells i and magnitude bins m, we had the highest value of λemi for that mi cell. We evaluate the performance of eleven submitted forecasts in two ways. First, we determine the number of mi cells for which the forecast λemi was the largest, the best forecast is the one with the highest number. Second, we determine the mean value of λemi for the 27 mi cells for each forecast. The best forecast has the highest mean value of λemi. The success of a forecast during the test period is dependent on the allocation of the probabilities λemi between the mi cells, since the sum over the mi cells is unity. We illustrate the forecast distributions of λemi and discuss their differences. We conclude that the RELM test was successful in

  6. Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California.

    Science.gov (United States)

    Lee, Ya-Ting; Turcotte, Donald L; Holliday, James R; Sachs, Michael K; Rundle, John B; Chen, Chien-Chih; Tiampo, Kristy F

    2011-10-04

    The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ≥ 4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ≥ 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor-Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.

  7. Alaska North Slope regional gas hydrate production modeling forecasts

    Science.gov (United States)

    Wilson, S.J.; Hunter, R.B.; Collett, T.S.; Hancock, S.; Boswell, R.; Anderson, B.J.

    2011-01-01

    A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside

  8. Regional forecasting with global atmospheric models; Final report

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-05-01

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

  9. Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

    Indian Academy of Sciences (India)

    Someshwar Das; Raghavendra Ashrit; Gopal Raman Iyengar; Saji Mohandas; M Das Gupta; John P George; E N Rajagopal; Surya Kanti Dutta

    2008-10-01

    Performance of four mesoscale models namely,the MM5,ETA,RSM and WRF,run at NCMRWF for short range weather forecasting has been examined during monsoon-2006.Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind,temperature,specific humidity,geopotential height,rainfall,systematic errors,root mean square errors and specific events like the monsoon depressions. It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none ’.Perhaps an ensemble approach would be the best.However, if we must make a final verdict,it can be stated that in general,(i)the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and,the MM5 is able to produce best All India rainfall forecasts in day-3,but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India,(ii)the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time,and (iii)the RSM is able to produce least errors in the day-1 forecasts of the tracks,while the ETA model produces least errors in the day-3 forecasts.

  10. Regional forecasting with global atmospheric models; Fourth year report

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-05-01

    The scope of the report is to present the results of the fourth year`s work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals.

  11. Regional forecasting with global atmospheric models; Third year report

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-05-01

    This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third year`s work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain several studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals.

  12. Tourism demand in the Algarve region: Evolution and forecast using SVARMA models

    Science.gov (United States)

    Lopes, Isabel Cristina; Soares, Filomena; Silva, Eliana Costa e.

    2017-06-01

    Tourism is one of the Portuguese economy's key sectors, and its relative weight has grown over recent years. The Algarve region is particularly focused on attracting foreign tourists and has built over the years a large offer of diversified hotel units. In this paper we present multivariate time series approach to forecast the number of overnight stays in hotel units (hotels, guesthouses or hostels, and tourist apartments) in Algarve. We adjust a seasonal vector autoregressive and moving averages model (SVARMA) to monthly data between 2006 and 2016. The forecast values were compared with the actual values of the overnight stays in Algarve in 2016 and led to a MAPE of 15.1% and RMSE= 53847.28. The MAPE for the Hotel series was merely 4.56%. These forecast values can be used by a hotel manager to predict their occupancy and to determine the best pricing policy.

  13. Forecast Verification for North American Mesoscale (NAM) Operational Model over Karst/Non-Karst regions

    Science.gov (United States)

    Sullivan, Z.; Fan, X.

    2014-12-01

    Karst is defined as a landscape that contains especially soluble rocks such as limestone, gypsum, and marble in which caves, underground water systems, over-time sinkholes, vertical shafts, and subterranean river systems form. The cavities and voids within a karst system affect the hydrology of the region and, consequently, can affect the moisture and energy budget at surface, the planetary boundary layer development, convection, and precipitation. Carbonate karst landscapes comprise about 40% of land areas over the continental U.S east of Tulsa, Oklahoma. Currently, due to the lack of knowledge of the effects karst has on the atmosphere, no existing weather model has the capability to represent karst landscapes and to simulate its impact. One way to check the impact of a karst region on the atmosphere is to check the performance of existing weather models over karst and non-karst regions. The North American Mesoscale (NAM) operational forecast is the best example, of which historical forecasts were archived. Variables such as precipitation, maximum/minimum temperature, dew point, evapotranspiration, and surface winds were taken into account when checking the model performance over karst versus non-karst regions. The forecast verification focused on a five-year period from 2007-2011. Surface station observations, gridded observational dataset, and North American Regional Reanalysis (for certain variables with insufficient observations) were used. Thirteen regions of differing climate, size, and landscape compositions were chosen across the Contiguous United States (CONUS) for the investigation. Equitable threat score (ETS), frequency bias (fBias), and root-mean-square error (RMSE) scores were calculated and analyzed for precipitation. RMSE and mean bias (Bias) were analyzed for other variables. ETS, fBias, and RMSE scores show generally a pattern of lower forecast skills, a greater magnitude of error, and a greater under prediction of precipitation over karst than

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-03 (NODC Accession 0001531)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-12 (NODC Accession 0002659)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-06 (NODC Accession 0002406)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-07 (NODC Accession 0001523)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-09 (NODC Accession 0001525)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-04 (NODC Accession 0001520)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-06 (NODC Accession 0001522)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-05 (NODC Accession 0001533)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-03 (NODC Accession 0001519)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-05 (NODC Accession 0001545)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-10 (NODC Accession 0043271)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-03 (NODC Accession 0002742)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-04 (NODC Accession 0001532)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-11 (NODC Accession 0001527)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-09 (NODC Accession 0043270)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-10 (NODC Accession 0001550)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-12 (NODC Accession 0043273)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-02 (NODC Accession 0001554)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-09 (NODC Accession 0001549)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-02 (NODC Accession 0001542)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-02 (NODC Accession 0001530)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-11 (NODC Accession 0001587)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-05 (NODC Accession 0001569)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-08 (NODC Accession 0001524)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-01 (NODC Accession 0001529)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-03 (NODC Accession 0001603)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-05 (NODC Accession 0043281)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-07 (NODC Accession 0001571)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-05 (NODC Accession 0001521)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-04 (NODC Accession 0001568)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-07 (NODC Accession 0001559)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-03 (NODC Accession 0001591)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-08 (NODC Accession 0043284)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-10 (NODC Accession 0001562)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01 (NODC Accession 0001517)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-03 (NODC Accession 0002162)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-04 (NODC Accession 0043262)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-01 (NODC Accession 0001577)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-12 (NODC Accession 0001588)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-09 (NODC Accession 0001573)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-01 (NODC Accession 0001565)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-08 (NODC Accession 0002504)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-02 (NODC Accession 0001590)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-06 (NODC Accession 0043282)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-07 (NODC Accession 0001583)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-08 (NODC Accession 0001560)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-08 (NODC Accession 0043268)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-09 (NODC Accession 0001585)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-06 (NODC Accession 0001558)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-07 (NODC Accession 0043267)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-03 (NODC Accession 0001579)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-06 (NODC Accession 0001582)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-03 (NODC Accession 0001567)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-11 (NODC Accession 0001551)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-01 (NODC Accession 0002660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-02 (NODC Accession 0001518)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-12 (NODC Accession 0001576)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-08 (NODC Accession 0001596)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-12 (NODC Accession 0001564)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-09 (NODC Accession 0001597)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-04 (NODC Accession 0001556)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-04 (NODC Accession 0001604)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-02 (NODC Accession 0001566)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-07 (NODC Accession 0043283)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-05 (NODC Accession 0001593)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-11 (NODC Accession 0001599)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-06 (NODC Accession 0001594)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-12 (NODC Accession 0001600)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-08 (NODC Accession 0001584)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-04 (NODC Accession 0002340)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-09 (NODC Accession 0001561)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-04 (NODC Accession 0001592)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-02 (NODC Accession 0002160)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-08 (NODC Accession 0001572)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-01 (NODC Accession 0002159)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-01 (NODC Accession 0001601)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-11 (NODC Accession 0001575)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-11 (NODC Accession 0001563)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-10 (NODC Accession 0001598)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-05 (NODC Accession 0043265)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-06 (NODC Accession 0001570)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-07 (NODC Accession 0001595)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-07 (NODC Accession 0001535)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-12 (NODC Accession 0001528)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-05 (NODC Accession 0001581)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-08 (NODC Accession 0002154)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-05 (NODC Accession 0002373)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-02 (NODC Accession 0001578)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-01 (NODC Accession 0001589)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-06 (NODC Accession 0002151)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-06 (NODC Accession 0043266)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-09 (NODC Accession 0002505)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-10 (NODC Accession 0001526)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-06 (NODC Accession 0001534)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-08 (NODC Accession 0001548)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-04 (NODC Accession 0001544)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-07 (NODC Accession 0001547)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-02 (NODC Accession 0001602)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-10 (NODC Accession 0001586)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-01 (NODC Accession 0001541)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-12 (NODC Accession 0001540)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-04 (NODC Accession 0001580)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-06 (NODC Accession 0001546)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-03 (NODC Accession 0001543)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-09 (NODC Accession 0001537)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-10 (NODC Accession 0002156)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-11 (NODC Accession 0002652)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. Northeast Coastal Ocean Forecast System (NECOFS): A Multi-scale Global-Regional-Estuarine FVCOM Model

    Science.gov (United States)

    Beardsley, R. C.; Chen, C.

    2014-12-01

    The Northeast Coastal Ocean Forecast System (NECOFS) is a global-regional-estuarine integrated atmosphere/surface wave/ocean forecast model system designed for the northeast US coastal region covering a computational domain from central New Jersey to the eastern end of the Scotian Shelf. The present system includes 1) the mesoscale meteorological model WRF (Weather Research and Forecasting); 2) the regional-domain FVCOM covering the Gulf of Maine/Georges Bank/New England Shelf region (GOM-FVCOM); 3) the unstructured-grid surface wave model (FVCOM-SWAVE) modified from SWAN with the same domain as GOM-FVCOM; 3) the Mass coastal FVCOM with inclusion of inlets, estuaries and intertidal wetlands; and 4) three subdomain wave-current coupled inundation FVCOM systems in Scituate, MA, Hampton River, NH and Mass Bay, MA. GOM-FVCOM grid features unstructured triangular meshes with horizontal resolution of ~ 0.3-25 km and a hybrid terrain-following vertical coordinate with a total of 45 layers. The Mass coastal FVCOM grid is configured with triangular meshes with horizontal resolution up to ~10 m, and 10 layers in the vertical. Scituate, Hampton River and Mass Bay inundation model grids include both water and land with horizontal resolution up to ~5-10 m and 10 vertical layers. GOM-FVCOM is driven by surface forcing from WRF model output configured for the region (with 9-km resolution), the COARE3 bulk air-sea flux algorithm, local river discharges, and tidal forcing constructed by eight constituents and subtidal forcing on the boundary nested to the Global-FVCOM. SWAVE is driven by the same WRF wind field with wave forcing at the boundary nested to Wave Watch III configured for the northwestern Atlantic region. The Mass coastal FVCOM and three inundation models are connected with GOM-FVCOM through one-way nesting in the common boundary zones. The Mass coastal FVCOM is driven by the same surface forcing as GOM-FVCOM. The nesting boundary conditions for the inundation models

  2. Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

    Institute of Scientific and Technical Information of China (English)

    XU Jianming; HE Jinhai; YANG Yuanqin; WANG Jiahe; XU Xiangde; LIU Yu; DING Guoan; CHEN Huailiang; HU Jiangkai; ZHANG Jianchun; WU Hao; LI Weiliang

    2005-01-01

    Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution

  3. The consistency evaluation of the climate version of the Eta regional forecast model developed for regional climate downscaling

    CERN Document Server

    Pisnichenko, I A

    2007-01-01

    The regional climate model prepared from Eta WS (workstation) forecast model has been integrated over South America with the horizontal resolution of 40 km for the period of 1961-1977. The model was forced at its lateral boundaries by the outputs of HadAMP. The data of HadAMP represent the simulation of modern climate with the resolution about150 km. In order to prepare climate regional model from the Eta forecast model was added new blocks and multiple modifications and corrections was made in the original model. The running of climate Eta model was made on the supercomputer SX-6. The detailed analysis of the results of dynamical downscaling experiment includes an investigation of a consistency between the regional and AGCM models as well as of ability of the regional model to resolve important features of climate fields on the finer scale than that resolved by AGCM. In this work we show the results of our investigation of the consistency of the output fields of the Eta model and HadAMP. We have analysed geo...

  4. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Ekman, Annica [Stockholm Univ. (Sweden). Dept. of Meteorology

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO{sub 2}(a)), sulfate in air (SO{sub 4}(a)), sulfate in cloud water (SO{sub 4}(aq)) and hydrogen peroxide (H{sub 2}O{sub 2}). Modeled concentrations of SO{sub 2}(a), SO{sub 4}(a) and SO{sub 4}(aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO{sub 2}(a) concentration and underpredicts the SO{sub 4}(a) concentration. The agreement between modeled and observed SO{sub 4}(aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO{sub 2}(a) and 2-2.5 days for SO{sub 4}(a). For SO{sub 2}(a) this is in accordance with earlier simulated global turnover times, but for SO{sub 4}(a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO{sub 4}(a) and SO{sub 4}(aq), if an additional oxidant for converting SO{sub 2}(a) to SO{sub 4}(a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations.

  5. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Ekman, Annica [Stockholm Univ. (Sweden). Dept. of Meteorology

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO{sub 2}(a)), sulfate in air (SO{sub 4}(a)), sulfate in cloud water (SO{sub 4}(aq)) and hydrogen peroxide (H{sub 2}O{sub 2}). Modeled concentrations of SO{sub 2}(a), SO{sub 4}(a) and SO{sub 4}(aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO{sub 2}(a) concentration and underpredicts the SO{sub 4}(a) concentration. The agreement between modeled and observed SO{sub 4}(aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO{sub 2}(a) and 2-2.5 days for SO{sub 4}(a). For SO{sub 2}(a) this is in accordance with earlier simulated global turnover times, but for SO{sub 4}(a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO{sub 4}(a) and SO{sub 4}(aq), if an additional oxidant for converting SO{sub 2}(a) to SO{sub 4}(a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations.

  6. Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

    Science.gov (United States)

    David Hui; Karen Shum; Ji Chen; Shyh-Chin Chen; Jack Ritchie; John Roads

    2007-01-01

    Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of July–August–September 2003 and April–...

  7. A hierarchical Bayesian model for regionalized seasonal forecasts: Application to low flows in the northeastern United States

    Science.gov (United States)

    Ahn, Kuk-Hyun; Palmer, Richard; Steinschneider, Scott

    2017-01-01

    This study presents a regional, probabilistic framework for seasonal forecasts of extreme low summer flows in the northeastern United States conditioned on antecedent climate and hydrologic conditions. The model is developed to explore three innovations in hierarchical modeling for seasonal forecasting at ungaged sites: (1) predictive climate teleconnections are inferred directly from ocean fields instead of predefined climate indices, (2) a parsimonious modeling structure is introduced to allow climate teleconnections to vary spatially across streamflow gages, and (3) climate teleconnections and antecedent hydrologic conditions are considered jointly for regional forecast development. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with five simpler nested formulations to test specific hypotheses embedded in the full model structure. Results indicate that each of the three innovations improve out-of-sample summer low-flow forecasts, with the greatest benefits derived from the spatially heterogeneous effect of climate teleconnections. We conclude with a discussion of possible model improvements from a better representation of antecedent hydrologic conditions at ungaged sites.

  8. Regional Integrated Meteorological Forecasting and Warning Model for Geological Hazards Based on Logistic Regression

    Institute of Scientific and Technical Information of China (English)

    XU Jing; YANG Chi; ZHANG Guoping

    2007-01-01

    Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as "likely enough to release warning messages". The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.

  9. The ScaLIng Macroweather Model (SLIMM) and monthly and inter annual regional forecasting.

    Science.gov (United States)

    Lovejoy, S.; Del Rio Amador, L.; Sloman, L.

    2015-12-01

    By exploiting the sensitive dependence on initial conditions, GCM's can generate a statistical ensemble of future states in which the high frequency "weather" is treated as a driving noise. Following Hasselman, 1976, this has lead to stochastic models that directly generate the noise, and model the low frequencies using systems of integer ordered linear ordinary differential equations, the most well known are the linear inverse models (LIM). These have been presented as a benchmark for decadal surface temperature forecast. Using the LIM, hindcast skills comparable to and sometimes even better than the skill of (coupled) Global Circulation Models (GCM's) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Nevertheless, the short range exponential temporal decorrelations implicit in the LIM models are unrealistic (the true decorrelations are closer to long range power laws), and - as a consequence - the useful limit to the forecast horizon is roughly one year: it enormously underestimates the memory of the system. In presentation, we make a scaling analogue of the LIM: ScaLIng Macroweather Model (SLIMM) that exploits the power law (scaling) behavior in time of the temperature field and consequently, make use of the long history dependence of the data to improve the skill. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5º x 5º regions on the planet using the Twentieth Century Reanalysis as a reference datasets. As a first step, we removed the anthropogenic component of each time series based on its sensitivity to equivalent CO2 concentration for the last 130 years, the residues are our estimates of the natural variability that SLIMM predicts. This residues were treated as fractional Gaussian noise processes with scaling exponent H between -0.5 and 0. The value of H for each grid-point can be obtained directly from the data. We report maps of theoretical skill predicted by the model and we

  10. Regional demand forecasting and simulation model: user's manual. Task 4, final report

    Energy Technology Data Exchange (ETDEWEB)

    Parhizgari, A M

    1978-09-25

    The Department of Energy's Regional Demand Forecasting Model (RDFOR) is an econometric and simulation system designed to estimate annual fuel-sector-region specific consumption of energy for the US. Its purposes are to (1) provide the demand side of the Project Independence Evaluation System (PIES), (2) enhance our empirical insights into the structure of US energy demand, and (3) assist policymakers in their decisions on and formulations of various energy policies and/or scenarios. This report provides a self-contained user's manual for interpreting, utilizing, and implementing RDFOR simulation software packages. Chapters I and II present the theoretical structure and the simulation of RDFOR, respectively. Chapter III describes several potential scenarios which are (or have been) utilized in the RDFOR simulations. Chapter IV presents an overview of the complete software package utilized in simulation. Chapter V provides the detailed explanation and documentation of this package. The last chapter describes step-by-step implementation of the simulation package using the two scenarios detailed in Chapter III. The RDFOR model contains 14 fuels: gasoline, electricity, natural gas, distillate and residual fuels, liquid gases, jet fuel, coal, oil, petroleum products, asphalt, petroleum coke, metallurgical coal, and total fuels, spread over residential, commercial, industrial, and transportation sectors.

  11. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model

    Energy Technology Data Exchange (ETDEWEB)

    Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

    2010-12-15

    In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. (author)

  12. Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

    Science.gov (United States)

    Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

    2010-01-01

    A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve

  13. Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

    Science.gov (United States)

    Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

    2010-01-01

    A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve

  14. Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

    Science.gov (United States)

    Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

    2017-04-01

    Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models

  15. A static and dynamic factorscoupled forecasting model of regional rainfall-induced landslides:A case study of Shenzhen

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Most rainfall-induced landslide forecasting models focus on the relation between landslides and rainfall,which is one of the dynamic factors,and seldom consider the stacitc factors,such as geological and geograpical factors.Landslide susceptibility,however,is determinded by both static and dynamic factors.This article proposes a static and dynamic factors-coupled forecasting model(SDFCFM) of regional rainfall-induced landslides,which quantitatively considers both the static and dynamic factors that affect landslides.The generalized additive model(GAM) is applied to coupling both factors to get the landslide susceptibility.In the case study,SDFCFM is applied to forecast the landslide occurrences in Shenzhen during a rainfall process in 2008.Compared with the rainfall logistic regression model,the resulting landslide susceptibility map illustrates that SDFCFM can reduce the forecast redundancy and improve the hit ratio.It is both applicable and practical.The application of SDFCFM in landslide warning and prevention system will improve its efficiency and also cut down the cost of human and matreial resources.

  16. Regional forecast model for the Olea pollen season in Extremadura (SW Spain)

    Science.gov (United States)

    Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Silva-Palacios, Inmaculada; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela

    2016-10-01

    The olive tree ( Olea europaea) is a predominantly Mediterranean anemophilous species. The pollen allergens from this tree are an important cause of allergic problems. Olea pollen may be relevant in relation to climate change, due to the fact that its flowering phenology is related to meteorological parameters. This study aims to investigate airborne Olea pollen data from a city on the SW Iberian Peninsula, to analyse the trends in these data and their relationships with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1994 to 2013 in Badajoz (SW Spain) using a 7-day Hirst-type volumetric sampler. The main Olea pollen season lasted an average of 34 days, from May 4th to June 7th. The model proposed to forecast airborne pollen concentrations, described by one equation. This expression is composed of two terms: the first term represents the resilience of the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term was obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological variables multiplied by a fitting coefficient. Due to the allergenic characteristics of this pollen type, it should be necessary to forecast its short-term prevalence using a long record of data in a city with a Mediterranean climate. The model obtained provides a suitable level of confidence to forecast Olea airborne pollen concentration.

  17. Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts

    Directory of Open Access Journals (Sweden)

    Siti Choirun Nisak

    2016-06-01

    Full Text Available Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS assumes the variance-covariance matrix has a constant error ~(, but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR approach is used to accommodate the weakness of the OLS. SUR assumption is ~(, for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS. Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1(1,12,36,(0,(1-SUR with determination coefficient generated with the average of 57.726%.

  18. Estimating a-priori kinematic wave model parameters based on regionalization for flash flood forecasting in the Conterminous United States

    Science.gov (United States)

    Vergara, Humberto; Kirstetter, Pierre-Emmanuel; Gourley, Jonathan J.; Flamig, Zachary L.; Hong, Yang; Arthur, Ami; Kolar, Randall

    2016-10-01

    This study presents a methodology for the estimation of a-priori parameters of the widely used kinematic wave approximation to the unsteady, 1-D Saint-Venant equations for hydrologic flow routing. The approach is based on a multi-dimensional statistical modeling of the macro scale spatial variability of rating curve parameters using a set of geophysical factors including geomorphology, hydro-climatology and land cover/land use over the Conterminous United States. The main goal of this study was to enable prediction at ungauged locations through regionalization of model parameters. The results highlight the importance of regional and local geophysical factors in uniquely defining characteristics of each stream reach conforming to physical theory of fluvial hydraulics. The application of the estimates is demonstrated through a hydrologic modeling evaluation of a deterministic forecasting system performed on 1672 gauged basins and 47,563 events extracted from a 10-year simulation. Considering the mean concentration time of the basins of the study and the target application on flash flood forecasting, the skill of the flow routing simulations is significantly high for peakflow and timing of peakflow estimation, and shows consistency as indicated by the large sample verification. The resulting a-priori estimates can be used in any hydrologic model that employs the kinematic wave model for flow routing. Furthermore, probabilistic estimates of kinematic wave parameters are enabled based on uncertainty information that is generated during the multi-dimensional statistical modeling. More importantly, the methodology presented in this study enables the estimation of the kinematic wave model parameters anywhere over the globe, thus allowing flood modeling in ungauged basins at regional to global scales.

  19. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts

    NARCIS (Netherlands)

    Wit, de A.J.W.; Diepen, van C.A.

    2007-01-01

    Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil

  20. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  1. Seasonal Verification of Dust Forecast over the Indian Region

    Science.gov (United States)

    Fatima, Hashmi; George, John P.; Rajagopal, E. N.; Basu, Swati

    2017-07-01

    The medium-range forecast of the dust aerosols over Indian region produced by the NCMRWF numerical weather prediction model with mineral dust scheme from May 2013 to May 2014 is examined in this study. Coarse mode aerosol observations are only used for comparison with dust forecast with the assumption that coarse mode aerosol over Indian region largely represents dust aerosol, especially over the areas of high dust load. Accuracy and trends of the day-to-day dust forecast are studied at three AERONET locations in Indo-Gangetic Plains (IGP) using surface and MODIS satellite retrievals of coarse mode aerosol optical depth for entire one year (May 2013-May 2014). Seasonal mean geographical distribution of the medium-range forecast of dust by the model over Indian region is validated with different satellite retrievals for all four seasons. Availability of suitable observations is one of the limiting factors and big challenges for the validation of the dust forecast. The main focus of this study is to assess dust forecast by the model over Indian region for all seasons, to know the biases and errors of the model forecast for its optimal use. The study finds that model dust forecast is comparable to AERONET observations over three locations for all seasons except monsoon season.

  2. Environmental forecasting and turbulence modeling

    Science.gov (United States)

    Hunt, J. C. R.

    This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a limited period of time into the future and over a limited space-time ‘target’, and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space-time targets, so that they only depend on the statistical averages of the initial and ‘edge’ conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the ‘reductionist’ methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or ‘blocked’ anticyclones) to small scale vortices. These eigen states can be predicted

  3. 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.

  4. 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.

  5. Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

    Science.gov (United States)

    Tariku, Tebikachew Betru; Gan, Thian Yew

    2017-08-01

    Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional

  6. The Chemistry CATT-BRAMS model : a regional atmospheric model system for integrated air quality and weather forecasting and research

    National Research Council Canada - National Science Library

    Longo, K. M; Freitas, S. R; Pirre, M; Marécal, V; Rodrigues, L. F; Panetta, J; Alonso, M. F; Rosário, N. E; Moreira, D. S; Gácita, M. S; Arteta, J; Fonseca, R; Stockler, R; Katsurayama, D. M; Fazenda, A; Bela, M

    2013-01-01

    ... (CCATT-BRAMS, version 4.5) is an on-line regional chemical transport model designed for local and regional studies of atmospheric chemistry from the surface to the lower stratosphere suitable both for operational and research purposes...

  7. Using the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale

    Directory of Open Access Journals (Sweden)

    A. Pietroniro

    2006-08-01

    Full Text Available Environment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire – MEC, which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC – Surface and Hydrology, a configuration of MEC currently under development, is specialized for coupled land-surface and hydrological models. To determine the specific requirements for MESH, its different components were implemented on the Laurentian Great Lakes watershed, situated on the Canada–U.S. border. This experiment showed that MESH can help us better understand the behaviour of different land-surface models, test different schemes for producing ensemble streamflow forecasts, and provide a means of sharing the data, the models and the results with collaborators and end-users. This modelling framework is at the heart of a testbed proposal for the Hydrologic Ensemble Prediction Experiment (HEPEX which should allow us to make use of the North American Ensemble Forecasting System (NAEFS to improve streamflow forecasts of the Great Lakes tributaries, and demonstrate how MESH can contribute to a Community Hydrologic Prediction System (CHPS.

  8. Forecasting and Analysis of Agricultural Product Logistics Demand in Tibet Based on Combination Forecasting Model

    Institute of Scientific and Technical Information of China (English)

    Wenfeng; YANG

    2015-01-01

    Over the years,the logistics development in Tibet has fallen behind the transport. Since the opening of Qinghai-Tibet Railway in2006,the opportunity for development of modern logistics has been brought to Tibet. The logistics demand analysis and forecasting is a prerequisite for regional logistics planning. By establishing indicator system for logistics demand of agricultural products,agricultural product logistics principal component regression model,gray forecasting model,BP neural network forecasting model are built. Because of the single model’s limitations,quadratic-linear programming model is used to build combination forecasting model to predict the logistics demand scale of agricultural products in Tibet over the next five years. The empirical analysis results show that combination forecasting model is superior to single forecasting model,and it has higher precision,so combination forecasting model will have much wider application foreground and development potential in the field of logistics.

  9. The impact of convection in the West African monsoon region on global weather forecasts - explicit vs. parameterised convection simulations using the ICON model

    Science.gov (United States)

    Pante, Gregor; Knippertz, Peter

    2017-04-01

    The West African monsoon is the driving element of weather and climate during summer in the Sahel region. It interacts with mesoscale convective systems (MCSs) and the African easterly jet and African easterly waves. Poor representation of convection in numerical models, particularly its organisation on the mesoscale, can result in unrealistic forecasts of the monsoon dynamics. Arguably, the parameterisation of convection is one of the main deficiencies in models over this region. Overall, this has negative impacts on forecasts over West Africa itself but may also affect remote regions, as waves originating from convective heating are badly represented. Here we investigate those remote forecast impacts based on daily initialised 10-day forecasts for July 2016 using the ICON model. One set of simulations employs the default setup of the global model with a horizontal grid spacing of 13 km. It is compared with simulations using the 2-way nesting capability of ICON. A second model domain over West Africa (the nest) with 6.5 km grid spacing is sufficient to explicitly resolve MCSs in this region. In the 2-way nested simulations, the prognostic variables of the global model are influenced by the results of the nest through relaxation. The nest with explicit convection is able to reproduce single MCSs much more realistically compared to the stand-alone global simulation with parameterised convection. Explicit convection leads to cooler temperatures in the lower troposphere (below 500 hPa) over the northern Sahel due to stronger evaporational cooling. Overall, the feedback of dynamic variables from the nest to the global model shows clear positive effects when evaluating the output of the global domain of the 2-way nesting simulation and the output of the stand-alone global model with ERA-Interim re-analyses. Averaged over the 2-way nested region, bias and root mean squared error (RMSE) of temperature, geopotential, wind and relative humidity are significantly reduced in

  10. Forecasting High-Priority Infectious Disease Surveillance Regions: A Socioeconomic Model

    OpenAIRE

    2012-01-01

    We explored a potential role for socioeconomic factors in modeling of national rates of infectious disease outbreaks. The final model included child measles immunization rate and telephone line density. Understanding socioeconomic factors could help improve the understanding of outbreak risk.

  11. Empirical regional models for the short-term forecast of M3000F2 during not quiet geomagnetic conditions over Europe

    Directory of Open Access Journals (Sweden)

    M. Pietrella

    2013-10-01

    Full Text Available Twelve empirical local models have been developed for the long-term prediction of the ionospheric characteristic M3000F2, and then used as starting point for the development of a short-term forecasting empirical regional model of M3000F2 under not quiet geomagnetic conditions. Under the assumption that the monthly median measurements of M3000F2 are linearly correlated to the solar activity, a set of regression coefficients were calculated over 12 months and 24 h for each of 12 ionospheric observatories located in the European area, and then used for the long-term prediction of M3000F2 at each station under consideration. Based on the 12 long-term prediction empirical local models of M3000F2, an empirical regional model for the prediction of the monthly median field of M3000F2 over Europe (indicated as RM_M3000F2 was developed. Thanks to the IFELM_foF2 models, which are able to provide short-term forecasts of the critical frequency of the F2 layer (foF2STF up to three hours in advance, it was possible to considerer the Brudley–Dudeney algorithm as a function of foF2STF to correct RM_M3000F2 and thus obtain an empirical regional model for the short-term forecasting of M3000F2 (indicated as RM_M3000F2_BD up to three hours in advance under not quiet geomagnetic conditions. From the long-term predictions of M3000F2 provided by the IRI model, an empirical regional model for the forecast of the monthly median field of M3000F2 over Europe (indicated as IRI_RM_M3000F2 was derived. IRI_RM_M3000F2 predictions were modified with the Bradley–Dudeney correction factor, and another empirical regional model for the short-term forecasting of M3000F2 (indicated as IRI_RM_M3000F2_BD up to three hours ahead under not quiet geomagnetic conditions was obtained. The main results achieved comparing the performance of RM_M3000F2, RM_M3000F2_BD, IRI_RM_M3000F2, and IRI_RM_M3000F2_BD are (1 in the case of moderate geomagnetic activity, the Bradley–Dudeney correction

  12. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  13. Implementation and testing of a simple data assimilation algorithm in the regional air pollution forecast model, DEOM

    DEFF Research Database (Denmark)

    Frydendall, Jan; Brandt, J.; Christensen, J. H.

    2009-01-01

    A simple data assimilation algorithm based on statistical interpolation has been developed and coupled to a long-range chemistry transport model, the Danish Eulerian Operational Model (DEOM), applied for air pollution forecasting at the National Environmental Research Institute (NERI), Denmark....... In this paper, the algorithm and the results from experiments designed to find the optimal setup of the algorithm are described. The algorithm has been developed and optimized via eight different experiments where the results from different model setups have been tested against measurements from the EMEP...... configuration of the data assimilation algorithm, were found. The data assimilation algorithm will in the future be used in the operational THOR integrated air pollution forecast system, which includes the DEOM....

  14. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

    2008-02-15

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

  15. Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model

    OpenAIRE

    Khandakar Md Habib Al Razi, Moritomi Hiroshi

    2013-01-01

    The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km) over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry) model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA) during the summer of 14–18 August 2010 was investigated u...

  16. Forecasting with Dynamic Regression Models

    CERN Document Server

    Pankratz, Alan

    2012-01-01

    One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

  17. U.S. Regional Demand Forecasts Using NEMS and GIS

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

    2005-07-01

    The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of

  18. The chemistry CATT–BRAMS model (CCATT–BRAMS 4.5: a regional atmospheric model system for integrated air quality and weather forecasting and research

    Directory of Open Access Journals (Sweden)

    K. M. Longo

    2013-02-01

    Full Text Available The Coupled Chemistry Aerosol-Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CCATT–BRAMS, version 4.5 is an online regional chemical transport model designed for local and regional studies of atmospheric chemistry from surface to the lower stratosphere suitable both for operational and research purposes. It includes gaseous/aqueous chemistry, photochemistry, scavenging and dry deposition. The CCATT–BRAMS model takes advantages of the BRAMS specific development for the tropics/subtropics and of the recent availability of preprocessing tools for chemical mechanisms and of fast codes for photolysis rates. BRAMS includes state-of-the-art physical parameterizations and dynamic formulations to simulate atmospheric circulations of scales down to meters. The online coupling between meteorology and chemistry allows the system to be used for simultaneous atmospheric weather and chemical composition forecasts as well as potential feedbacks between them. The entire system comprises three preprocessing software tools for chemical mechanism (which are user defined, aerosol and trace gases emission fields and atmospheric and chemistry fields for initial and boundary conditions. In this paper, the model description is provided along evaluations performed using observational data obtained from ground-based stations, instruments aboard of aircrafts and retrieval from space remote sensing. The evaluation takes into account model application on different scales from megacities and Amazon Basin up to intercontinental region of the Southern Hemisphere.

  19. The Chemistry CATT-BRAMS model (CCATT-BRAMS 4.5: a regional atmospheric model system for integrated air quality and weather forecasting and research

    Directory of Open Access Journals (Sweden)

    K. M. Longo

    2013-09-01

    Full Text Available Coupled Chemistry Aerosol-Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CCATT-BRAMS, version 4.5 is an on-line regional chemical transport model designed for local and regional studies of atmospheric chemistry from the surface to the lower stratosphere suitable both for operational and research purposes. It includes gaseous/aqueous chemistry, photochemistry, scavenging and dry deposition. The CCATT-BRAMS model takes advantage of BRAMS-specific development for the tropics/subtropics as well as the recent availability of preprocessing tools for chemical mechanisms and fast codes for photolysis rates. BRAMS includes state-of-the-art physical parameterizations and dynamic formulations to simulate atmospheric circulations down to the meter. This on-line coupling of meteorology and chemistry allows the system to be used for simultaneous weather and chemical composition forecasts as well as potential feedback between the two. The entire system is made of three preprocessing software tools for user-defined chemical mechanisms, aerosol and trace gas emissions fields and the interpolation of initial and boundary conditions for meteorology and chemistry. In this paper, the model description is provided along with the evaluations performed by using observational data obtained from ground-based stations, instruments aboard aircrafts and retrieval from space remote sensing. The evaluation accounts for model applications at different scales from megacities and the Amazon Basin up to the intercontinental region of the Southern Hemisphere.

  20. Implementation and testing of a simple data assimilation algorithm in the regional air pollution forecast model, DEOM

    Directory of Open Access Journals (Sweden)

    J. Frydendall

    2009-08-01

    Full Text Available A simple data assimilation algorithm based on statistical interpolation has been developed and coupled to a long-range chemistry transport model, the Danish Eulerian Operational Model (DEOM, applied for air pollution forecasting at the National Environmental Research Institute (NERI, Denmark. In this paper, the algorithm and the results from experiments designed to find the optimal setup of the algorithm are described. The algorithm has been developed and optimized via eight different experiments where the results from different model setups have been tested against measurements from the EMEP (European Monitoring and Evaluation Programme network covering a half-year period, April–September 1999. The best performing setup of the data assimilation algorithm for surface ozone concentrations has been found, including the combination of determining the covariances using the Hollingsworth method, varying the correlation length according to the number of adjacent observation stations and applying the assimilation routine at three successive hours during the morning. Improvements in the correlation coefficient in the range of 0.1 to 0.21 between the results from the reference and the optimal configuration of the data assimilation algorithm, were found. The data assimilation algorithm will in the future be used in the operational THOR integrated air pollution forecast system, which includes the DEOM.

  1. Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

    Directory of Open Access Journals (Sweden)

    D. L. Shrestha

    2013-05-01

    Full Text Available The quality of precipitation forecasts from four Numerical Weather Prediction (NWP models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT and regional (ACCESS-R NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

  2. A comparison of boundary-layer heights inferred from wind-profiler backscatter profiles with diagnostic calculations using regional model forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Baltink, H.K.; Holtslag, A.A.M. [Royal Netherlands Meteorological Inst., KNMI, De Bilt (Netherlands)

    1997-10-01

    From October 1994 through January 1997 the Tropospheric Energy Budget Experiment (TEBEX) was executed by KNMI. The main objectives are to study boundary layer processes and cloud variability on the sub-grid scale of present Global Climate Models and to improve the related sub-grid parametrizations. A suite of instruments was deployed to measure a large number of variables. Measurements to characterize ABL processes were focussed around the 200 m high meteorological observation tower of the KNMI in Cabauw. In the framework of TEBEX a 1290 MHz wind-profiler/RASS was installed in July 1994 at 300 m from tower. Data collected during TEBEX are used to assess the performance of a Regional Atmospheric Climate Model (RACMO). This climate model runs also in a operational forecast mode once a day. The diagnostic ABL-height (h{sub model}) is calculated from the RACMO forecast output. A modified Richardson`s number method extended with an excess parcel temperature is applied for all stability conditions. We present the preliminary results of a comparison of h{sub model} from forecasts with measured h{sub TS} derived from profiler and sodar data for July 1995. (au)

  3. ECONOMETRIC FORECAST OF AGRICULTURAL SECTOR INVESTING IN LVOV REGION

    Directory of Open Access Journals (Sweden)

    Rostyslav Lytvyn

    2014-07-01

    Full Text Available Purpose of economic processes forecasting in agriculture is more relevant and urgent in recent years with application of applied econometric methods. In represented research paper, these methods are used to forecast investment and the main agricultural industry indicators of Lvov region of Ukraine. The linear trend model, the parabolic trend model and the exponential trend model were elaborated from the period from 2000 to 2009 in this scientific study using applied statistical tool STATGRAFICS and EXCEL spreadsheets. And with assistance of these models forecast for investment on the basis of data of essential indicators of agrarian sector of the region for 2010 and 2011 was made. All models with probability р=0,95 are adequate experimental data for 2000-2009 years, that allow to make the forecast of investments and main agricultural indicators of the researched region by these models for 2010 and 2011 years. Nevertheless, it should be pointed out that, because of small amount of input data analysis of regression equations coefficients have more qualitative than quantitative influence upon resulting variable y6.

  4. Flood forecasting for River Mekong with data-based models

    Science.gov (United States)

    Shahzad, Khurram M.; Plate, Erich J.

    2014-09-01

    In many regions of the world, the task of flood forecasting is made difficult because only a limited database is available for generating a suitable forecast model. This paper demonstrates that in such cases parsimonious data-based hydrological models for flood forecasting can be developed if the special conditions of climate and topography are used to advantage. As an example, the middle reach of River Mekong in South East Asia is considered, where a database of discharges from seven gaging stations on the river and 31 rainfall stations on the subcatchments between gaging stations is available for model calibration. Special conditions existing for River Mekong are identified and used in developing first a network connecting all discharge gages and then models for forecasting discharge increments between gaging stations. Our final forecast model (Model 3) is a linear combination of two structurally different basic models: a model (Model 1) using linear regressions for forecasting discharge increments, and a model (Model 2) using rainfall-runoff models. Although the model based on linear regressions works reasonably well for short times, better results are obtained with rainfall-runoff modeling. However, forecast accuracy of Model 2 is limited by the quality of rainfall forecasts. For best results, both models are combined by taking weighted averages to form Model 3. Model quality is assessed by means of both persistence index PI and standard deviation of forecast error.

  5. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  6. Regional hydrological models for distributed flash-floods forecasting: towards an estimation of potential impacts and damages

    Science.gov (United States)

    Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David

    2016-04-01

    Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches

  7. Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil

    Directory of Open Access Journals (Sweden)

    Victor Brunini Moreto

    2015-10-01

    Full Text Available Forecast is the act of estimating a future event based on current data. Ten-day period (TDP meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF and surplus (EXC and soil water storage (SWS. Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1.

  8. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Halbleib, Roxana; Voev, Valeri

    2011-01-01

    This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical appl...

  9. Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model

    Energy Technology Data Exchange (ETDEWEB)

    Habib Al Razi, Khandakar Md; Hiroshi, Moritomi [Environmental and Renewable Energy System, Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu City, 501-1193 (Japan)

    2013-07-01

    The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km) over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry) model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA) during the summer of 14–18 August 2010 was investigated using the observation data. We analyzed the ozone and other trace gas concentrations, as well as the corresponding weather conditions in this high ozone episode by WRF/Chem model. The simulation results revealed that the analyzed episode was mainly caused by the impact of accumulation of pollution rich in ozone over the Greater Tokyo Area. WRF/Chem has shown relatively good performance in modeling of this continuous high ozone episode, the simulated and the observed concentrations of ozone, NOx and NO2 are basically in agreement at Kawasaki City, with best correlation coefficients of 0.87, 0.70 and 0.72 respectively. Moreover, the simulations of WRF/Chem with WRF preprocessing software (WPS) show a better agreement with meteorological observations such as surface winds and temperature profiles in the ground level of this area. As a result the surface ozone simulation performances have been enhanced in terms of the peak ozone and spatial patterns, whereas WRF/Chem has been succeeded to generate meteorological fields as well as ozone, NOx, NO2 and NO.

  10. Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem model

    Directory of Open Access Journals (Sweden)

    Khandakar Md Habib Al Razi, Moritomi Hiroshi

    2013-01-01

    Full Text Available The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA during the summer of 14–18 August 2010 was investigated using the observation data. We analyzed the ozone and other trace gas concentrations, as well as the corresponding weather conditions in this high ozone episode by WRF/Chem model. The simulation results revealed that the analyzed episode was mainly caused by the impact of accumulation of pollution rich in ozone over the Greater Tokyo Area. WRF/Chem has shown relatively good performance in modeling of this continuous high ozone episode, the simulated and the observed concentrations of ozone, NOx and NO2 are basically in agreement at Kawasaki City, with best correlation coefficients of 0.87, 0.70 and 0.72 respectively. Moreover, the simulations of WRF/Chem with WRF preprocessing software (WPS show a better agreement with meteorological observations such as surface winds and temperature profiles in the ground level of this area. As a result the surface ozone simulation performances have been enhanced in terms of the peak ozone and spatial patterns, whereas WRF/Chem has been succeeded to generate meteorological fields as well as ozone, NOx, NO2 and NO.

  11. FORECAST OF THE AGRICULTURAL DEVELOPMENT FOR THE AMUR REGION

    Directory of Open Access Journals (Sweden)

    Reymer V. V.

    2015-12-01

    Full Text Available This article explains the relevance of evaluation of agricultural growth, which can be achieved through the implementation of agricultural sectors’ innovative potential. The opportunities of agricultural growth are defined by the set of macroeconomic, sectoral and regional factors as well as the type of enterprises that have different levels of innovative susceptibility. The authors give an overview of the main methods of social and economic forecasting and justify the choice of the ARIMA (Autoregressive integrated moving average as a tool for forecasting regional development of agriculture. The article presents the experts’ estimatesbased values of integrated indicators of agricultural exogenous factors and the ARIMA-parameters based on the use of these indicators for time series prediction of agricultural production in the Amur region. The authors conclude that the time series ARIMA-model of the gross agricultural production, taking into account the influence of innovation potential factors, demonstrate a good approximation to the Amur region data. This article also compares the forecasts of agricultural production on inertial and innovative scenario for the Amur region, and provides an estimation of innovation potential growth of the agricultural branches

  12. Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts

    Science.gov (United States)

    Saide, P. E.; Carmichael, G. R.; Liu, Z.; Schwartz, C. S.; Lin, H. C.; da Silva, A. M.; Hyer, E.

    2013-10-01

    An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM2.5 predictions reduced at over 90% of the stations and AOD errors reduced at 100% of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size

  13. 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.

  14. The Red Sea Modeling and Forecasting System

    KAUST Repository

    Hoteit, Ibrahim

    2015-04-01

    Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

  15. A Simple Fuzzy Time Series Forecasting Model

    DEFF Research Database (Denmark)

    Ortiz-Arroyo, Daniel

    2016-01-01

    In this paper we describe a new first order fuzzy time series forecasting model. We show that our automatic fuzzy partitioning method provides an accurate approximation to the time series that when combined with rule forecasting and an OWA operator improves forecasting accuracy. Our model does...... not attempt to provide the best results in comparison with other forecasting methods but to show how to improve first order models using simple techniques. However, we show that our first order model is still capable of outperforming some more complex higher order fuzzy time series models....

  16. Ionospheric forecasts for the European region for space weather applications

    Directory of Open Access Journals (Sweden)

    Tsagouri Ioanna

    2015-01-01

    Full Text Available This paper discusses recent advances in the implementation and validation of the Solar Wind driven autoregression model for Ionospheric short-term Forecast (SWIF that is running in the European Digital upper Atmosphere Server (DIAS to release ionospheric forecasting products for the European region. The upgraded implementation plan expands SWIF’s capabilities in the high latitude ionosphere while the extensive validation tests in the two solar cycles 23 and 24 allow the comprehensive analysis of the model’s performance in all terms. Focusing on disturbed conditions, the results demonstrate that SWIF’s alert detection algorithm forecasts the occurrence of ionospheric storm time disturbances with probability of detection up to 98% under intense geomagnetic storm conditions and up to 63% when storms of moderate intensity are also considered. The forecasts show relative improvement over climatology of about 30% in middle-to-low and high latitudes and 40% in middle-to-high latitudes. This indicates that SWIF is able to capture on average more than one third (35% of the storm-associated ionospheric disturbances. Regarding the accuracy, the averaged mean relative error during storm conditions usually ranges around 20% in middle-to-low and high latitudes and 24% in the middle-to-high latitudes. Our analysis shows clearly that SWIF alert criteria were designed to effectively anticipate the ionospheric storm time effects that occurred under specific interplanetary conditions, e.g., cloud Interplanetary Coronal Mass Ejections (ICMEs and/or associated sheaths. The results provide valuable input in advancing our ability in predicting the space weather effects in the ionosphere for future developments, and further work is proposed to enhance the model forecasting efficiency to support operational applications.

  17. Construction of the migration flows forecasting into Russian regions

    Directory of Open Access Journals (Sweden)

    Aleksandr Aleksandrovich Tarasyev

    2013-06-01

    Full Text Available This paper presents a dynamic model that can predict the dynamics of migration flows between source countries and host regions, as well as the dynamics of wage levels there. The model is constructed within the framework of neoclassical economics and human capital theory in continuous time. Thanks to liberalization of migration policy in Russia in 2007, the model could be successfully employed to Russian regions and the Commonwealth of Independent States (CIS, which have visa-free entry regulations with the Russian Federation. Employing the model on statistical data, we forecast the number and origin composition of foreign labor force from the CIS into Russian regions for 2010-2016. The purpose of our further research is to classify migrants by skills

  18. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...

  19. Short-term load forecasting based on a multi-model

    Energy Technology Data Exchange (ETDEWEB)

    Faller, C. [ETH, Zurich (Switzerland). Faculty of Electrical Engineering; Dvorakova, R.; Horacek, P. [Czech Technical University (Czech Republic). Faculty of Electrical Engineering

    2000-07-01

    Two algorithms for short-term electricity demand forecasting in the regional electricity distribution network are presented. Several approaches - feedforward neural network, adaptive modelling and fuzzy modelling - are applied to the forecast. Two different models are designed. A one hour forecasting is based on the General Regression Neural Network (GRNN) model and Principle Component Analysis. The multi-model with adaptive features and fuzzy reasoning is used for a longer-term forecast. (author)

  20. Forecast of useful energy for the TIMES-Norway model

    Energy Technology Data Exchange (ETDEWEB)

    Rosenberg, Eva

    2012-07-25

    A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

  1. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

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

    1999-01-01

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

  2. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

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

    1999-01-01

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

  3. STUDY OF THE EFFECTS OF REDUCING SYSTEMATIC ERRORS ON MONTHLY REGIONAL CLIMATE DYNAMICAL FORECAST

    Institute of Scientific and Technical Information of China (English)

    ZENG Xin-min; XI Chao-li

    2009-01-01

    A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model tbr monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system,and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC),U.S.A.,which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surthce air temperature,it is found that the corrected forecast is apparently better than the original,suggesting that the approach can be applied for improving monthly-scale regional climate dynamical lbrecast.

  4. The Barcelona Dust Forecast Center: The first WMO regional meteorological center specialized on atmospheric sand and dust forecast

    Science.gov (United States)

    Basart, Sara; Terradellas, Enric; Cuevas, Emilio; Jorba, Oriol; Benincasa, Francesco; Baldasano, Jose M.

    2015-04-01

    The World Meteorological Organization's Sand and Dust Storm Warning Advisory and Assessment System (WMO SDS-WAS, http://sds-was.aemet.es/) project has the mission to enhance the ability of countries to deliver timely and quality sand and dust storm forecasts, observations, information and knowledge to users through an international partnership of research and operational communities. The good results obtained by the SDS-WAS Northern Africa, Middle East and Europe (NAMEE) Regional Center and the demand of many national meteorological services led to the deployment of operational dust forecast services. On June 2014, the first WMO Regional Meteorological Center Specialized on Atmospheric Sand and Dust Forecast, the Barcelona Dust Forecast Center (BDFC; http://dust.aemet.es/), was publicly presented. The Center operationally generates and distributes predictions for the NAMEE region. The dust forecasts are based on the NMMB/BSC-Dust model developed at the Barcelona Supercomputing Center (BSC-CNS). The present contribution will describe the main objectives and capabilities of BDFC. One of the activities performed by the BDFC is to establish a protocol to routinely exchange products from dust forecast models as dust load, dust optical depth (AOD), surface concentration, surface extinction and deposition. An important step in dust forecasting is the evaluation of the results that have been generated. This process consists of the comparison of the model results with multiple kinds of observations (i.e. AERONET and MODIS) and is aimed to facilitate the understanding of the model capabilities, limitations, and appropriateness for the purpose for which it was designed. The aim of this work is to present different evaluation approaches and to test the use of different observational products in the evaluation system.

  5. A forecasting model of gaming revenues in Clark County, Nevada

    Energy Technology Data Exchange (ETDEWEB)

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

    1992-04-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain.

  6. Evaluation of a regional air quality forecast model for tropospheric NO2 columns using the OMI/Aura satellite tropospheric NO2 product

    Directory of Open Access Journals (Sweden)

    J. K. Vaughan

    2010-09-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, are compared to OMI tropospheric NO2 integrated column densities for an 18 month period over the Pacific Northwest. AIRPACT column densities are well correlated (r=0.75 to cloud-free (2 for monthly averages without wildfires, but are poorly correlated (r=0.21 with significant model over-predictions for months with wildfires when OMI and AIRPACT are compared over the entire domain. AIRPACT predicts higher NO2 in some northwestern US urban areas, and lower NO2 in the Vancouver, BC urban area, when compared to OMI. Model results are spatially averaged to the daily OMI swath. The Dutch KNMI (DOMINO and NASA (Standard Product retrievals of tropospheric NO2 from OMI (Collection-3 are compared. The NASA product is shown to be significantly different than the KNMI tropospheric NO2 product. The average difference in tropospheric columns, after applying the averaging kernels of the respective products to the model results, is shown to be larger in the summer (±50% than winter (±20%.

  7. Modelling and forecasting WIG20 daily returns

    DEFF Research Database (Denmark)

    Amado, Cristina; Silvennoinen, Annestiina; Terasvirta, Timo

    of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity....

  8. Midway Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Midway Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...

  9. Bermuda Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Bermuda Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  10. Operational flood forecasting system of Umbria Region "Functional Centre

    Science.gov (United States)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according

  11. Demand forecast model based on CRM

    Science.gov (United States)

    Cai, Yuancui; Chen, Lichao

    2006-11-01

    With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

  12. 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.

  13. Application of hydrologic forecast model.

    Science.gov (United States)

    Hua, Xu; Hengxin, Xue; Zhiguo, Chen

    2012-01-01

    In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasible and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.

  14. 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

  15. Forecasting elections in Europe: Synthetic models

    Directory of Open Access Journals (Sweden)

    Michael S. Lewis-Beck

    2015-01-01

    Full Text Available Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to tap omitted variables. We apply this model to a sample of three Western European countries: Germany, Ireland, and the United Kingdom. This combinatory strategy appears to offer clear forecasting gains, in terms of lead and accuracy.

  16. Econometric Models for Forecasting of Macroeconomic Indices

    Science.gov (United States)

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

    2016-01-01

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

  17. Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

    Science.gov (United States)

    Suharsono, Agus; Suhartono, Masyitha, Aulia; Anuravega, Arum

    2015-12-01

    The purpose of the study is to forecast the outflow and inflow of currency at Indonesian Central Bank or Bank Indonesia (BI) in Sulawesi Region. The currency outflow and inflow data tend to have a trend pattern which is influenced by calendar variation effects. Therefore, this research focuses to apply some forecasting methods that could handle calendar variation effects, i.e. Time Series Regression (TSR) and ARIMAX models, and compare the forecast accuracy with ARIMA model. The best model is selected based on the lowest of Root Mean Squares Errors (RMSE) at out-sample dataset. The results show that ARIMA is the best model for forecasting the currency outflow and inflow at South Sulawesi. Whereas, the best model for forecasting the currency outflow at Central Sulawesi and Southeast Sulawesi, and for forecasting the currency inflow at South Sulawesi and North Sulawesi is TSR. Additionally, ARIMAX is the best model for forecasting the currency outflow at North Sulawesi. Hence, the results show that more complex models do not neccessary yield more accurate forecast than the simpler one.

  18. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Chiriac, Roxana; Voev, Valeri

    This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions....... We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...

  19. Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region

    Indian Academy of Sciences (India)

    S K Roy Bhowmik; V R Durai

    2012-04-01

    India Meteorological Department has implemented district level medium range rainfall forecast system applying multimodel ensemble technique, making use of model outputs of state-of-the-art global models from the five leading global NWP centres. The pre-assigned grid point weights on the basis of anomaly correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.25° × 0.25° utilizing two season datasets (1 June–30 September, 2007 and 2008) and the multimodel ensemble forecasts (day-1 to day-5 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district, taking the average value of all grid points falling in a particular district. In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most states of the country, particularly over the states where the monsoon systems are more dominant.

  20. Forecasting elections in Europe: Synthetic models

    OpenAIRE

    Michael S. Lewis-Beck; Ruth Dassonneville

    2015-01-01

    Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to ...

  1. Combining SKU-level sales forecasts from models and experts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)

    2009-01-01

    textabstractWe study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we

  2. Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

    Science.gov (United States)

    Roberts, J. Brent; Roberts, Franklin R.

    2013-01-01

    The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

  3. A Forecast Model for Unemployment by Education

    DEFF Research Database (Denmark)

    Tranæs, Torben; Larsen, Anders Holm; Groes, Niels

    1994-01-01

    We present a dynamic forecast model for the labour market: demand for labour by education and the distribution of labour by education among industries are determined endogenously with overall demand by industry given exogenously. The model is derived from a simple behavioural equation based...... on a strong relationship between the “strength” in the struggle for jobs of an educational group, and the change in relative supply. This relationship proves to be significant in the data. Furthermore, when used to forecast employment by education on real data, the model predicts reasonably well even...... for educational groups, where the initial forecast year is a change point for unemployment....

  4. Nambe Pueblo Water Budget and Forecasting model.

    Energy Technology Data Exchange (ETDEWEB)

    Brainard, James Robert

    2009-10-01

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

  5. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-02-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  6. Modeling olive-crop forecasting in Tunisia

    Science.gov (United States)

    Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

    2016-01-01

    Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

  7. Modelling and forecasting Australian domestic tourism

    OpenAIRE

    2006-01-01

    In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models to forecast the domestic tourism demand. Combining these two frameworks, we build innovation state s...

  8. Forecasting drug utilization and expenditure in a metropolitan health region

    Directory of Open Access Journals (Sweden)

    Korkmaz Seher

    2010-05-01

    Full Text Available Abstract Background New pharmacological therapies are challenging the healthcare systems, and there is an increasing need to assess their therapeutic value in relation to existing alternatives as well as their potential budget impact. Consequently, new models to introduce drugs in healthcare are urgently needed. In the metropolitan health region of Stockholm, Sweden, a model has been developed including early warning (horizon scanning, forecasting of drug utilization and expenditure, critical drug evaluation as well as structured programs for the introduction and follow-up of new drugs. The aim of this paper is to present the forecasting model and the predicted growth in all therapeutic areas in 2010 and 2011. Methods Linear regression analysis was applied to aggregate sales data on hospital sales and dispensed drugs in ambulatory care, including both reimbursed expenditure and patient co-payment. The linear regression was applied on each pharmacological group based on four observations 2006-2009, and the crude predictions estimated for the coming two years 2010-2011. The crude predictions were then adjusted for factors likely to increase or decrease future utilization and expenditure, such as patent expiries, new drugs to be launched or new guidelines from national bodies or the regional Drug and Therapeutics Committee. The assessment included a close collaboration with clinical, clinical pharmacological and pharmaceutical experts from the regional Drug and Therapeutics Committee. Results The annual increase in total expenditure for prescription and hospital drugs was predicted to be 2.0% in 2010 and 4.0% in 2011. Expenditures will increase in most therapeutic areas, but most predominantly for antineoplastic and immune modulating agents as well as drugs for the nervous system, infectious diseases, and blood and blood-forming organs. Conclusions The utilisation and expenditure of drugs is difficult to forecast due to uncertainties about the rate

  9. Forecasting drug utilization and expenditure in a metropolitan health region

    Science.gov (United States)

    2010-01-01

    Background New pharmacological therapies are challenging the healthcare systems, and there is an increasing need to assess their therapeutic value in relation to existing alternatives as well as their potential budget impact. Consequently, new models to introduce drugs in healthcare are urgently needed. In the metropolitan health region of Stockholm, Sweden, a model has been developed including early warning (horizon scanning), forecasting of drug utilization and expenditure, critical drug evaluation as well as structured programs for the introduction and follow-up of new drugs. The aim of this paper is to present the forecasting model and the predicted growth in all therapeutic areas in 2010 and 2011. Methods Linear regression analysis was applied to aggregate sales data on hospital sales and dispensed drugs in ambulatory care, including both reimbursed expenditure and patient co-payment. The linear regression was applied on each pharmacological group based on four observations 2006-2009, and the crude predictions estimated for the coming two years 2010-2011. The crude predictions were then adjusted for factors likely to increase or decrease future utilization and expenditure, such as patent expiries, new drugs to be launched or new guidelines from national bodies or the regional Drug and Therapeutics Committee. The assessment included a close collaboration with clinical, clinical pharmacological and pharmaceutical experts from the regional Drug and Therapeutics Committee. Results The annual increase in total expenditure for prescription and hospital drugs was predicted to be 2.0% in 2010 and 4.0% in 2011. Expenditures will increase in most therapeutic areas, but most predominantly for antineoplastic and immune modulating agents as well as drugs for the nervous system, infectious diseases, and blood and blood-forming organs. Conclusions The utilisation and expenditure of drugs is difficult to forecast due to uncertainties about the rate of adoption of new

  10. Research activities at the Australian Bureau of Meteorology for the regional ionospheric specification and forecasting

    Science.gov (United States)

    Bouya, Zahra; Terkildsen, Michael

    2016-07-01

    The Australian Space Forecast Centre (ASFC) provides space weather forecasts to a diverse group of customers. Space Weather Services (SWS) within the Australian Bureau of Meteorology is focussed both on developing tailored products and services for the key customer groups, and supporting ASFC operations. Research in SWS is largely centred on the development of data-driven models using a range of solar-terrestrial data. This paper will cover some data requirements , approaches and recent SWS activities for data driven modelling with a focus on the regional Ionospheric specification and forecasting.

  11. 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.

  12. The AviaDem forecasting model: illustration of a forecasting case at Amsterdam Schiphol Airport

    NARCIS (Netherlands)

    Veldhuis, J.; Lieshout, R.

    2010-01-01

    The paper describes an aviation market forecasting model which focuses on market forecasts for airports. Most forecasting models in use today assess aviation trends resulting from macroeconomic trends. The model described in this paper has this feature built in, but the added value of this model is

  13. 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.

  14. Hydrological model calibration for enhancing global flood forecast skill

    Science.gov (United States)

    Hirpa, Feyera A.; Beck, Hylke E.; Salamon, Peter; Thielen-del Pozo, Jutta

    2016-04-01

    Early warning systems play a key role in flood risk reduction, and their effectiveness is directly linked to streamflow forecast skill. The skill of a streamflow forecast is affected by several factors; among them are (i) model errors due to incomplete representation of physical processes and inaccurate parameterization, (ii) uncertainty in the model initial conditions, and (iii) errors in the meteorological forcing. In macro scale (continental or global) modeling, it is a common practice to use a priori parameter estimates over large river basins or wider regions, resulting in suboptimal streamflow estimations. The aim of this work is to improve flood forecast skill of the Global Flood Awareness System (GloFAS; www.globalfloods.eu), a grid-based forecasting system that produces flood forecast unto 30 days lead, through calibration of the distributed hydrological model parameters. We use a combination of in-situ and satellite-based streamflow data for automatic calibration using a multi-objective genetic algorithm. We will present the calibrated global parameter maps and report the forecast skill improvements achieved. Furthermore, we discuss current challenges and future opportunities with regard to global-scale early flood warning systems.

  15. Data-Driven Techniques for Regional Groundwater Level Forecasts

    Science.gov (United States)

    Chang, F. J.; Chang, L. C.; Tsai, F. H.; Shen, H. Y.

    2015-12-01

    Data-Driven Techniques for Regional Groundwater Level Forecasts Fi-John Changa, Li-Chiu Changb, Fong He Tsaia, Hung-Yu Shenba Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC. b Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan, ROC..Correspondence to: Fi-John Chang (email: changfj@ntu.edu.tw)The alluvial fan of the Zhuoshui River in Taiwan is a good natural recharge area of groundwater. However, the over extraction of groundwater occurs in the coastland results in serious land subsidence. Groundwater systems are heterogeneous with diverse temporal-spatial patterns, and it is very difficult to quantify their complex processes. Data-driven methods can effectively capture the spatial-temporal characteristics of input-output patterns at different scales for accurately imitating dynamic complex systems with less computational requirements. In this study, we implement various data-driven methods to suitably predict the regional groundwater level variations for making countermeasures in response to the land subsidence issue in the study area. We first establish the relationship between regional rainfall, streamflow as well as groundwater levels and then construct intelligent groundwater level prediction models for the basin based on the long-term (2000-2013) regional monthly data sets collected from the Zhuoshui River basin. We analyze the interaction between hydrological factors and groundwater level variations; apply the self-organizing map (SOM) to obtain the clustering results of the spatial-temporal groundwater level variations; and then apply the recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to predicting the monthly groundwater levels. As a consequence, a regional intelligent groundwater level prediction model can be constructed based on the adaptive results of the SOM. Results demonstrate that the development

  16. Modeling and forecasting petroleum futures volatility

    Energy Technology Data Exchange (ETDEWEB)

    Sadorsky, Perry [York Univ., Schulich School of Business, Toronto, ON (Canada)

    2006-07-15

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

  17. 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.

  18. Using Adjoint-Based Forecast Sensitivity Method to Evaluate TAMDAR Data Impacts on Regional Forecasts

    Directory of Open Access Journals (Sweden)

    Xiaoyan Zhang

    2015-01-01

    Full Text Available This study evaluates the impact of Tropospheric Airborne Meteorological Data Reporting (TAMDAR observations on regional 24-hour forecast error reduction over the Continental United States (CONUS domain using adjoint-based forecast sensitivity to observation (FSO method as the diagnostic tool. The relative impact of TAMDAR observations on reducing the forecast error was assessed by conducting the WRFDA FSO experiments for two two-week-long periods, one in January and one in June 2010. These experiments assimilated operational TAMDAR data and other conventional observations, as well as GPS refractivity (GPSREF. FSO results show that rawinsonde soundings (SOUND and TAMDAR exhibit the largest observation impact on 24 h WRF forecast, followed by GeoAMV, aviation routine weather reports (METAR, GPSREF, and synoptic observations (SYNOP. At 0000 and 1200 UTC, TAMDAR has an equivalent impact to SOUND in reducing the 24-hour forecast error. However, at 1800 UTC, TAMDAR has a distinct advantage over SOUND, which has the sparse observation report at these times. In addition, TAMDAR humidity observations at lower levels of the atmosphere (700 and 850 hPa have a significant impact on 24 h forecast error reductions. TAMDAR and SOUND observations present a qualitatively similar observation impact between FSO and Observation System Experiments (OSEs.

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

    Science.gov (United States)

    Fernández-Vázquez, Esteban; Moreno, Blanca

    2017-08-01

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

  20. 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.

  1. Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model

    Science.gov (United States)

    Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.

    2015-12-01

    Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral

  2. SARS epidemical forecast research in mathematical model

    Institute of Scientific and Technical Information of China (English)

    DING Guanghong; LIU Chang; GONG Jianqiu; WANG Ling; CHENG Ke; ZHANG Di

    2004-01-01

    The SIJR model, simplified from the SEIJR model, is adopted to analyze the important parameters of the model of SARS epidemic such as the transmission rate, basic reproductive number. And some important parameters are obtained such as the transmission rate by applying this model to analyzing the situation in Hong Kong, Singapore and Canada at the outbreak of SARS. Then forecast of the transmission of SARS is drawn out here by the adjustment of parameters (such as quarantined rate) in the model. It is obvious that inflexion lies on the crunode of the graph, which indicates the big difference in transmission characteristics between the epidemic under control and not under control. This model can also be used in the comparison of the control effectiveness among different regions. The results from this model match well with the actual data in Hong Kong, Singapore and Canada and as a by-product, the index of the effectiveness of control in the later period can be acquired. It offers some quantitative indexes, which may help the further research in epidemic diseases.

  3. Overview of Urban PM2. 5Numerical Forecast Models in China

    Institute of Scientific and Technical Information of China (English)

    Nianliang; CHENG; Hongxia; LI; Fan; MENG; Fahe; CHAI

    2015-01-01

    This paper made an overview and introduction of urban PM2. 5numerical forecast models in China,and mainly introduced air quality simulated forecast system of Beijing,Shanghai,and Nanjing. On this basis,it discussed development direction and existing problems of urban PM2. 5forecast models in China. Besides,it revealed significance of numerical models for air quality forecast. In a heavy air pollution of Beijing- Tianjin- Hebei in October 6- 12 th of 2014,the forecast results indicated that pollutants was transported from south to north,so the regional transport exerts great influence on concentration of PM2. 5.

  4. The role of component-wise boosting for regional economic forecasting

    OpenAIRE

    Lehmann, Robert; Wohlrabe, Klaus

    2015-01-01

    This paper applies component-wise boosting to the topic of regional economic forecasting. By using unique quarterly gross domestic product data for one German state for the period from 1996 to 2013, in combination with a large data set of 253 monthly indicators, we show how accurate forecasts obtained from component-wise boosting are compared to a simple benchmark model. We additionally take a closer look into the algorithm and evaluate whether a stable pattern of selected indicators exists o...

  5. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2017-09-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  6. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2016-10-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  7. Comparison of two analog-based downscaling methods for regional reference evapotranspiration forecasts

    Science.gov (United States)

    Tian, Di; Martinez, Christopher J.

    2012-12-01

    SummaryThe objective of this study was to compare the performance of natural analog (NA) and constructed analog (CA) methods to produce both probabilistic and deterministic downscaled daily reference evapotranspiration (ETo) forecasts in the southeastern United States. The 1-15 day, 15-member ETo forecasts were produced from 1979 to 2009 using the Penman-Monteith equation and a forecast analog approach with a combination of the Global Forecast System (GFS) reforecasts and NCEP-DOE Reanalysis 2 climatology, and were downscaled using the North American Regional Reanalysis (NARR). The Pearson correlation coefficient (R), mean squared error skill score (MSESS), and Bias were used to evaluate the skill of downscaled deterministic forecasts. The Linear Error in Probability Space (LEPS) skill score, Brier Skill Score (BSS), relative operating characteristic, and reliability diagrams were used to evaluate the skill of downscaled probabilistic forecasts. Overall, CA showed slightly higher skill than NA in terms of the metrics for deterministic forecasts, while for probabilistic forecasts NA showed higher skill than CA regarding the BSS in five categories (terciles, and 10th and 90th percentiles) and lower skill than CA regarding the LEPS skill score. Both CA and NA produced skillful deterministic results in the first 3 lead days, while the skill was higher for CA than for NA. Probabilistic NA forecasts exhibited higher resolution and reliability than CA, likely due to a larger ensemble size. Forecasts by both methods showed the lowest skill in the Florida peninsula and in mountainous areas, likely due to the fact that these features were not well-resolved in the model forecast.

  8. Sequential correction of ensemble regional weather predictions for forecasting reference evapotranspiration

    Science.gov (United States)

    Pelosi, Anna; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    This study explores the performance of an adaptive procedure for correcting the ensemble numerical weather outputs applied to the probabilistic forecast of reference evapotranspiration (ETo). This procedure is proposed as an effective forecast correction method when the available dataset is not large enough for the calibration of statistical batch procedures. The numerical weather prediction outputs are those provided by COSMO-LEPS, an ensemble-based Limited Area Model, with 16 members and 7.5 km spatial resolution, with forecast lead-time up to 5 days. ETo forecasts are computed according to the FAO Penman-Monteith (FAO-PM) equation, which requires data of five weather variables: air temperature, relative humidity, solar radiation and wind speed. The performance of the proposed procedure is evaluated at eighteen monitoring stations, located in Campania region (Southern Italy), with two alternative strategies: i) correction applied to the raw ensemble forecasts of the five weather variables prior applying the FAO-PM equation; ii) correction applied to the ensemble output of the ETo forecasts obtained with FAO-PM equation after using the raw ensemble weather forecasts as input. In both cases the suggested post-processing procedure was able to significantly increase the accuracy and reduce the uncertainty of the ETo forecasts.

  9. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Chiriac, Roxana; Voev, Valeri

    . We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...

  10. Pollen Forecast and Dispersion Modelling

    Science.gov (United States)

    Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello

    2014-05-01

    The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE

  11. Forecasting Models in the State Education System

    Directory of Open Access Journals (Sweden)

    Gintautas DZEMYDA

    2003-04-01

    Full Text Available This paper presents model-based assessment and forecasting of the Lithuanian education system in the period of 2001-2010. In order to obtain satisfactory forecasting results, constructing of models used for these aims should be grounded on some interactive data mining. Data mining of data stored in the system of the Lithuanian teacher's database and of data from other sources representing the state of education system and the demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, the analysis of flow of teachers and pupils, the clustering of schools, the model of dynamics of pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.

  12. Evaluation of the performance of DIAS ionospheric forecasting models

    Directory of Open Access Journals (Sweden)

    Tsagouri Ioanna

    2011-08-01

    Full Text Available Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr. Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i the solar wind driven autoregression model for ionospheric short-term forecast (SWIF, which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii the geomagnetically correlated autoregression model (GCAM, which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models’ predictions with actual observations obtained over almost one solar cycle (1998–2007 at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome and on the comparison of the models’ performance against two simple prediction strategies, the median- and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.

  13. The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

    Science.gov (United States)

    Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

    2014-01-01

    The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

    Shukla, S.; McNally, A.; Husak, G.; Funk, C.

    2014-03-01

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

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

    Directory of Open Access Journals (Sweden)

    S. Shukla

    2014-03-01

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

  17. Developing and testing solar irradiance forecasting techniques in the Hawaiian Islands region

    Science.gov (United States)

    Matthews, D. K.

    2015-12-01

    The Hawaíi Natural Energy Institute (HNEI) is developing an operational solar forecasting for the Hawaiian Islands. The system comprises the following three components, covering forecasting horizons from seconds to days ahead. (i) A ground-observation driven advection model, using sky imagery and cloud height data. (ii) A satellite-image based advection model, primarily driven by Geostationary Operational Environmental Satellite (GOES) imagery. (iii) A coupled ocean-atmosphere model, using the Regional Ocean Modeling System (ROMS) model and the Weather Research and Forecasting (WRF) model, including newly available microphysics, shallow convection parameterization, and radiative transfer physics options. The satellite and NWP components provide coverage for the entire island chain, however, lack the resolution in time and space, to accurately forecast ramp events (large changes in irradiance that occur over a short period of time). Knowledge of the magnitude, duration and timing of ramp events are particularly important in Hawaíi due to the small size of the electric grids. Currently, HNEI employs a sky imager and ceilometer installed on the University of Hawaíi campus for high resolution forecasting, however, instrument design and cost limit widespread deployment. We discuss the development and preliminary validation of a new forecasting system based on inexpensive, panoramic (large FOV), off-the-shelf cameras with a cloud base height retrieval algorithm that does not require additional instrumentation.

  18. Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

    Science.gov (United States)

    Cipullo, Michelle; Molthan, Andrew; Shafer, Jackie; Case, Jonathan; Jedlovec, Gary

    2011-01-01

    Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat

  19. Forecasting Exchange Rates with Mixed Models

    Directory of Open Access Journals (Sweden)

    Laura Maria Badea

    2013-06-01

    Full Text Available Gaining accuracy in exchange rate forecasting applications provides true benefits for financial activities. Supported today by the advancements in computing power, machine learning techniques provide good alternatives to traditional time series estimation methods. Very approached in time series forecasting are Artificial Neural Networks (ANNs which offer robust results and allow a flexible data manipulation. When integrating both, the “white-box” feature of conventional methods and the complexity of machine learning techniques, forecasting models perform even better in terms of generated errors. In this study, input variables (independent variables are selected using an ARIMA technique and are further employed in differently configured multilayered feed-forward neural networks using Broyden-Fletcher-Goldfarb-Shanno (BFGS optimization algorithm to perform predictions on EUR/RON and CHF/RON exchange rates. Results in terms of mean squared error highlight good results when using mixed models.

  20. An Econometric Model for Forecasting Income and Employment in Hawaii.

    Science.gov (United States)

    Chau, Laurence C.

    This report presents the methodology for short-run forecasting of personal income and employment in Hawaii. The econometric model developed in the study is used to make actual forecasts through 1973 of income and employment, with major components forecasted separately. Several sets of forecasts are made, under different assumptions on external…

  1. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 2: The added value from climate forecast models

    Science.gov (United States)

    Yuan, Xing

    2016-06-01

    This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed

  2. Ionospheric scintillation forecasting model based on NN-PSO technique

    Science.gov (United States)

    Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.

    2017-09-01

    The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.

  3. Verification of a probabilistic flood forecasting system for an Alpine Region of northern Italy

    Science.gov (United States)

    Laiolo, P.; Gabellani, S.; Rebora, N.; Rudari, R.; Ferraris, L.; Ratto, S.; Stevenin, H.

    2012-04-01

    Probabilistic hydrometeorological forecasting chains are increasingly becoming an operational tool used by civil protection centres for issuing flood alerts. One of the most important requests of decision makers is to have reliable systems, for this reason an accurate verification of their predictive performances become essential. The aim of this work is to validate a probabilistic flood forecasting system: Flood-PROOFS. The system works in real time, since 2008, in an alpine Region of northern Italy, Valle d'Aosta. It is used by the Civil Protection regional service to issue warnings and by the local water company to protect its facilities. Flood-PROOFS uses as input Quantitative Precipitation Forecast (QPF) derived from the Italian limited area model meteorological forecast (COSMO-I7) and forecasts issued by regional expert meteorologists. Furthermore the system manages and uses both real time meteorological and satellite data and real time data on the maneuvers performed by the water company on dams and river devices. The main outputs produced by the computational chain are deterministic and probabilistic discharge forecasts in different cross sections of the considered river network. The validation of the flood prediction system has been conducted on a 25 months period considering different statistical methods such as Brier score, Rank histograms and verification scores. The results highlight good performances of the system as support system for emitting warnings but there is a lack of statistics especially for huge discharge events.

  4. Impact of High Resolution SST Data on Regional Weather Forecasts

    Science.gov (United States)

    Jedlovec, Gary J.; Case, Jonathon; LaFontaine, Frank; Vazquez, Jorge; Mattocks, Craig

    2010-01-01

    Past studies have shown that the use of coarse resolution SST products such as from the real-time global (RTG) SST analysis[1] or other coarse resolution once-a-day products do not properly portray the diurnal variability of fluxes of heat and moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. For example, the use of high resolution MODIS SST composite [2] to initialize the Advanced Research Weather Research and Forecast (WRF) (ARW) [3] has been shown to improve the prediction of sensible weather parameters in coastal regions [4][5}. In an extend study, [6] compared the MODIS SST composite product to the RTG SST analysis and evaluated forecast differences for a 6 month period from March through August 2007 over the Florida coastal regions. In a comparison to buoy data, they found that that the MODIS SST composites reduced the bias and standard deviation over that of the RTG data. These improvements led to significant changes in the initial and forecasted heat fluxes and the resulting surface temperature fields, wind patterns, and cloud distributions. They also showed that the MODIS composite SST product, produced for the Terra and Aqua satellite overpass times, captured a component of the diurnal cycle in SSTs not represented in the RTG or other one-a-day SST analyses. Failure to properly incorporate these effects in the WRF initialization cycle led to temperature biases in the resulting short term forecasts. The forecast impact was limited in some situations however, due to composite product inaccuracies brought about by data latency during periods of long-term cloud cover. This paper focuses on the forecast impact of an enhanced MODIS/AMSR-E composite SST product designed to reduce inaccuracies due data latency in the MODIS only composite product.

  5. 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.

  6. Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-05-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.

  7. Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-09-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short

  8. Towards Disaggregate Dynamic Travel Forecasting Models

    Institute of Scientific and Technical Information of China (English)

    Moshe Ben-Akiva; Jon Bottom; Song Gao; Haris N. Koutsopoulos; Yang Wen

    2007-01-01

    The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models.The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network performance,through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.

  9. Application Study of Empirical Model and Xiaohuajian Flood Forecasting Model in the Middle Yellow River

    Science.gov (United States)

    Hu, Caihong

    2013-04-01

    Xiaolandi-Huayuankou region is an important rainstorm centre in the middle Yellow river, which drainage area of 35883km2. A set of forecasting methods applied in this region was formed throughout years of practice. The Xiaohuajian flood forecasting model and empirical model were introduced in this paper. The simulated processes of the Xiaohuajian flood forecasting model include evapotranspiration, infiltration, runoff, river flow. Infiltration and surface runoff are calculated utilizing the Horton model for infiltration into multilayered soil profiles. Overland flow is routed by Nash instantaneous unit hydrograph and Section Muskingum method. The empirical model are simulated using P~Pa~R and empirical relation approach for runoff generation and concentration. The structures of these two models were analyzed and compared in detail. Yihe river basin located in Xiaolandi-Huayuankou region was selected for the purpose of the study. The results show that the accuracy of the two methods are similar, however, the accuracy of Xiaohuajian flood forecasting model for flood forecasting is relatively higher, especially the process of the flood; the accuracy of the empirical methods is much worse, but it can also be accept. The two models are both practicable, so the two models can be combined to apply. The result of the Xiaohuajian flood forecasting model can be used to guide the reservoir for flood control, and the result of empirical methods can be as a reference.

  10. Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

    2015-12-01

    Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

  11. Forecasting the Unit Cost of a Product with Some Linear Fuzzy Collaborative Forecasting Models

    Directory of Open Access Journals (Sweden)

    Toly Chen

    2012-10-01

    Full Text Available Forecasting the unit cost of every product type in a factory is an important task. However, it is not easy to deal with the uncertainty of the unit cost. Fuzzy collaborative forecasting is a very effective treatment of the uncertainty in the distributed environment. This paper presents some linear fuzzy collaborative forecasting models to predict the unit cost of a product. In these models, the experts’ forecasts differ and therefore need to be aggregated through collaboration. According to the experimental results, the effectiveness of forecasting the unit cost was considerably improved through collaboration.

  12. Forecasting characteristic earthquakes in a minimalist model

    DEFF Research Database (Denmark)

    Vázquez-Prada, M.; Pacheco, A.; González, Á.

    2003-01-01

    Using error diagrams, we quantify the forecasting of characteristic-earthquake occurence in a recently introduced minimalist model. Initially we connect the earthquake alarm at a fixed time after the occurence of a characteristic event. The evaluation of this strategy leads to a one-dimensional n...

  13. Applications products of aviation forecast models

    Science.gov (United States)

    Garthner, John P.

    1988-01-01

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

  14. An updated subgrid orographic parameterization for global atmospheric forecast models

    Science.gov (United States)

    Choi, Hyun-Joo; Hong, Song-You

    2015-12-01

    A subgrid orographic parameterization (SOP) is updated by including the effects of orographic anisotropy and flow-blocking drag (FBD). The impact of the updated SOP on short-range forecasts is investigated using a global atmospheric forecast model applied to a heavy snowfall event over Korea on 4 January 2010. When the SOP is updated, the orographic drag in the lower troposphere noticeably increases owing to the additional FBD over mountainous regions. The enhanced drag directly weakens the excessive wind speed in the low troposphere and indirectly improves the temperature and mass fields over East Asia. In addition, the snowfall overestimation over Korea is improved by the reduced heat fluxes from the surface. The forecast improvements are robust regardless of the horizontal resolution of the model between T126 and T510. The parameterization is statistically evaluated based on the skill of the medium-range forecasts for February 2014. For the medium-range forecasts, the skill improvements of the wind speed and temperature in the low troposphere are observed globally and for East Asia while both positive and negative effects appear indirectly in the middle-upper troposphere. The statistical skill for the precipitation is mostly improved due to the improvements in the synoptic fields. The improvements are also found for seasonal simulation throughout the troposphere and stratosphere during boreal winter.

  15. Regional Ocean Modeling System (ROMS): Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the islands of Samoa at approximately 3-km resolution. While considerable...

  16. Regional Ocean Modeling System (ROMS): CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the Commonwealth of the Northern Mariana Islands (CNMI) at approximately...

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

    Science.gov (United States)

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

    2014-01-01

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

  18. FORECASTING OF ECONOMIC GROWTH OF REGION IN CONDITIONS OF DEFICIENCY OF THE INFORMATION

    Directory of Open Access Journals (Sweden)

    S.L. Sadov

    2007-12-01

    Full Text Available The new approach to forecasting economic growth of the region, showing minimal requirements to a supply with information, is offered in clause. It is based on combinatory likelihood modelling of dependence of a parameter of economic growth from its reliability. The method we shall apply to regions with the expressed branch specialization for the period before realization of structural reorganization of economy. In the conclusion the forecast of growth of Republic Komi VRP up to 2020 is given − is shown, that it will make 4 − 6% a year at preservation of energy raw specializations.

  19. The CYCOFOS new forecasting systems at regional and sub-regional scales for supporting the marine safety

    Science.gov (United States)

    Zodiatis, George; Radhakrishnan, Hari; Galanis, George; Nikolaidis, Andreas; Emmanouil, George; Nikolaidis, Georgios; Lardner, Robin; Sofianos, Sarantis; Stylianou, Stavros; Nikolaidis, Marios

    2016-04-01

    The CYCOFOS new forecasting systems at regional and sub-regional scales for supporting the marine safety George Zodiatis1, Hari Radhakrishnan1, George Galanis1,2, Andreas Nikolaidis1, George Emmanouil1,2, Georgios Nikolaidis1, Robin Lardner1, Sarantis Sofianos3, Stavros Stylianou1 and Marios Nikolaidis1 1Oceanography Centre, University of Cyprus, Nicosia 1678, Cyprus 2 Hellenic Naval Academy, Section of Mathematics, Piraeus 18539, Greece 3 University of Athens, Ocean Physics and Modeling Group, Athens 15784, Greece The Cyprus Coastal Ocean FOrecasting System-CYCOFOS has been providing operational hydrodynamic and sea state forecasts in the Eastern Mediterranean since early 2002. Recently, it has been improved with the implementation of new hydrodynamic, wave and atmospheric models, targeting larger and higher resolution domains at regional and sub-regional scales. For the new CYCOFOS hydrodynamic system a novel parallel version of POM has been implemented. The new flow model covers the Eastern Mediterranean with a resolution of 2 km and the Levantine with 500 m, both nested in Copernicus Marine Environmental Monitoring Service-CMEMS. The CYCOFOS hydrodynamic model is coupled with the latest ECMWF WAM model. The surface currents produced from the Copernicus marine service and CYCOFOS has been incorporated in the wave integration, providing a second independent forcing input to the new CYCOFOS wave model, in addition to the winds. The Weather Research and Forecasting atmospheric model-WRF has been implemented in the same domain as SKIRON atmospheric model, in order to provide the backup forcing for the CYCOFOS models. The improved CYCOFOS forecasting data are used for the EU CISE 2020 project to establish an ΕU Common Information Sharing Environment to improve the Maritime Situational Awareness, particularly for SAR operations, as well as for the MEDESS4MS multi model oil spill prediction service, for operational oil spill predictions in the Mediterranean.

  20. [Development of forecasting models for fatal road traffic injuries].

    Science.gov (United States)

    Tan, Aichun; Tian, Danping; Huang, Yuanxiu; Gao, Lin; Deng, Xin; Li, Li; He, Qiong; Chen, Tianmu; Hu, Guoqing; Wu, Jing

    2014-02-01

    To develop the forecasting models for fatal road traffic injuries and to provide evidence for predicting the future trends on road traffic injuries. Data on the mortality of road traffic injury including factors as gender and age in different countries, were obtained from the World Health Organization Mortality Database. Other information on GDP per capita, urbanization, motorization and education were collected from online resources of World Bank, WHO, the United Nations Population Division and other agencies. We fitted logarithmic models of road traffic injury mortality by gender and age group, including predictors of GDP per capita, urbanization, motorization and education. Sex- and age-specific forecasting models developed by WHO that including GDP per capita, education and time etc. were also fitted. Coefficient of determination(R(2)) was used to compare the performance between our modes and WHO models. 2 626 sets of data were collected from 153 countries/regions for both genders, between 1965 and 2010. The forecasting models of road traffic injury mortality based on GDP per capita, motorization, urbanization and education appeared to be statistically significant(P forecasting models that we developed seemed to be better than those developed by WHO.

  1. Challenging Issues on fog forecast with a three-dimensional fog forecast model

    Science.gov (United States)

    Masbou, M.

    2012-12-01

    Fog has a significant impact on economical aspect (traffic management and safety) as well as on environmental issues (fresh water source for the population and the biosphere in arid region). However, reliable fog and visibility forecasts stay challenging issue. Fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, topography, vegetation, turbulent mixing at the surface as well as its microphysical structure. In order to consider these intertwined processes, the three-dimensional fog forecast model, COSMO-FOG, with a high vertical resolution with different microphysical complexity has been developed. This model includes a microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Moreover, the turbulence scheme, based on a Mellor-Yamada 2.5 order and a closure of a 2nd order has been modified to improve the model behaviour in case of a stable atmosphere structure, occurring typically during night radiative fog episodes. The potential of COSMO-FOG will be presented in some realistic fog situations (flat, bumpy and complex terrain). The fog spatial extension will be compared with MSG satellite products for fog and low cloud. The interplays between dynamical, thermodynamical patterns and the soil-atmosphere interactions will be presented.

  2. An analysis of seasonal forecasts from POAMA and SCOPIC in the Pacific region

    Science.gov (United States)

    Cottrill, Andrew; Charles, Andrew; Kuleshov, Yuriy

    2013-04-01

    The Australian Bureau of Meteorology (BoM), as part of the Pacific Island Climate Prediction Project (PI-CPP), has developed seasonal forecasts for ten National Meteorological Services (NMS) in the Pacific region for nearly a decade, to improve seasonal forecast services to local communities and industry. As part of this project, a new statistical model called SCOPIC (Seasonal Climate Outlooks for Pacific Island Countries) was developed to provide partner countries with the ability to produce their own seasonal climate outlooks. In 2010, as part of the Pacific Adaptation Strategy and Assistance Programme (PASAP), the BoM developed a seasonal outlook portal for Pacific NMS as an alternative source of seasonal forecasts based on the Bureau's dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia). This dynamical model is a coupled ocean-atmosphere model, which has been developed by the Bureau for over ten years for forecasting research in Australia. However, no formal assessment of the skill of the two forecast systems (POAMA and SCOPIC) has been carried out using a number of skill metrics for the Pacific region. Although the skill of POAMA in the Australian region is now well documented, the forecast skill is even higher in the Pacific region due to its proximity to the tropical ocean, where the El Niño-Southern Oscillation (ENSO) provides the main source of tropical climate variability and predictability on seasonal time scales. The statistical model (SCOPIC) uses discriminant analysis (multiple linear regression) and the relationships of sea surface temperatures (SST) or the Southern Oscillation Index (predictors) and monthly rainfall (predictands) to predict rainfall at various lead times. In contrast, POAMA uses the current state of the climate (initial ocean and atmospheric conditions) and model physics to predict forecasts of many climate variables at all locations across the globe and also at various lead times. Here we demonstrate the skill

  3. Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

    2010-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

  4. Time series modelling and forecasting of emergency department overcrowding.

    Science.gov (United States)

    Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian

    2014-09-01

    Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

  5. Mesoscale Modeling, Forecasting and Remote Sensing Research.

    Science.gov (United States)

    remote sensing , cyclonic scale diagnostic studies and mesoscale numerical modeling and forecasting are summarized. Mechanisms involved in the release of potential instability are discussed and simulated quantitatively, giving particular attention to the convective formulation. The basic mesoscale model is documented including the equations, boundary condition, finite differences and initialization through an idealized frontal zone. Results of tests including a three dimensional test with real data, tests of convective/mesoscale interaction and tests with a detailed

  6. EXPENSES FORECASTING MODEL IN UNIVERSITY PROJECTS PLANNING

    Directory of Open Access Journals (Sweden)

    Sergei A. Arustamov

    2016-11-01

    Full Text Available The paper deals with mathematical model presentation of cash flows in project funding. We describe different types of expenses linked to university project activities. Problems of project budgeting that contribute most uncertainty have been revealed. As an example of the model implementation we consider calculation of vacation allowance expenses for project participants. We define problems of forecast for funds reservation: calculation based on methodology established by the Ministry of Education and Science calculation according to the vacation schedule and prediction of the most probable amount. A stochastic model for vacation allowance expenses has been developed. We have proposed methods and solution of the problems that increase the accuracy of forecasting for funds reservation based on 2015 data.

  7. Interval forecasts of a novelty hybrid model for wind speeds

    Directory of Open Access Journals (Sweden)

    Shanshan Qin

    2015-11-01

    Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.

  8. Feasibility of large-scale water monitoring and forecasting in the Asia-Pacific region

    Science.gov (United States)

    van Dijk, A. I. J. M.; Peña-Arancibia, J. L.; Sardella, C. S. E.

    2012-04-01

    The Asian-Pacific region (including China, India and Pakistan) is home to 51% of the global population. It accounts for 53% of agricultural and 32% of domestic water use world wide. Due to the influence of Pacific Ocean and Indian Ocean circulation patterns, the region experiences strong inter-annual variations in water availability and occurrence of drought, flood and severe weather. Some of the countries in the region have national water monitoring or forecasting systems, but they are typically of fairly narrow scope. We investigated the feasibility and utility of an integrated regional water monitoring and forecasting system for water resources, floods and drought. In particular, we assessed the quality of information that can be achieved by relying on internationally available data sources, including numerical weather prediction (NWP) and satellite observations of precipitation, soil moisture and vegetation. Combining these data sources with a large scale hydrological model, we produced monitoring and forecast information for selected retrospective case studies. The information was compared to that from national systems, both in terms of information content and system characteristics (e.g. scope, data sources, and information latency). While national systems typically have better access to national observation systems, they do not always make effective use of the available data, science and technology. The relatively slow changing nature of important Pacific and Indian Ocean circulation patterns adds meaningful seasonal forecast skill for some regions. Satellite and NWP precipitation estimates can add considerable value to the national gauge networks: as forecasts, as near-real time observations and as historic reference data. Satellite observations of soil moisture and vegetation are valuable for drought monitoring and underutilised. Overall, we identify several important opportunities for better water monitoring and forecasting in the Asia-Pacific region.

  9. Towards operational modeling and forecasting of the Iberian shelves ecosystem.

    Directory of Open Access Journals (Sweden)

    Martinho Marta-Almeida

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

  10. operational modelling and forecasting of the Iberian shelves ecosystem

    Science.gov (United States)

    Marta-Almeida, M.; Reboreda, R.; Rocha, C.; Dubert, J.; Nolasco, R.; Cordeiro, N.; Luna, T.; Rocha, A.; Silva, J. Lencart e.; Queiroga, H.; Peliz, A.; Ruiz-Villarreal, M.

    2012-04-01

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

  11. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011-2014

    Science.gov (United States)

    Giri, R. K.; Panda, Jagabandhu; Rath, Sudhansu S.; Kumar, Ravindra

    2016-06-01

    In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011-2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1-10 and 11-25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products.

  12. CFORS - Regional Chemical and Weather Forecast System in Support of Field Experiments

    Science.gov (United States)

    Yienger, J. J.; Uno, I.; Guttikunda, S. K.; Carmichael, G. R.; Tang, Y.; Thongboonchoo, N.; Woo, J.; Dorwart, J.; Streets, D.

    2001-12-01

    In this paper we will present the development, evaluation, and use of improved modeling techniques and methodologies for the integration of meteorological forecasts with air pollution forecasts in support of field operations during the TRACE-P and Ace-Asia experiments in East Asia. During the campaign period we provided a variety of forecast products using our regional modeling system built upon the dynamic meteorological model RAMS and the 3-D regional chemical transport models STEM-III. These models were run in both on-line and off-line modes, and the results integrated into an interactive web-based data mining and analysis framework. This resulting Chemical Weather Forecasting System CFORS, was run operationally for the period February through May 2001, and provided 72-hr forecasts of a variety of aerosol, chemical and air mass and emission marker quantities. These included aerosol mass distribution and optical depth by major component (e.g., dust, sea salt, black carbon, organic carbon, and sulfate), photochemical quantities including ozone and OH/HO2, and air mass & emissions markers including lightning, volcanic, mega-cities, and biomass burning. These model products were presented along with meteorological forecasts and satellite products, and used to help determine the flight plans, the positioning of the ship, and to alert surface stations of upcoming events (such as dust storms). The use of CFORS forecasts (along with other model results) models were shown to provide important new information and level of detail into mission planning. For example many of the mission objectives required designing flight paths that sampled across gradients of optical depth, or flew above, below and through vertical layers of aerosol, intercepted biomass emission plumes, or sampled dust storms. CFORS, forecasts of dust outbreaks and plume locations, etc., proved to be very useful in designing missions that meet these objective. In this paper we will present an overview of

  13. Forecasting telecommunications data with linear models

    OpenAIRE

    Madden, Gary G; Tan, Joachim

    2007-01-01

    For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given....

  14. Prospective and retrospective evaluation of five-year earthquake forecast models for California

    Science.gov (United States)

    Strader, Anne; Schneider, Max; Schorlemmer, Danijel

    2017-10-01

    The Collaboratory for the Study of Earthquake Predictability was developed to prospectively test earthquake forecasts through reproducible and transparent experiments within a controlled environment. From January 2006 to December 2010, the Regional Earthquake Likelihood Models (RELM) Working Group developed and evaluated thirteen time-invariant prospective earthquake mainshock forecasts. The number, spatial and magnitude components of the forecasts were compared to the observed seismicity distribution using a set of likelihood-based consistency tests. In this RELM experiment update, we assess the long-term forecasting potential of the RELM forecasts. Additionally, we evaluate RELM forecast performance against the Uniform California Earthquake Rupture Forecast (UCERF2) and the National Seismic Hazard Mapping Project (NSHMP) forecasts, which are used for seismic hazard analysis for California. To test each forecast's long-term stability, we also evaluate each forecast from January 2006 to December 2015, which contains both five-year testing periods, and the 40-year period from January 1967 to December 2006. Multiple RELM forecasts, which passed the N-test during the retrospective (January 2006 to December 2010) period, overestimate the number of events from January 2011 to December 2015, although their forecasted spatial distributions are consistent with observed earthquakes. Both the UCERF2 and NSHMP forecasts pass all consistency tests for the two five-year periods; however, they tend to underestimate the number of observed earthquakes over the 40-year testing period. The smoothed seismicity model Helmstetter-et-al.Mainshock outperforms both United States Geological Survey (USGS) models during the second five-year experiment, and contains higher forecasted seismicity rates than the USGS models at multiple observed earthquake locations.

  15. Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

    Science.gov (United States)

    Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

    2010-05-01

    The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

  16. NEW CAR DEMAND MODELING AND FORECASTING USING BASS DIFFUSION MODEL

    Directory of Open Access Journals (Sweden)

    Zuhaimy Ismail

    2013-01-01

    Full Text Available Forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. The building of Bass diffusion model for forecasting new product within the Malaysian society is presented in this study. The proposed model represents the spread level of new Proton car among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new car. With the limited amount of data available for the new car, a robust Bass model was developed to forecast the sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed model and numerical calculation shows that the proposed diffusion model is robust and effective for forecasting demand of new Proton car. The proposed diffusion model is shown to forecast more effectively and accurately even with insufficient previous data on the new product.

  17. Stochastic model of forecasting spare parts demand

    Directory of Open Access Journals (Sweden)

    Ivan S. Milojević

    2012-01-01

    hypothesis of the existence of phenomenon change trends, the next step in the methodology of forecasting is the determination of a specific growth curve that describes the regularity of the development in time. These curves of growth are obtained by the analytical representation (expression of dynamic lines. There are two basic stages in the process of expression and they are: - The choice of the type of curve the shape of which corresponds to the character of the dynamic order variation - the determination of the number of values (evaluation of the curve parameters. The most widespread method of forecasting is the trend extrapolation. The basis of the trend extrapolation is the continuing of past trends in the future. The simplicity of the trend extrapolation process, on the one hand, and the absence of other information on the other hand, are the main reasons why the trend extrapolation is used for forecasting. The trend extrapolation is founded on the following assumptions: - The phenomenon development can be presented as an evolutionary trajectory or trend, - General conditions that influenced the trend development in the past will not undergo substantial changes in the future. Spare parts demand forecasting is constantly being done in all warehouses, workshops, and at all levels. Without demand forecasting, neither planning nor decision making can be done. Demand forecasting is the input for determining the level of reserve, size of the order, ordering cycles, etc. The question that arises is the one of the reliability and accuracy of a forecast and its effects. Forecasting 'by feeling' is not to be dismissed if there is nothing better, but in this case, one must be prepared for forecasting failures that cause unnecessary accumulation of certain spare parts, and also a chronic shortage of other spare parts. All this significantly increases costs and does not provide a satisfactory supply of spare parts. The main problem of the application of this model is that each

  18. Kalman filter estimation model in flood forecasting

    Science.gov (United States)

    Husain, Tahir

    Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.

  19. Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg

    forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...

  20. Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate

    Directory of Open Access Journals (Sweden)

    Mark R. Jury

    2014-01-01

    Full Text Available This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3. Early season forecasts from the coupled forecast system (CFS are steadier than European community medium range forecast (ECMWF. CFS and ECMWF April forecasts of June–August (JJA rainfall achieve significant fit (r2=0.27, 0.25, resp., but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.

  1. Real-time Social Internet Data to Guide Forecasting Models

    Energy Technology Data Exchange (ETDEWEB)

    Del Valle, Sara Y. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-20

    Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.

  2. Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

    Science.gov (United States)

    Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

    2016-06-01

    Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

  3. Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting

    Science.gov (United States)

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

    2008-05-01

    from flood modelling on the Odra River in Poland show that this model system can perform as well as traditional models and gives good predictions in mountainous catchments. By allowing different process representations to be applied within the same framework, it is possible to develop hydrological models in a phased manner. This phased approach was used for example in the Napa Valley, California where it is important to balance water demands for urban areas, agriculture, and ecosystem preservation while maintaining flood protection and water quality. A first regional model was developed with a detailed description of the surface process and a simple linear reservoir was used to simulate the groundwater component. Then a more detailed fully-distributed finite-difference groundwater model was constructed within the same framework while maintaining the surface water components. In the DMIP case study, Blue River, Oklahoma, this flexibility has been used to evaluate the performance of different model structures, and to determine the impact of grid resolution on model accuracy. The results show clear limits to the benefit attained by increasing model complexity and resolution. In contrast, detailed flood mapping using high resolution topography carried out with this tool in South Boulder Creek, Colorado show that very detailed description of the topography and flows paths are required for accurate flood mapping and determination of the flood risk. This framework is now being used to develop a flood forecasting system for the Big Cypress Basin in Florida.

  4. Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

    Science.gov (United States)

    Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

    2010-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

  5. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  6. Drought Forecasting Using Stochastic Models in a Hyper-Arid Climate

    Directory of Open Access Journals (Sweden)

    Amr Mossad

    2015-03-01

    Full Text Available Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI in a hyper-arid climate. The results reveal that all developed ARIMA models demonstrate the potential ability to forecast drought over different time scales. In these models, the p, d, q, P, D and Q values are quite similar for the same SPEI time scale. This is in correspondence with autoregressive (AR and moving average (MA parameter estimate values, which are also similar. Therefore, the ARIMA model (1, 1, 0 (2, 0, 1 could be considered as a general model for the Al Qassim region. Meanwhile, the ARIMA model (1, 0, 3 (0, 0, 0 at 3-SPEI and the ARIMA model (1, 1, 1 (2, 0, 1 at 24-SPEI could be generalized for the Hail region. The ARIMA models at the 24-SPEI time scale is the best forecasting models with high R2 (more than 0.9 and lower values of RMSE and MAE, while they are the least forecasting at the 3-SPEI time scale. Accordingly, this study recommends that ARIMA models can be very useful tools for drought forecasting that can help water resource managers and planners to take precautions considering the severity of drought in advance.

  7. Constrained regression models for optimization and forecasting

    Directory of Open Access Journals (Sweden)

    P.J.S. Bruwer

    2003-12-01

    Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.

  8. Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System

    Institute of Scientific and Technical Information of China (English)

    WANG Wei-gong; LI Zheng; CHENG Mei-ling

    2005-01-01

    The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.

  9. PI forecast with or without de-clustering: an experiment for the Sichuan-Yunnan region

    Directory of Open Access Journals (Sweden)

    C. S. Jiang

    2011-03-01

    Full Text Available Pattern Informatics (PI algorithm uses earthquake catalogues for estimating the increase of the probability of strong earthquakes. The main measure in the algorithm is the number of earthquakes above a threshold magnitude. Since aftershocks occupy a significant proportion of the total number of earthquakes, whether de-clustering affects the performance of the forecast is one of the concerns in the application of this algorithm. This problem is of special interest after a great earthquake, when aftershocks become predominant in regional seismic activity. To investigate this problem, the PI forecasts are systematically analyzed for the Sichuan-Yunnan region of southwest China. In this region there have occurred some earthquakes larger than MS 7.0, including the 2008 Wenchuan earthquake. In the analysis, the epidemic-type aftershock sequences (ETAS model was used for de-clustering. The PI algorithm was revised to consider de-clustering, by replacing the number of earthquakes by the sum of the ETAS-assessed probability for an event to be a "background event" or a "clustering event". Case studies indicate that when an intense aftershock sequence is included in the "sliding time window", the hotspot picture may vary, and the variation lasts for about one year. PI forecasts seem to be affected by the aftershock sequence included in the "anomaly identifying window", and the PI forecast using "background events" seems to have a better performance.

  10. 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

  11. PETRA. The Forecast Model. Synthesis report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-09-01

    The aim of the PETRA project was to develop a model that could recreate the main aspects involved in the demand for travel. The attainment of this objective requires that the model system should retain a high degree of detail and be based on disaggregate models. This was both to ensure an accurate representation of the underlying behavioural intentions, and allow analysis of the underlying travel demand and related aspects across a number of dimensions. This has been achieved in all main respects. The model system is capable of close reproduction of the observed behaviour and generally responds as expected to changes, exhibiting consistent and plausible reactions. The dis-aggregation of the forecast population, according to the various criteria, allows the model to clearly illustrates the behavioural differences between different population segments. Thus, it seems reasonable to conclude that PETRA is capable of detailed analyses of the distributional and behavioural effects of policy changes. (au) EFP-94. 20 refs.

  12. Forecasting wind power production from a wind farm using the RAMS model

    DEFF Research Database (Denmark)

    Tiriolo, L.; Torcasio, R. C.; Montesanti, S.;

    2015-01-01

    The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex...... and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution...... of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available...

  13. Dynamically downscaled multi-model ensemble seasonal forecasts over Ethiopia

    Science.gov (United States)

    Asharaf, Shakeel; Fröhlich, Kristina; Fernandez, Jesus; Cardoso, Rita; Nikulin, Grigory; Früh, Barbara

    2016-04-01

    Truthful and reliable seasonal rainfall predictions have an important social and economic value for the east African countries as their economy is highly dependent on rain-fed agriculture and pastoral systems. Only June to September (JJAS) seasonal rainfall accounts to more than 80% crop production in Ethiopia. Hence, seasonal foresting is a crucial concern for the region. The European Provision of Regional Impact Assessment on a seasonal to decadal timescale (EUPORIAS) project offers a common framework to understand hindcast uncertainties through the use of multi-model and multi-member simulations over east Africa. Under this program, the participating regional climate models (RCMs) were driven by the atmospheric-only version of the ECEARTH global climate model, which provides hindcasts of a five-months period (May to September) from 1991-2012. In this study the RCMs downscaled rainfall is evaluated with respect to the observed JJAS rainfall over Ethiopia. Both deterministic and probabilistic based forecast skills are assessed. Our preliminary results show the potential usefulness of multi-model ensemble simulations in forecasting the seasonal rainfall over the region.

  14. With string model to time series forecasting

    OpenAIRE

    Pinčák, Richard; Bartoš, Erik

    2015-01-01

    Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial ma...

  15. Guidance on the Choice of Threshold for Binary Forecast Modeling

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

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

  16. Predictability and uncertainty of the GloFAS forecasts in the Pacific region of Peru

    Science.gov (United States)

    Boelee, Leonore; Samuals, Paul; Lumbroso, Darren; Zsoter, Ervin; Stephens, Elisabeth; Cloke, Hannah; Baso, Juan

    2017-04-01

    GloFAS is a global flood awareness system based on a distributed hydrological model forced with numerical ensemble weather predictions (Alfieri et al. 2013). Results are published on a password-protected website. Forecasts from the GloFAS are currently limited in resolution and quality, but are nonetheless being used by humanitarian and aid organisations and a small number of forecasting agencies. One such agency is SENHAMI in Peru. To get around the limited accuracy issue, SENHAMI are applying a simple bias correction to the initial conditions of the GloFAS forecasts. This process is reliant on in situ measurements being available and reliable, therefore limiting the locations which can be corrected. Also, the uncertainties of the initials conditions are reduced but the remaining uncertainties will continue limiting the predictability of the forecasts. This research aims to understand and quantify the inaccuracy and uncertainties in the GloFAS forecasts for the Pacific region of Peru. The work will explore ways of improving the predictability of the forecasts within the GloFAS framework. The research will start with looking at the performance of the three main components of the GloFAS forecasting system: the forcing data, the runoff component and the flow routing component. The forcing data, consisting of the ERA-Intrim (Dee et al. 2011) and Variable Resolution Ensemble Prediction System (Miller et al. 2010),will be validated. The starting point will be finding if the weak rainfall along the Pacific coast caused by the large-scale mid tropospheric subsidence over the southeaster subtropical Pacific Ocean and enhanced by the coastal upwelling of cold air (Garreaud, Rutliant, and Fuenzalida 2002), is present in the forcing data. The representation of the hydrological processes, as done by HTESSEL, will be analysed focussing on the surface runoff, subsurface runoff and soil moisture. The results of the flow routing model, LisFlood-Global, will be validated, focussing

  17. A Regional Ensemble Forecast System for Stratiform Precipitation Events in Northern China.Part Ⅰ: A Case Study

    Institute of Scientific and Technical Information of China (English)

    ZHU Jiangshan; Fanyou KONG; LEI Hengchi

    2012-01-01

    A single-model,short-range,ensemble forecasting system (Institute of Atmospheric Physics,Regional Ensemble Forecast System,IAP REFS) with 15-km grid spacing,configured with multiple initial conditions,multiple lateral boundary conditions,and multiple physics parameterizations with 11 ensemble members,was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China.This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework.The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System (NCEP GEFS) forecasts,and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region.Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system.The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system.The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts.Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts (PQPF).However,the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables,and its influence on PQPF resolution was limited as well.

  18. The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

    Science.gov (United States)

    Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas

    2016-04-01

    As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared

  19. Impact of various observing systems on weather analysis and forecast over the Indian region

    Science.gov (United States)

    Singh, Randhir; Ojha, Satya P.; Kishtawal, C. M.; Pal, P. K.

    2014-09-01

    To investigate the potential impact of various types of data on weather forecast over the Indian region, a set of data-denial experiments spanning the entire month of July 2012 is executed using the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system. The experiments are designed to allow the assessment of mass versus wind observations and terrestrial versus space-based instruments, to evaluate the relative importance of the classes of conventional instrument such as radiosonde, and finally to investigate the role of individual spaceborne instruments. The moist total energy norm is used for validation and forecast skill assessment. The results show that the contribution of wind observations toward error reduction is larger than mass observations in the short range (48 h) forecast. Terrestrial-based observations generally contribute more than space-based observations except for the moisture fields, where the role of the space-based instruments becomes more prevalent. Only about 50% of individual instruments are found to be beneficial in this experiment configuration, with the most important role played by radiosondes. Thereafter, Meteosat Atmospheric Motion Vectors (AMVs) (only for short range forecast) and Special Sensor Microwave Imager (SSM/I) are second and third, followed by surface observations, Sounder for Probing Vertical Profiles of Humidity (SAPHIR) radiances and pilot observations. Results of the additional experiments of comparative performance of SSM/I total precipitable water (TPW), Microwave Humidity Sounder (MHS), and SAPHIR radiances indicate that SSM/I is the most important instrument followed by SAPHIR and MHS for improving the quality of the forecast over the Indian region. Further, the impact of single SAPHIR instrument (onboard Megha-Tropiques) is significantly larger compared to three MHS instruments (onboard NOAA-18/19 and MetOp-A).

  20. 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.

  1. PV power forecast using a nonparametric PV model

    OpenAIRE

    Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis

    2015-01-01

    Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...

  2. Exploiting teleconnection indices for probabilistic forecasting of drought class transitions in Sicily region (Italy)

    Science.gov (United States)

    Bonaccorso, Brunella; Cancelliere, Antonino

    2015-04-01

    In the present study two probabilistic models for short-medium term drought forecasting able to include information provided by teleconnection indices are proposed and applied to Sicily region (Italy). Drought conditions are expressed in terms of the Standardized Precipitation-Evapotranspiration Index (SPEI) at different aggregation time scales. More specifically, a multivariate approach based on normal distribution is developed in order to estimate: 1) on the one hand transition probabilities to future SPEI drought classes and 2) on the other hand, SPEI forecasts at a generic time horizon M, as functions of past values of SPEI and the selected teleconnection index. To this end, SPEI series at 3, 4 and 6 aggregation time scales for Sicily region are extracted from the Global SPEI database, SPEIbase , available at Web repository of the Spanish National Research Council (http://sac.csic.es/spei/database.html), and averaged over the study area. In particular, SPEIbase v2.3 with spatial resolution of 0.5° lat/lon and temporal coverage between January 1901 and December 2013 is used. A preliminary correlation analysis is carried out to investigate the link between the drought index and different teleconnection patterns, namely: the North Atlantic Oscillation (NAO), the Scandinavian (SCA) and the East Atlantic-West Russia (EA-WR) patterns. Results of such analysis indicate a strongest influence of NAO on drought conditions in Sicily with respect to other teleconnection indices. Then, the proposed forecasting methodology is applied and the skill in forecasting of the proposed models is quantitatively assessed through the application of a simple score approach and of performance indices. Results indicate that inclusion of NAO index generally enhance model performance thus confirming the suitability of the models for short- medium term forecast of drought conditions.

  3. Grey forecasting model for active vibration control systems

    Science.gov (United States)

    Lihua, Zou; Suliang, Dai; Butterworth, John; Ma, Xing; Dong, Bo; Liu, Aiping

    2009-05-01

    Based on the grey theory, a GM(1,1) forecasting model and an optimal GM(1,1) forecasting model are developed and assessed for use in active vibration control systems for earthquake response mitigation. After deriving equations for forecasting the control state vector, design procedures for an optimal active control method are proposed. Features of the resulting vibration control and the influence on it of time-delay based on different sampling intervals of seismic ground motion are analysed. The numerical results show that the forecasting models based on the grey theory are reliable and practical in structural vibration control fields. Compared with the grey forecasting model, the optimal forecasting model is more efficient in reducing the influences of time-delay and disturbance errors.

  4. Forecasting auroras from regional and global magnetic field measurements

    Science.gov (United States)

    Kauristie, Kirsti; Myllys, Minna; Partamies, Noora; Viljanen, Ari; Peitso, Pyry; Juusola, Liisa; Ahmadzai, Shabana; Singh, Vikramjit; Keil, Ralf; Martinez, Unai; Luginin, Alexej; Glover, Alexi; Navarro, Vicente; Raita, Tero

    2016-06-01

    We use the connection between auroral sightings and rapid geomagnetic field variations in a concept for a Regional Auroral Forecast (RAF) service. The service is based on statistical relationships between near-real-time alerts issued by the NOAA Space Weather Prediction Center and magnetic time derivative (dB/dt) values measured by five MIRACLE magnetometer stations located in Finland at auroral and sub-auroral latitudes. Our database contains NOAA alerts and dB/dt observations from the years 2002-2012. These data are used to create a set of conditional probabilities, which tell the service user when the probability of seeing auroras exceeds the average conditions in Fennoscandia during the coming 0-12 h. Favourable conditions for auroral displays are associated with ground magnetic field time derivative values (dB/dt) exceeding certain latitude-dependent threshold values. Our statistical analyses reveal that the probabilities of recording dB/dt exceeding the thresholds stay below 50 % after NOAA alerts on X-ray bursts or on energetic particle flux enhancements. Therefore, those alerts are not very useful for auroral forecasts if we want to keep the number of false alarms low. However, NOAA alerts on global geomagnetic storms (characterized with Kp values > 4) enable probability estimates of > 50 % with lead times of 3-12 h. RAF forecasts thus rely heavily on the well-known fact that bright auroras appear during geomagnetic storms. The additional new piece of information which RAF brings to the previous picture is the knowledge on typical storm durations at different latitudes. For example, the service users south of the Arctic Circle will learn that after a NOAA ALTK06 issuance in night, auroral spotting should be done within 12 h after the alert, while at higher latitudes conditions can remain favourable during the next night.

  5. Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

    Science.gov (United States)

    Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati; Pal, P. K.

    2016-10-01

    An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6-8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

  6. A Neural Network Model for Forecasting CO2 Emission

    Directory of Open Access Journals (Sweden)

    C. Gallo

    2014-06-01

    Full Text Available Air pollution is today a serious problem, caused mainly by human activity. Classical methods are not considered able to efficiently model complex phenomena as meteorology and air pollution because, usually, they make approximations or too rigid schematisations. Our purpose is a more flexible architecture (artificial neural network model to implement a short-term CO2 emission forecasting tool applied to the cereal sector in Apulia region – in Southern Italy - to determine how the introduction of cultural methods with less environmental impact acts on a possible pollution reduction.

  7. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

  8. Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition

    Energy Technology Data Exchange (ETDEWEB)

    Rogers, J.; Porter, K.

    2011-03-01

    The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

  9. Forecast of future aviation fuels: The model

    Science.gov (United States)

    Ayati, M. B.; Liu, C. Y.; English, J. M.

    1981-01-01

    A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

  10. Integration of a relocatable ocean model in the Mediterranean Forecasting System

    Directory of Open Access Journals (Sweden)

    A. Russo

    2006-10-01

    Full Text Available The MFS (Mediterranean Forecasting System project and its follower MFSTEP (Mediterranean ocean Forecasting System–Towards Environmental Prediction are being covering the Mediterranean Sea with operational Ocean General Circulation Models (OGCMs at horizontal resolution varying from about 12 km till 2005 to 6.5 km in 2006 (reaching 3 km with some regional models and 1.5 km for few shelf models. Heat, water and momentum fluxes through the air-sea interface are derived from the European Center for Medium-range Weather Forecast (ECMWF output at 0.5° horizontal resolution. Such horizontal resolutions could be not able to provide the needed forecast accuracy in some cases (localized emergencies at sea, e.g. oil spill; need for high resolution current forecasts, e.g. offshore works. A solution to this problem is represented by relocatable models able to be rapidly deployed and to produce forecasts starting from the MFS products. The Harvard Ocean Prediction System (HOPS has been chosen as base of the relocatable model and it has been interfaced with the MFSTEP OGCM and one regional model. The relocatable model has demonstrated capability to produce forecasts within 2-3 days in many cases, and more rapid implementation may be obtained.

  11. Neural network versus classical time series forecasting models

    Science.gov (United States)

    Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

    2017-05-01

    Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

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

    Science.gov (United States)

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

    2015-12-01

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

  13. A Stochastic-Dynamic Model for Real Time Flood Forecasting

    Science.gov (United States)

    Chow, K. C. A.; Watt, W. E.; Watts, D. G.

    1983-06-01

    A stochastic-dynamic model for real time flood forecasting was developed using Box-Jenkins modelling techniques. The purpose of the forecasting system is to forecast flood levels of the Saint John River at Fredericton, New Brunswick. The model consists of two submodels: an upstream model used to forecast the headpond level at the Mactaquac Dam and a downstream model to forecast the water level at Fredericton. Inputs to the system are recorded values of the water level at East Florenceville, the headpond level and gate position at Mactaquac, and the water level at Fredericton. The model was calibrated for the spring floods of 1973, 1974, 1977, and 1978, and its usefulness was verified for the 1979 flood. The forecasting results indicated that the stochastic-dynamic model produces reasonably accurate forecasts for lead times up to two days. These forecasts were then compared to those from the existing forecasting system and were found to be as reliable as those from the existing system.

  14. Forecasting the Polish zloty with non-linear models

    OpenAIRE

    Michal Rubaszek; Pawel Skrzypczynski; Grzegorz Koloch

    2011-01-01

    The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on ex...

  15. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  16. Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis

    Science.gov (United States)

    2014-04-01

    the WRF Model as a High Resolution Regional Climate Model : Model Intercomparison Study. Seventh International Conference on Urban Climate , Yokohama...surface observation data versus 1-km model forecast data for all data points (all geographic regions and all times) used in analyses...temperature (K) data set showing surface observation data versus 3-km model forecast data for all data points (all geographic regions and all times) used in

  17. Modeling of spatial dependence in wind power forecast uncertainty

    DEFF Research Database (Denmark)

    Papaefthymiou, George; Pinson, Pierre

    2008-01-01

    It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus neg...

  18. Daily air quality index forecasting with hybrid models: A case in China.

    Science.gov (United States)

    Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

    2017-09-19

    Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the

  19. 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.

  20. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

    Science.gov (United States)

    Ehsan, Muhammad Azhar; Tippett, Michael K.; Almazroui, Mansour; Ismail, Muhammad; Yousef, Ahmed; Kucharski, Fred; Omar, Mohamed; Hussein, Mahmoud; Alkhalaf, Abdulrahman A.

    2016-07-01

    Northern Hemisphere winter precipitation reforecasts from the European Centre for Medium Range Weather Forecast System-4 and six of the models in the North American Multi-Model Ensemble are evaluated, focusing on two regions (Region-A: 20°N-45°N, 10°E-65°E and Region-B: 20°N-55°N, 205°E-255°E) where winter precipitation is a dominant fraction of the annual total and where precipitation from mid-latitude storms is important. Predictability and skill (deterministic and probabilistic) are assessed for 1983-2013 by the multimodel composite (MME) of seven prediction models. The MME climatological mean and variability over the two regions is comparable to observation with some regional differences. The statistically significant decreasing trend observed in Region-B precipitation is captured well by the MME and most of the individual models. El Niño Southern Oscillation is a source of forecast skill, and the correlation coefficient between the Niño3.4 index and precipitation over region A and B is 0.46 and 0.35, statistically significant at the 95 % level. The MME reforecasts weakly reproduce the observed teleconnection. Signal, noise and signal to noise ratio analysis show that the signal variance over two regions is very small as compared to noise variance which tends to reduce the prediction skill. The MME ranked probability skill score is higher than that of individual models, showing the advantage of a multimodel ensemble. Observed Region-A rainfall anomalies are strongly associated with the North Atlantic Oscillation, but none of the models reproduce this relation, which may explain the low skill over Region-A. The superior quality of multimodel ensemble compared with individual models is mainly due to larger ensemble size.

  1. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

    Science.gov (United States)

    Ehsan, Muhammad Azhar; Tippett, Michael K.; Almazroui, Mansour; Ismail, Muhammad; Yousef, Ahmed; Kucharski, Fred; Omar, Mohamed; Hussein, Mahmoud; Alkhalaf, Abdulrahman A.

    2017-05-01

    Northern Hemisphere winter precipitation reforecasts from the European Centre for Medium Range Weather Forecast System-4 and six of the models in the North American Multi-Model Ensemble are evaluated, focusing on two regions (Region-A: 20°N-45°N, 10°E-65°E and Region-B: 20°N-55°N, 205°E-255°E) where winter precipitation is a dominant fraction of the annual total and where precipitation from mid-latitude storms is important. Predictability and skill (deterministic and probabilistic) are assessed for 1983-2013 by the multimodel composite (MME) of seven prediction models. The MME climatological mean and variability over the two regions is comparable to observation with some regional differences. The statistically significant decreasing trend observed in Region-B precipitation is captured well by the MME and most of the individual models. El Niño Southern Oscillation is a source of forecast skill, and the correlation coefficient between the Niño3.4 index and precipitation over region A and B is 0.46 and 0.35, statistically significant at the 95 % level. The MME reforecasts weakly reproduce the observed teleconnection. Signal, noise and signal to noise ratio analysis show that the signal variance over two regions is very small as compared to noise variance which tends to reduce the prediction skill. The MME ranked probability skill score is higher than that of individual models, showing the advantage of a multimodel ensemble. Observed Region-A rainfall anomalies are strongly associated with the North Atlantic Oscillation, but none of the models reproduce this relation, which may explain the low skill over Region-A. The superior quality of multimodel ensemble compared with individual models is mainly due to larger ensemble size.

  2. A first large-scale flood inundation forecasting model

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-11-04

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

  3. Development of golf tourism and golf tourism demand forecasts in Turkey: a study of Belek region

    Directory of Open Access Journals (Sweden)

    Murat Çuhadar

    2013-06-01

    Full Text Available Golf tourism has become one of the rapidly developing tourism types in Turkey, especially in the Belek region. In this study, detailed information about the development of golf tourism in Turkey from past to present was provided and golf tourism demand to Belek region which is a major golf tourism destinastion in the world and Turkey was modeled and forecasted monthly by Box-Jenkins methodology for the May 2013 –December 2014 period. As a measure of golf tourism demand, number of monthly golf games were taken in the study and the monthly number of golf game statistics of January 2001 – April 2013 in the golf establishments in Belek tourism center were used. By producing ex-ante forecasts it is aimed to create a basis for tourism development plans prepared by the management of private and public sector and to provide support for administrators’ monthly planning decisions.

  4. A Simple Hybrid Model for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Suseelatha Annamareddi

    2013-01-01

    Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

  5. Residential Saudi load forecasting using analytical model and Artificial Neural Networks

    Science.gov (United States)

    Al-Harbi, Ahmad Abdulaziz

    In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

  6. Dust modelling and forecasting in the Barcelona Supercomputing Center: Activities and developments

    Energy Technology Data Exchange (ETDEWEB)

    Perez, C; Baldasano, J M; Jimenez-Guerrero, P; Jorba, O; Haustein, K; Basart, S [Earth Sciences Department. Barcelona Supercomputing Center. Barcelona (Spain); Cuevas, E [Izanaa Atmospheric Research Center. Agencia Estatal de Meteorologia, Tenerife (Spain); Nickovic, S [Atmospheric Research and Environment Branch, World Meteorological Organization, Geneva (Switzerland)], E-mail: carlos.perez@bsc.es

    2009-03-01

    The Barcelona Supercomputing Center (BSC) is the National Supercomputer Facility in Spain, hosting MareNostrum, one of the most powerful Supercomputers in Europe. The Earth Sciences Department of BSC operates daily regional dust and air quality forecasts and conducts intensive modelling research for short-term operational prediction. This contribution summarizes the latest developments and current activities in the field of sand and dust storm modelling and forecasting.

  7. 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

  8. Factor Model Forecasting of Inflation in Croatia

    Directory of Open Access Journals (Sweden)

    Davor Kunovac

    2007-12-01

    Full Text Available This paper tests whether information derived from 144 economic variables (represented by only a few constructed factors can be used for the forecasting of consumer prices in Croatia. The results obtained show that the use of one factor enhances the precision of the benchmark model’s ability to forecast inflation. The methodology used is sufficiently general to be able to be applied directly for the forecasting of other economic variables.

  9. Factor Model Forecasts of Exchange Rates

    OpenAIRE

    Charles Engel; Nelson C. Mark; Kenneth D. West

    2012-01-01

    We construct factors from a cross section of exchange rates and use the idiosyncratic deviations from the factors to forecast. In a stylized data generating process, we show that such forecasts can be effective even if there is essentially no serial correlation in the univariate exchange rate processes. We apply the technique to a panel of bilateral U.S. dollar rates against 17 OECD countries. We forecast using factors, and using factors combined with any of fundamentals suggested by Taylor r...

  10. RESULTS OF INTERBANK EXCHANGE RATES FORECASTING USING STATE SPACE MODEL

    Directory of Open Access Journals (Sweden)

    Muhammad Kashif

    2008-07-01

    Full Text Available This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the best performance among all the models.

  11. Forecasting Drinking and Household Water Requirement of the Thrace Region

    Directory of Open Access Journals (Sweden)

    F. Konukcu

    2007-05-01

    Full Text Available This study aims at future forecasting drinking and household water requirements of the Thrace region by the aid of a scientific perspective. To realise this, first future population of the region was predicted and then the water requirements were calculated. As results, water requirements of the city and the countryside for the years 2020, 2030, 2040 and 2050 were computed as 1.45, 1.94, 2.58 and 3.44 km3, respectively. Beside, rapidly increasing drinking and household water requirements due to fast population growth and immense amount of migration into the region, demands by agriculture and intensive industry suggest that the present total water potential of about 4.0 km3 will not be sufficient and a great water crisis may be experienced. Adverse effects of a probable global climate change on water resources make the situation more acute. To overcome this crisis, governmental agencies and civil societies are called work together to produce and implement rational strategies.

  12. Shemya, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Shemya, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  13. Palm Beach, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Palm Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  14. Haleiwa, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Haleiwa, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  15. Key West, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Key West, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Sitka, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sitka, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  17. Monterey, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Monterey, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  18. Ponce, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ponce, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  19. Port Alexander, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Alexander, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  20. Port Orford, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Orford, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  1. Seward, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Seward, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  2. Nawiliwili, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nawiliwili, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  3. Montauk, New York Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Montauk, New York Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  4. San Juan, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Juan, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  5. Arecibo, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Arecibo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  6. Toke Point, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Toke Point, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  7. Hilo, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hilo, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  8. Ocean City, Maryland Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ocean City, Maryland Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  9. Keauhou, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Keauhou, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  10. Honolulu, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Honolulu, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  11. San Diego, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Diego, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  12. Adak, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Adak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  13. Garibaldi, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Garibaldi, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  14. Kihei, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kihei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  15. Kahului, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kahului, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Daytona Beach, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Daytona Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  17. Mayaguez, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Mayaguez, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  18. Savannah, Georgia Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Savannah, Georgia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  19. Homer, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Homer, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  20. King Cove, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The King Cove, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  1. Portland, Maine Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Portland, Maine Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  2. La Push, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The La Push, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  3. Seaside, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Seaside, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  4. Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Fajardo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  5. Kawaihae, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kawaihae, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  6. Nikolski, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nikolski, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  7. Kodiak, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kodiak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  8. Sand Point, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sand Point, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  9. Pearl Harbor, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Pearl Harbor, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  10. Florence, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Florence, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  11. Hanalei, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hanalei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  12. Lahaina, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Lahaina, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  13. Wake Island Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Wake Island Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  14. Kailua-Kona, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kailua-Kona, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  15. Apra Harbor, Guam Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Apra Harbor, Guam Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Westport, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Westport, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  17. Neah Bay, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Neah Bay, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  18. When mechanism matters: Bayesian forecasting using models of ecological diffusion

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

    2017-01-01

    Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

  19. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011–2014

    Indian Academy of Sciences (India)

    R K Giri; Jagabandhu Panda; Sudhansu S Rath; Ravindra Kumar

    2016-06-01

    In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitationis required. In view of this, the present study intends to validate the quantitative precipitationforecast (QPF) issued during southwest monsoon season for six river catchments (basin) under theflood meteorological office, Patna region. The forecast is analysed statistically by computing various skillscores of six different precipitation ranges during the years 2011–2014. The analysis of QPF validationindicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitationranges of 1–10 and 11–25 mm. However, the reliability decreases for higher ranges of rainfall and also forthe lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecastingfor QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It isrealized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively usefulfor issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However,QPF may be improved using satellite and radar products.

  20. Importance of three-dimensional grids and time-dependent factors for applications of earthquake forecasting models to subduction environments

    Science.gov (United States)

    Chan, Chung-Han

    2016-09-01

    This study provides some new insights into earthquake forecasting models that are applied to regions with subduction systems, including the depth component for forecasting grids and time-dependent factors. To demonstrate the importance of depth component, a forecasting approach, which incorporates three-dimensional grids, is compared with an approach with two-dimensional cells. Through application to the two subduction regions, Ryukyu and Kanto, it is shown that the approaches with three-dimensional grids always demonstrate a better forecasting ability. I thus confirm the importance of depth dependency for forecasting, especially for applications to a subduction environment or a region with non-vertical seismogenic structures. In addition, this study discusses the role of time-dependent factors for forecasting models and concludes that time dependency only becomes crucial during the period with significant seismicity rate change that follows a large earthquake.

  1. Drift dynamics in a coupled model initialized for decadal forecasts

    Science.gov (United States)

    Sanchez-Gomez, Emilia; Cassou, Christophe; Ruprich-Robert, Yohan; Fernandez, Elodie; Terray, Laurent

    2016-03-01

    Drifts are always present in models when initialized from observed conditions because of intrinsic model errors; those potentially affect any type of climate predictions based on numerical experiments. Model drifts are usually removed through more or less sophisticated techniques for skill assessment, but they are rarely analysed. In this study, we provide a detailed physical and dynamical description of the drifts in the CNRM-CM5 coupled model using a set of decadal retrospective forecasts produced within CMIP5. The scope of the paper is to give some physical insights and lines of approach to, on one hand, implement more appropriate techniques of initialisation that minimize the drift in forecast mode, and on the other hand, eventually reduce the systematic biases of the models. We first document a novel protocol for ocean initialization adopted by the CNRM-CERFACS group for forecasting purpose in CMIP5. Initial states for starting dates of the predictions are obtained from a preliminary integration of the coupled model where full-field ocean surface temperature and salinity are restored everywhere to observations through flux derivative terms and full-field subsurface fields (below the prognostic ocean mixed layer) are nudged towards NEMOVAR reanalyses. Nudging is applied only outside the 15°S-15°N band allowing for dynamical balance between the depth and tilt of the tropical thermocline and the model intrinsic biased wind. A sensitivity experiment to the latitudinal extension of no-nudging zone (1°S-1°N instead of 15°, hereafter referred to as NOEQ) has been carried out. In this paper, we concentrate our analyses on two specific regions: the tropical Pacific and the North Atlantic basins. In the Pacific, we show that the first year of the forecasts is characterized by a quasi-systematic excitation of El Niño-Southern Oscillation (ENSO) warm events whatever the starting dates. This, through ocean-to-atmosphere heat transfer materialized by diabatic heating

  2. Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-06-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R > 0.9 but were significantly biased (~25% with consistent under-predictions for spring months when there is significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ± 10 ppb but not found to significantly affect peak ozone surface concentrations.

  3. Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-02-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R>0.9 but were significantly biased (~25 % with significant under-predictions for spring months with significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ±10 ppb but not found to significantly affect peak ozone surface concentrations.

  4. Planetary Kp index forecast using autoregressive models

    CERN Document Server

    Gonzalez, Arian Ojeda; Odriozola, Siomel Savio; Rosa, Reinaldo Roberto; Mendes, Odim

    2014-01-01

    The geomagnetic Kp index is derived from the K index measurements obtained from thirteen stations located around the Earth geomagnetic latitudes between $48^\\circ$ and $63^\\circ$. This index is processed every three hours, is quasi-logarithmic and estimates the geomagnetic activity. The Kp values fall within a range of 0 to 9 and are organized as a set of 28 discrete values. The data set is important because it is used as one of the many input parameters of magnetospheric and ionospheric models. The objective of this work is to use historical data from the Kp index to develop a methodology to make a prediction in a time interval of at least three hours. Five different models to forecast geomagnetic indices Kp and ap are tested. Time series of values of Kp index from 1932 to 15/12/2012 at 21:00 UT are used as input to the models. The purpose of the model is to predict the three measured values after the last measured value of the Kp index (it means the next 9 hours values). The AR model provides the lowest com...

  5. Developing and testing solar irradiance forecasting techniques in the Hawaiian Islands region

    Science.gov (United States)

    Matthews, D. K.; Souza, J. M.; Stein, K.

    2014-12-01

    Irradiance variability, primarily driven by cloud formation and advection, can be problematic in the state of Hawaíi, because of the high penetration of distributed solar and the small scale of the island electrical grids. The Hawaíi Natural Energy Institute (HNEI) is developing an operational system in order to research and test new techniques to generate solar forecasts for the Hawaiian Islands. The operational system comprises the following three components.(i) A ground-observation-based advection model, using sky imagers and a ceilometer located at the University of Hawaíi at Mānoa. Every 10 minutes (during daylight hours), this component generates a high-resolution 1 hour Global Horizontal Irradiance (GHI) prediction for a region that is within ~15 km of the instrumentation. (ii) A satellite-image-based advection model, using Geostationary Operational Environmental Satellite (GOES) imagery and the Heliosat-II method. Every 30 minutes (during daylight hours), this component generates a 1 km resolution, 6 hour GHI prediction for the entire Hawaiian Archipelago. (iii) A coupled ocean-atmosphere model, using the Regional Ocean Modeling System (ROMS) model and the Weather Research and Forecasting (WRF) model, including newly available microphysics, shallow convection parameterization, and radiative transfer model options. Nightly, this component generates 48 hour GHI, Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) predictions for (a) a 10 km resolution domain covering the full Hawaiian Archipelago and (b) a nested 2 km resolution domain covering the islands of Maui, Óahu, and Hawaíi. We discuss the development and validation of the system, and the scales of forecasting accuracy for each component. We also examine the impact of the coupled model on the simulations of surface flux processeses and ocean-atmosphere feedbacks, both of which influence the prediction of regional cloud properties.

  6. Modelled seasonal forecasts of snow water equivalent and runoff in alpine catchments

    Science.gov (United States)

    Förster, Kristian; Hanzer, Florian; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich

    2016-04-01

    Seasonal forecasts of water balance components are becoming increasingly important for hydrological applications. These forecasts are typically derived from coupled atmosphere-ocean climate models, which enable physically based seasonal forecasts. In mountainous regions, however, topography is complex whilst typical spatial resolutions of the climate models are still comparably coarse, i.e in the data, ridges and valleys are not represented with sufficient accuracy. Therefore, seasonal predictions of atmospheric variables require consideration of representative gradients. We present first results of seasonal forecasts and re-forecasts processed by the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2). These are prepared for monthly time steps in order to be used for ensemble runs of water balance simulation using the Alpine Water balance And Runoff Estimation model (AWARE). This model has been designed for monthly seasonal predictions in ice- and snowmelt dominated catchments. The study area is the Inn catchment in Tyrol/Austria, including its headwaters in Switzerland. Results are evaluated for both anomalies of meteorological input data (temperature and precipitation), as well as balance components including snow water equivalent and runoff, both simulated with AWARE. Based on model skill evaluations derived from forecasts and observations, the model chain CFSv2 - AWARE proves helpful to analyse possible future hydrological system states of mountainous catchments with emphasis on spatio-temporal snow cover evolution.

  7. Forecasting Financial Time Series Using Model Averaging

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco)

    2007-01-01

    textabstractIn almost all cases a decision maker cannot identify ex ante the true process. This observation has led researchers to introduce several sources of uncertainty in forecasting exercises. In this context, the research reported in these pages finds an increase of forecasting power o

  8. Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS-Terra Fractional Snow Cover Products

    Directory of Open Access Journals (Sweden)

    Xiaoduo Pan

    2017-07-01

    Full Text Available Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB, a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1 FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2 Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3 Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale.

  9. Evaluation and Correction of Quantitative Precipitation Forecast by Storm-Scale NWP Model in Jiangsu, China

    Directory of Open Access Journals (Sweden)

    Gaili Wang

    2016-01-01

    Full Text Available With the development of high-performance computer systems and data assimilation techniques, storm-scale numerical weather prediction (NWP models are gradually used for short-term deterministic forecasts. The primary objective of this study is to evaluate and correct precipitation forecasts of a storm-scale NWP model called the advanced regional prediction system (ARPS. The evaluation and correction consider five heavy precipitation events that occurred in the summer of 2015 in Jiangsu, China. The performances of the original and corrected ARPS precipitation forecasts are evaluated as a function of lead time using standard measurements and a spatial verification method called Structure-Amplitude-Location (SAL. In general, the ARPS could not produce optimal forecasts for very short lead times, and the forecast accuracy improves with increasing lead time. The ARPS overestimates precipitation for all lead times, which is confirmed by large bias in many forecasts in the first and second quadrant of the diagram of SAL, especially at the 1 h lead time. The amplitude correction is performed by matching percentile values of the ARPS precipitation forecasts and observations for each lead time. Amplitude correction significantly improved the ARPS precipitation forecasts in terms of the considered performance indices of standard measures and A-component and S-component of SAL.

  10. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    Science.gov (United States)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  11. Forecasting project schedule performance using probabilistic and deterministic models

    Directory of Open Access Journals (Sweden)

    S.A. Abdel Azeem

    2014-04-01

    Full Text Available Earned value management (EVM was originally developed for cost management and has not widely been used for forecasting project duration. In addition, EVM based formulas for cost or schedule forecasting are still deterministic and do not provide any information about the range of possible outcomes and the probability of meeting the project objectives. The objective of this paper is to develop three models to forecast the estimated duration at completion. Two of these models are deterministic; earned value (EV and earned schedule (ES models. The third model is a probabilistic model and developed based on Kalman filter algorithm and earned schedule management. Hence, the accuracies of the EV, ES and Kalman Filter Forecasting Model (KFFM through the different project periods will be assessed and compared with the other forecasting methods such as the Critical Path Method (CPM, which makes the time forecast at activity level by revising the actual reporting data for each activity at a certain data date. A case study project is used to validate the results of the three models. Hence, the best model is selected based on the lowest average percentage of error. The results showed that the KFFM developed in this study provides probabilistic prediction bounds of project duration at completion and can be applied through the different project periods with smaller errors than those observed in EV and ES forecasting models.

  12. 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.

  13. Operational forecasting based on a modified Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

  14. Testing probabilistic adaptive real-time flood forecasting models

    NARCIS (Netherlands)

    Smith, P.J.; Beven, K.J.; Leedal, D.; Weerts, A.H.; Young, P.C.

    2014-01-01

    Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties in the forecasting process. This paper reviews recent applications of data-based mechanistic (DBM) models within the operational UK Natio

  15. Combined forecasts from linear and nonlinear time series models

    NARCIS (Netherlands)

    N. Terui (Nobuhiko); H.K. van Dijk (Herman)

    1999-01-01

    textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)line

  16. Forecasts of time averages with a numerical weather prediction model

    Science.gov (United States)

    Roads, J. O.

    1986-01-01

    Forecasts of time averages of 1-10 days in duration by an operational numerical weather prediction model are documented for the global 500 mb height field in spectral space. Error growth in very idealized models is described in order to anticipate various features of these forecasts and in order to anticipate what the results might be if forecasts longer than 10 days were carried out by present day numerical weather prediction models. The data set for this study is described, and the equilibrium spectra and error spectra are documented; then, the total error is documented. It is shown how forecasts can immediately be improved by removing the systematic error, by using statistical filters, and by ignoring forecasts beyond about a week. Temporal variations in the error field are also documented.

  17. Multilayer stock forecasting model using fuzzy time series.

    Science.gov (United States)

    Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

    2014-01-01

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

  18. Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

    Science.gov (United States)

    Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

    2017-04-01

    The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

  19. Forecasting carbon budget under climate change and CO2 fertilization for subtropical region in China using integrated biosphere simulator (IBIS) model

    Science.gov (United States)

    Zhu, Q.; Jiang, H.; Liu, J.; Peng, C.; Fang, X.; Yu, S.; Zhou, G.; Wei, X.; Ju, W.

    2011-01-01

    The regional carbon budget of the climatic transition zone may be very sensitive to climate change and increasing atmospheric CO2 concentrations. This study simulated the carbon cycles under these changes using process-based ecosystem models. The Integrated Biosphere Simulator (IBIS), a Dynamic Global Vegetation Model (DGVM), was used to evaluate the impacts of climate change and CO2 fertilization on net primary production (NPP), net ecosystem production (NEP), and the vegetation structure of terrestrial ecosystems in Zhejiang province (area 101,800 km2, mainly covered by subtropical evergreen forest and warm-temperate evergreen broadleaf forest) which is located in the subtropical climate area of China. Two general circulation models (HADCM3 and CGCM3) representing four IPCC climate change scenarios (HC3AA, HC3GG, CGCM-sresa2, and CGCM-sresb1) were used as climate inputs for IBIS. Results show that simulated historical biomass and NPP are consistent with field and other modelled data, which makes the analysis of future carbon budget reliable. The results indicate that NPP over the entire Zhejiang province was about 55 Mt C yr-1 during the last half of the 21st century. An NPP increase of about 24 Mt C by the end of the 21st century was estimated with the combined effects of increasing CO2 and climate change. A slight NPP increase of about 5 Mt C was estimated under the climate change alone scenario. Forests in Zhejiang are currently acting as a carbon sink with an average NEP of about 2.5 Mt C yr-1. NEP will increase to about 5 Mt C yr-1 by the end of the 21st century with the increasing atmospheric CO2 concentration and climate change. However, climate change alone will reduce the forest carbon sequestration of Zhejiang's forests. Future climate warming will substantially change the vegetation cover types; warm-temperate evergreen broadleaf forest will be gradually substituted by subtropical evergreen forest. An increasing CO2 concentration will have little

  20. Modeling and Forecasting Volatility of the Malaysian Stock Markets

    Directory of Open Access Journals (Sweden)

    Ahmed Shamiri

    2009-01-01

    Full Text Available Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian. Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.

  1. Climate information based streamflow and rainfall forecasts for Huai River Basin using Hierarchical Bayesian Modeling

    Directory of Open Access Journals (Sweden)

    X. Chen

    2013-09-01

    Full Text Available A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.

  2. Port San Luis, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port San Luis, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  3. Charlotte Amalie, Virgin Islands Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Charlotte Amalie, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami...

  4. Pago Pago, American Samoa Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Pago Pago, American Samoa Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  5. Point Reyes, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Point Reyes, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  6. Morehead City, North Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Morehead City, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  7. Cordova, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cordova, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  8. Craig, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Craig, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  9. Virginia Beach Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Virginia Beach, Virginia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  10. Unalaska, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Unalaska, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  11. A simulation model for forecasting downhill ski participation

    Science.gov (United States)

    Daniel J. Stynes; Daniel M. Spotts

    1980-01-01

    The purpose of this paper is to describe progress in the development of a general computer simulation model to forecast future levels of outdoor recreation participation. The model is applied and tested for downhill skiing in Michigan.

  12. Santa Barbara, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Barbara, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  13. Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Elfin Cove, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  14. Los Angeles, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Los Angeles, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  15. British Columbia, Canada Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The British Columbia, Canada Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  16. Cape Hatteras, North Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cape Hatteras, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  17. Myrtle Beach, South Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Myrtle Beach, South Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  18. San Francisco, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Francisco, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  19. Santa Monica, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Monica, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  20. Atlantic City, New Jersey Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Atlantic City, New Jersey Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  1. Atka, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Atka, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...

  2. Nantucket, Massachusetts Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nantucket, Massachusetts Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  3. Crescent City, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Crescent City, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  4. Chignik, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Chignik, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  5. Port Angeles, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Angeles, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  6. Christiansted, Virgin Islands Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Christiansted, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  7. Eureka, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Eureka, California Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  8. With string model to time series forecasting

    Science.gov (United States)

    Pinčák, Richard; Bartoš, Erik

    2015-10-01

    Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.

  9. Sea Fog Forecasting with Lagrangian Models

    Science.gov (United States)

    Lewis, J. M.

    2014-12-01

    In 1913, G. I. Taylor introduced us to a Lagrangian view of sea fog formation. He conducted his study off the coast of Newfoundland in the aftermath of the Titanic disaster. We briefly review Taylor's classic work and then apply these same principles to a case of sea fog formation and dissipation off the coast of California. The resources used in this study consist of: 1) land-based surface and upper-air observations, 2) NDBC (National Data Buoy Center) observations from moored buoys equipped to measure dew point temperature as well as the standard surface observations at sea (wind, sea surface temperature, pressure, and air temperature), 3) satellite observations of cloud, and 4) a one-dimensional (vertically directed) boundary layer model that tracks with the surface air motion and makes use of sophisticated turbulence-radiation parameterizations. Results of the investigation indicate that delicate interplay and interaction between the radiation and turbulence processes makes accurate forecasts of sea fog onset unlikely in the near future. This pessimistic attitude stems from inadequacy of the existing network of observations and uncertainties in modeling dynamical processes within the boundary layer.

  10. Impact on the short-term forecast using radar data assimilation on the South and Southeast region of Brazil

    Science.gov (United States)

    Herdies, Dirceu; Viana, Liviany; Souza, Diego; Vendrasco, Eder

    2017-04-01

    The objective of this study was to analyze the behavior of the precipitation related to the numerical weather forecast employing the Atmospheric Weather Research and Forecasting model (WRF) and the Data assimilation Weather Research and Forecasting model Data Assimilation Three Dimensional-Variational (WRFDA / 3D-VAR) system for a Convective system occurred in the summer of 2015/2016 on the southern and southeastern regions of Brazil. The datasets used were radar data in the region of interest and observational data from the Global Telecommunications System (GTS). The data assimilated were radial velocity (directly) and reflectivity (indirectly) and variables of the state - air temperature, surface pressure, wind speed and direction, among others. Three experiments were performed to evaluate the weather forecast for the selected case: i) without any type of assimilation, (ii) assimilated GTS data, and (iii) assimilated data from available radars. The prediction until to 6 hours of convective system intensity was evaluated, which were validated with the combined precipitation data from satellites and surface. The results showed the positive impact of the short-term forecast using experiments with the radar and GTS data when compared to the experiment without using them. Thus, this study is expected to contribute to the development of modeling and the operation of the assimilation of radar data in the numerical weather prediction over the regions of study.

  11. Overview of the Diagnostic Cloud Forecast Model at the Air Force Weather Agency

    Science.gov (United States)

    Hildebrand, E. P.

    2014-12-01

    The Air Force Weather Agency (AFWA) is responsible for running and maintaining the Diagnostic Cloud Forecast (DCF) model to support DoD missions and those of their external partners. The DCF model generates three-dimensional cloud forecasts for global and regional domains at various resolutions. Regional domains are chosen based on Air Force mission needs. DCF is purely a statistical model that can be appended to any numerical weather prediction (NWP) model. Operationally, AFWA runs the DCF model deterministically using GFS data from NCEP and WRF data that are created in-house. In addition, AFWA also runs an ensemble version of the DCF model using the Mesoscale Ensemble Prediction System (MEPS). The deterministic DCF uses predictor variables from the WRF or GFS models, depending on whether the domain is regional or global, and statistically relates them to observed cloud cover from the World-Wide Merged Cloud Analysis (WWMCA). The forecast process of the model uses an ordinal logistic regression to predict membership in one of 101 groups (every 1% from 0-100%). The predicted group membership then is translated into a cloud amount. This is performed on 21 pressure levels ranging from 1000 hPa to 100 hPa. Cloud amount forecasts on these 21 levels are used along with the NWP geopotential height forecasts to estimate the base and top heights of cloud layers in the vertical. DCF also includes routines to estimate the amount and type of cloud within each layer. Forecasts of total cloud amount are verified using the WWMCA, as well as independent sources of cloud data. This presentation will include an overview of the DCF model and its use at AFWA. Results will be presented to show that DCF adds value over the raw cloud forecasts from NWP models. Ideas for future work also will be addressed.

  12. Evaluation of statistical models for forecast errors from the HBV model

    Science.gov (United States)

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

    2010-04-01

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

  13. Using initial and boundary condition perturbations in medium-range regional ensemble forecasting with two nested domains

    Science.gov (United States)

    Jiang, J.; Koracin, D.; Vellore, R.; Xiao, M.; Lewis, J. M.

    2010-12-01

    Simulated evolution of climate and weather is sensitive to the specification of their initial state. Small errors in the initial state could lead the forecast into a different direction. It is essential to estimate the impact of the uncertainty in initial conditions on the forecast accuracy. For limited-area or regional forecasting, lateral boundary conditions also have considerable influence on the development of mesoscale or local-scale phenomena. Strong lateral boundary conditions derived from a larger scale environment could significantly alter or even remove local-scale components. This study investigates the impact of uncertainty in initial and lateral boundary conditions on medium-range regional forecasting using the Advanced Weather Research and Forecasting (WRF) model. The WRF model was configured with two nested domains: the parent domain has a 108 km horizontal resolution, and a nested domain with 36 km resolution covers the western U.S. The ensemble forecasting was conducted with 50 ensemble members using random perturbations in the initial conditions (ICs) and lateral boundary conditions (LBCs). A case period of 15 days in December 2008 is chosen, during which two intense frontal passages occurred in the western U.S. Results show that, applying only IC perturbations, the contribution from the IC perturbations to the ensemble spread decreases with time. Using both randomly perturbed LBCs and ICs from the coarser domain, the inner nested domain shows a wider ensemble spread. The resulting ensemble forecasting can be interpreted as a probabilistic prediction for wind energy, especially for wind gust and wind turbine operational cut-off. The analysis also includes an efficiency comparison of using coarser ensemble forecasting vs. a higher resolution single control run.

  14. Decision Making Models Using Weather Forecast Information

    OpenAIRE

    Hiramatsu, Akio; Huynh, Van-Nam; Nakamori, Yoshiteru

    2007-01-01

    The quality of weather forecast has gradually improved, but weather information such as precipitation forecast is still uncertainty. Meteorologists have studied the use and economic value of weather information, and users have to translate weather information into their most desirable action. To maximize the economic value of users, the decision maker should select the optimum course of action for his company or project, based on an appropriate decision strategy under uncertain situations. In...

  15. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Marin Cerjan

    2014-05-01

    Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

  16. Modeling and Forecasting the Volatility of Eastern European Emerging Markets

    Directory of Open Access Journals (Sweden)

    Sang Hoon Kang

    2009-06-01

    Full Text Available This study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, naThis study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, namely, Hungary, Poland, Russia, and Slovakia. From the results of our empirical analysis, we found that the FIGARCH model is better equipped to capture the long memory property in the volatility of these markets than the GARCH and IGARCH models. More importantly, the FIGARCH model is found to provide superior performance in one-day-ahead volatility forecasts. Thus, this study recommends researchers, portfolio managers, and traders to use the long memory FIGARCH model in analyzing and forecasting the volatility dynamics of Eastern European emerging markets.

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

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

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

  18. Verification of short lead time forecast models: applied to Kp and Dst forecasting

    Science.gov (United States)

    Wintoft, Peter; Wik, Magnus

    2016-04-01

    In the ongoing EU/H2020 project PROGRESS models that predicts Kp, Dst, and AE from L1 solar wind data will be used as inputs to radiation belt models. The possible lead times from L1 measurements are shorter (10s of minutes to hours) than the typical duration of the physical phenomena that should be forecast. Under these circumstances several metrics fail to single out trivial cases, such as persistence. In this work we explore metrics and approaches for short lead time forecasts. We apply these to current Kp and Dst forecast models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637302.

  19. a system approach to the long term forecasting of the climat data in baikal region

    Science.gov (United States)

    Abasov, N.; Berezhnykh, T.

    2003-04-01

    The Angara river running from Baikal with a cascade of hydropower plants built on it plays a peculiar role in economy of the region. With view of high variability of water inflow into the rivers and lakes (long-term low water periods and catastrophic floods) that is due to climatic peculiarities of the water resource formation, a long-term forecasting is developed and applied for risk decreasing at hydropower plants. Methodology and methods of long-term forecasting of natural-climatic processes employs some ideas of the research schools by Academician I.P.Druzhinin and Prof. A.P.Reznikhov and consists in detailed investigation of cause-effect relations, finding out physical analogs and their application to formalized methods of long-term forecasting. They are divided into qualitative (background method; method of analogs based on solar activity), probabilistic and approximative methods (analog-similarity relations; discrete-continuous model). These forecasting methods have been implemented in the form of analytical aids of the information-forecasting software "GIPSAR" that provides for some elements of artificial intelligence. Background forecasts of the runoff of the Ob, the Yenisei, the Angara Rivers in the south of Siberia are based on space-time regularities that were revealed on taking account of the phase shifts in occurrence of secular maxima and minima on integral-difference curves of many-year hydrological processes in objects compared. Solar activity plays an essential role in investigations of global variations of climatic processes. Its consideration in the method of superimposed epochs has allowed a conclusion to be made on the higher probability of the low-water period in the actual inflow to Lake Baikal that takes place on the increasing branch of solar activity of its 11-year cycle. The higher probability of a high-water period is observed on the decreasing branch of solar activity from the 2nd to the 5th year after its maximum. Probabilistic method

  20. 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.

  1. TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?

    Institute of Scientific and Technical Information of China (English)

    YU Lean; WANG Shouyang; K. K. Lai; Y.Nakamori

    2005-01-01

    Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.

  2. A Modeler's Perspective on Space Weather Forecasting (Invited)

    Science.gov (United States)

    Wiltberger, M. J.

    2010-12-01

    Space physics is moving into a new era where numerical models originally developed for answering science questions are used as the basis for making operational space weather forecasts. Answering this challenge requires developments on multiple fronts requiring collaborations across space physics disciplines and between the research and operations communities. Since space weather in geospace is driven by the solar wind conditions a natural solution to improving the forecast lead time is to couple geospace models to heliospheric models. The quality of these forecast is dependent upon the ability of the heliospheric models to accurately model IMF Bz. Another challenge presented by moving into the forecasting arena is preparing the models for real-time operation which includes both computational performance and data redundancy issues. Moving into operations also presents modelers with an opportunity to assess their models performance over calculation intervals unprecedented duration. A key collaboration in the transition of models to operation is the discussion between forecasters and developers on what forecast parameters can accurately be predicted by the current generation of numerical models. This collaboration naturally includes a discussion of the definition of the best metrics to be used in quantitatively assessing performance.

  3. Comparative Election Forecasting: Further Insights from Synthetic Models

    OpenAIRE

    Michael S. Lewis-Beck; Dassonneville, Ruth

    2015-01-01

    As an enterprise, election forecasting has spread and grown. Initial work began in the 1980s in the United States, eventually travelling to Western Europe, where it finds a current outlet in the most of the region’s democracies. However, that work has been confined to traditional approaches – statistical modeling or poll-watching. We import a new approach, which we call synthetic modeling. These forecasts come from hybrid models blending structural knowledge with contemporary p...

  4. Forcing the snow-cover model SNOWPACK with forecasted weather data

    Directory of Open Access Journals (Sweden)

    S. Bellaire

    2011-12-01

    Full Text Available Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km2 and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.

  5. A model to forecast magma chamber rupture

    Science.gov (United States)

    Browning, John; Drymoni, Kyriaki; Gudmundsson, Agust

    2016-04-01

    An understanding of the amount of magma available to supply any given eruption is useful for determining the potential eruption magnitude and duration. Geodetic measurements and inversion techniques are often used to constrain volume changes within magma chambers, as well as constrain location and depth, but such models are incapable of calculating total magma storage. For example, during the 2012 unrest period at Santorini volcano, approximately 0.021 km3 of new magma entered a shallow chamber residing at around 4 km below the surface. This type of event is not unusual, and is in fact a necessary condition for the formation of a long-lived shallow chamber. The period of unrest ended without culminating in eruption, i.e the amount of magma which entered the chamber was insufficient to break the chamber and force magma further towards the surface. Using continuum-mechanics and fracture-mechanics principles, we present a model to calculate the amount of magma contained at shallow depth beneath active volcanoes. Here we discuss our model in the context of Santorini volcano, Greece. We demonstrate through structural analysis of dykes exposed within the Santorini caldera, previously published data on the volume of recent eruptions, and geodetic measurements of the 2011-2012 unrest period, that the measured 0.02% increase in volume of Santorini's shallow magma chamber was associated with magmatic excess pressure increase of around 1.1 MPa. This excess pressure was high enough to bring the chamber roof close to rupture and dyke injection. For volcanoes with known typical extrusion and intrusion (dyke) volumes, the new methodology presented here makes it possible to forecast the conditions for magma-chamber failure and dyke injection at any geodetically well-monitored volcano.

  6. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

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

  7. Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold

    Directory of Open Access Journals (Sweden)

    Qianjin Dong

    2015-11-01

    Full Text Available It is essential to consider the acceptable threshold in the assessment of a hydrological model because of the scarcity of research in the hydrology community and errors do not necessarily cause risk. Two forecast errors, including rainfall forecast error and peak flood forecast error, have been studied based on the reliability theory. The first order second moment (FOSM and bound methods are used to identify the reliability. Through the case study of the Dahuofang (DHF Reservoir, it is shown that the correlation between these two errors has great influence on the reliability index of hydrological model. In particular, the reliability index of the DHF hydrological model decreases with the increasing correlation. Based on the reliability theory, the proposed performance evaluation framework incorporating the acceptable forecast error threshold and correlation among the multiple errors can be used to evaluate the performance of a hydrological model and to quantify the uncertainties of a hydrological model output.

  8. A refined fuzzy time series model for stock market forecasting

    Science.gov (United States)

    Jilani, Tahseen Ahmed; Burney, Syed Muhammad Aqil

    2008-05-01

    Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.

  9. Forecasting electricity usage using univariate time series models

    Science.gov (United States)

    Hock-Eam, Lim; Chee-Yin, Yip

    2014-12-01

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

  10. 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.

  11. 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.

  12. The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

    Science.gov (United States)

    Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

    2015-10-01

    The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

  13. A Hybrid Model for the Mid-Long Term Runoff Forecasting by Evolutionary Computaion Techniques

    Institute of Scientific and Technical Information of China (English)

    Zou Xiu-fen; Kang Li-shan; Cae Hong-qing; Wu Zhi-jian

    2003-01-01

    The mid-long term hydrology forecasting is one of most challenging problems in hydrological studies. This paper proposes an efficient dynamical system prediction model using evolutionary computation techniques. The new model overcomes some disadvantages of conventional hydrology fore casting ones. The observed data is divided into two parts: the slow "smooth and steady" data, and the fast "coarse and fluctuation" data. Under the divide and conquer strategy, the behavior of smooth data is modeled by ordinary differential equations based on evolutionary modeling, and that of the coarse data is modeled using gray correlative forecasting method. Our model is verified on the test data of the mid-long term hydrology forecast in tbe northeast region of China. The experimental results show that the model is superior to gray system prediction model (GSPM).

  14. Forecasting Models for Hydropower Unit Stability Using LS-SVM

    Directory of Open Access Journals (Sweden)

    Liangliang Qiao

    2015-01-01

    Full Text Available This paper discusses a least square support vector machine (LS-SVM approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in Y-direction of lower generator bearing (LGB and pressure in draft tube (DT. A heuristic method such as a neural network using Backpropagation (NNBP is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.

  15. Regional Ocean Modeling System (ROMS): Oahu South Shore

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 2-day, 3-hourly forecast for the region surrounding the south shore of the island of Oahu at approximately 200-m resolution....

  16. Regional Ocean Modeling System (ROMS): Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the main Hawaiian islands at approximately 4-km resolution. While...

  17. Forecasting Forest Type and Age Classes in the Appalachian-Cumberland Subregion of the Central Hardwood Region

    Science.gov (United States)

    David N. Wear; Robert Huggett

    2011-01-01

    This chapter describes how forest type and age distributions might be expected to change in the Appalachian-Cumberland portions of the Central Hardwood Region over the next 50 years. Forecasting forest conditions requires accounting for a number of biophysical and socioeconomic dynamics within an internally consistent modeling framework. We used the US Forest...

  18. Verification of operational solar flare forecast: Case of Regional Warning Center Japan

    Science.gov (United States)

    Kubo, Yûki; Den, Mitsue; Ishii, Mamoru

    2017-08-01

    In this article, we discuss a verification study of an operational solar flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has been issuing four-categorical deterministic solar flare forecasts for a long time. In this forecast verification study, we used solar flare forecast data accumulated over 16 years (from 2000 to 2015). We compiled the forecast data together with solar flare data obtained with the Geostationary Operational Environmental Satellites (GOES). Using the compiled data sets, we estimated some conventional scalar verification measures with 95% confidence intervals. We also estimated a multi-categorical scalar verification measure. These scalar verification measures were compared with those obtained by the persistence method and recurrence method. As solar activity varied during the 16 years, we also applied verification analyses to four subsets of forecast-observation pair data with different solar activity levels. We cannot conclude definitely that there are significant performance differences between the forecasts of RWC Japan and the persistence method, although a slightly significant difference is found for some event definitions. We propose to use a scalar verification measure to assess the judgment skill of the operational solar flare forecast. Finally, we propose a verification strategy for deterministic operational solar flare forecasting. For dichotomous forecast, a set of proposed verification measures is a frequency bias for bias, proportion correct and critical success index for accuracy, probability of detection for discrimination, false alarm ratio for reliability, Peirce skill score for forecast skill, and symmetric extremal dependence index for association. For multi-categorical forecast, we propose a set of verification measures as marginal distributions of forecast and observation for bias, proportion correct for accuracy, correlation coefficient and joint probability distribution for association, the

  19. Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

    Science.gov (United States)

    Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

    2017-09-01

    In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The

  20. Medium Range Forecast (MRF) and Nested Grid Model (NGM)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nested Grid Model (NGM) and Medium Range Forecast (MRF) Archive is historical digital data set DSI-6140, archived at the NOAA National Centers for Environmental...

  1. Formation of an Integrated Stock Price Forecast Model in Lithuania

    Directory of Open Access Journals (Sweden)

    Audrius Dzikevičius

    2016-12-01

    Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

  2. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

    Science.gov (United States)

    Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew

    2016-10-01

    We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.

  3. Operational forecasting of daily temperatures in the Valencia Region. Part II: minimum temperatures in winter.

    Science.gov (United States)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of minimum temperatures during winter is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, winter minimum temperatures are considered a parameter of interest and concern since persistent cold-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict cold-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that low temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily minimum temperatures during winter over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the winter forecast period from 1 December 2007 - 31 March 2008. The results obtained are encouraging and indicate a good agreement between the observed and simulated minimum temperatures. Moreover, the model captures quite well the temperatures in the extreme cold episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia

  4. Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer.

    Science.gov (United States)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of summer maximum temperatures is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, summer maximum daily temperatures are considered a parameter of interest and concern since persistent heat-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict heat-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily maximum temperatures during summer over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the summer forecast period of 1 June - 30 September, 2007. The results obtained are encouraging and indicate a good agreement between the observed and simulated maximum temperatures. Moreover, the model captures quite well the temperatures in the extreme heat episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

  5. 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.

  6. Modeling And Forecasting Exchange-Rate Shocks

    OpenAIRE

    Andreou, A. S.; Zombanakis, George A.; Likothanassis, S. D.; Georgakopoulos, E.

    1998-01-01

    This paper considers the extent to which the application of neural networks methodology can be used in order to forecast exchange-rate shocks. Four major foreign currency exchange rates against the Greek Drachma as well as the overnight interest rate in the Greek market are employed in an attempt to predict the extent to which the local currency may be suffering an attack. The forecasting is extended to the estimation of future exchange rates and interest rates. The MLP proved to be highly ...

  7. Spatio-temporal modeling for real-time ozone forecasting.

    Science.gov (United States)

    Paci, Lucia; Gelfand, Alan E; Holland, David M

    2013-05-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.

  8. Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates

    Directory of Open Access Journals (Sweden)

    Piotr Białowolski

    2012-03-01

    Full Text Available The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators (BACE is employed. The models are atheoretical (i.e. they do not reflect causal relationships postulated by the macroeconomic theory and the role of regressors is played by business and consumer tendency survey-based indicators. Additionally, survey-based indicators are included with a lag that enables to forecast the variables of interest (GDP, unemployment, and inflation for the four forthcoming quarters without the need to make any additional assumptions concerning the values of predictor variables in the forecast period.  Bayesian Averaging of Classical Estimators is a method allowing for full and controlled overview of all econometric models which can be obtained out of a particular set of regressors. In this paper authors describe the method of generating a family of econometric models and the procedure for selection of a final forecasting model. Verification of the procedure is performed by means of out-of-sample forecasts of main economic variables for the quarters of 2011. The accuracy of the forecasts implies that there is still a need to search for new solutions in the atheoretical modelling.

  9. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  10. Modelling and forecasting Turkish residential electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Dilaver, Zafer, E-mail: Z.dilaver@surrey.ac.uk [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom); The Republic of Turkey Prime Ministry, PK 06573, Ankara (Turkey); Hunt, Lester C [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom)

    2011-06-15

    This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008. - Research Highlights: > Estimated short run and long run expenditure elasticities of 0.38 and 1.57, respectively. > Estimated short run and long run price elasticities of -0.09 and -0.38, respectively. > Estimated UEDT has increasing (i.e. energy using) and decreasing (i.e. energy saving) periods. > Predicted Turkish residential electricity demand between 48 and 80 TWh in 2020.

  11. A New Method for Grey Forecasting Model Group

    Institute of Scientific and Technical Information of China (English)

    李峰; 王仲东; 宋中民

    2002-01-01

    In order to describe the characteristics of some systems, such as the process of economic and product forecasting, a lot of discrete data may be used. Although they are discrete, the inside law can be-founded by some methods. For a series that the discrete degree is large and the integrated tendency is ascending, a new method for grey forecasting model group is given by the grey system theory. The method is that it firstly transforms original data, chooses some clique values and divides original data into groups by different clique values; then, it establishes non-equigap GM(1, 1) model for different groups and searches forecasting area of original data by the solution of model. At the end of the paper, the result of reliability of forecasting value is obtained. It is shown that the method is feasible.

  12. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  13. Modeling and forecasting the peak flows of a river

    Directory of Open Access Journals (Sweden)

    Mario Lefebvre

    2002-01-01

    Full Text Available A stochastic model is found for the value of the peak flows of the Mistassibi river in Québec, Canada, when the river is in spate. Next, the objective is to forecast the value of the coming peak flow about four days in advance, when the flow begins to show a marked increase. Both the stochastic model proposed in the paper and a model based on linear regression are used to produce the forecasts. The quality of the forecasts is assessed by considering the standard errors and the peak criterion. The forecasts are much more accurate than those obtained by taking the mean value of the previous peak flows.

  14. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  15. ECONOMIC FORECASTS BASED ON ECONOMETRIC MODELS USING EViews 5

    Directory of Open Access Journals (Sweden)

    Cornelia TomescuDumitrescu,

    2009-05-01

    Full Text Available The forecast of evolution of economic phenomena represent on the most the final objective of econometrics. It withal represent a real attempt of validity elaborate model. Unlike the forecasts based on the study of temporal series which have an recognizable inertial character the forecasts generated by econometric model with simultaneous equations are after to contour the future of ones of important economic variables toward the direct and indirect influences bring the bear on their about exogenous variables. For the relief of the calculus who the realization of the forecasts based on the econometric models its suppose is indicate the use of the specialized informatics programs. One of this is the EViews which is applied because it reduces significant the time who is destined of the econometric analysis and it assure a high accuracy of calculus and of the interpretation of results.

  16. Local and Nonlocal Impacts of Soil Moisture Initialization on AGCM Seasonal Forecasts: A Model Sensitivity Study.

    Science.gov (United States)

    Zhang, H.; Frederiksen, C. S.

    2003-07-01

    Using a version of the Australian Bureau of Meteorology Research Centre (BMRC) atmospheric general circulation model, this study investigates the model's sensitivity to different soil moisture initial conditions in its dynamically extended seasonal forecasts of June-August 1998 climate anomalies, with focus on the south and northeast China regions where severe floods occurred. The authors' primary aim is to understand the model's responses to different soil moisture initial conditions in terms of the physical and dynamical processes involved. Due to a lack of observed global soil moisture data, the efficacy of using soil moisture anomalies derived from the NCEP-NCAR reanalysis is assessed. Results show that by imposing soil moisture percentile anomalies derived from the reanalysis data into the BMRC model initial condition, the regional features of the model's simulation of seasonal precipitation and temperature anomalies are modulated. Further analyses reveal that the impacts of soil moisture conditions on the model's surface temperature forecasts are mainly from localized interactions between land surface and the overlying atmosphere. In contrast, the model's sensitivity in its forecasts of rainfall anomalies is mainly due to the nonlocal impacts of the soil moisture conditions. Over the monsoon-dominated east Asian region, the contribution from local water recycling, through surface evaporation, to the model simulation of precipitation is limited. Rather, it is the horizontal moisture transport by the regional atmospheric circulation that is the dominant factor in controlling the model rainfall. The influence of different soil moisture conditions on the model forecasts of rainfall anomalies is the result of the response of regional circulation to the anomalous soil moisture condition imposed. Results from the BMRC model sensitivity study support similar findings from other model studies that have appeared in recent years and emphasize the importance of improving

  17. Flood Forecast and Early Warning with High-Resolution Ensemble Rainfall from Numerical Weather Prediction Model

    OpenAIRE

    Yu, Wansik; NAKAKITA, Eiichi; Jung, Kwansue

    2016-01-01

    This paper investigates the applicability of ensemble forecasts of numerical weather prediction (NWP) model for flood forecasting. In this study, 10 km resolution ensemble rainfalls forecast and their downscaled forecasts of 2 km resolution were used in the hydrologic model as input data for flood forecasting and application of flood early warning. Ensemble data consists of 51 members and 48 hr forecast time. Ensemble outputs are verified spatially whether they can produce suitable rainfall p...

  18. Attractor-based models for individual and groups’ forecasting

    Science.gov (United States)

    Astakhova, N. N.; Demidova, L. A.; Kuzovnikov, A. V.; Tishkin, R. V.

    2017-02-01

    In this paper the questions of the attractors’ application in case of the development of the forecasting models on the base of the strictly binary trees have been considered. Usually, these models use the short time series as the training data sequence. The application of the principles of the attractors’ forming on the base of the long time series will allow creating the training data sequence more reasonably. The offered approach to creation of the training data sequence for the forecasting models on the base of the strictly binary trees was applied for the individual and groups’ forecasting of time series. At the same time the problems of one-objective and multiobjective optimization on the base of the modified clonal selection algorithm have been considered. The reviewed examples confirm the efficiency of the attractors’ application in sense of minimization of the used quality indicators of the forecasting models, and also the forecasting errors on 1 – 5 steps forward. Besides, the minimization of time expenditures for the development of the forecasting models is provided.

  19. Coupling meteorological and hydrological models for flood forecasting

    Directory of Open Access Journals (Sweden)

    Bartholmes

    2005-01-01

    Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

  20. Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

    Science.gov (United States)

    Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

    2011-12-01

    The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.