WorldWideScience

Sample records for regional ensemble forecasts

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Multimodel SuperEnsemble technique for quantitative precipitation forecasts in Piemonte region

    Directory of Open Access Journals (Sweden)

    D. Cane

    2010-02-01

    Full Text Available The Multimodel SuperEnsemble technique is a powerful post-processing method for the estimation of weather forecast parameters reducing direct model output errors. It has been applied to real time NWP, TRMM-SSM/I based multi-analysis, Seasonal Climate Forecasts and Hurricane Forecasts. The novelty of this approach lies in the methodology, which differs from ensemble analysis techniques used elsewhere.

    Several model outputs are put together with adequate weights to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure, the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts, involving a new accurate statistical method for bias correction and a wide spectrum of results over Piemonte very dense non-GTS weather station network.

  9. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  10. CME Ensemble Forecasting - A Primer

    Science.gov (United States)

    Pizzo, V. J.; de Koning, C. A.; Cash, M. D.; Millward, G. H.; Biesecker, D. A.; Codrescu, M.; Puga, L.; Odstrcil, D.

    2014-12-01

    SWPC has been evaluating various approaches for ensemble forecasting of Earth-directed CMEs. We have developed the software infrastructure needed to support broad-ranging CME ensemble modeling, including composing, interpreting, and making intelligent use of ensemble simulations. The first step is to determine whether the physics of the interplanetary propagation of CMEs is better described as chaotic (like terrestrial weather) or deterministic (as in tsunami propagation). This is important, since different ensemble strategies are to be pursued under the two scenarios. We present the findings of a comprehensive study of CME ensembles in uniform and structured backgrounds that reveals systematic relationships between input cone parameters and ambient flow states and resulting transit times and velocity/density amplitudes at Earth. These results clearly indicate that the propagation of single CMEs to 1 AU is a deterministic process. Thus, the accuracy with which one can forecast the gross properties (such as arrival time) of CMEs at 1 AU is determined primarily by the accuracy of the inputs. This is no tautology - it means specifically that efforts to improve forecast accuracy should focus upon obtaining better inputs, as opposed to developing better propagation models. In a companion paper (deKoning et al., this conference), we compare in situ solar wind data with forecast events in the SWPC operational archive to show how the qualitative and quantitative findings presented here are entirely consistent with the observations and may lead to improved forecasts of arrival time at Earth.

  11. A Regional Ensemble Forecast System for Stratiform Precipitation Events in the Northern China Region.Part Ⅱ: Seasonal Evaluation for Summer 2010

    Institute of Scientific and Technical Information of China (English)

    ZHU Jiangshan; KONG Fanyou; LEI Hengchi

    2013-01-01

    In this study,the Institute of Atmospheric Physics,Chinese Academy of Sciences-regional ensemble forecast system (IAP-REFS) described in Part Ⅰ was further validated through a 65-day experiment using the summer season of 2010.The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts (QPF) and probabilistic QPF,but it has a systematic bias in forecasting near-surface variables.Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts.In this study,the perturbation extraction and inflation method (proposed with the single case study in Part Ⅰ) was further applied to the full season with different inflation factors.This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables.The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.

  12. Ensemble Forecasting of Major Solar Flares

    CERN Document Server

    Guerra, J A; Uritsky, V M

    2015-01-01

    We present the results from the first ensemble prediction model for major solar flares (M and X classes). Using the probabilistic forecasts from three models hosted at the Community Coordinated Modeling Center (NASA-GSFC) and the NOAA forecasts, we developed an ensemble forecast by linearly combining the flaring probabilities from all four methods. Performance-based combination weights were calculated using a Monte Carlo-type algorithm by applying a decision threshold $P_{th}$ to the combined probabilities and maximizing the Heidke Skill Score (HSS). Using the probabilities and events time series from 13 recent solar active regions (2012 - 2014), we found that a linear combination of probabilities can improve both probabilistic and categorical forecasts. Combination weights vary with the applied threshold and none of the tested individual forecasting models seem to provide more accurate predictions than the others for all values of $P_{th}$. According to the maximum values of HSS, a performance-based weights ...

  13. 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

  14. Total probabilities of ensemble runoff forecasts

    Science.gov (United States)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2017-04-01

    Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes

  15. Total probabilities of ensemble runoff forecasts

    Science.gov (United States)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2016-04-01

    Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative

  16. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    OpenAIRE

    Jinyin Ye; Yuehong Shao; Zhijia Li

    2016-01-01

    TIGGE (THORPEX International Grand Global Ensemble) was a major part of the THORPEX (Observing System Research and Predictability Experiment). It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability fo...

  17. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller

    2008-12-01

    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  18. Analysis of mesoscale forecasts using ensemble methods

    CERN Document Server

    Gross, Markus

    2016-01-01

    Mesoscale forecasts are now routinely performed as elements of operational forecasts and their outputs do appear convincing. However, despite their realistic appearance at times the comparison to observations is less favorable. At the grid scale these forecasts often do not compare well with observations. This is partly due to the chaotic system underlying the weather. Another key problem is that it is impossible to evaluate the risk of making decisions based on these forecasts because they do not provide a measure of confidence. Ensembles provide this information in the ensemble spread and quartiles. However, running global ensembles at the meso or sub mesoscale involves substantial computational resources. National centers do run such ensembles, but the subject of this publication is a method which requires significantly less computation. The ensemble enhanced mesoscale system presented here aims not at the creation of an improved mesoscale forecast model. Also it is not to create an improved ensemble syste...

  19. Precipitation and temperature ensemble forecasts from single-value forecasts

    Directory of Open Access Journals (Sweden)

    J. Schaake

    2007-04-01

    Full Text Available A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004. The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved.

    Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods.

    The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other

  20. Ozone ensemble forecast with machine learning algorithms

    OpenAIRE

    Mallet, Vivien; Stoltz, Gilles; Mauricette, Boris

    2009-01-01

    International audience; We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrati...

  1. 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.

  2. Seamless Hourly Rainfall Ensemble Forecasts for 0 - 10 days

    Science.gov (United States)

    Cooper, Shaun; Seed, Alan

    2014-05-01

    The Australian Bureau of Meteorology uses a number of Numerical Weather Prediction (NWP) models to generate deterministic rainfall forecasts over a range of lead-times, each with a different resolution in space and time and with different forecast domains. High resolution regional NWP models are used to generate forecasts for the first three days, and are typically more accurate than lower resolution Global NWP models that produce forecasts for longer lead times. Consequently, there is a requirement for a seamless forecast system that is able to blend the various NWP forecasts into a single forecast with a uniform resolution over the entire forecast period. NWP rainfall forecasts contain errors at scales that are significant for even large river basins, and ensemble hydrological prediction systems require ensembles of the order of 100 members, which is well beyond the size that can be generated by NWP ensemble systems. The idea, therefore, is to blend the NWP models in such a way that recognises the skill of the NWP at a particular scale and lead time and to use a stochastic model of forecast errors to perturb the blended deterministic forecast to generate a large ensemble. NWP uncertainties are scale and forecast lead time dependent, especially at long forecast lead times, and are characteristic to each model. By blending the models scale by scale it is possible to recognise the increased skill of the models at larger spatial scales and shorter lead times. The stochastic model is applied at each scale, adding increasingly more variability at smaller spatial scales, while preserving the space-time structure of rain. This process allows an ensemble to be generated by blending deterministic forecasts. Two NWP models from the Bureau, ACCESS-G (Global) (~40 km by ~40 km, 3 hourly out to 10 days) and ACCESS-R (Regional) (~12 km by ~12 km, 1 hourly out to 3 days), are downscaled and blended with the stochastic model to produce an ensemble of hourly forecasts out to 10

  3. Layered Ensemble Architecture for Time Series Forecasting.

    Science.gov (United States)

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  4. Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

    Science.gov (United States)

    Bergeron, Jean M.; Trudel, Mélanie; Leconte, Robert

    2016-10-01

    The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered

  5. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    Directory of Open Access Journals (Sweden)

    Jinyin Ye

    2016-01-01

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

  6. Global Ensemble Forecast System (GEFS) [2.5 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  7. Towards reliable seasonal ensemble streamflow forecasts for ephemeral rivers

    Science.gov (United States)

    Bennett, James; Wang, Qj; Li, Ming; Robertson, David

    2016-04-01

    Despite their inherently variable nature, ephemeral rivers are an important water resource in many dry regions. Water managers are likely benefit considerably from even mildly skilful ensemble forecasts of streamflow in ephemeral rivers. As with any ensemble forecast, forecast uncertainty - i.e., the spread of the ensemble - must be reliably quantified to allow users of the forecasts to make well-founded decisions. Correctly quantifying uncertainty in ephemeral rivers is particularly challenging because of the high incidence of zero flows, which are difficult to handle with conventional statistical techniques. Here we apply a seasonal streamflow forecasting system, the model for generating Forecast Guided Stochastic Scenarios (FoGSS), to 26 Australian ephemeral rivers. FoGSS uses post-processed ensemble rainfall forecasts from a coupled ocean-atmosphere prediction system to force an initialised monthly rainfall runoff model, and then applies a staged hydrological error model to describe and propagate hydrological uncertainty in the forecast. FoGSS produces 12-month streamflow forecasts; as forecast skill declines with lead time, the forecasts are designed to transit seamlessly to stochastic scenarios. The ensemble rainfall forecasts used in FoGSS are known to be unbiased and reliable, and we concentrate here on the hydrological error model. The FoGSS error model has several features that make it well suited to forecasting ephemeral rivers. First, FoGSS models the error after data is transformed with a log-sinh transformation. The log-sinh transformation is able to normalise even highly skewed data and homogenise its variance, allowing us to assume that errors are Gaussian. Second, FoGSS handles zero values using data censoring. Data censoring allows streamflow in ephemeral rivers to be treated as a continuous variable, rather than having to model the occurrence of non-zero values and the distribution of non-zero values separately. This greatly simplifies parameter

  8. Seasonal hydrological ensemble forecasts over Europe

    Science.gov (United States)

    Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian

    2015-04-01

    Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called Ensemble Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.

  9. Ensemble postprocessing for probabilistic quantitative precipitation forecasts

    Science.gov (United States)

    Bentzien, S.; Friederichs, P.

    2012-12-01

    Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop ensemble prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an ensemble setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an ensemble system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. Ensemble systems like COSMO-DE-EPS are often limited with respect to ensemble size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient ensemble spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated ensemble system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical

  10. Ensemble Forecasting of Major Solar Flares -- First Results

    Science.gov (United States)

    Pulkkinen, A. A.; Guerra, J. A.; Uritsky, V. M.

    2015-12-01

    We present the results from the first ensemble prediction model for major solar flares (M and X classes). Using the probabilistic forecasts from three models hosted at the Community Coordinated Modeling Center (NASA-GSFC) and the NOAA forecasts, we developed an ensemble forecast by linearly combining the flaring probabilities from all four methods. Performance-based combination weights were calculated using a Monte-Carlo-type algorithm that applies a decision threshold PthP_{th} to the combined probabilities and maximizing the Heidke Skill Score (HSS). Using the data for 13 recent solar active regions between years 2012 - 2014, we found that linear combination methods can improve the overall probabilistic prediction and improve the categorical prediction for certain values of decision thresholds. Combination weights vary with the applied threshold and none of the tested individual forecasting models seem to provide more accurate predictions than the others for all values of PthP_{th}. According to the maximum values of HSS, a performance-based weights calculated by averaging over the sample, performed similarly to a equally weighted model. The values PthP_{th} for which the ensemble forecast performs the best are 25 % for M-class flares and 15 % for X-class flares. When the human-adjusted probabilities from NOAA are excluded from the ensemble, the ensemble performance in terms of the Heidke score, is reduced.

  11. Dynamic Analogue Initialization for Ensemble Forecasting

    Institute of Scientific and Technical Information of China (English)

    LI Shan; RONG Xingyao; LIU Yun; LIU Zhengyu; Klaus FRAEDRICH

    2013-01-01

    This paper introduces a new approach for the initialization of ensemble numerical forecasting:Dynamic Analogue Initialization (DAI).DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future.As such,DAI performs the ensemble forecast using the best analogues from a full size ensemble.As a pilot study,the Lorenz63 and Lorenz96 models were used to test DAI's effectiveness independently.Results showed that DAI can improve the forecast significantly.Especially in lower-dimensional systems,DAI can reduce the forecast RMSE by ~50% compared to the Monte Carlo forecast (MC).This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error.Meanwhile,a potential improvement of DAI is also proposed,and that is to find the optimal range of embedding time based on the error's growing speed.

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

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

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

  13. Seasonal hydrological ensemble forecasts over Europe

    Science.gov (United States)

    Arnal, Louise; Wetterhall, Fredrik; Stephens, Elisabeth; Cloke, Hannah; Pappenberger, Florian

    2016-04-01

    This study investigates the limits of predictability in dynamical seasonal discharge forecasting, in both space and time, over Europe. Seasonal forecasts have an important socioeconomic value. Applications are numerous and cover hydropower management, spring flood prediction, low flow prediction for navigation and agricultural water demands. Additionally, the constant increase in NWP skill for longer lead times and the predicted increase in the intensity and frequency of hydro-meteorological extremes, have amplified the incentive to promote and further improve hydrological forecasts on sub-seasonal to seasonal timescales. In this study, seasonal hydrological forecasts (SEA), driven by the ECMWF's System 4 in hindcast mode, were analysed against an Ensemble Streamflow Prediction (ESP) benchmark. The ESP was forced with an ensemble of resampled historical meteorological observations and started with perfect initial conditions. Both forecasts were produced by the LISFLOOD model, run on the pan-European scale with a spatial resolution of 5 by 5 km. The forecasts were issued monthly on a daily time step, from 1990 until the current time, up to a lead time of 7 months. The seasonal discharge forecasts were analysed against the ESP on a catchment scale in terms of their accuracy, skill and sharpness, using a diverse set of verification metrics (e.g. KGE, CRPSS and ROC). Additionally, a reverse-ESP was constructed by forcing the LISFLOOD model with a single perfect meteorological set of observations and initiated from an ensemble of resampled historical initial conditions. The comparison of the ESP with the reverse-ESP approach enabled the identification of the respective contribution of meteorological forcings and hydrologic initial conditions errors to seasonal discharge forecasting uncertainties in Europe. These results could help pinpoint target elements of the forecasting chain which, after being improved, could lead to substantial increase in discharge predictability

  14. Probabilistic flood forecasting on the Rhone River: evaluation with ensemble and analogue-based precipitation forecasts

    Directory of Open Access Journals (Sweden)

    Bellier Joseph

    2016-01-01

    Full Text Available Hydrological ensemble forecasting performances are analysed over 5 basins up to 2000 km2 in the French Upper Rhone region. Streamflow forecasts are issued at an hourly time step from lumped ARX rainfall-runoff models forced by different precipitation forecasts. Ensemble meteorological forecasts from ECMWF and NCEP are considered, as well as analogue-based forecasts fed by their corresponding control forecast. Analogue forecasts are rearranged using an adaptation of the Schaake-Shuffle method in order to ensure the temporal coherence. A new evaluation approach is proposed, separating forecasting performances on peak amplitudes and peak timings for high flow events. Evaluation is conducted against both simulated and observed streamflow (so that relative meteorological and hydrological uncertainties can be assessed, by means of CRPS and rank histograms, over the 2007-2014 period. Results show a general agreement of the forecasting performances when averaged over the 5 basins. However, ensemble-based and analogue-based streamflow forecasts produce a different signature on peak events in terms of bias, spread and reliability. Strengths and weaknesses of both approaches are discussed as well as potential improvements, notably towards their merging.

  15. Evaluating Tropical Cyclone Forecast Track Uncertainty Using A Grand Ensemble of Ensemble Prediction Systems

    Science.gov (United States)

    2011-09-01

    variable is appropriately sized for the region ( UCAR 2010). 4. An Isotropic Joint-Ensemble Majumdar and Finochio (2010) develop a probability circle...Forecasting, 22, 671–675. UCAR , cited 2010: NCEP Perturbation Method. [Available online at http://www.meted.ucar.edu/nwp/pcu2/ens_matrix

  16. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    CERN Document Server

    Bouallegue, Zied Ben; Theis, Susanne E; Pinson, Pierre

    2015-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost.For example, the ensemble copula coupling (ECC) method consists in rebuilding the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach which preserves the dynamical development of the ensemble members is called dynamic ensemble copula coupling (...

  17. Role of Hydrological Ensemble Forecasts in Operational Decision Making

    Science.gov (United States)

    Ramaswamy, V.; Saleh, F.; Georgas, N.; Blumberg, A. F.

    2016-12-01

    Considerable importance has been placed on addressing uncertainties in hydrologic forecasts, particularly in regards to operational decision making. This work investigates the utility of short term hydrological ensemble forecasts for operational decision making using meteorological inputs from more than 100 ensemble members from different numerical weather prediction (NWP) models. To this end, an advanced automated hydrologic framework comprising of a regional scale hydrologic model, GIS datasets and the meteorological ensemble predictions from different weather prediction facilities was implemented over the Hudson and Raritan River basins, USA. The uncertainties associated with ensemble streamflow forecasts was analysed for three different flood events classified as minor, moderate and major. This was done by visually and statistically comparing the spread, magnitude and timing of the peak of the hydrologic outputs. Results from this work demonstrate the effectiveness of different NWP models for different operational scenarios, thus providing a better understanding of the uncertainties and risks associated with decision making. In addition to gaining insights into the risks associated with issuing flood alerts, this work also offers useful perspectives on the operationally managing water resources.

  18. 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.

  19. 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.

  20. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2015-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost.For example, the ensemble copula coupling (ECC) method consists in rebuilding the multivariate aspect of the forecast...... from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error...

  1. Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2016-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from...... the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new...

  2. Ensemble data assimilation with an adjusted forecast spread

    Directory of Open Access Journals (Sweden)

    Sabrina Rainwater

    2013-04-01

    Full Text Available Ensemble data assimilation typically evolves an ensemble of model states whose spread is intended to represent the algorithm's uncertainty about the state of the physical system that produces the data. The analysis phase treats the forecast ensemble as a random sample from a background distribution, and it transforms the ensemble according to the background and observation error statistics to provide an appropriate sample for the next forecast phase. We find that in the presence of model nonlinearity and model error, it can be fruitful to rescale the ensemble spread prior to the forecast and then reverse this rescaling after the forecast. We call this approach forecast spread adjustment, which we discuss and test in this article using an ensemble Kalman filter and a 2005 model due to Lorenz. We argue that forecast spread adjustment provides a tunable parameter, that is, complementary to covariance inflation, which cumulatively increases ensemble spread to compensate for underestimation of uncertainty. We also show that as the adjustment parameter approaches zero, the filter approaches the extended Kalman filter if the ensemble size is sufficiently large. We find that varying the adjustment parameter can significantly reduce analysis and forecast errors in some cases. We evaluate how the improvement provided by forecast spread adjustment depends on ensemble size, observation error and model error. Our results indicate that the technique is most effective for small ensembles, small observation error and large model error, though the effectiveness depends significantly on the nature of the model error.

  3. Evaluation of seasonal ensemble forecasts in Norway

    Science.gov (United States)

    Tore Sinnes, Svein; Engeland, Kolbjørn; Langsholt, Elin; Roar Sælthun, Nils

    2017-04-01

    Throughout the winter and spring season, seasonal forecasts are used by the Norwegian Water Resources and Energy Directorate (NVE) in order to assess the probability for sever floods or for low seasonal runoff volumes. The latter is especially important for hydropower production. The seasonal forecasts are generated by a set of 145 lumped, elevation distributed HBV models distributed all over Norway. The observed weather is used to establish the initial snow cover, soil moisture and groundwater levels in the HBV model. Subsequently, scenarios are created by using time series of observed weather the previous 50 years, creating a total of 50 ensembles. The predictability of this seasonal forecasting system depends therefore on the importance of the initial conditions, and in Norway the seasonal snow cover is especially important. The aim of this study is to evaluate the performance of the seasonal forecasts of flood peaks and seasonal runoff volumes and especially to evaluate of the predictability depends on (i) catchment climatology and (ii) issue dates and lead times. For achieving these aims, evaluation criterions assessing reliability and sharpness were used. The results shows that the predictability is the highest for catchments where the spring runoff is dominated by snow melt. The predictability is the highest for the shortest lead times (up to 1 months ahead).The predictive performance is higher for runoff volumes than for the flood peaks.

  4. Demonstrating the value of larger ensembles in forecasting physical systems

    Directory of Open Access Journals (Sweden)

    Reason L. Machete

    2016-12-01

    Full Text Available Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model's dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target differs from the forecasting context, where one is given a high fidelity (but imperfect model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation, the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by

  5. Discrete post-processing of total cloud cover ensemble forecasts

    Science.gov (United States)

    Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

    2017-04-01

    This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

  6. The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across England and Wales

    OpenAIRE

    J. Schellekens; Weerts, A. H.; Moore, R J; Pierce, C.E.; S. Hildon

    2011-01-01

    Operational flood forecasting systems share a fundamental challenge: forecast uncertainty which needs to be considered when making a flood warning decision. One way of representing this uncertainty is through employing an ensemble approach. This paper presents research funded by the Environment Agency in which ensemble rainfall forecasts are utilised and tested for operational use. The form of ensemble rainfall forecast used is the Met Office short-range product called MOGRE...

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

    Directory of Open Access Journals (Sweden)

    P. Tao

    2015-05-01

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

  8. Stochastic and dynamical downscaling of ensemble precipitation forecasts

    Science.gov (United States)

    Brussolo, E.; von Hardenberg, J.; Rebora, N.

    2009-04-01

    Forecasting hydrogeological risk in small basins requires quantitative forecasts and an estimate of the probability of occurrence of severe, localized precipitation events at spatial scales of the order of tens of kilometers or less, significantly smaller than those currently provided by large scale, global, ensemble forecasting systems (EPS). Dynamically based forecasts at these scales can be obtained extending EPS scenarios with high-resolution, non-hydrostatic, limited area ensemble prediction systems. An alternative is represented by the direct application of stochastic downscaling techniques to the large scale ensemble forecasts. This work compares the performances of these two very different ensemble forecast downscaling approaches. To this purpose we consider ensemble forecasts provided by the ECMWF EPS, downscaled in space using the RainFARM stochastic technique [1], and ensembles of forecasts obtained from the COSMO-LEPS limited area prediction system (which also uses ECMWF EPS ensemble members as boundary conditions), for three intense precipitation events over northern Italy in 2006. The statistical properties of the fields produced with these two techniques are compared and the skill of the resulting ensembles is verified against direct precipitation measurements from a dense network of rain gauges. Reference: 1. Rebora, N., L. Ferraris, J. von Hardenberg, and A. Provenzale, 2006: The RainFARM: Rainfall Downscaling by a Filtered AutoRegressive Model. J. Hydrometeorol., 7, 724-738.

  9. Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)

    Science.gov (United States)

    Schunk, R. W.; Scherliess, L.; Eccles, V.; Gardner, L. C.; Sojka, J. J.; Zhu, L.; Pi, X.; Mannucci, A. J.; Butala, M.; Wilson, B. D.; Komjathy, A.; Wang, C.; Rosen, G.

    2016-07-01

    The goal of the Multimodel Ensemble Prediction System (MEPS) program is to improve space weather specification and forecasting with ensemble modeling. Space weather can have detrimental effects on a variety of civilian and military systems and operations, and many of the applications pertain to the ionosphere and upper atmosphere. Space weather can affect over-the-horizon radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the Federal Aviation Administration (FAA's) Wide Area Augmentation System (WAAS). Because of its importance, numerous space weather forecasting approaches are being pursued, including those involving empirical, physics-based, and data assimilation models. Clearly, if there are sufficient data, the data assimilation modeling approach is expected to be the most reliable, but different data assimilation models can produce different results. Therefore, like the meteorology community, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere-Thermosphere-Electrodynamics (ITE) system that is based on different data assimilation models. The MEPS ensemble is composed of seven physics-based data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and middle to low latitude ionosphere-electrodynamics. Hence, multiple data assimilation models can be used to describe each region. A selected storm event that was reconstructed with four different data assimilation models covering the middle and low latitude ionosphere is presented and discussed. In addition, the effect of different data types on the reconstructions is shown.

  10. Ensemble Forecasting of Volcanic Emissions in Hawai’i

    Directory of Open Access Journals (Sweden)

    Andre Kristofer Pattantyus

    2015-03-01

    Full Text Available Deterministic model forecasts do not convey to the end users the forecast uncertainty the models possess as a result of physics parameterizations, simplifications in model representation of physical processes, and errors in initial conditions. This lack of understanding leads to a level of uncertainty in the forecasted value when only a single deterministic model forecast is available. Increasing computational power and parallel software architecture allows multiple simulations to be carried out simultaneously that yield useful measures of model uncertainty that can be derived from ensemble model results. The Hybrid Single Particle Lagrangian Integration Trajectory and Dispersion model has the ability to generate ensemble forecasts. A meteorological ensemble was formed to create probabilistic forecast products and an ensemble mean forecast for volcanic emissions from the Kilauea volcano that impacts the state of Hawai’i. The probabilistic forecast products show uncertainty in pollutant concentrations that are especially useful for decision-making regarding public health. Initial comparison of the ensemble mean forecasts with observations and a single model forecast show improvements in event timing for both sulfur dioxide and sulfate aerosol forecasts.  

  11. Statistical ensemble postprocessing for precipitation forecasting during the West African Monsoon

    Science.gov (United States)

    Vogel, Peter; Gneiting, Tilmann; Knippertz, Peter; Fink, Andreas H.; Schlüter, Andreas

    2017-04-01

    Precipitation forecasts for one up to several days are of high socioeconomic importance for agriculturally dominated societies in West Africa. In this contribution, we evaluate the performance of operational European Centre for Medium-Range Weather Forecasts (ECWMF) raw ensemble and statistically postprocessed against climatological precipitation forecasts for accumulation periods of 1 to 5 days for the monsoon periods (May to mid-October) from 2007 to 2014. We use Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) as state-of-the-art postprocessing methods and verify against station and gridded Tropical Rainfall Measuring Mission (TRMM) observations. Based on a subset of past forecast—observation-pairs, statistical postprocessing corrects ensemble forecasts for biases and dispersion errors. For the midlatitudes, statistical postprocessing has demonstrated its added value for a wide range of meteorological quantities and this contribution is the first to apply it to precipitation forecasts over West Africa, where the high degree of convective organization at the mesoscale makes precipitation forecasts particularly challenging. The raw ECMWF ensemble predictions of accumulated precipitation are poor compared to climatological forecasts and exhibit strong dispersion errors and biases. For the Guinea Coast, we find a substantial wet bias of the ECMWF ensemble and more than every second ensemble forecast fails to capture the verifying observation within its forecast range. Postprocessed forecasts clearly outperform ECMWF raw ensemble forecasts by correcting for biases and dispersion errors, but disappointingly reveal only slight, if any, improvements compared to climatological forecasts. These results hold across verification regions and years, for 1 to 5-day accumulated precipitation forecasts, and for station and gridded observations. We investigate different spatial accumulation sizes from 0.25 x 0.25° to 5 x 2° longitude

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

    OpenAIRE

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

    2015-01-01

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

  13. Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting

    Science.gov (United States)

    Hopson, T. M.

    2014-12-01

    One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.

  14. A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

    Directory of Open Access Journals (Sweden)

    G. Thirel

    2010-08-01

    Full Text Available The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF 10-day Ensemble Prediction System (EPS. Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm

  15. 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.

  16. Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

    Science.gov (United States)

    Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

    2017-04-01

    ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i

  17. Adaptive calibration of (u,v)‐wind ensemble forecasts

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    Ensemble forecasts of (u,v)‐wind are of crucial importance for a number of decision‐making problems related to e.g. air traffic control, ship routeing and energy management. The skill of these ensemble forecasts as generated by NWP‐based models can be maximised by correcting for their lack...... of sufficient reliability. The original framework introduced here allows for an adaptive bivariate calibration of these ensemble forecasts. The originality of this methodology lies in the fact that calibrated ensembles still consist of a set of (space–time) trajectories, after translation and dilation...... on the adaptive calibration of ECMWF ensemble forecasts of (u,v)‐wind at 10 m above ground level over Europe over a three‐year period between December 2006 and December 2009. Substantial improvements in (bivariate) reliability and in various deterministic/probabilistic scores are observed. Finally, the maps...

  18. Ensemble Bayesian forecasting system Part I: Theory and algorithms

    Science.gov (United States)

    Herr, Henry D.; Krzysztofowicz, Roman

    2015-05-01

    The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of

  19. Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

    Science.gov (United States)

    Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

    2013-04-01

    Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the

  20. Theory and Practice of Phase-aware Ensemble Forecasting

    Science.gov (United States)

    Schulte, J. A.; Georgas, N.

    2016-12-01

    The timing of events represents a source of uncertainty in ensemble forecasting that can produce misleading ensemble statistics. A general theory is presented to overcome drawbacks of traditional ensemble forecasting statistics that perform poorly in the presence of timing disagreements among ensemble members. It was shown, in particular, that ensemble forecasts containing substantial uncertainty in timing can produce non-trivial higher-order statistical moments, rendering the ensemble mean inappropriate as a best available estimate of the future state of the forecast parameter in question. A set of theoretical experiments showed that the existence of large timing differences among ensemble members can produce negative ensemble skewness even when the ensemble members are sinusoids whose amplitudes are drawn from a normal distribution: Consistently, the ensemble mean will tend to fall on the left tail of the normal distribution representing the originally sampled amplitudes, rather than at the mean or median. To remedy the left-tail placement problem of the ensemble mean, a new generally applicable ensemble statistic - the phase-aware ensemble mean - is proposed that is more robust against ensemble skewness resulting from timing spread. The computation of the phase-aware mean involves the transformation of all ensemble members to wavelet space and the subsequent inverse wavelet transformation of the product of the ensemble mean wavelet phase and modulus back to the time domain. The new methods were applied to storm surge reforecasts for Hurricane Irene and Sandy at 8 stations located around the New York City metropolitan area. The phase-aware ensemble mean was found to perform better at detecting the magnitude of events compared to the traditional ensemble mean, consistent with the results from theoretical experiments. The ensemble mean, moreover, was found to be consistently located on the left tail of distributions representing future peak storm surge outcomes. A

  1. Trends in the predictive performance of raw ensemble weather forecasts

    Science.gov (United States)

    Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

    2015-04-01

    Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near

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

    Science.gov (United States)

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

    2016-04-01

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

  3. A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

    Directory of Open Access Journals (Sweden)

    G. Thirel

    2010-04-01

    Full Text Available The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Such systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF 10-day Ensemble Prediction System (EPS. Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc., especially

  4. Skill forecasting from ensemble predictions of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

    2009-01-01

    Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...

  5. Verification of ensemble forecasts of Mediterranean high-impact weather events against satellite observations

    Directory of Open Access Journals (Sweden)

    J.-P. Chaboureau

    2012-08-01

    Full Text Available Ensemble forecasts at kilometre scale of two severe storms over the Mediterranean region are verified against satellite observations. In complement to assessing the forecasts against ground-based measurements, brightness temperature (BT images are computed from forecast fields and directly compared to BTs observed from satellite. The so-called model-to-satellite approach is very effective in identifying systematic errors in the prediction of cloud cover for BTs in the infrared window and in verifying the forecasted convective activity with BTs in the microwave range. This approach is combined with the calculation of meteorological scores for an objective evaluation of ensemble forecasts. The application of the approach is shown in the context of two Mediterranean case studies, a tropical-like storm and a heavy precipitating event. Assessment of cloud cover and convective activity using satellite observations in the infrared (10.8 μm and microwave regions (183–191 GHz provides results consistent with other traditional methods using rainfall measurements. In addition, for the tropical-like storm, differences among forecasts occur much earlier in terms of cloud cover and deep convective activity than they do in terms of deepening and track. Further, the underdispersion of the ensemble forecasts of the two high-impact weather events is easily identified with satellite diagnostics. This suggests that such an approach could be a useful method for verifying ensemble forecasts, particularly in data-sparse regions.

  6. Extracting Value from Ensembles for Cloud-Free Forecasting

    Science.gov (United States)

    2011-09-01

    for Medium range Weather Forecasting EMean Ensemble mean ETR Ensemble transform with rescaling EUMETSAT European Organization for the...transform method (ET) with rescaling ( ETR ) to define the initial atmospheric uncertainty (Wei et al. 2008). Adapted from the ET method devised by...variances of each grid point to further restrain the initial ensemble spread. The ETR method replaced the breeding method in GEFS during NCEP’s May

  7. Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting

    Directory of Open Access Journals (Sweden)

    B. Vincendon

    2011-05-01

    Full Text Available Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.

  8. Online probabilistic learning with an ensemble of forecasts

    Science.gov (United States)

    Thorey, Jean; Mallet, Vivien; Chaussin, Christophe

    2016-04-01

    Our objective is to produce a calibrated weighted ensemble to forecast a univariate time series. In addition to a meteorological ensemble of forecasts, we rely on observations or analyses of the target variable. The celebrated Continuous Ranked Probability Score (CRPS) is used to evaluate the probabilistic forecasts. However applying the CRPS on weighted empirical distribution functions (deriving from the weighted ensemble) may introduce a bias because of which minimizing the CRPS does not produce the optimal weights. Thus we propose an unbiased version of the CRPS which relies on clusters of members and is strictly proper. We adapt online learning methods for the minimization of the CRPS. These methods generate the weights associated to the members in the forecasted empirical distribution function. The weights are updated before each forecast step using only past observations and forecasts. Our learning algorithms provide the theoretical guarantee that, in the long run, the CRPS of the weighted forecasts is at least as good as the CRPS of any weighted ensemble with weights constant in time. In particular, the performance of our forecast is better than that of any subset ensemble with uniform weights. A noteworthy advantage of our algorithm is that it does not require any assumption on the distributions of the observations and forecasts, both for the application and for the theoretical guarantee to hold. As application example on meteorological forecasts for photovoltaic production integration, we show that our algorithm generates a calibrated probabilistic forecast, with significant performance improvements on probabilistic diagnostic tools (the CRPS, the reliability diagram and the rank histogram).

  9. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

    Science.gov (United States)

    Liu, Li; Xu, Yue-Ping

    2017-04-01

    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  10. Performance comparison of meso-scale ensemble wave forecasting systems for Mediterranean sea states

    Science.gov (United States)

    Pezzutto, Paolo; Saulter, Andrew; Cavaleri, Luigi; Bunney, Christopher; Marcucci, Francesca; Torrisi, Lucio; Sebastianelli, Stefano

    2016-08-01

    This paper compares the performance of two wind and wave short range ensemble forecast systems for the Mediterranean Sea. In particular, it describes a six month verification experiment carried out by the U.K. Met Office and Italian Air Force Meteorological Service, based on their respective systems: the Met Office Global-Regional Ensemble Prediction System and the Nettuno Ensemble Prediction System. The latter is the ensemble version of the operational Nettuno forecast system. Attention is focused on the differences between the two implementations (e.g. grid resolution and initial ensemble members sampling) and their effects on the prediction skill. The cross-verification of the two ensemble systems shows that from a macroscopic point of view the differences cancel out, suggesting similar skill. More in-depth analysis indicates that the Nettuno wave forecast is better resolved but, on average, slightly less reliable than the Met Office product. Assessment of the added value of the ensemble techniques at short range in comparison with the deterministic forecast from Nettuno, reveals that adopting the ensemble approach has small, but substantive, advantages.

  11. Combining 2-m temperature nowcasting and short range ensemble forecasting

    Directory of Open Access Journals (Sweden)

    A. Kann

    2011-12-01

    Full Text Available During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG since 2006 (Haiden et al., 2011, provides short range deterministic forecasts at high temporal (15 min–60 min and spatial (1 km resolution. An INCA Ensemble (INCA-EPS of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting which is running operationally at ZAMG (Wang et al., 2011. The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR which yields a statistical mbox{correction} of the first and second moment (mean bias and dispersion for Gaussian distributed continuous

  12. 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...

  13. Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. The relevance and benefits of employing a newly developed orthogonal fitting method for the power curve model over the traditional least-squares one are discussed...... predictive distributions. Such a methodology has the benefit of yielding predictive distributions that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts, while taking advantage of their high resolution....... of probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble predictions as input. The paper concentrates on the test case of the Horns Rev wind farm over a period of approximately one year, in order to describe, apply and discuss a complete ensemble-based forecasting...

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

    Science.gov (United States)

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

    2016-04-01

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

  15. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  16. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-10-02

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  17. Accounting for three sources of uncertainty in ensemble hydrological forecasting

    Directory of Open Access Journals (Sweden)

    A. Thiboult

    2015-07-01

    Full Text Available Seeking for more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the Ensemble Kalman Filter, multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims at untangling the sources of uncertainty by studying the combination of these tools and assessing their contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stage in the forecasting process by using different means. Their combination outperforms any of the tool used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial condition uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improve the general forecasting performance and prevents from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the Ensemble Kalman Filter tuning to avoid overlapping in error deciphering.

  18. 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.

  19. The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

    Directory of Open Access Journals (Sweden)

    K. Liechti

    2013-10-01

    Full Text Available This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues, an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

  20. 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.

  1. Dispersion of aerosol particles in the free atmosphere using ensemble forecasts

    Directory of Open Access Journals (Sweden)

    T. Haszpra

    2013-10-01

    Full Text Available The dispersion of aerosol particle pollutants is studied using 50 members of an ensemble forecast in the example of a hypothetical free atmospheric emission above Fukushima over a period of 2.5 days. Considerable differences are found among the dispersion predictions of the different ensemble members, as well as between the ensemble mean and the deterministic result at the end of the observation period. The variance is found to decrease with the particle size. The geographical area where a threshold concentration is exceeded in at least one ensemble member expands to a 5–10 times larger region than the area from the deterministic forecast, both for air column "concentration" and in the "deposition" field. We demonstrate that the root-mean-square distance of any particle from its own clones in the ensemble members can reach values on the order of one thousand kilometers. Even the centers of mass of the particle cloud of the ensemble members deviate considerably from that obtained by the deterministic forecast. All these indicate that an investigation of the dispersion of aerosol particles in the spirit of ensemble forecast contains useful hints for the improvement of risk assessment.

  2. Using a mesoscale ensemble to predict forecast error and perform targeted observation

    Institute of Scientific and Technical Information of China (English)

    DU Jun; YU Rucong; CUI Chunguang; LI Jun

    2014-01-01

    Using NCEP short range ensemble forecast (SREF) system, demonstrated two fundamental on-going evolu-tions in numerical weather prediction (NWP) are through ensemble methodology. One evolution is the shift from traditional single-value deterministic forecast to flow-dependent (not statistical) probabilistic forecast to address forecast uncertainty. Another is from a one-way observation-prediction system shifting to an in-teractive two-way observation-prediction system to increase predictability of a weather system. In the first part, how ensemble spread from NCEP SREF predicting ensemble-mean forecast error was evaluated over a period of about a month. The result shows that the current capability of predicting forecast error by the 21-member NCEP SREF has reached to a similar or even higher level than that of current state-of-the-art NWP models in predicting precipitation, e.g., the spatial correlation between ensemble spread and absolute fore-cast error has reached 0.5 or higher at 87 h (3.5 d) lead time on average for some meteorological variables. This demonstrates that the current operational ensemble system has already had preliminary capability of predicting the forecast error with usable skill, which is a remarkable achievement as of today. Given the good spread-skill relation, the probability derived from the ensemble was also statistically reliable, which is the most important feature a useful probabilistic forecast should have. The second part of this research tested an ensemble-based interactive targeting (E-BIT) method. Unlike other mathematically-calculated objec-tive approaches, this method is subjective or human interactive based on information from an ensemble of forecasts. A numerical simulation study was performed to eight real atmospheric cases with a 10-member, bred vector-based mesoscale ensemble using the NCEP regional spectral model (RSM, a sub-component of NCEP SREF) to prove the concept of this E-BIT method. The method seems to work most

  3. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system

    NARCIS (Netherlands)

    Reggiani, P.; Renner, M.; Weerts, A.H.; Van Gelder, P.A.H.J.M.

    2009-01-01

    Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive

  4. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system

    NARCIS (Netherlands)

    Reggiani, P.; Renner, M.; Weerts, A.H.; Van Gelder, P.A.H.J.M.

    2009-01-01

    Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive

  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. First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

    Science.gov (United States)

    Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

    2017-04-01

    Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for

  7. Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics

    CERN Document Server

    Scheuerer, Michael

    2013-01-01

    Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the Germa...

  8. Calibrating 2 m Temperature Forecast for the Regional Ensemble Prediction System at NMC%区域集合预报系统2m温度预报的校准技术

    Institute of Scientific and Technical Information of China (English)

    王敏; 李晓莉; 范广洲; 李泽椿

    2012-01-01

    采用非齐次高斯回归(NGR)技术对国家气象中心区域集合预报系统的2 m温度预报结果开展了一阶偏差和二阶离散度的校准研究.对预报结果比较详尽的检验分析表明:校准后的2 m温度预报可靠性和预报技巧均显著提高,表现为校准后集合预报成员的均方根误差与离散度更为接近;原Talagrand直方图中的“L”形分布现象得到有效改善;Brier评分、最小连续分级概率评分(CRPS)明显减小,相对作用特征(ROC)面积增大,说明校准后的2 m温度预报表现出更好的预报技能.此外,NGR技术与自适应误差订正技术的对比试验表明,NGR在消除集合平均偏差和提高集合离散度两个方面均有优势.%It's known that ensemble forecasts provide a flow-dependent sample of the probability distribution of possible future atmospheric states instead of the single and deterministic prediction. Ideally, the probability of any event could be skillfully estimated directly from the relative event frequency in the ensemble. Unfortunately, although the quality of ensemble prediction systems (EPS) has been improved greatly, the direct output of EPS generally is subject to the systematic deficiencies, especially for surface variables. They are under-dispersive and lack of reliability. Therefore, statistical post-processing methods have been developed to improve direct model output. The nonhomogeneous Gaussian regression (NGR) is used to calibrate 2 m temperature forecast of the regional EPS at NMC/CMA. The NGR is the statistical correction method with the first and the second moment (mean bias and dispersion) for Gaussian-distributed continuous variable. This method is based on the multiple linear regression technique and provides a predictive probability density function (PDF) in terms of the normal distribution. The method of minimum continuous ranked probability score (CRPS) estimation is used to fit the regression coefficients of PDF. It can be

  9. Mesoscale hydrological ensemble forecasting for water resources management

    Energy Technology Data Exchange (ETDEWEB)

    Fortin, V. [Meteorological Research Div., Environment Canada, Dorval, Quebec (Canada); Turcotte, R. [Government of Quebec, Centre of Quebec' s Water Expertise, Quebec (Canada); Anctil, F. [Univ. Laval, Dept. of Civil Engineering, Laval, Quebec (Canada); Favre, A.-C. [INRS, Water Earth and Environment, Quebec (Canada); Petit, Th. [Univ. Laval, Dept. of Civil Engineering, Laval, Quebec (Canada)

    2008-07-01

    This poster discusses meso-scale hydrological ensemble forecasting for water resources management. Environment Canada will produce a 20 member meso-scale (35 km), short range (48 h) meteorological ensemble prediction system (M-EPS). Each of the 20 members of the global EPS will be dynamically down scaled from 100 to 35 km over North America using limited area model GEM-LAM. Preliminary tests have been conducted on Lie'vre watershed to assess the impact of using short range M-EPS for hydrological forecasting.

  10. ENSEMBLE methods to reconcile disparate national long range dispersion forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Mikkelsen, T.; Galmarini, S.; Bianconi, R.; French, S. (eds.)

    2003-11-01

    ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparate national forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidental atmospheric release of radioactive material. A series of new decision-making 'ENSEMBLE' procedures and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecasts from meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national emergency and meteorological forecasting centres, which may choose to integrate them directly into operational emergency information systems, or possibly use them as a basis for future system development. (au)

  11. A Robust Multimodel Framework for Ensemble Seasonal Hydroclimatic Forecasts

    Science.gov (United States)

    Mendoza, P. A.; Rajagopalan, B.; Clark, M. P.; Cortés, G.; McPhee, J. P.

    2014-12-01

    We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

  12. Rainfall downscaling of weekly ensemble forecasts using self-organising maps

    Directory of Open Access Journals (Sweden)

    Masamichi Ohba

    2016-03-01

    Full Text Available This study presents an application of self-organising maps (SOMs to downscaling medium-range ensemble forecasts and probabilistic prediction of local precipitation in Japan. SOM was applied to analyse and connect the relationship between atmospheric patterns over Japan and local high-resolution precipitation data. Multiple SOM was simultaneously employed on four variables derived from the JRA-55 reanalysis over the area of study (south-western Japan, and a two-dimensional lattice of weather patterns (WPs was obtained. Weekly ensemble forecasts can be downscaled to local precipitation using the obtained multiple SOM. The downscaled precipitation is derived by the five SOM lattices based on the WPs of the global model ensemble forecasts for a particular day in 2009–2011. Because this method effectively handles the stochastic uncertainties from the large number of ensemble members, a probabilistic local precipitation is easily and quickly obtained from the ensemble forecasts. This downscaling of ensemble forecasts provides results better than those from a 20-km global spectral model (i.e. capturing the relatively detailed precipitation distribution over the region. To capture the effect of the detailed pattern differences in each SOM node, a statistical model is additionally concreted for each SOM node. The predictability skill of the ensemble forecasts is significantly improved under the neural network-statistics hybrid-downscaling technique, which then brings a much better skill score than the traditional method. It is expected that the results of this study will provide better guidance to the user community and contribute to the future development of dam-management models.

  13. Flood forecast sensitivity to temperature using ECMWF ensembles for 145 catchments in Norway

    Science.gov (United States)

    Jahr Hegdahl, Trine; Engeland, Kolbjørn; Grønbech, Bård Johan; Steinsland, Ingelin; Merete Tallaksen, Lena

    2017-04-01

    The Norwegian flood forecasting service is based on a flood-forecasting model run on 145 basins. The basins are located all across Norway and differ in both size and hydrological regime. Current flood forecasting system is based on deterministic meteorological forecasts, and uses an auto-regressive procedure to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The aim of our study is to establish and assess the performance of both meteorological and hydrological ensembles for 145 catchments in Norway, which differ in size, elevation and hydrological regime. We identify regional differences and improvements in performance for preprocessed meteorological forecasts. A separate study further investigates the sensitivity to forecasted temperature for specific snowmelt induced floods. In Norway, snowmelt and combined rain and snowmelt floods are frequent. Hence, temperature is important for correct calculations of snowmelt. Temperature and precipitation ensembles are derived from ECMWF covering a period of nearly three years (01.03.2013 to 31.12.2015). To improve the spread and reduce bias we used standard methods provided by the Norwegian Meteorological Institute. Precipitation is corrected applying a zero-adjusted gamma distribution method (correcting the spread), and temperature is bias corrected using a quantile-quantile mapping (using Hirlam (RCM) 5 km temperature grid as a reference). Observed temperature and precipitation data are station data for all of Norway, interpolated to a 1×1 km2 grid (SeNorge.no). Streamflow observations are available from the NVE database. The hydrological model is the flood-forecasting operational HBV model, run with daily catchment average values. The results show that the methods applied to meteorological ensemble data reduce the cold bias present in the ECMWF temperature ensembles. Catchments on the western coast

  14. Local polynomial method for ensemble forecast of time series

    Directory of Open Access Journals (Sweden)

    S. Regonda

    2005-01-01

    Full Text Available We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (D with a delay time (τ. To obtain a forecast from a given time point t, three steps are involved: (i the current state of the system is mapped on to the state space, known as the feature vector, (ii a small number (K=α*n, α=fraction (0,1] of the data, n=data length of neighbors (and their future evolution to the feature vector are identified in the state space, and (iii a polynomial of order p is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (D, τ, α, p is selected based on an objective criterion, called the Generalized Cross Validation (GCV. All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time t, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF. The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index. This framework can also be used to forecast a vector of response variables based on a vector of predictors.

  15. Ambiguity in Ensemble Forecasting: Evolution, Estimate Validation and Value

    Science.gov (United States)

    2009-09-01

    background-error covariance ( bP̂ ) from Equation (12)(c) must be estimated diagnostically from the ensemble of background states using Equation (12)(a...Although the beta- fit does not always provide a quality fit to the ˆTp data, it was sufficient for the pedagogical purpose here. For this forecast

  16. Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

    Directory of Open Access Journals (Sweden)

    Zied Ben Bouallègue

    2013-02-01

    Full Text Available COSMO-DE-EPS, a convection-permitting ensemble prediction system based on the high-resolution numerical weather prediction model COSMO-DE, is pre-operational since December 2010, providing probabilistic forecasts which cover Germany. This ensemble system comprises 20 members based on variations of the lateral boundary conditions, the physics parameterizations and the initial conditions. In order to increase the sample size in a computationally inexpensive way, COSMO-DE-EPS is combined with alternative ensemble techniques: the neighborhood method and the time-lagged approach. Their impact on the quality of the resulting probabilistic forecasts is assessed. Objective verification is performed over a six months period, scores based on the Brier score and its decomposition are shown for June 2011. The combination of the ensemble system with the alternative approaches improves probabilistic forecasts of precipitation in particular for high precipitation thresholds. Moreover, combining COSMO-DE-EPS with only the time-lagged approach improves the skill of area probabilities for precipitation and does not deteriorate the skill of 2 m-temperature and wind gusts forecasts.

  17. Ensemble-based Probabilistic Forecasting at Horns Rev

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2009-01-01

    of probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble predictions as input. The paper concentrates on the test case of the Horns Rev wind form over a period of approximately 1 year, in order to describe, apply and discuss a complete ensemble-based probabilistic...... the benefit of yielding predictive distributions that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts, at the some time taking advantage of their high resolution. Copyright (C) 2008 John Wiley & Sons, Ltd....... are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which ore recursively estimated in order to maximize the overall skill of obtained predictive distributions. Such a methodology has...

  18. Hindcast experiments of ensemble streamflow forecasting for the Paraopeba river (Brazil)

    Science.gov (United States)

    Collischonn, W.; Meller, A.; Dias, P. L. S.; Moreira, D. S.

    2012-04-01

    Streamflow forecasts are routinely produced and used in Brazil to predict inflow to major hydropower reservoirs . In this field of application quantitative precipitation forecasts are becoming increasingly used to extend the range and increase the skill of streamflow forecasts. Forecasting systems designed to provide flood alert, on the other side, are relatively rare in Brazil, and are often based on simplified river routing models. However, a number of recent floods with significant loss of lives and economical impact is now motivating the creation of a new governmental institution dedicated to natural disaster and flood forecasting. This will further motivate the incorporation of numerical weather predictions (NWP) as input data to hydrological flood forecasting models, with the aim of increasing forecast lead time. In this context ensemble meteorological forecasts will be increasingly useful, since it is expected that ensembles can give some idea of the confidence level of the forecasts, and that extremes can be better captured by a high number of NWP runs with different initial conditions, or with different meteorological models. Silva Dias and Moreira (2006) organized a grand ensemble including several different models and model members for South America. We used forecasts of individual models of this grand ensemble to run a series of streamflow hindcast experiments (in forecast mode), using the MGB-IPH hydrological model. These tests were conducted in the Paraopeba river basin, which is a tributary of the São Francisco river, located in Minas Gerais State, in a Tropical region in the range from 21 S to 19 S. Results of 72 hour streamflow forecasts were compared to hourly observed discharge at Porto Mesquita gauging station, were the drainage area is 10280 square kilometers, during the Austral Summer of 2011. Results were assessed by visual inspection of hydrographs and by the analysis of a number of summary statistics. These preliminary results suggest that

  19. Optimization of multi-model ensemble forecasting of typhoon waves

    Directory of Open Access Journals (Sweden)

    Shun-qi Pan

    2016-01-01

    Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.

  20. GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

    Science.gov (United States)

    Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

    2017-04-01

    Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal

  1. Bayesian Processor of Ensemble for Precipitation Forecasting: A Development Plan

    Science.gov (United States)

    Toth, Z.; Krzysztofowicz, R.

    2006-05-01

    The Bayesian Processor of Ensemble (BPE) is a new, theoretically-based technique for probabilistic forecasting of weather variates. It is a generalization of the Bayesian Processor of Output (BPO) developed by Krzysztofowicz and Maranzano for processing single values of multiple predictors into a posterior distribution function of a predictand. The BPE processes an ensemble of a predictand generated by multiple integrations of a numerical weather prediction (NWP) model, and optimally fuses the ensemble with climatic data in order to quantify uncertainty about the predictand. As is well known, Bayes theorem provides the optimal theoretical framework for fusing information from different sources and for obtaining the posterior distribution function of a predictand. Using a family of such distribution functions, a given raw ensemble can be mapped into a posterior ensemble, which is well calibrated, has maximum informativeness, and preserves the spatio-temporal and cross-variate dependence structure of the NWP output fields. The challenge is to develop and test the BPE suitable for operational forecasting. This talk will present the basic design components of the BPE, along with a discussion of the climatic and training data to be used in its potential application at the National Centers for Environmental Prediction (NCEP). The technique will be tested first on quasi-normally distributed variates and next on precipitation variates. For reasons of economy, the BPE will be applied on the relatively coarse resolution grid corresponding to the ensemble output, and then the posterior ensemble will be downscaled to finer grids such as that of the National Digital Forecast Database (NDFD).

  2. Integrating Ensemble Forecasts of Precipitation and Streamflow into Decision Support for Reservoir Operations in North Central Texas

    Science.gov (United States)

    Kim, S.; Limon, R. A.; Alizadeh, B.; Seo, D. J.; Fincannon, T. J.; Winguth, A. M. E.; Brown, J.; Blaylock, L.; Lampe, M.; Philpott, A.; Bell, F.

    2016-12-01

    North Central Texas relies heavily on surface water for water supply. To meet the growing demand, large raw water suppliers, such as the Tarrant Regional Water District (TRWD), operate systems of reservoirs that are connected by extensive networks of pipelines over long distances. To ensure water supply at all times, while minimizing flooding risks and pumping cost, TRWD utilizes a suite of decision support tools. This research aims to improve the operating efficiency of the water delivery system by providing skillful ensemble precipitation and inflow forecasts that can be used to optimize the water supply operations under uncertain environmental conditions. To assess the value of medium- and long-range forecasts of precipitation and inflow to operation and management of the TRWD's reservoir-pipeline system, a set of hindcasting and verification experiments is being carried out. The hindcasting experiments use weather and climate reforecasts from the Global Ensemble Forecast System and the Climate Forecast System Version 2, and the forecasting and verification tools of the National Weather Service's Hydrologic Ensemble Forecast Service, the Community Hydrologic Prediction System, and the Ensemble Verification System. The value of ensemble forecasts will be demonstrated via the TRWD forecasting model, which uses RiverWare. We also present a multiscale bias correction procedure for post-processing the raw streamflow ensembles.

  3. Multimodel Ensemble Forecasts for Precipitations in China in 1998

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the "Climate of the 20th Century Experiment" (20C3M) for the Fourth IPCC Assessment Report. Climate Research Unit (CRU)data were chosen for the observation analysis field. Root mean square (RMS) error and correlation coefficients (R) are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression (MLR) performs best. However, for bias-corrected data, the superensemble based on singular value decomposition (SVD) produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.

  4. Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

    Science.gov (United States)

    Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

    2017-04-01

    Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

  5. Wave Extremes in the North East Atlantic from Ensemble Forecasts

    CERN Document Server

    Breivik, Øyvind; Bidlot, Jean-Raymond; Carrasco, Ana; Saetra, Øyvind; 10.1175/JCLI-D-12-00738.1

    2013-01-01

    A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the North-East Atlantic, the Norwegian Sea and the North Sea are computed from archived +240-h forecasts of the ECMWF ensemble prediction system (EPS) from 1999 to 2009. We make three assumptions: First, each forecast is representative of a six-hour interval and collectively the data set is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which we confirm by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. We find anomaly correlations of 0.20, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the data set we also find that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The a...

  6. Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    . The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...

  7. Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model

    Institute of Scientific and Technical Information of China (English)

    Xiaqiong ZHOU; Johnny C.L.CHEN

    2006-01-01

    The purpose of this study is to investigate the effectiveness of two different ensemble forecasting (EF) techniques-the lagged-averaged forecast (LAF) and the breeding of growing modes (BGM). In the BGM experiments, the vortex and the environment are perturbed separately (named BGMV and BGME).Tropical cyclone (TC) motions in two difficult situations are studied: a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM-90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also used to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the

  8. Application of evolutionary computation on ensemble forecast of quantitative precipitation

    Science.gov (United States)

    Dufek, Amanda S.; Augusto, Douglas A.; Dias, Pedro L. S.; Barbosa, Helio J. C.

    2017-09-01

    An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembles' languages were applied to several real-world datasets over southern, southeastern and central Brazil during the rainy period from October to February of 2008-2013. According to the results, the GP algorithms performed better than two traditional statistical techniques, with errors 27-57% lower than simple ensemble mean and the MASTER super model ensemble system. In addition, the results revealed that GP algorithms outperformed the best individual forecasts, reaching an improvement of 34-42%. On the other hand, the GP algorithms had a similar performance with respect to each other and to the Bayesian model averaging, but the former are far more versatile techniques. Although the results for the six ensembles' languages are almost indistinguishable, our most complex linear language turned out to be the best overall proposal. Moreover, some meteorological attributes, including the weather patterns over Brazil, seem to play an important role in the prediction of daily rainfall amount.

  9. Ensemble stream flow predictions using the ECMWF forecasts

    Science.gov (United States)

    Kiczko, Adam; Romanowicz, Renata; Osuch, Marzena; Pappenberger, Florian; Karamuz, Emilia

    2015-04-01

    Floods and low flows in rivers are seasonal phenomena that can cause several problems to society. To anticipate high and low flow events, flow forecasts are crucial. They are of particular importance in mountainous catchments, where the lead time of forecasts is usually short. In order to prolong the forecast lead-time, numerical weather predictions (NWPs) are used as a hydrological model driving force. The forecasted flow is commonly given as one value, even though it is uncertain. There is an increasing interest in accounting for the uncertainty in flood early warning and decision support systems. When NWP are given in the form of ensembles, such as the ECMWF forecasts, the uncertainty of these forecasts can be accounted for. Apart from the forecast uncertainty the uncertainty related to the hydrological model used also plays an important role in the uncertainty of the final flow prediction. The aim of this study is the development of a stream flow prediction system for the Biała Tarnowska, a mountainous catchment in the south of Poland. We apply two different hydrological models. One is a conceptual HBV model for rainfall-flow predictions, applied within a Generalised Likelihood Uncertainty Estimation (GLUE) framework, the second is a data-based DBM model, adjusted for Polish conditions by adding the Soil Moisture Accounting (SMA) and snow-melt modules. Both models provide the uncertainty of the predictions, but the DBM approach is much more numerically efficient, therefore more suitable for the real-time forecasting.. The ECMWF forecasts require bias reduction in order to correspond to observations. Therefore we applied Quantile Mapping with and without seasonal adjustment for bias correction. Up to seven-days ahead forecast skills are compared using the Relative Operation Characteristic (ROC) graphs, for the flood warning and flood alarm flow value thresholds. The ECMWF forecasts are obtained from the project TIGGE (http

  10. Assessment and added value estimation of an ensemble approach with a focus on global radiation forecasts

    CERN Document Server

    Bouallegue, Zied Ben

    2015-01-01

    The assessment of the high-resolution ensemble weather prediction system COSMO-DE-EPS is achieved with the perspective of using it for renewable energy applications. The performance of the ensemble forecast is explored focusing on global radiation, the main weather variable affecting solar power production, and on quantile forecasts, key probabilistic products for the energy sector. First, the ability of the ensemble system to capture and resolve the observation variability is assessed. Secondly, the potential benefit of the ensemble forecasting strategy compared to a single forecast approach is quantitatively estimated. A new metric called ensemble added value is proposed, aiming at a fair comparison of an ensemble forecast with a single forecast, when optimized to the users' needs. Hourly mean forecasts are verified against pyranometer measurements over verification periods covering 2013. The results show in particular that the added value of the ensemble approach is season-dependent and increases with the ...

  11. Medium Range Ensembles Flood Forecasts for Community Level Applications

    Science.gov (United States)

    Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

    2013-05-01

    Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

  12. Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

    NARCIS (Netherlands)

    Mainardi Fan, F.; Schwanenberg, D.; Collischonn, W.; Weerts, A.H.

    2015-01-01

    Study region This paper describes a major ensemble-forecasts verification effort for inflows of three large-scale river basins of Brazil: Upper São Francisco, Doce, and Tocantins Rivers. Study focus In experimental scenarios, inflow forecasts were generated forcing one hydrological model with

  13. Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

    NARCIS (Netherlands)

    Mainardi Fan, F.; Schwanenberg, D.; Collischonn, W.; Weerts, A.H.

    2015-01-01

    Study region This paper describes a major ensemble-forecasts verification effort for inflows of three large-scale river basins of Brazil: Upper São Francisco, Doce, and Tocantins Rivers. Study focus In experimental scenarios, inflow forecasts were generated forcing one hydrological model with quanti

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

    Science.gov (United States)

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

    2013-12-01

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

  15. Potentialities of ensemble strategies for flood forecasting over the Milano urban area

    Science.gov (United States)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Homar, Víctor; Romero, Romu; Lombardi, Gabriele; Mancini, Marco

    2016-08-01

    Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme. Here, we examine two severe hydrometeorological episodes that affected the Milano urban area and for which the complex flood protection system of the city did not completely succeed. Indeed, flood damage have exponentially increased during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. First, we examine how land-use changes due to urban development have altered the hydrological response to intense rainfalls. Second, we test a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. Accurate forecasts of deep moist convection and extreme precipitation are difficult to be predicted due to uncertainties arising from the numeric weather prediction (NWP) physical parameterizations and high sensitivity to misrepresentation of the atmospheric state; however, two hydrological ensemble prediction systems (HEPS) have been designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. No substantial differences in skill have been found between both ensemble strategies when considering an enhanced diversity of IC/LBCs for the perturbed initial conditions ensemble. Furthermore, no additional benefits have been found by considering more frequent LBCs in a mixed physics ensemble, as ensemble spread seems to be reduced. These findings could help to design the most appropriate ensemble strategies before these hydrometeorological extremes, given the computational

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

    Science.gov (United States)

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

    2010-09-01

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

  17. Development and evaluation of novel forecasting adaptive ensemble model

    Directory of Open Access Journals (Sweden)

    C.M. Anish

    2016-09-01

    Full Text Available This paper proposes a new ensemble based adaptive forecasting structure for efficient different interval days' ahead prediction of five different asset values (NAV. In this approach three individual adaptive structures such as adaptive moving average (AMA, adaptive auto regressive moving average (AARMA and feedback radial basis function network (FRBF are employed to first train with conventional LMS, conventional forward-backward LMS and corresponding learning algorithm of FRBF respectively. After successful validation of each model the output obtained by each individual model is optimally weighted using Genetic algorithm (GA as well as particle swarm optimization (PSO based techniques to produce the best possible different days ahead prediction accuracy. Finally the results of prediction obtained of the NAV values are compared with the results obtained by individual predictors as well as by other four existing ensemble schemes. It is in general demonstrated that in all cases the proposed forecasting scheme outperforms other competitive methods.

  18. A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

    Science.gov (United States)

    Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

    2017-04-01

    ' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.

  19. Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

    Directory of Open Access Journals (Sweden)

    Xia Li

    2014-01-01

    Full Text Available Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.

  20. Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

    Science.gov (United States)

    Omi, Takahiro; Ogata, Yosihiko; Hirata, Yoshito; Aihara, Kazuyuki

    2015-04-01

    Because aftershock occurrences can cause significant seismic risks for a considerable time after the main shock, prospective forecasting of the intermediate-term aftershock activity as soon as possible is important. The epidemic-type aftershock sequence (ETAS) model with the maximum likelihood estimate effectively reproduces general aftershock activity including secondary or higher-order aftershocks and can be employed for the forecasting. However, because we cannot always expect the accurate parameter estimation from incomplete early aftershock data where many events are missing, such forecasting using only a single estimated parameter set (plug-in forecasting) can frequently perform poorly. Therefore, we here propose Bayesian forecasting that combines the forecasts by the ETAS model with various probable parameter sets given the data. By conducting forecasting tests of 1 month period aftershocks based on the first 1 day data after the main shock as an example of the early intermediate-term forecasting, we show that the Bayesian forecasting performs better than the plug-in forecasting on average in terms of the log-likelihood score. Furthermore, to improve forecasting of large aftershocks, we apply a nonparametric (NP) model using magnitude data during the learning period and compare its forecasting performance with that of the Gutenberg-Richter (G-R) formula. We show that the NP forecast performs better than the G-R formula in some cases but worse in other cases. Therefore, robust forecasting can be obtained by employing an ensemble forecast that combines the two complementary forecasts. Our proposed method is useful for a stable unbiased intermediate-term assessment of aftershock probabilities.

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

    Institute of Scientific and Technical Information of China (English)

    Seung-Woo LEE; Dong-Kyou LEE

    2011-01-01

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

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

    Science.gov (United States)

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

    2016-07-01

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

  3. Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS

    Directory of Open Access Journals (Sweden)

    Sheng-Chi Yang

    2014-01-01

    Full Text Available During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs.

  4. Probabilistic Ensemble Forecast of Summertime Temperatures in Pakistan

    Directory of Open Access Journals (Sweden)

    Muhammad Hanif

    2014-01-01

    Full Text Available Snowmelt flooding triggered by intense heat is a major temperature related weather hazard in northern Pakistan, and the frequency of such extreme flood events has increased during the recent years. In this study, the probabilistic temperature forecasts at seasonal and subseasonal time scales based on hindcasts simulations from three state-of-the-art models within the DEMETER project are assessed by the relative operating characteristic (ROC verification method. Results based on direct model outputs reveal significant skill for hot summers in February 3–5 (ROC area=0.707 with lower 95% confidence limit of 0.538 and February 4-5 (ROC area=0.771 with lower 95% confidence limit of 0.623 forecasts when validated against observations. Results for ERA-40 reanalysis also show skill for hot summers. Skilful probabilistic ensemble forecasts of summertime temperatures may be valuable in providing the foreknowledge of snowmelt flooding and water management in Pakistan.

  5. Uncertain Climate Forecasts From Multimodel Ensembles: When to Use Them and When to Ignore Them

    CERN Document Server

    Jewson, Stephen

    2010-01-01

    Uncertainty around multimodel ensemble forecasts of changes in future climate reduces the accuracy of those forecasts. For very uncertain forecasts this effect may mean that the forecasts should not be used. We investigate the use of the well-known Bayesian Information Criterion (BIC) to make the decision as to whether a forecast should be used or ignored.

  6. Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

    Directory of Open Access Journals (Sweden)

    Fernando Mainardi Fan

    2015-09-01

    New hydrological insights for the region: This work presents one of the first extensive efforts to evaluate ensemble forecasts for large-scale basins in South America using TIGGE archive data. Results from these scenarios provide validation criteria and confirm that ensemble forecasts depend on the particular EPS used to run the hydrological model and on the basin studied. Furthermore, the use of the Super Ensemble seems to be a good strategy in terms of performance and robustness. The importance of the TIGGE database is also highlighted.

  7. GEOWOW - Benefits of TIGGE ensemble forecast data for the GEOSS community

    Science.gov (United States)

    Zsoter, Ervin

    2014-05-01

    GEOWOW (GEOSS interoperability for Weather, Ocean and Water) is an EU-funded project with the main challenge to improve Earth Observation data discovery, accessibility and exploitability, and to evolve the Global Earth Observation System of Systems (GEOSS) for the benefit of all Societal Benefit Areas (SBAs) with focus on Weather, Ocean Ecosystems and Water. There is a particular focus on supporting multi-disciplinary interoperability across different SBAs, including the use of weather forecast ensembles in applications. The weather community in GEOWOW, led by ECMWF, addresses the elements of the GEO Capacity Building Strategy by improving the access to TIGGE (THORPEX Interactive Grand Global Ensemble) data in which global ensemble predictions from 10 leading global NWP centres are collected in near real-time to support research on ensemble prediction. GEOWOW extends the TIGGE archive with ensemble weather forecasts from limited area models and will build a multi-model ensemble time-series archive (Global and LAM containing data only for specific points) to increase the accessibility of TIGGE for a wider community. GEOWOW work also includes the development and demonstration of (multi-model) products using TIGGE data to support high impact weather forecasting in areas such as tropical cyclone tracks, heavy precipitation events and strong winds. Testing and delivery of these products is in collaboration with participants in the WMO Severe Weather Forecasting Demonstration Project (SWFDP). The SWFDP is a WMO capacity building project which uses a Cascading Forecasting Process to support a basic capability to issue severe weather warnings in developing and least-developed countries. A specific area of work covers the demonstration and documentation of the potential use of TIGGE data in high impact weather forecasting through mainly case studies. Analysed cases include various types of severe weather and different regions - for example rainfall and flooding in West

  8. A Comparison of ETKF and Downscaling in a Regional Ensemble Prediction System

    Directory of Open Access Journals (Sweden)

    Hanbin Zhang

    2015-03-01

    Full Text Available Based on the operational regional ensemble prediction system (REPS in China Meteorological Administration (CMA, this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the operational REPS environment. The perturbation characteristics are analyzed and ensemble forecast verifications are conducted; furthermore, a TC case is investigated. The main conclusions are as follows: the ETKF perturbations contain more power at small scales while the ones derived from downscaling contain more power at large scales, and the relative difference of the two types of perturbations on scales become smaller with forecast lead time. The growth of downscaling perturbations is more remarkable, and the downscaling perturbations have larger magnitude than ETKF perturbations at all forecast lead times. However, the ETKF perturbation variance can represent the forecast error variance better than downscaling. Ensemble forecast verification shows slightly higher skill of downscaling ensemble over ETKF ensemble. A TC case study indicates that the overall performance of the two systems are quite similar despite the slightly smaller error of DOWN ensemble than ETKF ensemble at long range forecast lead times.

  9. 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)

  10. On evaluation of ensemble precipitation forecasts with observation-based ensembles

    Directory of Open Access Journals (Sweden)

    S. Jaun

    2007-04-01

    Full Text Available Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS. The observational references in the evaluation are (a analyzed rain gauge data by ordinary Kriging and (b ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2 of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

  11. Development and Evaluation of Storm Surge Ensemble Forecasting for the Philippines Using JMA Storm Surge Model

    Science.gov (United States)

    Lapidez, J. P. B.; Tablazon, J. P.; Lagmay, A. M. F. A.; Suarez, J. K. B.; Santiago, J. T.

    2014-12-01

    The Philippines is one of the countries most vulnerable to storm surge. It is located in the North-western Pacific basin which is the most active basin in the planet. An average of 20 tropical cyclones enters the Philippine area of responsibility (PAR) every year. The archipelagic nature of the country with regions having gently sloping coasts and shallow bays also contribute to the formation of extreme surges. Last November 2013, storm surge brought by super typhoon Haiyan severely damaged several coastal regions in the Visayan Islands. Haiyan left more than 6 300 casualties and damages amounting to more than $ 2 billion. Extreme storm surge events such as this highlight the need to establish a storm surge early warning system for the country. This study explores the development and evaluation of storm surge ensemble forecasting for the Philippines using the Japan Meteorological Agency (JMA) storm surge model. 36-hour, 24-hour, and 12-hour tropical cyclone forecasts are used to generate an ensemble storm surge forecast to give the most probable storm surge height at a specific point brought by an incoming tropical cyclone. The result of the storm surge forecast is compared to tide gauge record to evaluate the accuracy. The total time of computation and dissemination of forecast result is also examined to assess the feasibility of using the JMA storm surge model for operational purposes.

  12. Evaluation of ECMWF System 4 product for ensemble streamflow forecast in Upper Hanjiang River Basin

    Science.gov (United States)

    Li, Yilu; Tian, Fuqiang

    2017-04-01

    This study attempts to investigate the application of ECMWF System 4 forecast dataset for long term streamflow forecasts with the lead time of 0-2 months in China. The case study is Upper Hanjiang River Basin (UHRB), where forecast results are essential for the central route of South to North Water Diversion Project (SNWDP) in China. A semi-distributed hydrological model (THREW) was applied to simulate the rainfall-runoff processes over the UHRB during the period of 2001-2008. The accuracy of streamflow prediction decreases with lead time, while it is no significant relationship with the drainage areas. All the stations become more reliable as lead time increases, but the Yangxian station shows less reliable than others. The forecast uncertainty is effectively estimated by applying the ECMWF System 4 forecast dataset for the ensemble streamflow forecasts. Significant differences in the performance of ECMWF system 4 are found in seasonal predictions. The forecast is more skillful in Post-dry season than otherwise in term of accuracy and reliability. This study will broaden the application field of ECMWF System 4 dataset to long term streamflow forecast for similar climate region. The results would provide effective guidelines for reservoir operation and be helpful for potential users to employ ECMWF System 4 dataset in other basins over China.

  13. Ensemble dispersion forecasting - Part 1. Concept, approach and indicators

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Klug, W.;

    2004-01-01

    The paper presents an approach to the treatment and analysis of long-range transport and dispersion model forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as continental scale. The method is called multi-model ensemble dispersion...... proposed are particularly suited for long-range transport and dispersion models although they can also be applied to short-range dispersion and weather fields. (C) 2004 Elsevier Ltd. All rights reserved....

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

    Science.gov (United States)

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

    2016-11-01

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

  15. Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale

    OpenAIRE

    Wansik Yu; Eiichi Nakakita; Sunmin Kim; Kosei Yamaguchi

    2016-01-01

    The common approach to quantifying the precipitation forecast uncertainty is ensemble simulations where a numerical weather prediction (NWP) model is run for a number of cases with slightly different initial conditions. In practice, the spread of ensemble members in terms of flood discharge is used as a measure of forecast uncertainty due to uncertain precipitation forecasts. This study presents the uncertainty propagation of rainfall forecast into hydrological response with catchment scale t...

  16. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    Science.gov (United States)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  17. Ovis: A framework for visual analysis of ocean forecast ensembles

    KAUST Repository

    Hollt, Thomas

    2014-08-01

    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.

  18. Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles.

    Science.gov (United States)

    Höllt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D; Hadwiger, Markus

    2014-08-01

    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

  19. An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    Directory of Open Access Journals (Sweden)

    J. A. Velázquez

    2009-07-01

    Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.

    In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  20. An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    Directory of Open Access Journals (Sweden)

    F. Anctil

    2009-11-01

    Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.

    In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  1. A short-term ensemble wind speed forecasting system for wind power applications

    Science.gov (United States)

    Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

    2011-12-01

    This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

  2. A Global Hydrological Ensemble Forecasting System: Uncertainty Quantification and Data Assimilation

    Science.gov (United States)

    Hong, Y.; Zhang, Y.; Xue, X.; Wang, X.; Gourley, J. J.; Kirstetter, P.

    2012-12-01

    A Global Hydrological Ensemble Forecasting System (GHEFS) driven by TRMM Multi-satellite Prediction Analysis (TMPA) precipitation ensembles and Global Ensemble Forecast System (GEFS) Quantitative Precipitation Forecast (QPF) ensembles, via the Coupled Routing and Excess STorage (CREST) distributed hydrological model, provides deterministic and probabilistic (e.g. 95% confidence boundaries) simulations of streamflow. The TMPA inputs enable flood monitoring and short-term forecasts while the GEFS ensembles provide for forecasts up to a seven-day lead time. This talk will focus on a quantification of the system's uncertainty and streamflow ensemble prediction generation using the following three techniques: 1) an error model that first quantifies and then perturbs both temporal and spatial variability of the real-time, TMPA precipitation estimates by considering the version-7 research product as the reference rainfall product; 2) in forecast mode, utilization of the Ensemble Transform method to account for the uncertainty of GEFS forecasts from its initial condition errors; 3) a sequential data assimilation approach - the Ensemble Square Root Kalman Filter (EnSRF) applied to update the CREST model's internal states whenever observations (e.g. streamflow, soil moisture, and actual ET etc.) are available. The GHEFS is validated in several basins in the U.S. and other continents in terms of flood detection capability (e.g. CSI, NSCE, Peak, Timing), showing improved prognostic capability by offering more time for responding agencies and yielding unique uncertainty information about the magnitude of the forecast impacts.

  3. Ensemble prediction of floods – catchment non-linearity and forecast probabilities

    Directory of Open Access Journals (Sweden)

    C. Reszler

    2007-07-01

    Full Text Available Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less, the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

  4. THE ENSEMBLE FORECASTING OF TROPICAL CYCLONE MOTION I:USING A PRIMITIVE EQUATION BAROTROPIC MODEL

    Institute of Scientific and Technical Information of China (English)

    周霞琼; 端义宏; 朱永禔

    2003-01-01

    Ensemble forecasting of tropical cyclone (TC) motion was studied usinga primitive equation barotropic model by perturbing initial position and structure for 1979 - 1993 TC. The results show that TC initial position perturbation affects its track, but the ensemble mean is close to control forecast. Experiments was also performed by perturbing TC initial parameters which were used to generate TC initial field, and more improvement can be obtained by taking ensemble mean of selective member than selecting members randomly. The skill of 60 % - 70 % of all cases is improved in selective ensemble mean. When the ambient steeringcurrent is weak, more improvement can be obtained over the control forecast.

  5. Forecasting of Severe Weather in Austria and Hungary Using High-Resolution Ensemble Prediction System

    Science.gov (United States)

    Szucs, Mihaly; Simon, Andre; Szintai, Balazs; Suklitsch, Martin; Wang, Yong; Wastl, Clemens; Boloni, Gergely

    2015-04-01

    The study presents and compares several approaches in EPS (ensemble prediction system) forecasting based on the non-hydrostatic, high resolution AROME model. The PEARP (global ARPEGE model EPS) was used for coupling. Besides, AROME-EPS was also generated upon hydrostatic ALADIN-EPS forecasts (LAEF), which were used as initial and lateral boundary conditions for each AROME-EPS run. The horizontal resolution of the AROME model is 2.5km and it uses 60 vertical levels for the vertical discretization. In most of the tests, the AROME-EPS run with 10+1 members in Hungarian and 16 members in Austrian implementation. The forecast length was usually set to 30-36 hours. The use of high-resolution EPS has advantages in almost all situations with severe convection (mostly in forecasting intense multicell thunderstorms or mesoscale convective systems of non-frontal origin). The possibility of severe thunderstorm was indicated by several EPS runs even if the deterministic (reference) AROME model failed to forecast the event. Similarly, it could be shown that the AROME-EPS can perform better than hydrostatic global or ALADIN-EPS models in situations with strong wind or heavy precipitation induced by large-scale circulation (mainly in mountain regions). Both EDA (Ensemble of Data Assimilation) and SPPT (Stochastically Perturbed Parameterized Tendencies) methods were tested as a potential perturbation generation method on limited area. The EDA method was able to improve the accuracy of single members through the reduction of the analysis error by applying local data assimilation. It was also able to increase the spread of the system in the early hours due to the additional analysis perturbations. The impact of the SPPT scheme was proven to be smaller in comparison to the impact of this method in global ensemble systems. Further possibilities of improving the assimilation methods and the setup of the AROME-EPS are also discussed.

  6. Probabilistic maize yield simulation over East Africa using ensemble seasonal climate forecasts

    Science.gov (United States)

    Ogutu, Geoffrey; Supit, Iwan; Hutjes, Ronald

    2016-04-01

    Seasonal climate variability influences crop yields, especially in areas where rain fed agriculture is widely practiced such as in the East African region. Assuming that seasonal climate prediction skill would translate to similarly skillful prediction of impacts, an ensemble seasonal climate hindcast (ECMWF system4 EPS) for the period 1981 to 2010 at different initialization dates (lead months) before sowing is used to drive a crop simulation model: the World Food Studies (WOFOST) model, implemented for a single season Maize crop. The water-limited yield predictions were assessed against reference yields produced by the same crop model forced by the WATCH Forcing Data ERA-Interim (WFDEI) at grid point level. We focus on the main sowing dates of June/July (Northern region), March (Equatorial East Africa) and November (Southern region). Deviation of yields from the mean over the study period is used to indicate regions in which probabilistic yield forecasts would be useful while the Relative Operating Curve Skill Score (ROCSS) indicates prediction skill of above normal, near normal and below normal yield prediction. Equatorial regions of East Africa and coastal Kenya with sowing date in March show a mean deviation of ≥ 500 Kg/ha. Here probabilistic yield forecasts can potentially be useful as opposed to the northern and southern regions with less deviation. The high deviation in this region may also be due to the existence of more than one cropping season corresponding to the bi-modal rainfall regime since the model only simulates a single season. A positive ROCSS over a large extent of the equatorial region show predictability skill of all the tercile forecasts simulated by forecasts initialized at the start of sowing date (March i.e. lead 0 forecasts) and no predictability at longer lead months. Over Ethiopia in the northern region of East Africa, November harvests with a sowing date of June show predictability of the upper, lower and middle terciles at lead-0

  7. Exploring the calibration of a wind forecast ensemble for energy applications

    Science.gov (United States)

    Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

    2015-04-01

    In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw

  8. Observation impact in short-range ensemble forecasts

    Science.gov (United States)

    Necker, Tobias; Weissmann, Martin; Sommer, Matthias

    2017-04-01

    Observation impact assessment offers a great potential for convective-scale data assimilation. It provides information on the contribution of various observations to the observing system and is crucial for the refinement of the observing network as well as the data assimilation system. In the framework of the Hans-Ertel Centre for Weather Research (HErZ), a method for an ensemble-based approximation of observation impact using an observation-based verification metric was developed over the past years. Instead of the subsequent analysis, the method uses subsequent observations for verification that are considerably more independent from the forecast. Recently, the method was adapted to use independent observation types for verification. Results of the impact assessment using radar-derived precipitation observations for verification are presented. Furthermore the impact time of different observation types is investigated. The study covers the high impact weather period in summer 2016 using the pre-operational convective-scale ensemble system of Deutscher Wetterdienst (KENDA/COSMO-DE).

  9. Application of a GCM Ensemble Seasonal Climate Forecasts to Crop Yield Prediction in East Africa

    Science.gov (United States)

    Ogutu, G.; Franssen, W.; Supit, I.; Hutjes, R. W. A.

    2016-12-01

    We evaluated the potential use of ECMWF System-4 seasonal climate forecasts (S4) for impacts analysis over East Africa. Using the 15 member, 7 months ensemble forecasts initiated every month for 1981-2010, we tested precipitation (tp), air temperature (tas) and surface shortwave radiation (rsds) forecast skill against the WATCH forcing Data ERA-Interim (WFDEI) re-analysis and other data. We used these forecasts as input in the WOFOST crop model to predict maize yields. Forecast skill is assessed using anomaly correlation (ACC), Ranked Probability Skill Score (RPSS) and the Relative Operating Curve Skill Score (ROCSS) for MAM, JJA and OND growing seasons. Predicted maize yields (S4-yields) are verified against historical observed FAO and nationally reported (NAT) yield statistics, and yields from the same crop model forced by WFDEI (WFDEI-yields). Predictability of the climate forecasts vary with season, location and lead-time. The OND tp forecasts show skill over a larger area up to three months lead-time compared to MAM and JJA. Upper- and lower-tercile tp forecasts are 20-80% better than climatology. Good tas forecast skill is apparent with three months lead-time. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher against others. S4-forecasts captures ENSO related anomalous years with region dependent skill. Anomalous ENSO influence is also seen in simulated yields. Focussing on the main sowing dates in the northern (July), equatorial (March-April) and southern (December) regions, WFDEI-yields are lower than FAO and NAT but anomalies are comparable. Yield anomalies are predictable 3-months before sowing in most of the regions. Differences in interannual variability in the range of ±40% may be related to sensitivity of WOFOST to drought stress while the ACCs are largely positive ranging from 0.3 to 0.6. Above and below-normal yields are predictable with 2-months lead time. We evidenced a potential use of seasonal

  10. 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.

  11. Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale

    Directory of Open Access Journals (Sweden)

    Wansik Yu

    2016-01-01

    Full Text Available The common approach to quantifying the precipitation forecast uncertainty is ensemble simulations where a numerical weather prediction (NWP model is run for a number of cases with slightly different initial conditions. In practice, the spread of ensemble members in terms of flood discharge is used as a measure of forecast uncertainty due to uncertain precipitation forecasts. This study presents the uncertainty propagation of rainfall forecast into hydrological response with catchment scale through distributed rainfall-runoff modeling based on the forecasted ensemble rainfall of NWP model. At first, forecast rainfall error based on the BIAS is compared with flood forecast error to assess the error propagation. Second, the variability of flood forecast uncertainty according to catchment scale is discussed using ensemble spread. Then we also assess the flood forecast uncertainty with catchment scale using an estimation regression equation between ensemble rainfall BIAS and discharge BIAS. Finally, the flood forecast uncertainty with RMSE using specific discharge in catchment scale is discussed. Our study is carried out and verified using the largest flood event by typhoon “Talas” of 2011 over the 33 subcatchments of Shingu river basin (2,360 km2, which is located in the Kii Peninsula, Japan.

  12. Verification of Ensemble Forecasts for the New York City Operations Support Tool

    Science.gov (United States)

    Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

    2012-12-01

    The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble

  13. A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days

    Science.gov (United States)

    Bennett, James C.; Robertson, David E.; Shrestha, Durga Lal; Wang, Q. J.; Enever, David; Hapuarachchi, Prasantha; Tuteja, Narendra K.

    2014-11-01

    This study describes a System for Continuous Hydrological Ensemble Forecasting (SCHEF) designed to forecast streamflows to lead times of 9 days. SCHEF is intended to support optimal management of water resources for consumptive and environmental purposes and ultimately to support the management of impending floods. Deterministic rainfall forecasts from the ACCESS-G numerical weather prediction (NWP) model are post-processed using a Bayesian joint probability model to correct biases and quantify uncertainty. Realistic temporal and spatial characteristics are instilled in the rainfall forecast ensemble with the Schaake shuffle. The ensemble rainfall forecasts are then used as inputs to the GR4H hydrological model to produce streamflow forecasts. A hydrological error correction is applied to ensure forecasts transit smoothly from recent streamflow observations. SCHEF forecasts streamflows skilfully for a range of hydrological and climate conditions. Skill is particularly evident in forecasts of streamflows at lead times of 1-6 days. Forecasts perform best in temperate perennially flowing rivers, while forecasts are poorest in intermittently flowing rivers. The poor streamflow forecasts in intermittent rivers are primarily the result of poor rainfall forecasts, rather than an inadequate representation of hydrological processes. Forecast uncertainty becomes more reliably quantified at longer lead times; however there is considerable scope for improving the reliability of streamflow forecasts at all lead times. Additionally, we show that the choice of forecast time-step can influence forecast accuracy: forecasts generated at a 1-h time-step tend to be more accurate than at longer time-steps (e.g. 1-day). This is largely because at shorter time-steps the hydrological error correction is able to correct streamflow forecasts with more recent information, rather than the ability of GR4H to simulate hydrological processes better at shorter time-steps. SCHEF will form the

  14. Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

    Directory of Open Access Journals (Sweden)

    Constantin Junk

    2015-04-01

    Full Text Available Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn, which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS minimization and a principal component analysis (PCA of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF, including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10‑m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

  15. Comparison of extended medium-range forecast skill between KMA ensemble, ocean coupled ensemble, and GloSea5

    Science.gov (United States)

    Park, Sangwook; Kim, Dong-Joon; Lee, Seung-Woo; Lee, Kie-Woung; Kim, Jongkhun; Song, Eun-Ji; Seo, Kyong-Hwan

    2017-08-01

    This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.

  16. 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.

  17. Constraining the Ensemble Kalman Filter for improved streamflow forecasting

    Science.gov (United States)

    Maxwell, Deborah; Jackson, Bethanna; McGregor, James

    2016-04-01

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

  18. Operational Ensemble River Forecasting in the United States and Australia: Practices and Challenges

    Science.gov (United States)

    Pagano, T. C.

    2012-04-01

    Operational river forecasts have been long produced to support water resources management in the United States and Australia. These forecasts cover a range of timescales from flash flooding (e.g. minutes to hours ahead) to seasonal (e.g. months ahead) and are generated by a range of statistical (e.g. regression-based) and dynamical (e.g. rainfall-runoff) model based techniques. Forecast uncertainty is commonly estimated operationally by using an ensemble of future precipitation scenarios and/or a measure of historical model error. Retrospective ensemble forecasting and the use of reforecasts for bias-adjustment and post-processing have become popular research topics and a few successful demonstration projects exist in both countries. Practical methods of post-processing, such as ensemble dressing, have been used to improve the probabilistic reliability of forecasts. The translation of predictions of probability distributions of streamflow into temporally and spatially consistent ensemble hydrographs remains an area for further development. However, probabilistic forecast communication and use remains a stumbling block for many. Furthermore, ensemble generation and post-processing typically require completely automated systems, making it difficult for humans to contribute their expertise to the forecasting process. This talk draws on ten years of experience as an operational forecaster with the US Department of Agriculture and as a developer of short-term flood forecasting systems to support the Australian Bureau of Meteorology.

  19. Application of a 3-D Super Ensemble to ocean forecast

    Science.gov (United States)

    Lenartz, F.; Barth, A.; Beckers, J.-M.; Vandenbulcke, L.; Rixen, M.

    2009-04-01

    Super Ensemble (SE) techniques have recently allowed improving the forecast of various important oceanographic parameters, such as the significant wave height, the speed of sound or the surface drift, by correcting the prediction at a single or multiple locations, where data were available during the whole training period. However, nowadays common observation systems, such as satellite imagery or drifters, do not always provide information at the exact same locations, hence it is necessary to generalize the approach in order to take benefit of every image or track available. In this study, we try and apply a SE, fed with remote sensing and gliders data, to 3-D hydrodynamic models. The basic idea on which rely the SE methods is that a certain combination of several model runs and possibly data could yield better results than just one single model, even if it has a higher temporal or spatial resolution. As the most efficient techniques are the ones using observations, they rapidly developed and increased in complexity by copying what had been done in the data assimilation community; getting from the simple ensemble mean of the model outputs to their linear combination based on a particle filter. In our present study, we have decided to use the Kalman filter (KF) as it alleviates the need of an a priori determination of the training period length, and does not require the run of a very large ensemble of members. In addition, we apply it in a 3-D framework in order to take benefit of the spatial information contained by each source of measurements. For example, satellite images of sea surface temperature (SST) are very useful to correct the value of this parameter, but depending on the structure of the water column, it can also give a precious guess of how warm or cold is the ocean at 20 m deep. In our experiment the domain of interest is the Ligurian Sea during the last week of September, when part of the set-up for the CalVal08 campaign (SiC Charles Trees) had

  20. Maximization of seasonal forecasts performance combining Grand Multi-Model Ensembles

    Science.gov (United States)

    Alessandri, Andrea; De Felice, Matteo; Catalano, Franco; Lee, Doo Young; Yoo, Jin Ho; Lee, June-Yi; Wang, Bin

    2014-05-01

    Multi-Model Ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model errors. Previous works suggested that the potential benefit that can be expected by using a MME amplify with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two Multi Model Ensemble (MME) Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (CliPAS/APCC) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from CliPAS/APCC have been evaluated. The grand ENSEMBLES-CliPAS/APCC Multi-Model enhances significantly the skill compared to previous estimates from the contributing MMEs. The combinations of SPSs maximizing the skill that is currently attainable for specific predictands/phenomena is evaluated. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and CliPAS/APCC models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration. As an example for the tropical Pacific, the maximum performance is obtained with only the combination of 5-to-6 SPSs from the grand ENSEMBLES-CliPAS/APCC MME. With particular focus over Tropical Pacific, the relationship between performance and bias of the grand-MME combinations is evaluated. The skill of the grand-MME combinations over Euro-Mediterranean and East-Asia regions is further evaluated as a function of the capability of the selected contributing SPSs to forecast anomalies of the Polar/Siberian highs during winter and of the Asian summer monsoon precipitation during summer. Our results indicate that, combining SPSs from independent MME sources is a good

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

    Science.gov (United States)

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

    2016-07-01

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

  2. Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

    Science.gov (United States)

    Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

    2016-12-01

    Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

  3. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

    Directory of Open Access Journals (Sweden)

    Y. Xuan

    2009-03-01

    Full Text Available Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1 The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2 The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3 the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

  4. Climate Prediction Center(CPC)Ensemble Canonical Correlation Analysis Forecast of Temperature

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) temperature forecast is a 90-day (seasonal) outlook of US surface temperature anomalies. The ECCA uses Canonical...

  5. Climate Prediction Center (CPC)Ensemble Canonical Correlation Analysis 90-Day Seasonal Forecast of Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) precipitation forecast is a 90-day (seasonal) outlook of US surface precipitation anomalies. The ECCA uses...

  6. Using the TIGGE database for ensemble hydrological forecasting: a study on 74 catchments in France (Invited)

    Science.gov (United States)

    Ramos, M.; Zalachori, I.; Mathevet, T.; Loumagne, C.

    2010-12-01

    This study assesses the quality of streamflow forecasts issued by the GRP rainfall-runoff model, driven by ensemble weather predictions from the TIGGE database. The GRP is a lumped soil-moisture-accounting type model developed at Cemagref in France for operational flood forecasting. In this study, the model is run at daily time steps and its assimilation procedure makes use of the last observed discharge at the time of the forecast to update the state of the model routing store. Ensemble predictions from 8 meteorological centres from the TIGGE archive are considered. They are available over a 2-year period, from October 2006 to October 2008. Single ensembles from each centre, with a number of members ranging from 15 to 51 ensembles, are considered in the hydrological model, as well as a combined multi-model ensemble that takes into account all members available at each day of the forecast period. Forecast data were spatially averaged over the studied catchments to obtain the areal forecast precipitation at each lead time. Hydrological forecast evaluation is performed at catchment-based spatial scales and for lead-times up to 15 days. Simulations were carried out over 74 catchments in France, with areas ranging from 1000 km2 to 44,000 km2. Forecasts are compared to observed data and typical skill scores used for forecast verification are computed. Issues related to the quality of the ensemble hydrological forecasts, and the impacts of factors like catchment area and hydrological regime type on the performance of the forecasts are discussed.

  7. 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.

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

    Science.gov (United States)

    Khajehei, Sepideh; Moradkhani, Hamid

    2017-03-01

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

  9. National Weather Service (NWS) Implementation of the Hydrologic Ensemble Forecast Service

    Science.gov (United States)

    Hartman, R. K.; Fresch, M. A.; Wells, E.

    2015-12-01

    Operational hydrologic forecasters as well as the communities that they serve have long recognized the value of including uncertainty in hydrologic projections. While single value (deterministic) forecasts are easy to understand and link to specific mitigation actions, the potential for using modern risk management strategies is very limited. This is particularly evident at lead times beyond a few days when forecast skill may be low but the value (and costs) of mitigation actions may be quite high. Based on nearly ten years of research and development, the NWS's National Water Center (NWC, formerly the Office of Hydrologic Development) implemented and evaluated the Hydrologic Ensemble Forecast Service (HEFS, see Demargne et al. 2014 Brown et al., 2013, Brown et al., 2014a/b/c). The HEFS provides hydrologic forecasts that reflect the total uncertainty, including that contributed by the meteorological forcing and the hydrologic modeling. The HEFS leverages the skill in weather and climate forecasts to produce ensemble forecasts of precipitation, temperature and streamflow at forecast lead times ranging from one hour to one year. The resulting ensembles represent a rich dataset from which a wide variety of risk-based decision support information can be derived. The NWS River Forecast Centers (RFCs) are starting to incorporate the Hydrologic Ensemble Forecast Service (HEFS,) into their routine operations. In 2012, five (of thirteen) RFCs began running and testing HEFS in an experimental mode. In 2015, HEFS was deployed (including training and software support) to the eight remaining RFCs. Currently, all RFCs are running the HEFS every day in real-time for an increasing number of forecast locations. Eventually, forecasts from the HEFS will be integrated into the warning/hazard services at the NWS Weather Forecast Offices (WFOs). This contribution describes the HEFS framework, the development and deployment strategy, and the operational plans for HEFS going forward.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-02-02

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

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  12. Evaluation of bias-correction methods for ensemble streamflow volume forecasts

    Directory of Open Access Journals (Sweden)

    T. Hashino

    2007-01-01

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

  13. A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting

    KAUST Repository

    Raboudi, Naila

    2016-11-01

    The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme\\'s behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated

  14. Sand-Dust Storm Ensemble Forecast Model Based on Rough Set

    Institute of Scientific and Technical Information of China (English)

    LU Zhiying; YANG Le; LI Yanying; ZHAO Zhichao

    2007-01-01

    To improve the accuracy of sand-dust storm forecast system, a sand-dust storm ensemble forecast model based on rough set (RS) is proposed. The feature data are extracted from the historical data sets using the self-organization map (SOM) clustering network and single fields forecast to form the feature values with low dimensions. Then, the unwanted attributes are reduced according to RS to discretize the continuous feature values. Lastly, the minimum decision rules are constructed according to the remainder attributes, namely sand-dust storm ensemble forecast model based on RS is constructed. Results comparison between the proposed model and the back propagation neural network model show that the sand-storm forecast model based on RS has better stability, faster running speed, and its forecasting accuracy ratio is increased from 17.1% to 86.21%.

  15. Ensemble-based Regional Climate Prediction: Political Impacts

    Science.gov (United States)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  16. Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa

    Science.gov (United States)

    Shukla, Shraddhanand; Roberts, Jason; Hoell, Andrew; Funk, Christopher C.; Robertson, Franklin; Kirtman, Ben

    2016-07-01

    The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March-May (MAM), July-September (JAS), and October-December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

  17. Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa

    Science.gov (United States)

    Shukla, Shraddhanand; Roberts, Jason B.; Hoell. Andrew,; Funk, Chris; Robertson, Franklin R.; Kirtmann, Benjamin

    2016-01-01

    The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

  18. Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions

    Directory of Open Access Journals (Sweden)

    Rachida El Ouaraini

    2015-12-01

    Full Text Available The implementation of a regional ensemble data assimilation and forecasting system requires the specification of appropriate perturbations of lateral boundary conditions (LBCs, in order to simulate associated errors. The sensitivity of analysis and 6-h forecast ensemble spread to these perturbations is studied here formally and experimentally by comparing three different LBC configurations for the ensemble data assimilation system of the ALADIN-France limited-area model (LAM. While perturbed initial LBCs are provided by the perturbed LAM analyses in each ensemble, the three ensemble configurations differ with respect to LBCs used at 3- and 6-h forecast ranges, which respectively correspond to: (1 perturbed LBCs provided by the operational global ensemble data assimilation system (GLBC, which is considered as a reference configuration; (2 unperturbed LBCs (ULBC obtained from the global deterministic model; (3 perturbed LBCs obtained by adding random draws of an error covariance model (PLBC to the global deterministic system. A formal analysis of error and perturbation equations is first carried out, in order to provide an insight of the relative effects of observation perturbations and of LBC perturbations at different ranges, in the various ensemble configurations. Horizontal variations of time-averaged ensemble spread are then examined for 6-h forecasts. Despite the use of perturbed initial LBCs, the regional ensemble ULBC is underdispersive not only near the lateral boundaries, but also in approximately one-third of the inner area, due to advection during the data assimilation cycle. This artefact is avoided in PLBC through the additional use of non-zero LBC perturbations at 3- and 6-h ranges, and the sensitivity to the amplitude scaling of the covariance model is illustrated for this configuration. Some aspects of the temporal variation of ensemble spread and associated sensitivities to LBC perturbations are also studied. These results

  19. A Robust and Effective Multivariate Post-processing approach: Application on North American Multi-Model Ensemble Climate Forecast over the CONUS

    Science.gov (United States)

    Khajehei, Sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

    2017-04-01

    The North American Multi-model Ensemble (NMME) forecasting system has been providing valuable information using a large number of contributing models each consisting of several ensemble members. Despite all the potential benefits that the NMME offers, the forecasts are prone to bias in many regions. In this study, monthly precipitation from 11 contributing models totaling 128 ensemble members in the NMME are assessed and bias corrected. All the models are regridded to 0.5 degree spatial resolution for a more detailed assessment. The goals of this study are as follows: 1. Evaluating the performance of the NMME models over the Contiguous United States using the probabilistic and deterministic measures. 2. Introducing the Copula based ensemble post-processing (COP-EPP) method rooted in Bayesian methods for conditioning the forecast on the observations to improve the performance of NMME predictions. 3. Comparing the forecast skill of the NMME at four different lead-times (lead-0 to lead-3) across the western US, and assessing the effectiveness of COP-EPP in post-processing of precipitation forecasts. Results revealed that NMME models are highly biased in central and western US, while they provide acceptable performance in the eastern regions. The new approach demonstrates substantial improvement over the raw NMME forecasts. However, regional assessment indicates that the COP-EPP is superior to the commonly used Quantile Matching (QM) approach. Also, this method is showing considerable improvements on the seasonal NMME forecasts at all lead times.

  20. Navigating a Path Toward Operational, Short-term, Ensemble Based, Probablistic Streamflow Forecasts

    Science.gov (United States)

    Hartman, R. K.; Schaake, J.

    2004-12-01

    The National Weather Service (NWS) has federal responsibility for issuing public flood warnings in the United States. Additionally, the NWS has been engaged in longer range water resources forecasts for many years, particularly in the Western U.S. In the past twenty years, longer range forecasts have increasingly incorporated ensemble techniques. Ensemble techniques are attractive because they allow a great deal of flexibility, both temporally and in content. This technique also provides for the influence of additional forcings (i.e. ENSO), through either pre or post processing techniques. More recently, attention has turned to the use of ensemble techniques in the short-term streamflow forecasting process. While considerably more difficult, the development of reliable short-term probabilistic streamflow forecasts has clear application and value for many NWS customers and partners. During flood episodes, expensive mitigation actions are initialed or withheld and critical reservoir management decisions are made in the absence of uncertainty and risk information. Limited emergency services resources and the optimal use of water resources facilities necessitates the development of a risk-based decision making process. The development of reliable short-term probabilistic streamflow forecasts are an essential ingredient in the decision making process. This paper addresses the utility of short-term ensemble streamflow forecasts and the considerations that must be addressed as techniques and operational capabilities are developed. Verification and validation information are discussed from both a scientific and customer perspective. Education and training related to the interpretation and use of ensemble products are also addressed.

  1. Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

    Science.gov (United States)

    Pinson, Pierre

    2016-04-01

    The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

  2. Multi-Model Long-Range Ensemble Forecast for Decision Support in Hydroelectric Operations

    Science.gov (United States)

    Kunkel, M. L.; Parkinson, S.; Blestrud, D.; Holbrook, V. P.

    2014-12-01

    Idaho Power Company (IPC) is a hydroelectric based utility serving over a million customers in southern Idaho and eastern Oregon. Hydropower makes up ~50% of our power generation and accurate predictions of streamflow and precipitation drive our long-term planning and decision support for operations. We investigate the use of a multi-model ensemble approach for mid and long-range streamflow and precipitation forecasts throughout the Snake River Basin. Forecast are prepared using an Idaho Power developed ensemble forecasting technique for 89 locations throughout the Snake River Basin for periods of 3 to 18 months in advance. A series of multivariable linear regression, multivariable non-linear regression and multivariable Kalman filter techniques are combined in an ensemble forecast based upon two data types, historical data (streamflow, precipitation, climate indices [i.e. PDO, ENSO, AO, etc…]) and single value decomposition derived values based upon atmospheric heights and sea surface temperatures.

  3. A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

    Directory of Open Access Journals (Sweden)

    Yiqi Chu

    2016-10-01

    Full Text Available Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

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

    Science.gov (United States)

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

    2016-08-01

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

  5. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    Science.gov (United States)

    Candogan Yossef, Naze; van Beek, Rens; Weerts, Albrecht; Winsemius, Hessel; Bierkens, Marc F. P.

    2017-08-01

    In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

  6. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    Directory of Open Access Journals (Sweden)

    N. Candogan Yossef

    2017-08-01

    Full Text Available In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS. FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance. We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

  7. Hydrological-oriented verification for ensemble forecasting systems: the case of the PIT diagram

    Science.gov (United States)

    Bourgin, François; Ramos, Maria-Helena; Perrin, Charles; Renard, Benjamin

    2017-04-01

    The most common way to communicate uncertainty in streamflow predictions for water resources and risk management is through the use of ensemble scenarios or prediction intervals. While the advantages of probabilistic flow forecasting for decision-making are recognized, the evaluation of the quality of ensemble-based or probabilistic forecasts remains a challenge. Reliability is a fundamental attribute when evaluating the quality of probabilistic flow predictions. It is related to the statistical coherence of the associated uncertainty estimates. Reliable predictions are thus important for users who take actions based on prediction intervals (e.g., reservoir inflow volume forecasts) or on the forecast probability of a given critical event (e.g., exceedance of a flood threshold). However, forecast systems are usually developed to serve many users and, in general, they are evaluated without considering the user's specific decision-making problem. This means that a forecasting system must be reliable in all situations (for normal, high or low flows; for peak flow probabilities or volume probabilities of occurrence), regardless of the event of interest for the user. At the same time, users are often interested in knowing if a forecasting system performs well for their case of application. Application-focused evaluations of the quality of a forecast are thus also important to enhance the usefulness of a forecasting system. Here, we investigate the specificities of hydrological-oriented verification of reliability that is commonly assessed with the Probability Integral Transform (PIT) diagram. We applied an ensemble forecasting system to a large set of catchments in France to assess the impact of conditioning strategies used to stratifying the data on the evaluation of forecast performance. For example, we considered separating low and high flows, or focusing on rainfall-driven or recession parts of the hydrographs. We show that the use of conditioning strategies can

  8. The value of the North American Multi Model Ensemble phase 2 for sub-seasonal hydrological forecasting

    Science.gov (United States)

    Wanders, Niko; Wood, Eric

    2016-04-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. For example, seasonal forecasts of drought risk can enable farmers to make adaptive choices on crop varieties, labour usage, and technology investments. Seasonal and sub-seasonal predictions can increase preparedness to hydrological extremes that regularly occur in all regions of the world with large impacts on society. We investigated the skill of six seasonal forecast models from the NMME-2 ensemble coupled to two global hydrological models (VIC and PCRGLOBWB) for the period 1982-2012. The 31 years of NNME-2 hindcast data is used in combination with an ensemble mean and ESP forecast, to forecast important hydrological variables (e.g. soil moisture, groundwater storage, snow, reservoir levels and river discharge). By using two global hydrological models we are able to quantify both the uncertainty in the meteorological input and the uncertainty created by the different hydrological models. We show that the NMME-2 forecast outperforms the ESP forecasts in terms of anomaly correlation and brier skill score for all forecasted hydrological variables, with a low uncertainty in the performance amongst the hydrological models. However, the continuous ranked probability score (CRPS) of the NMME-2 ensemble is inferior to the ESP due to a large spread between the individual ensemble members. We use a cost analysis to show that the damage caused by floods and droughts in large scale rivers can globally be reduced by 48% (for leads from 1-2 months) to 20% (for leads between 6-9 months) when precautions are taken based on the NMME-2 ensemble instead of an ESP forecast. In collaboration with our local partner in West Africa (AGHRYMET), we looked at the performance of the sub-seasonal forecasts for crop planting dates and high flow season in West Africa. We show that the uncertainty in the optimal planting date is reduced

  9. 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.

  10. Integrating weather and climate predictions for seamless hydrologic ensemble forecasting: A case study in the Yalong River basin

    Science.gov (United States)

    Ye, Aizhong; Deng, Xiaoxue; Ma, Feng; Duan, Qingyun; Zhou, Zheng; Du, Chao

    2017-04-01

    Despite the tremendous improvement made in numerical weather and climate models over the recent years, the forecasts generated by those models still cannot be used directly for hydrological forecasting. A post-processor like the Ensemble Pre-Processor (EPP) developed by U.S. National Weather Service must be used to remove various biases and to extract useful predictive information from those forecasts. In this paper, we investigate how different designs of canonical events in the EPP can help post-process precipitation forecasts from the Global Ensemble Forecast System (GEFS) and Climate Forecast System Version 2 (CFSv2). The use of canonical events allow those products to be linked seamlessly and then the post-processed ensemble precipitation forecasts can be generated using the Schaake Shuffle procedure. We used the post-processed ensemble precipitation forecasts to drive a distributed hydrological model to obtain ensemble streamflow forecasts and evaluated those forecasts against the observed streamflow. We found that the careful design of canonical events can help extract more useful information, especially when up-to-date observed precipitation is used to setup the canonical events. We also found that streamflow forecasts using post-processed precipitation forecasts have longer lead times and higher accuracy than streamflow forecasts made by traditional Extend Streamflow Prediction (ESP) and the forecasts based on original GEFS and CFSv2 precipitation forecasts.

  11. Using ensemble weather forecast in a risk based real time optimization of urban drainage systems

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  12. Real-time flood forecasting coupling different postprocessing techniques of precipitation forecast ensembles with a distributed hydrological model. The case study of may 2008 flood in western Piemonte, Italy

    Directory of Open Access Journals (Sweden)

    D. Cane

    2013-02-01

    Full Text Available In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques. The region of interest is Piemonte, northwestern Italy, a complex orography area close to the Mediterranean Sea where the forecast are often a challenge for weather models. The May 2008 flood is here used as a case study, and the very dense weather station network allows us for a very good description of the event and initialization of the hydrological model. The ensemble probabilistic forecasts of the rainfall fields are obtained with the Bayesian model averaging, with the classical poor man ensemble approach and with a new technique, the Multimodel SuperEnsemble Dressing. In this case study, the meteo-hydrological chain initialized with the Multimodel SuperEnsemble Dressing is able to provide more valuable discharge ranges with respect to the one initialized with Bayesian model averaging multi-model.

  13. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities.

    Science.gov (United States)

    Morabito, Marco; Pavlinic, Daniela Z; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  14. Spatial-temporal fractions verification for high-resolution ensemble forecasts

    Directory of Open Access Journals (Sweden)

    Le Duc

    2013-04-01

    Full Text Available Experiments with two ensemble systems of resolutions 10 km (MF10km and 2 km (MF2km were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighbourhood in FSS to the time and ensemble dimension. The extension was carried out by recognising that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighbourhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems. The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called the ensemble FSS, which is a good representative of FSS for ensemble forecast in comparison with the FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rains, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells.

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

    Science.gov (United States)

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

    2017-04-01

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

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

    DEFF Research Database (Denmark)

    Lundholm, Sisse Camilla

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

  17. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

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

    Science.gov (United States)

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

    2017-04-01

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

  19. The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

    OpenAIRE

    Liechti, K.; L. Panziera; U. Germann; Zappa, M.

    2013-01-01

    This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowca...

  20. Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting

    Science.gov (United States)

    Bourgin, François; Ramos, Maria-Helena; Thirel, Guillaume; Andreassian, Vazken

    2015-04-01

    Statistical post-processing and data assimilation (also called real-time model updating in the engineering community), although generally dealt with separately, can be intrinsically related in the hydrological forecasting framework. Both represent techniques that may be used in a forecasting system to improve the quality of the forecasts (i.e., to provide more accurate and reliable forecasts) and to, ultimately, enhance the usefulness of the forecasts in decision-making. In this study, we investigate how data assimilation and post-processing contribute, either separately or together, to the skill of a hydrological ensemble forecasting system. Based on a set of 202 unregulated catchments spread over France, we compare four forecasting options: without data assimilation and post-processing, without data assimilation but with post-processing, with data assimilation but without post-processing, and with both data assimilation and post-processing. A short-range meteorological ensemble prediction system, the Météo-France PEARP EPS, and the GRP hydrological model, a continuous, lumped storage-type model specifically designed for flood forecasting, were used to produce hourly hydrological ensemble forecasts. The data assimilation procedure used exploits the last available observed discharge to directly update the routing store state of the hydrological model, and the last relative error to correct the model output with a multiplicative coefficient. For post-processing, we used a hydrological uncertainty processor (HUP) that assesses model simulation uncertainties and compute empirical uncertainty bounds to flow simulations. Results indicate that both strategies have complementary effects. Data assimilation has mainly a very positive effect on forecast accuracy. Its impact however decreases with increasing lead time. Post-processing, by accounting specifically for hydrological uncertainty, has a very positive and longer lasting effect on forecast reliability. As a

  1. Ensemble-based air quality forecasts: A multimodel approach applied to ozone

    OpenAIRE

    Mallet, Vivien; Sportisse, Bruno

    2006-01-01

    International audience; The potential of ensemble techniques to improve ozone forecasts is investigated. Ensembles with up to 48 members (models) are generated using the modeling system Polyphemus. Members differ in their physical parameterizations, their numerical approximations, and their input data. Each model is evaluated during 4 months (summer 2001) over Europe with hundreds of stations from three ozone-monitoring networks. We found that several linear combinations of models have the po...

  2. The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

    Directory of Open Access Journals (Sweden)

    Amir Givati

    2017-01-01

    Full Text Available This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME. The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

  3. SVR-Boosting ensemble model for electricity price forecasting in electric power market

    Institute of Scientific and Technical Information of China (English)

    ZHOU Dian-min; GAO Lin; GUAN Xiao-hong; GAO Feng

    2008-01-01

    A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boos-ting) is presented in this paper for electricity price forecasting in electric power market. In the light of charac-the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accu-racy, and possess comparatively satisfactory generalization capability.

  4. The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

    Science.gov (United States)

    Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

    2015-08-01

    The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

  5. Anticipatory Water Management: Using ensemble weather forecasts for critical events

    OpenAIRE

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuation of local inhabitants. Therefore, the use of weather forecast information with hydrological models can be invaluable for the operational water manager to expand the forecast horizon and to have ti...

  6. Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

    Directory of Open Access Journals (Sweden)

    Bo Qu

    2017-01-01

    Full Text Available Statistical post-processing for multi-model grand ensemble (GE hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members, using seven different “THORPEX Interactive Grand Global Ensemble” (TIGGE weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs, over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.

  7. A component-resampling approach for estimating probability distributions from small forecast ensembles

    Science.gov (United States)

    Dettinger, M.

    2006-01-01

    In many meteorological and climatological modeling applications, the availability of ensembles of predictions containing very large numbers of members would substantially ease statistical analyses and validations. This study describes and demonstrates an objective approach for generating large ensembles of "additional" realizations from smaller ensembles, where the additional ensemble members share important first-and second-order statistical characteristics and some dynamic relations within the original ensemble. By decomposing the original ensemble members into assuredly independent time-series components (using a form of principal component decomposition) that can then be resampled randomly and recombined, the component-resampling procedure generates additional time series that follow the large and small scale structures in the original ensemble members, without requiring any tuning by the user. The method is demonstrated by applications to operational medium-range weather forecast ensembles from a single NCEP weather model and application to a multi-model, multi-emission-scenarios ensemble of 21st Century climate-change projections. ?? Springer 2006.

  8. Visualizing uncertainties in a storm surge ensemble data assimilation and forecasting system

    KAUST Repository

    Hollt, Thomas

    2015-01-15

    We present a novel integrated visualization system that enables the interactive visual analysis of ensemble simulations and estimates of the sea surface height and other model variables that are used for storm surge prediction. Coastal inundation, caused by hurricanes and tropical storms, poses large risks for today\\'s societies. High-fidelity numerical models of water levels driven by hurricane-force winds are required to predict these events, posing a challenging computational problem, and even though computational models continue to improve, uncertainties in storm surge forecasts are inevitable. Today, this uncertainty is often exposed to the user by running the simulation many times with different parameters or inputs following a Monte-Carlo framework in which uncertainties are represented as stochastic quantities. This results in multidimensional, multivariate and multivalued data, so-called ensemble data. While the resulting datasets are very comprehensive, they are also huge in size and thus hard to visualize and interpret. In this paper, we tackle this problem by means of an interactive and integrated visual analysis system. By harnessing the power of modern graphics processing units for visualization as well as computation, our system allows the user to browse through the simulation ensembles in real time, view specific parameter settings or simulation models and move between different spatial and temporal regions without delay. In addition, our system provides advanced visualizations to highlight the uncertainty or show the complete distribution of the simulations at user-defined positions over the complete time series of the prediction. We highlight the benefits of our system by presenting its application in a real-world scenario using a simulation of Hurricane Ike.

  9. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

    Science.gov (United States)

    Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas

    2016-01-01

    An ensemble of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically downscale global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.

  10. Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins.

    Science.gov (United States)

    Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn

    2016-04-01

    Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

  11. Ensemble forecasting shifts in climatically suitable areas for Tropidacris cristata (Orthoptera: Acridoidea: Romaleidae)

    DEFF Research Database (Denmark)

    Diniz, J.A.F.; Nabout, J.C.; Bini, L.M.;

    2010-01-01

    1. The effects of climate change on species' ranges have been usually inferred using niche-based models creating bioclimatic envelopes that are projected into geographical space. Here, we apply an ensemble forecasting approach for niche models in the Neotropical grasshopper Tropidacris cristata...

  12. Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

    DEFF Research Database (Denmark)

    Pinson, Pierre; Hagedorn, Renate

    2012-01-01

    A framework for the verification of ensemble forecasts of near-surface wind speed is described. It is based on existing scores and diagnostic tools, though considering observations from synoptic stations as reference instead of the analysis. This approach is motivated by the idea of having a user...

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  14. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change

    DEFF Research Database (Denmark)

    Diniz-Filho, José Alexandre F.; Bini, Luis Mauricio; Rangel, Thiago Fernando

    2009-01-01

    of uncertainty in ensembles of forecasts is presented. We model the distributions of 3837 New World birds and project them into 2080. We then quantify and map the relative contribution of different sources of uncertainty from alternative methods for niche modeling, general circulation models (AOGCM...

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  16. Multi-objective calibration of forecast ensembles using Bayesian model averaging

    NARCIS (Netherlands)

    Vrugt, J.A.; Clark, M.P.; Diks, C.G.H.; Duan, Q.; Robinson, B.A.

    2006-01-01

    Bayesian Model Averaging (BMA) has recently been proposed as a method for statistical postprocessing of forecast ensembles from numerical weather prediction models. The BMA predictive probability density function (PDF) of any weather quantity of interest is a weighted average of PDFs centered on the

  17. Statistical-dynamical long-range seasonal forecasting of streamflow with the North-American Multi Model Ensemble (NMME)

    Science.gov (United States)

    Slater, Louise; Villarini, Gabriele

    2017-04-01

    There are two main approaches to long-range (monthly to seasonal) streamflow forecasting: statistical approaches that typically relate climate precursors directly to streamflow, and dynamical physically-based approaches in which spatially distributed models are forced with downscaled meteorological forecasts. While the former approach is potentially limited by a lack of physical causality, the latter tends to be complex and time-consuming to implement. In contrast, hybrid statistical-dynamical techniques that use global climate model (GCM) ensemble forecasts as inputs to statistical models are both physically-based and rapid to run, but are a relatively new field of research. Here, we conduct the first systematic multimodel statistical-dynamical forecasting of streamflow using NMME climate forecasts from eight GCMs (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) across a broad region. At several hundred U.S. Midwest stream gauges with long (50+ continuous years) streamflow records, we fit probabilistic statistical models for seasonal streamflow percentiles ranging from minimum to maximum flows. As predictors, we use basin-averaged values of precipitation, antecedent wetness, temperature, agricultural row crop acreage, and population density. Using the observed data, we select the best-fitting probabilistic model for every site, season, and streamflow percentile (ranging from low to high flows). The best-fitting models are then used to obtain streamflow predictions by incorporating the NMME climate forecasts and the extrapolated agricultural and population time series as predictors. The forecasting skill of our models is assessed using both deterministic and probabilistic verification measures. The influence of the different predictors is evaluated for all streamflow percentiles and across the full range of lead times. Our findings reveal that statistical-dynamical streamflow forecasting produces promising results, which may enable water managers

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

    KAUST Repository

    Dreano, Denis

    2017-05-24

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

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

    Science.gov (United States)

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

    2017-07-01

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

  20. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

    Science.gov (United States)

    Rubin, Juli I.; Reid, Jeffrey S.; Hansen, James A.; Anderson, Jeffrey L.; Collins, Nancy; Hoar, Timothy J.; Hogan, Timothy; Lynch, Peng; McLay, Justin; Reynolds, Carolyn A.; Sessions, Walter R.; Westphal, Douglas L.; Zhang, Jianglong

    2016-03-01

    An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS) at 1 × 1°, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact through the ensemble data

  1. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    Science.gov (United States)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water

  2. Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

    Science.gov (United States)

    Lahmiri, Salim; Boukadoum, Mounir

    2015-08-01

    We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

  3. Ensemble forecasting of potential habitat for three invasive fishes

    Science.gov (United States)

    Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

    2012-01-01

    Aquatic invasive species pose major ecological and economic threats to aquatic ecosystems worldwide via displacement, predation, or hybridization with native species and the alteration of aquatic habitats and hydrologic cycles. Modeling the habitat suitability of alien aquatic species through spatially explicit mapping is an increasingly important risk assessment tool. Habitat modeling also facilitates identification of key environmental variables influencing invasive species distributions. We compared four modeling methods to predict the potential continental United States distributions of northern snakehead Channa argus (Cantor, 1842), round goby Neogobius melanostomus (Pallas, 1814), and silver carp Hypophthalmichthys molitrix (Valenciennes, 1844) using maximum entropy (Maxent), the genetic algorithm for rule set production (GARP), DOMAIN, and support vector machines (SVM). We used inventory records from the USGS Nonindigenous Aquatic Species Database and a geographic information system of 20 climatic and environmental variables to generate individual and ensemble distribution maps for each species. The ensemble maps from our study performed as well as or better than all of the individual models except Maxent. The ensemble and Maxent models produced significantly higher accuracy individual maps than GARP, one-class SVMs, or DOMAIN. The key environmental predictor variables in the individual models were consistent with the tolerances of each species. Results from this study provide insights into which locations and environmental conditions may promote the future spread of invasive fish in the US.

  4. Validation of time-lagged ensemble forecasts relevant for predicting aircraft wake vortices

    Energy Technology Data Exchange (ETDEWEB)

    Dengler, Klaus; Anger, Johanna [DLR, Oberpfaffenhofen (Germany). Inst. fuer Physik der Atmosphaere; Keil, Christian [Ludwig-Maximilians-Univ. Muenchen (Germany). Meteorologisches Inst.

    2011-12-15

    Time-Lagged Ensemble (TLE) forecasts of the high-resolution model COSMO-FRA with a horizontal resolution of 2.8 km, an hourly update cycle and a lead time of six hours are validated against measurements of a Wind-Temperature Radar (WTR) for three cases representing different weather regimes: a frontal passage, a stormy situation and a high pressure system. Forecasts and measurements are available at high temporal resolution every 10 minutes. The present study focuses on parameters relevant for forecasting aircraft wake vortices, namely wind, virtual potential temperature and Turbulent Kinetic Energy (TKE) for altitudes below 1600 m. When compared with a deterministic run started once a day at 00 UTC with a lead time of 24 h a reduction of forecast error in the first two forecast hours is visible for all wind components. For wind speed at low levels around 400 m this reduction is of order 1.7 ms{sup -1} for the 1-hour forecast member and 0.9 ms{sup -1} for the equally weighted ensemble mean depending on weather situation. TLE forecasts of virtual potential temperature show an improvement of about 1 K except for the high pressure system while forecast errors of TKE are reduced in all cases below 900 m. Since no data assimilation is used presently in COSMO-FRA, the improvements of the forecasts are the result of the hourly update cycle which benefits from most recent initial conditions provided by the COSMO-EU analysis. The results of this study may contribute to improve the quality of wake vortex predictions and encourage the assimilation of temporal high resolution wind data into COSMO-FRA to further improve the very short range forecasts. (orig.)

  5. EXPERIMENTS OF ENSEMBLE FORECAST OF TYPHOON TRACK USING BDA PERTURBING METHOD

    Institute of Scientific and Technical Information of China (English)

    HUANG Yan-yan; WAN Qi-lin; YUAN Jin-nan; DING Wei-yu

    2006-01-01

    A new method, BDA perturbing, is used in ensemble forecasting of typhoon track. This method is based on the Bogus Data Assimilation scheme. It perturbs the initial position and intensity of typhoons and gets a series of bogus vortex. Then each bogus vortex is used in data assimilation to obtain initial conditions. Ensemble forecast members are constructed by conducting simulation with these initial conditions. Some cases of typhoon are chosen to test the validity of this new method and the results show that: using the BDA perturbing method to perturb initial position and intensity of typhoon for track forecast can improve accuracy, compared with the direct use of the BDA assimilation scheme. And it is concluded that a perturbing amplitude of intensity of 5 hPa is probably more appropriate than 10 hPa if the BDA perturbing method is used in combination with initial position perturbation.

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

    Science.gov (United States)

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

    2010-05-01

    Ensemble and probabilistic forecasts have many advantages over deterministic ones, both in meteorology and hydrology (e.g. Krzysztofowicz, 2001). Mainly, they inform the user on the uncertainty linked to the forecast. It has been brought to attention that such additional information could lead to improved decision making (e.g. Wilks and Hamill, 1995; Mylne, 2002; Roulin, 2007), but very few studies concentrate on operational situations involving the use of such forecasts. In addition, many authors have demonstrated that ensemble forecasts outperform deterministic forecasts in terms of performance (e.g. Jaun et al., 2005; Velazquez et al., 2009; Laio and Tamea, 2007). However, such performance is mostly assessed on the basis of numerical scoring rules, which compare the forecasts to the observations, and seldom in terms of management gains. The proposed case study adopts an operational point of view, on the basis that a novel forecasting system has value only if it leads to increase monetary and societal gains (e.g. Murphy, 1994; Laio and Tamea, 2007). More specifically, Environment Canada operational ensemble precipitation forecasts are used to drive the HYDROTEL distributed hydrological model (Fortin et al., 1995), calibrated on the Gatineau watershed located in Québec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Québec SOHO stochastic management optimization tool that automatically search for optimal operation decisions for the all reservoirs and hydropower plants located on the basin. The timeline of the study is the fall season of year 2003. This period is especially relevant because of high precipitations that nearly caused a major spill, and forced the preventive evacuation of a portion of the population located near one of the dams. We show that the use of the ensemble forecasts would have reduced the occurrence of spills and flooding, which is of particular importance for dams located in populous area, and

  7. Ensemble approach to wheat yield forecasting in Ukraine

    Science.gov (United States)

    Kussul, Nataliia; Kolotii, Andrii; Skakun, Sergii; Shelestov, Andrii; Kussul, Olga; Kravchenko, Oleksii

    2014-05-01

    Crop yield forecasting is an extremely important component of the agriculture monitoring domain. In our previous study [1], we assessed relative efficiency and feasibility of using an NDVI-based empirical model for winter wheat yield forecasting at oblast level in Ukraine. Though the NDVI-based model provides minimum data requirements, it has some limitations since NDVI is indirectly related just to biomass but not meteorological conditions. Therefore, it is necessary to assess satellite-derived parameters that incorporate meteorology while maintaining the requirement of minimum data inputs. The objective of the proposed paper is several-fold: (i) to assess efficiency of using biophysical satellite-derived parameters for crop yield forecasting for Ukraine and select the optimal ones based on rigorous feature selection procedure; (ii) to assimilate predictions made by models built on various satellite-derived parameters. Two new parameters are considered in the paper: (i) vegetation health index (VHI) at 4 km spatial resolution derived from a series of NOAA satellites; (ii) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SPOT-VEGETATION at 1 km spatial resolution. VHI data are provided as weekly composites and FAPAR data are provided as decadal composites. The particular advantage of using VHI is that it incorporates moisture and thermal conditions of vegetation canopy, while FAPAR is directly related to the primary productivity of photosynthesis It is required to find a day of the year for which a parameter is taken and used in the empirical model. For this purpose, a Random Forest feature selection procedure is applied. It is found that VHI and FAPAR values taken in April-May provided the minimum error value when comparing to the official statistics, thus enabling forecasts 2-3 months prior to harvest, and this corresponds to results derived from LOOCV procedure. The best timing for making reliable yield forecasts is nearly the same

  8. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Zack, J; Natenberg, E; Young, S; Manobianco, J; Kamath, C

    2010-02-21

    measure in order to get the maximum positive impact on forecast performance for a particular site and short-term look-ahead period. Both tools rely on the use of NWP models to assess the sensitivity of a forecast for a particular location to measurements made at a prior time (i.e. the look-ahead period) at points surrounding the target location. The fundamental hypothesis is that points and variables with high sensitivity are good candidates for measurements since information at those points are likely to have the most impact on the forecast for the desired parameter, location and look-ahead period. One approach is called the adjoint method (Errico and Vukicevic, 1992; Errico, 1997) and the other newer approach is known as Ensemble Sensitivity Analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008). Both approaches have been tested on large-scale atmospheric prediction problems (e.g. forecasting pressure or precipitation over a relatively large region 24 hours ahead) but neither has been applied to mesoscale space-time scales of winds or any other variables near the surface of the earth. A number of factors suggest that ESA is better suited for short-term wind forecasting applications. One of the most significant advantages of this approach is that it is not necessary to linearize the mathematical representation of the processes in the underlying atmospheric model as required by the adjoint approach. Such a linearization may be especially problematic for the application of short-term forecasting of boundary layer winds in complex terrain since non-linear shifts in the structure of boundary layer due to atmospheric stability changes are a critical part of the wind power production forecast problem. The specific objective of work described in this paper is to test the ESA as a tool to identify measurement locations and variables that have the greatest positive impact on the accuracy of wind forecasts in the 0- to 6-hour look-ahead periods for the wind generation area of

  9. 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...

  10. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    Science.gov (United States)

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  11. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature

    Science.gov (United States)

    Baran, Sándor; Möller, Annette

    2017-02-01

    Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years, increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate distribution for wind vectors or even a more general setting allowing to combine any types of weather variables. In line with a recently proposed approach to model temperature and wind speed jointly by a bivariate BMA model, this paper introduces an EMOS model for these weather quantities based on a bivariate truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed forecasts of the 8-member University of Washington mesoscale ensemble and the 11-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to the performance of the bivariate BMA model and a multivariate Gaussian copula approach, post-processing the margins with univariate EMOS. While the predictive skills of the compared methods are similar, the bivariate EMOS model requires considerably lower computation times than the bivariate BMA method.

  12. Benchmark verification of deterministic and ensemble forecasts for the river Rhine

    Science.gov (United States)

    van Osnabrugge, Bart; Weerts, Albrecht; Verkade, Jan; den Toom, Matthijs; Uijlenhoet, Remko; Sprokkereef, Eric

    2016-04-01

    As part of the IMPREX project (IMproving PRedictions and management of EXtremes) we perform a benchmark verification study (tier 1) of an operational active hydrological forecasting system of the river Rhine. We assess the current forecast skill and contributions of current DA methods (e.g. error correction) to forecast skill. Our results will be compared with previous verification studies (Renner et al., 2009, Verkade et al., 2013) for the Rhine. Based on the results we will identify weaknesses and opportunities for future improvements. After model improvements and introduction of more advanced (ensemble) DA methods the benchmark will be conducted again later in the project (tier 2). From these experiments, we hope to show the relative importance of data assimilation, the availability of real-time hydrologic measured data in comparison with (improved) meteorological forecast skill and their effect on hydrologic predictability for the river Rhine.

  13. Ensemble forecast of human West Nile virus cases and mosquito infection rates

    Science.gov (United States)

    Defelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

    2017-02-01

    West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

  14. Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting

    OpenAIRE

    B. Vincendon; V. Ducrocq; Nuissier, O.; B. Vié

    2011-01-01

    Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new metho...

  15. Developing Multi-model Ensemble for Precipitation and Temperature Seasonal Forecasts: Implications for Karkheh River Basin in Iran

    Science.gov (United States)

    Najafi, Husain; Massah Bavani, Ali Reza; Wanders, Niko; Wood, Eric; Irannejad, Parviz; Robertson, Andrew

    2017-04-01

    Water resource managers can utilize reliable seasonal forecasts for allocating water between different users within a water year. In the west of Iran where a decline of renewable water resources has been observed, basin-wide water management has been the subject of many inter-provincial conflicts in recent years. The problem is exacerbated when the environmental water requirements is not provided leaving the Hoor-al-Azim marshland in the downstream dry. It has been argued that information on total seasonal rainfall can support the Iranian Ministry of Energy within the water year. This study explores the skill of the North America Multi Model Ensemble for Karkheh River Basin in the of west Iran. NMME seasonal precipitation and temperature forecasts from eight models are evaluated against PERSIANN-CDR and Climate Research Unit (CRU) datasets. Analysis suggests that anomaly correlation for both precipitation and temperature is greater than 0.4 for all individual models. Lead time-dependent seasonal forecasts are improved when a multi-model ensemble is developed for the river basin using stepwise linear regression model. MME R-squared exceeds 0.6 for temperature for almost all initializations suggesting high skill of NMME in Karkheh river basin. The skill of MME for rainfall forecasts is high for 1-month lead time for October, February, March and October initializations. However, for months when the amount of rainfall accounts for a significant proportion of total annual rainfall, the skill of NMME is limited a month in advance. It is proposed that operational regional water companies incorporate NMME seasonal forecasts into water resource planning and management, especially during growing seasons that are essential for agricultural risk management.

  16. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    Science.gov (United States)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological

  17. Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

    Science.gov (United States)

    Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

    2016-04-01

    Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending

  18. Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

    Science.gov (United States)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

    2017-04-01

    Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the

  19. The effects of land surface process perturbations in a global ensemble forecast system

    Science.gov (United States)

    Deng, Guo; Zhu, Yuejian; Gong, Jiandong; Chen, Dehui; Wobus, Richard; Zhang, Zhe

    2016-10-01

    Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

  20. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    Science.gov (United States)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2016-09-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  1. Real-time demonstration and evaluation of over-the-loop short to medium-range ensemble streamflow forecasting

    Science.gov (United States)

    Wood, A. W.; Clark, E.; Newman, A. J.; Nijssen, B.; Clark, M. P.; Gangopadhyay, S.; Arnold, J. R.

    2015-12-01

    The US National Weather Service River Forecasting Centers are beginning to operationalize short range to medium range ensemble predictions that have been in development for several years. This practice contrasts with the traditional single-value forecast practice at these lead times not only because the ensemble forecasts offer a basis for quantifying forecast uncertainty, but also because the use of ensembles requires a greater degree of automation in the forecast workflow than is currently used. For instance, individual ensemble member forcings cannot (practically) be manually adjusted, a step not uncommon with the current single-value paradigm, thus the forecaster is required to adopt a more 'over-the-loop' role than before. The relative lack of experience among operational forecasters and forecast users (eg, water managers) in the US with over-the-loop approaches motivates the creation of a real-time demonstration and evaluation platform for exploring the potential of over-the-loop workflows to produce usable ensemble short-to-medium range forecasts, as well as long range predictions. We describe the development and early results of such an effort by a collaboration between NCAR and the two water agencies, the US Army Corps of Engineers and the US Bureau of Reclamation. Focusing on small to medium sized headwater basins around the US, and using multi-decade series of ensemble streamflow hindcasts, we also describe early results, assessing the skill of daily-updating, over-the-loop forecasts driven by a set of ensemble atmospheric outputs from the NCEP GEFS for lead times from 1-15 days.

  2. Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

    Science.gov (United States)

    Villarini, Gabriele; Luitel, Beda; Vecchi, Gabriel A.; Ghosh, Joyee

    2016-09-01

    North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical-dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982-2015. The skill of these statistical-dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

  3. Extending the square root method to account for additive forecast noise in ensemble methods

    CERN Document Server

    Raanes, Patrick N; Bertino, Laurent

    2015-01-01

    A square root approach is considered for the problem of accounting for model noise in the forecast step of the ensemble Kalman filter (EnKF) and related algorithms. The primary aim is to replace the method of simulated, pseudo-random, additive noise so as to eliminate the associated sampling errors. The core method is based on the analysis step of ensemble square root filters, and consists in the deterministic computation of a transform matrix. The theoretical advantages regarding dynamical consistency are surveyed, applying equally well to the square root method in the analysis step. A fundamental problem due to the limited size of the ensemble subspace is discussed, and novel solutions that complement the core method are suggested and studied. Benchmarks from twin experiments with simple, low-order dynamics indicate improved performance over standard approaches such as additive, simulated noise and multiplicative inflation.

  4. Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

    Directory of Open Access Journals (Sweden)

    J. Dietrich

    2009-08-01

    Full Text Available Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.

  5. Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting

    National Research Council Canada - National Science Library

    Vincendon, B; Ducrocq, V; Nuissier, O; Vié, B

    2011-01-01

      Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP...

  6. Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

    Science.gov (United States)

    Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

    2016-06-01

    Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

  7. 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

  8. Evaluation of WRF-based convection-permitting multi-physics ensemble forecasts over China for an extreme rainfall event on 21 July 2012 in Beijing

    Science.gov (United States)

    Zhu, Kefeng; Xue, Ming

    2016-11-01

    On 21 July 2012, an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm, occurred in Beijing, China. Most operational models failed to predict such an extreme amount. In this study, a convective-permitting ensemble forecast system (CEFS), at 4-km grid spacing, covering the entire mainland of China, is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event, the predicted maximum is 415 mm d-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing, as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas, the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower (higher) Brier score and a higher resolution than the global ensemble for precipitation, indicating more reliable probabilistic forecasting by CEFS. Additionally, forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation, and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions, and, to less of an extent, the model physics.

  9. The Performance and Feasibility of Ensemble Forecast Sensitivity to Observations-based Proactive Quality Control Scheme

    Science.gov (United States)

    Chen, T. C.; Hotta, D.; Kalnay, E.

    2015-12-01

    Operational numerical weather prediction (NWP) systems occasionally exhibit "forecast skill dropouts" in which the forecast skill drops to an abnormally low level, due in part to the assimilation of flawed observational data. Recent studies have shown that a diagnostic technique called Ensemble Forecast Sensitivity to Observations (EFSO) can detect such observations (Kalnay et.al 2012; Ota et al. 2013, Tellus A). Based on this technique, a new Quality Control (QC) scheme called Proactive QC (PQC) has been proposed which detects "flawed" observations using EFSO after just 6 hours forecast, when the analysis at the next cycle becomes available for verification and then repeats the analysis and forecast without using the detected observations (Hotta 2014). In Hotta (2014), it was shown using the JCSDA S4 Testbed that the 6hr PQC reduces the 24-hour forecast errors from the detected skill dropout events. With such encouraging results we are performing preliminary experiments towards operational implementation. First, we show that offline PQC correction can significantly reduce forecast errors up to 5 days, and that the reduction and improved areal coverage can grow with synoptic weather disturbances for several days. Second, with online PQC cycle experiment the reduction of forecast error is shown to be even larger than in the offline version, since the effect could accumulate over each time we perform a PQC correction. Finally, the operational center imposes very tight schedule in order to deliver the products on time, thus the computational cost has to be minimized in order for PQC to be implemented. To avoid performing the analysis twice, which is the most expensive part of PQC, we test the accuracy of constant-K approximation, which assumes the Kalman gain K doesn't change much given the fact that only a small subset of observation is rejected. In this presentation, we will demonstrate the performance and feasibility of PQC implementation in real-time operational

  10. Image Region Selection and Ensemble for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    Xin Geng; Zhi-Hua Zhou

    2006-01-01

    In this paper, a novel framework for face recognition, namely Selective Ensemble of Image Regions (SEIR), is proposed. In this framework, all possible regions in the face image are regarded as a certain kind of features. There are two main steps in SEIR: the first step is to automatically select several regions from all possible candidates; the second step is to construct classifier ensemble from the selected regions. An implementation of SEIR based on multiple eigenspaces, namely SEME, is also proposed in this paper. SEME is analyzed and compared with eigenface, PCA + LDA, eigenfeature, and eigenface + eigenfeature through experiments. The experimental results show that SEME achieves the best performance.

  11. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

    Science.gov (United States)

    Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

    2016-09-01

    This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

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

    National Research Council Canada - National Science Library

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

    2016-01-01

      This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models...

  13. Combining stochastic weather generation and ensemble streamflow predictions for short to medium term flow forecasting over Quebec, Canada

    Science.gov (United States)

    Brissette, F.; Chen, J.; Li, Z.; Turcotte, R.

    2012-04-01

    Probabilistic streamflow forecasting has been an important research avenue over the past decade and such approaches are now more commonly being incorporated into operational forecasting systems within government agencies and industries dealing with water management. This work details a prototype for a streamflow forecast operational system in southern Quebec, Canada. The system uses ensemble meteorological forecasts for short term (less than 10 days) forecasting, switching to a stochastic weather generator for the period exceeding 10 days all the way to a three-month lead time. Precipitation and temperature series are then fed to one (or many) hydrological models to produce streamflow forecasts. The ensemble weather forecasts are corrected for biases and under dispersion using logistic regression. Results show that the ensemble streamflow forecasts resulting from the ensemble meteorological forecast have more skill than the deterministic forecasts. Preliminary results indicate that ensemble meteorological forecasts displayed skill for a period up to 5 days for precipitation and up to about 10 days for temperature. Past ten days, probabilistic streamflow forecasts are based on multiple synthetic times series obtained from a stochastic weather generator. The use of stochastic time series result in better forecasts then resampling the historical record and allows for better evaluation of extreme events. The weather generator can easily be linked to large scale seasonal global predictors, is such links exist. Over the tested basins (continental climate), the system forecast has skills up to a lead time of 4 weeks in the best case. For a lead-time between one and three months, using the forecast prototype yielded no better results than using the historical streamflow record. This work also investigated the uncertainty linked to the choice of one hydrology model and the ability of a multi-model approach to improve streamflow forecasting. Preliminary results showed that

  14. Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles

    Science.gov (United States)

    Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae

    2016-04-01

    Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.

  15. Ensemble Forecasting of Coronal Mass Ejections Using the WSA-ENLIL with CONED Model

    Science.gov (United States)

    Emmons, D.; Acebal, A.; Pulkkinen, A.; Taktakishvili, A.; MacNeice, P.; Odstricil, D.

    2013-01-01

    The combination of the Wang-Sheeley-Arge (WSA) coronal model, ENLIL heliospherical model version 2.7, and CONED Model version 1.3 (WSA-ENLIL with CONED Model) was employed to form ensemble forecasts for 15 halo coronal mass ejections (halo CMEs). The input parameter distributions were formed from 100 sets of CME cone parameters derived from the CONED Model. The CONED Model used image processing along with the bootstrap approach to automatically calculate cone parameter distributions from SOHO/LASCO imagery based on techniques described by Pulkkinen et al. (2010). The input parameter distributions were used as input to WSA-ENLIL to calculate the temporal evolution of the CMEs, which were analyzed to determine the propagation times to the L1 Lagrangian point and the maximum Kp indices due to the impact of the CMEs on the Earth's magnetosphere. The Newell et al. (2007) Kp index formula was employed to calculate the maximum Kp indices based on the predicted solar wind parameters near Earth assuming two magnetic field orientations: a completely southward magnetic field and a uniformly distributed clock-angle in the Newell et al. (2007) Kp index formula. The forecasts for 5 of the 15 events had accuracy such that the actual propagation time was within the ensemble average plus or minus one standard deviation. Using the completely southward magnetic field assumption, 10 of the 15 events contained the actual maximum Kp index within the range of the ensemble forecast, compared to 9 of the 15 events when using a uniformly distributed clock angle.

  16. 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.

  17. Ensemble of regional climate model projections for Ireland

    Science.gov (United States)

    Nolan, Paul; McGrath, Ray

    2016-04-01

    The method of Regional Climate Modelling (RCM) was employed to assess the impacts of a warming climate on the mid-21st-century climate of Ireland. The RCM simulations were run at high spatial resolution, up to 4 km, thus allowing a better evaluation of the local effects of climate change. Simulations were run for a reference period 1981-2000 and future period 2041-2060. Differences between the two periods provide a measure of climate change. To address the issue of uncertainty, a multi-model ensemble approach was employed. Specifically, the future climate of Ireland was simulated using three different RCMs, driven by four Global Climate Models (GCMs). To account for the uncertainty in future emissions, a number of SRES (B1, A1B, A2) and RCP (4.5, 8.5) emission scenarios were used to simulate the future climate. Through the ensemble approach, the uncertainty in the RCM projections can be partially quantified, thus providing a measure of confidence in the predictions. In addition, likelihood values can be assigned to the projections. The RCMs used in this work are the COnsortium for Small-scale MOdeling-Climate Limited-area Modelling (COSMO-CLM, versions 3 and 4) model and the Weather Research and Forecasting (WRF) model. The GCMs used are the Max Planck Institute's ECHAM5, the UK Met Office's HadGEM2-ES, the CGCM3.1 model from the Canadian Centre for Climate Modelling and the EC-Earth consortium GCM. The projections for mid-century indicate an increase of 1-1.6°C in mean annual temperatures, with the largest increases seen in the east of the country. Warming is enhanced for the extremes (i.e. hot or cold days), with the warmest 5% of daily maximum summer temperatures projected to increase by 0.7-2.6°C. The coldest 5% of night-time temperatures in winter are projected to rise by 1.1-3.1°C. Averaged over the whole country, the number of frost days is projected to decrease by over 50%. The projections indicate an average increase in the length of the growing season

  18. A Note on NCOM Temperature Forecast Error Calibration Using the Ensemble Transform

    Science.gov (United States)

    2009-01-01

    spatial distributions of the E Coelhn et a\\. ,’journal of Marine Systems 78 (2009) S272-S281 S27S t e IS ii 15 If ii .i s 51 s^ I! i| 1* 2 3...International Conference on Mathematics and Continuum Mechanics. Pub. Centro Internacional de Matematica (C1M). ISBN: 978-989-95011- 2-6, pp. 207-217. Porto...Hagerdorn. R., Palmer, T.N., 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting - II . Calibration and combination

  19. Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill

    Science.gov (United States)

    Rubin, Juli I.; Reid, Jeffrey S.; Hansen, James A.; Anderson, Jeffrey L.; Holben, Brent N.; Xian, Peng; Westphal, Douglas L.; Zhang, Jianglong

    2017-05-01

    Data assimilation of Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting currently makes use of a deterministic NAAPS simulation coupled to Navy Variational Data Assimilation System for aerosol optical depth, a two-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow-dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the two-dimensional variational system can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT > 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24 h forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24 h forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating that AERONET could serve as a backup observation network for over-land synoptic-scale aerosol events.

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

    KAUST Repository

    Altaf, Muhammad

    2013-08-01

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

  1. The Ensemble Framework for Flash Flood Forecasting: Global and CONUS Applications

    Science.gov (United States)

    Flamig, Z.; Vergara, H. J.; Clark, R. A.; Gourley, J. J.; Kirstetter, P. E.; Hong, Y.

    2015-12-01

    The Ensemble Framework for Flash Flood Forecasting (EF5) is a distributed hydrologic modeling framework combining water balance components such as the Variable Infiltration Curve (VIC) and Sacramento Soil Moisture Accounting (SAC-SMA) with kinematic wave channel routing. The Snow-17 snow pack model is included as an optional component in EF5 for basins where snow impacts are important. EF5 also contains the Differential Evolution Adaptive Metropolis (DREAM) parameter estimation scheme for model calibration. EF5 is made to be user friendly and as such training has been developed into a weeklong course. This course has been tested in modeling workshops held in Namibia and Mexico. EF5 has also been applied to specialized applications including the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH aims to provide flash flood monitoring and forecasting over the CONUS using Multi-Radar Multi-Sensor precipitation forcing. Using the extensive field measurements database from the 10,000 USGS measurement locations across the CONUS, parameters were developed for the kinematic wave routing in FLASH. This presentation will highlight FLASH performance over the CONUS on basins less than 1,000 km2 and discuss the development of simulated streamflow climatology over the CONUS for data mining applications. A global application of EF5 has also been developed using satellite based precipitation measurements combined with numerical weather prediction forecasts to produce flood and impact forecasts. The performance of this global system will be assessed and future plans detailed.

  2. Forecasting Computer Products Sales by Integrating Ensemble Empirical Mode Decomposition and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Chi-Jie Lu

    2012-01-01

    Full Text Available A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD, and extreme learning machine (ELM for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs. The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products.

  3. Automatic generation of large ensembles for air quality forecasting using the Polyphemus system

    Directory of Open Access Journals (Sweden)

    D. Garaud

    2009-07-01

    Full Text Available This paper describes a method to automatically generate a large ensemble of air quality simulations. This is achieved using the Polyphemus system, which is flexible enough to build various different models. The system offers a wide range of options in the construction of a model: many physical parameterizations, several numerical schemes and different input data can be combined. In addition, input data can be perturbed. In this paper, some 30 alternatives are available for the generation of a model. For each alternative, the options are given a probability, based on how reliable they are supposed to be. Each model of the ensemble is defined by randomly selecting one option per alternative. In order to decrease the computational load, as many computations as possible are shared by the models of the ensemble. As an example, an ensemble of 101 photochemical models is generated and run for the year 2001 over Europe. The models' performance is quickly reviewed, and the ensemble structure is analyzed. We found a strong diversity in the results of the models and a wide spread of the ensemble. It is noteworthy that many models turn out to be the best model in some regions and some dates.

  4. Ensemble flood forecasting to support dam water release operation using 10 and 2 km-resolution JMA Nonhydrostatic Model ensemble rainfalls

    Directory of Open Access Journals (Sweden)

    K. Kobayashi

    2015-12-01

    Full Text Available This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency Nonhydrostatic Model are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolution. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2 and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km-resolution JMA-NHM ensemble simulation are more appropriate than the 10 km-resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1; a threshold value for flood control. The inflows with the 10 km-resolution ensemble rainfall are all considerably smaller than the observations, while, at least one simulated discharge out of 11 ensemble members with the 2 km-resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls show much better results than the original ensemble discharge simulations.

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

    Science.gov (United States)

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

    2014-01-01

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

  6. ENSO Forecasts in the North American Multi-Model Ensemble: Composite Analysis and Verification

    Science.gov (United States)

    Chen, L. C.

    2015-12-01

    In this study, we examine precipitation and temperature forecasts during El Nino/Southern Oscillation (ENSO) events in six models in the North American Multi-Model Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, FLOR, GEOS5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982-2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal Ocean Nino Index (ONI) just prior to the date the forecasts were initiated. Two sets of composites are constructed over the North American continent: one based on precipitation and temperature anomalies, the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March, respectively, as well as to the five-month aggregates representing the winter conditions. For the anomaly composites, we use the anomaly correlation coefficient and root-mean-square error against the observed composites for evaluation. For the probability composites, unlike conventional probabilistic forecast verification assuming binary outcomes to the observations, both model and observed composites are expressed in probability terms. Performance metrics for such validation are limited. Therefore, we develop a probability anomaly correlation measure and a probability score for assessment, so the results are comparable to the anomaly composite evaluation. We found that all NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The skill is higher for the multi-model ensemble, as well as the five-month aggregates. Comparing to the anomaly composites, the probability composites have superior skill in predicting ENSO temperature patterns and are less sensitive to the sample used to construct the composites, suggesting that

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

    Science.gov (United States)

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

    2017-01-01

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

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. Using GEFS ensemble forecasts for decision making in reservoir management in California

    Science.gov (United States)

    Scheuerer, M.; Hamill, T.; Webb, R. S.

    2015-12-01

    Reservoirs such as Lake Mendocino in California's Russian River Basin provide flood control, water supply, recreation, and environmental stream flow regulation. Many of these reservoirs are operated by the U.S. Army Corps of Engineers (Corps) according to water control manuals that specify elevations for an upper volume of reservoir storage that must be kept available for capturing storm runoff and reducing flood risk, and a lower volume of storage that may be used for water supply. During extreme rainfall events, runoff is captured by these reservoirs and released as quickly as possible to create flood storage space for another potential storm. These flood control manuals are based on typical historical weather patterns - wet during the winter, dry otherwise - but are not informed directly by weather prediction. Alternative reservoir management approaches such as Forecast-Informed Reservoir Operations (FIRO), which seek to incorporate advances in weather prediction, are currently being explored as means to improve water supply availability while maintaining flood risk reduction and providing additional ecosystem benefits.We present results from a FIRO proof-of-concept study investigating the reliability of post-processed GEFS ensemble forecasts to predict the probability that day 6-to-10 precipitation accumulations in certain areas in California exceed a high threshold. Our results suggest that reliable forecast guidance can be provided, and the resulting probabilities could be used to inform decisions to release or hold water in the reservoirs. We illustrate the potential of these forecasts in a case study of extreme event probabilities for the Russian River Basin in California.

  13. 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

  14. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    Energy Technology Data Exchange (ETDEWEB)

    Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  15. An application and verification of ensemble forecasting on wind power to assess operational risk indicators in power grids

    Energy Technology Data Exchange (ETDEWEB)

    Alessandrini, S.; Ciapessoni, E.; Cirio, D.; Pitto, A.; Sperati, S. [Ricerca sul Sistema Energetico RSE S.p.A., Milan (Italy). Power System Development Dept. and Environment and Sustainable Development Dept.; Pinson, P. [Technical University of Denmark, Lyngby (Denmark). DTU Informatics

    2012-07-01

    Wind energy is part of the so-called not schedulable renewable sources, i.e. it must be exploited when it is available, otherwise it is lost. In European regulation it has priority of dispatch over conventional generation, to maximize green energy production. However, being variable and uncertain, wind (and solar) generation raises several issues for the security of the power grids operation. In particular, Transmission System Operators (TSOs) need as accurate as possible forecasts. Nowadays a deterministic approach in wind power forecasting (WPF) could easily be considered insufficient to face the uncertainty associated to wind energy. In order to obtain information about the accuracy of a forecast and a reliable estimation of its uncertainty, probabilistic forecasting is becoming increasingly widespread. In this paper we investigate the performances of the COnsortium for Small-scale MOdelling Limited area Ensemble Prediction System (COSMO-LEPS). First the ensemble application is followed by assessment of its properties (i.e. consistency, reliability) using different verification indices and diagrams calculated on wind power. Then we provide examples of how EPS based wind power forecast can be used in power system security analyses. Quantifying the forecast uncertainty allows to determine more accurately the regulation reserve requirements, hence improving security of operation and reducing system costs. In particular, the paper also presents a probabilistic power flow (PPF) technique developed at RSE and aimed to evaluate the impact of wind power forecast accuracy on the probability of security violations in power systems. (orig.)

  16. Wind and Wave Extremes over the World Oceans From Very Large Forecast Ensembles

    CERN Document Server

    Breivik, Øyvind; Abdalla, Saleh; Bidlot, Jean-Raymond

    2013-01-01

    Global return value estimates of significant wave height and 10-m neutral wind speed are estimated from very large aggregations of archived ECMWF ensemble forecasts at +240-h lead time from the period 2003-2012. The upper percentiles are found to match ENVISAT wind speed better than ERA-Interim (ERA-I), which tends to be biased low. The return estimates are significantly higher for both wind speed and wave height in the extratropics and the subtropics than what is found from ERA-I, but lower than what is reported by Caires and Sterl (2005) and Vinoth and Young (2011). The highest discrepancies between ERA-I and ENS240 are found in the hurricane-prone areas, suggesting that the ensemble comes closer than ERA-I in capturing the intensity of tropical cyclones. The width of the confidence intervals are typically reduced by 70% due to the size of the data sets. Finally, non-parametric estimates of return values were computed from the tail of the distribution. These direct return estimates compare very well with Ge...

  17. Ozone predictabilities due to meteorological uncertainties in the Mexico City basin using ensemble forecasts

    Directory of Open Access Journals (Sweden)

    N. Bei

    2010-07-01

    Full Text Available The purpose of the present study is to investigate the sensitivity of ozone (O3 predictions in the Mexico City Metropolitan Area (MCMA to meteorological initial uncertainties and planetary boundary layer (PBL parameterization schemes using state-of-the-art meteorological and photochemical prediction models through ensemble forecasts. The simulated periods (3, 9, 15 and 29 March 2006 represent four typical meteorological episodes ("South-Venting", "O3-North", "O3-South" and "Convection-North", respectively in the Mexico City basin during the MCMA-2006/MILAGRO campaign. Our results demonstrate that the uncertainties in meteorological initial conditions have significant impacts on O3 predictions, including peak time O3 concentrations ([O3], horizontal and vertical O3 distributions, and temporal variations. The ensemble spread of the simulated peak [O3] averaged over the city's ambient monitoring sites can reach up to 10 ppb. The increasing uncertainties in meteorological fields during peak O3 period contribute to the largest unpredictability in O3 simulations, while the impacts of wind speeds and PBL height on [O3] are more straightforward and important. The magnitude of the ensemble spreads varies with different PBL schemes and meteorological episodes. The uncertainties in O3 predictions caused by PBL schemes mainly come from their ability to represent the mixing layer height; but overall, these uncertainties are smaller than those from the uncertainties in meteorological initial conditions.

  18. Predictive Skill of Meteorological Drought Based on Multi-Model Ensemble Forecasts: A Real-Time Assessment

    Science.gov (United States)

    Chen, L. C.; Mo, K. C.; Zhang, Q.; Huang, J.

    2014-12-01

    Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Starting in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the North American Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the predictive skill of meteorological drought using real-time NMME forecasts for the period from May 2012 to May 2014. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation coefficient and root-mean-square errors against the observations, are used to evaluate forecast skill.Similar to the assessment based on NMME retrospective forecasts, predictive skill of monthly-mean precipitation (P) forecasts is generally low after the second month and errors vary among models. Although P forecast skill is not large, SPI predictive skill is high and the differences among models are small. The skill mainly comes from the P observations appended to the model forecasts. This factor also contributes to the similarity of SPI prediction among the six models. Still, NMME SPI ensemble forecasts have higher skill than those based on individual models or persistence, and the 6-month SPI forecasts are skillful out to four months. The three major drought events occurred during the 2012-2014 period, the 2012 Central Great Plains drought, the 2013 Upper Midwest flash drought, and 2013-2014 California drought, are used as examples to illustrate the system's strength and limitations. For precipitation-driven drought events, such as the 2012 Central Great Plains drought

  19. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

    Science.gov (United States)

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-06-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.

  20. Assessing the add value of ensemble forecast in a drought early warning

    Science.gov (United States)

    Calmanti, Sandro; Bosi, Lorenzo; Fernandez, Jesus; De Felice, Matteo

    2015-04-01

    The EU-FP7 project EUPORIAS is developing a prototype climate service to enhance the existing food security drought early warning system in Ethiopia. The Livelihoods, Early Assessment and Protection (LEAP) system is the Government of Ethiopia's national food security early warning system, established with the support of WFP and the World Bank in 2008. LEAP was designed to increase the predictability and timeliness of response to drought-related food crises in Ethiopia. It combines early warning with contingency planning and contingency funding, to allow the government, WFP and other partners to provide early assistance in anticipation of an impending catastrophes. Currently, LEAP uses satellite based rainfall estimates to monitor drought conditions and to compute needs. The main aim of the prototype is to use seasonal hindcast data to assess the added value of using ensemble climate rainfall forecasts to estimate the cost of assistance of population hit by major droughts. We outline the decision making process that is informed by the prototype climate service, and we discuss the analysis of the expected and skill of the available rainfall forecast data over Ethiopia. One critical outcome of this analysis is the strong dependence of the expected skill on the observational estimate assumed as reference. A preliminary evaluation of the full prototype products (drought indices and needs estimated) using hindcasts data will also be presented.

  1. Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting

    Directory of Open Access Journals (Sweden)

    Barthélémy Sébastien

    2016-01-01

    Full Text Available Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D, SCHAPI and CE-REMA resulted in the implementation of a Data Assimilation (DA method on top of MASCARET in the framework of real-time forecasting. This prototype was based on a simplified Kalman filter where the description of the background error covariances is prescribed based on off-line climatology constant over time. This approach showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations. An ensemble-based DA algorithm has recently been implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. It was demonstrated that the flow dependent description of the background error covariances with the EnKF algorithm leads to a more realistic correction of the hydraulic state with significant impact of the hydraulic network characteristics

  2. GloFAS – global ensemble streamflow forecasting and flood early warning

    Directory of Open Access Journals (Sweden)

    L. Alfieri

    2013-03-01

    Full Text Available Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS, which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

  3. DEWEPS - Development and Evaluation of new Wind forecasting tools with an Ensemble Prediction System

    Energy Technology Data Exchange (ETDEWEB)

    Moehrlen, C.; Joergensen, Jess

    2012-02-15

    There is an ongoing trend of increased privatization in the handling of renewable energy. This trend is required to ensure an efficient energy system, where improvements that make economic sense are prioritised. The reason why centralized forecasting can be a challenge in that matter is that the TSOs tend to optimize on physical error rather than cost. Consequently, the market is likely to speculate against the TSO, which in turn increases the cost of balancing. A privatized pool of wind and/or solar power is more difficult to speculate against, because the optimization criteria is unpredictable due to subjective risk considerations that may be taken into account at any time. Although there is and additional level of costs for the trading of the private volume, it can be argued that competition will accelerate efficiency from an economic perspective. The amount of power put into the market will become less predictable, when the wind power spot market bid takes place on the basis of a risk consideration in addition to the forecast information itself. The scope of this project is to contribute to more efficient wind power integration targeted both to centralised and decentralised cost efficient IT solutions, which will complement each other in market based energy systems. The DEWEPS project resulted in an extension of the number of Ensemble forecasts, an incremental trade strategy for balancing unpredictable power production, and an IT platform for efficient handling of power generation units. Together, these three elements contribute to less need for reserves, more capacity in the market, and thus more competition. (LN)

  4. Effect of targeted dropsonde observations and best track data on the track forecasts of Typhoon Sinlaku (2008 using an ensemble Kalman filter

    Directory of Open Access Journals (Sweden)

    BYoung-Joo Jung

    2012-01-01

    Full Text Available During August and September 2008, The Observing System Research and Predictability Experiment (THORPEX – Pacific Asian Regional Campaign (T-PARC was conducted to investigate the formation, structure, targeted observation, extratropical transition (ET and downstream effects of tropical cyclones (TCs in the Western North Pacific (WNP region. This study investigates the effect of targeted dropsonde observations from T-PARC and the TC best track data on the track forecast of Typhoon Sinlaku (2008. A WRF-based ensemble Kalman filter (EnKF is used for a series of observation system experiments (OSEs. From the innovation statistics and rank histograms, the EnKF behaves well in terms of ensemble spread, despite some spread deficiency in low-tropospheric winds and warm and moist biases. Assimilation of targeted dropsonde observations leads to improved initial position and subsequent track forecast compared with experiments that only assimilate conventional observations. In the meantime, assimilation of TC position reduces the initial position error, whereas assimilation of minimum sea level pressure (SLP information is efficient to analyse the strong vortex structures of TC and reduces track forecast errors. Assimilation of TC position and minimum SLP information is particularly beneficial when dropsonde observations do not exist.

  5. A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Zongxi Qu

    2016-01-01

    Full Text Available As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

  6. 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.

  7. Reduction of systematic biases in regional climate downscaling through ensemble forcing

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Hongwei; Wang, Bin [Chinese Academy of Sciences, LASG, Institute of Atmospheric Physics, Beijing (China); Wang, Bin [University of Hawaii at Manoa, Department of Meteorology, Honolulu, Hawaii (United States); University of Hawaii at Manoa, International Pacific Research Center, Honolulu, Hawaii (United States)

    2012-02-15

    Simulations of the East Asian summer monsoon for the period of 1979-2001 were carried out using the Weather Research and Forecast (WRF) model forced by three reanalysis datasets (NCEP-R2, ERA-40, and JRA-25). The experiments forced by different reanalysis data exhibited remarkable differences, primarily caused by uncertainties in the lateral boundary (LB) moisture fluxes over the Bay of Bengal and the Philippine Sea. The climatological mean water vapor convergence into the model domain computed from ERA-40 was about 24% higher than that from the NCEP-R2 reanalysis. We demonstrate that using the ensemble mean of NCEP-R2, ERA-40, and JRA-25 as LB forcing considerably reduced the biases in the model simulation. The use of ensemble forcing improved the performance in simulated mean circulation and precipitation, inter-annual variation in seasonal precipitation, and daily precipitation. The model simulated precipitation was superior to that in the reanalysis in both climatology and year-to-year variations, indicating the added value of dynamic downscaling. The results suggest that models having better performance under one set of LB forcing might worsen when another set of reanalysis data is used as LB forcing. Use of ensemble mean LB forcing for assessing regional climate model performance is recommended. (orig.)

  8. Lessons learned from four years of actively using River Forecast Center Ensemble Streamflow Predictions to inform reservoir management

    Science.gov (United States)

    Polebitski, A.; Palmer, R.; Meaker, B.

    2012-12-01

    The National Weather Service's River Forecast Centers (RFCs), located throughout the US, produce operational streamflow forecasts for short term application and long-term lead forecasts at selected locations. These forecasts are targeted for a variety of users, including water supply management, flood control, hydropower production, navigation, and recreation. This presentation highlights the challenges and successes associated with the use of RFC produced ensemble streamflow predictions (ESP) in generating system operations forecasts over the past four years for Snohomish County Public Utility District #1's (SnoPUD) Henry Jackson hydropower system. This research documents a multiyear collaboration between SnoPUD and academic researchers. The collaboration began with a proof of concept study in 2007 and evolved into a weekly decision support activity that has been ongoing since 2008 ( documented in Alemu et al. 2010). The Alemu et al. paper demonstrates the usefulness of ESP forecasts in hydropower operations decision making. This paper focuses on the value of forecasts and a decision support system (DSS) in improving skills in operating reservoir systems. During the application period, the model provided weekly guidance on meeting operational objectives and a probabilistic approach to quantifying system vulnerability during critical periods such as floods and drought. The ESP forecasts and the DSS were heavily used during periods of uncertainty and less so during periods of high system constraint or low system risk.

  9. Regional climate models downscaling in the Alpine area with Multimodel SuperEnsemble

    Directory of Open Access Journals (Sweden)

    D. Cane

    2012-08-01

    Full Text Available The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulations, however, even when obtained with Regional Climate Models (RCMs, are affected by strong errors where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization.

    Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area.

    For our purposes we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957–present were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique.

    Hence, we applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period.

    We propose also the first application to RCMS of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period.

  10. Development of web-based services for an ensemble flood forecasting and risk assessment system

    Science.gov (United States)

    Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

    2010-05-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability

  11. A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

    Science.gov (United States)

    Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2015-04-01

    Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple

  12. The very short-term rainfall forecasting for a mountainous watershed by means of an ensemble numerical weather prediction system in Taiwan

    Science.gov (United States)

    Wu, Ming-Chang; Lin, Gwo-Fong

    2017-03-01

    During typhoons, accurate forecasts of rainfall are always desired for various kinds of disaster warning systems to reduce the impact of rainfall-induced disasters. However, rainfall forecasting, especially the very short-term (hourly) rainfall, is one of the most difficult tasks in hydrology due to the high variability in space and time and the complex physical process. In this study, the purpose is to provide effective forecasts of very short-term rainfall by means of the ensemble numerical weather prediction system in Taiwan. To this end, the ensemble forecasts of hourly rainfall from this ensemble numerical weather prediction system are analyzed to evaluate the performance. Furthermore, a methodology, which is based on the principle of analogue prediction, is proposed to effectively process these ensemble forecasts for improving the performance on very short-term rainfall forecasting. To clearly demonstrate the advantage of the proposed methodology, actual application is conducted on a mountainous watershed to yield 1- to 6-h ahead forecasts during typhoon events. The results indicate that the proposed methodology is better performed and more flexible than the conventional one. Generally, the proposed methodology provides improved performance for very short-term rainfall forecasting, especially for 1- to 2-h ahead forecasting. The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems, such as flash-flood, landslide, and debris flow warning systems, during typhoons.

  13. Ensemble and probabilistic forecasting of (u,v)-wind for the energy application

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2011-01-01

    and Brazil are catching up by giving increasing importance to renewable energies in their electricity generation mix. The common and maybe most important characteristics of these renewable energy sources is that their power generation depends upon atmospheric and marine conditions, with a stochastic......Over the last decade, developments in the use of various renewable energy sources have been tremendous. Europe has been a pioneering region in opting for the large-scale deployment of wind energy, now being followed by solar and wave energy. Other countries like the United States, China, India...... and probabilistic forecasts are becoming increasingly popular among the actors of the power system and electricity markets. The energy application is particularly interesting since covering a variety of decision-making problems requiring different types of input forecasts. A few of them will be reviewed...

  14. Dynamical forecast vs Ensemble Streamflow Prediction (ESP): how sensitive are monthly and seasonal hydrological forecasts to the quality of rainfall drivers?

    Science.gov (United States)

    Tanguy, Maliko; Prudhomme, Christel; Harrigan, Shaun; Smith, Katie

    2017-04-01

    Seasonal forecasting of hydrological extremes is challenging for the hydro-meteorological modelling community, and the performance of hydrological forecasts at lead times over 1 month is still poor especially for catchments with limited hydrological memory. A considerable amount of effort is being invested within the meteorological community to improve dynamic meteorological forecasting which can then be used to drive hydrological models to produce physically-driven hydrological forecasts. However, currently for the UK, these meteorological forecasts are being produced at 1 month or seasonal time-step, whereas hydrological models often require daily or sub-daily time-steps. A simpler way to get seasonal forecasts is to use historical climate data to drive hydrological models using Ensemble Streamflow Prediction (ESP). This gives a range of possible future hydrological status given known initial conditions, but it does not contain any information on the future dynamic of the atmosphere. The error is highly dependent on the type of catchment, but ESP is an improvement compared to simply using climatology of river flows, especially in groundwater dominated catchments. The objective of this study is to find out how accurate the seasonal rainfall forecast has to be (in terms of total rainfall and temporal distribution) for the dynamical seasonal forecast to beat ESP. To this aim, we have looked at the sensitivity of hydrological models to the quality of driving rainfall input, proxy of 'best possible' forecasts. Study catchments representative of the range of UK's hydro-climatic conditions were selected. For these catchments, synthetic rainfall time series derived from observed data were created by increasingly degrading the data. The number of rainy days, their intensity and their sequencing were artificially modified to analyse which of these characteristics is most important to get a better hydrological forecast using a simple lumped hydrological model (GR4J), and

  15. Effect of Doubling the Ensemble Size on the Performance of Ensemble Prediction in the Warm Season Using MOGREPS Implemented at the KMA

    Institute of Scientific and Technical Information of China (English)

    Jun Kyung KAY; Hyun Mee KIM; Young-Youn PARK; Joohyung SON

    2013-01-01

    Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA),the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated.Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF),ensemble size is closely related to the efficiency of the ensemble prediction system.Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA.The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009.Several statistical verification scores were used to measure the accuracy,reliability,and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts.Even though the results were not significant,the accuracy of ensemble prediction improved slightly as ensemble size increased,especially for longer forecast times in the Northern Hemisphere.While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased,inconsistent results were obtained for the scores assessing the reliability of ensemble prediction.The overall performance of ensemble prediction in terms of accuracy,resolution,and reliability increased slightly with ensemble size,especially for longer forecast times.

  16. Ensemble-based data assimilation for operational flood forecasting - On the merits of state estimation for 1D hydrodynamic forecasting through the example of the ;Adour Maritime; river

    Science.gov (United States)

    Barthélémy, S.; Ricci, S.; Rochoux, M. C.; Le Pape, E.; Thual, O.

    2017-09-01

    This study presents the implementation and the merits of an Ensemble Kalman Filter (EnKF) algorithm with an inflation procedure on the 1D shallow water model MASCARET in the framework of operational flood forecasting on the ;Adour Maritime; river (South West France). In situ water level observations are sequentially assimilated to correct both water level and discharge. The stochastic estimation of the background error statistics is achieved over an ensemble of MASCARET integrations with perturbed hydrological boundary conditions. It is shown that the geometric characteristics of the network as well as the hydrological forcings and their temporal variability have a significant impact on the shape of the univariate (water level) and multivariate (water level and discharge) background error covariance functions and thus on the EnKF analysis. The performance of the EnKF algorithm is examined for observing system simulation experiments as well as for a set of eight real flood events (2009-2014). The quality of the ensemble is deemed satisfactory as long as the forecast lead time remains under the transfer time of the network, when perfect hydrological forcings are considered. Results demonstrate that the simulated hydraulic state variables can be improved over the entire network, even where no data are available, with a limited ensemble size and thus a computational cost compatible with operational constraints. The improvement in the water level Root-Mean-Square Error obtained with the EnKF reaches up to 88% at the analysis time and 40% at a 4-h forecast lead time compared to the standalone model.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. A comparison of the performance of the 3-D super-ensemble and an ensemble Kalman filter for short-range regional ocean prediction

    Directory of Open Access Journals (Sweden)

    Baptiste Mourre

    2014-01-01

    Full Text Available This study compares the ability of two approaches integrating models and data to forecast the Ligurian Sea regional oceanographic conditions in the short-term range (0–72 hours when constrained by a common observation dataset. The post-processing 3-D super-ensemble (3DSE algorithm, which uses observations to optimally combine multi-model forecasts into a single prediction of the oceanic variable, is first considered. The 3DSE predictive skills are compared to those of the Regional Ocean Modeling System model in which observations are assimilated through a more conventional ensemble Kalman filter (EnKF approach. Assimilated measurements include sea surface temperature maps, and temperature and salinity subsurface observations from a fleet of five underwater gliders. Retrospective analyses are carried out to produce daily predictions during the 11-d period of the REP10 sea trial experiment. The forecast skill evaluation based on a distributed multi-sensor validation dataset indicates an overall superior performance of the EnKF, both at the surface and at depth. While the 3DSE and EnKF perform comparably well in the area spanned by the incorporated measurements, the 3DSE accuracy is found to rapidly decrease outside this area. In particular, the univariate formulation of the method combined with the absence of regular surface salinity measurements produces large errors in the 3DSE salinity forecast. On the contrary, the EnKF leads to more homogeneous forecast errors over the modelling domain for both temperature and salinity. The EnKF is found to consistently improve the predictions with respect to the control solution without assimilation and to be positively skilled when compared to the climatological estimate. For typical regional oceanographic applications with scarce subsurface observations, the lack of physical spatial and multivariate error covariances applicable to the individual model weights in the 3DSE formulation constitutes a major

  2. 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.

  3. On the reliability of Seasonal Climate Forecasts

    CERN Document Server

    Weisheimer, Antje

    2013-01-01

    Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: How good are seasonal climate forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal climate forecasts are made from ensembles of integrations of numerical models of climate. We argue that goodness should be assessed primarily in terms of the probabilistic reliability of these ensemble-based forecasts and that a 5 should be reserved for systems which are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as a world leading operational institute producing seasonal climate forecasts. A wide range of goodness rankings, depending on regio...

  4. Extreme winds over Europe in the ENSEMBLES regional climate models

    Directory of Open Access Journals (Sweden)

    S. D. Outten

    2013-01-01

    Full Text Available Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate predictions of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES, and investigates the predicted changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the Generalised Pareto Distribution. The models show that for much of Europe the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.

  5. Extreme winds over Europe in the ENSEMBLES regional climate models

    Directory of Open Access Journals (Sweden)

    S. D. Outten

    2013-05-01

    Full Text Available Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate projections of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe following the SRES A1B scenario from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES. It investigates the projected changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the generalised Pareto distribution. The models show that, for much of Europe, the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.

  6. Development of web-based services for a novel ensemble flood forecasting and risk assessment system

    Science.gov (United States)

    He, Y.; Manful, D. Y.; Cloke, H. L.; Wetterhall, F.; Li, Z.; Bao, H.; Pappenberger, F.; Wesner, S.; Schubert, L.; Yang, L.; Hu, Y.

    2009-12-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust

  7. Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS: an attempt towards an ensemble forecasting method

    Directory of Open Access Journals (Sweden)

    Moslem Yousefi

    2015-12-01

    Full Text Available Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF and partial auto correlation factor (PACF on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate

  8. 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.

  9. Extended-Range Ensemble Predictions of Convection in the North Australian Monsoon Region

    Science.gov (United States)

    Drosdowsky, Wasyl; Wheeler, Matthew C.

    2017-04-01

    Extended-range (Bureau of Meteorology’s Predictive Ocean-Atmosphere Model for Australia (POAMA). Hindcasts from 1980-2011 are used, initialised on the 1st, 11th and 21st of each month, with a 33-member ensemble. The measure of convection is outgoing longwave radiation (OLR) averaged over the box 120ºE-150ºE, 5ºS-17.5ºS. This averaging serves to focus on the intraseasonal and longer time scales, and is an area of interest to users. The raw hindcasts of daily OLR show a strong systematic adjustment away from their initial values during the first week, and then converge to a mean seasonal cycle of similar amplitude and phase to observations. Hence, forecast OLR anomalies are formed by removing the model’s own seasonal cycle of OLR, which is a function of start time and lead time, a usual practice for dynamical seasonal prediction. Over all hindcasts, the model forecast root-mean-square (RMS) error is smaller than the RMS error of persistence and climatological reference forecasts for leads 3-35 days. Ensemble spread is less than the forecast RMS error (i.e. under-spread) for days 1-12, but slightly greater than the RMS error for longer leads. Binning the individual forecasts based on ensemble spread shows a generally positive relationship between spread and error. Therefore, greater certainty can be given for forecasts with smaller spread.

  10. 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

  11. Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event

    Directory of Open Access Journals (Sweden)

    J. Dietrich

    2008-03-01

    Full Text Available Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures on time. The accuracy and the lead time of the predictions for head waters primarily depend on the meteorological forecasts. Ensemble forecasts are a means of framing the uncertainty of the potential future development of the hydro-meteorological situation.

    This contribution presents a flood management strategy based on probabilistic hydrological forecasts driven by operational meteorological ensemble prediction systems. The meteorological ensemble forecasts are transformed into discharge ensemble forecasts by a rainfall-runoff model. Exceedance probabilities for critical discharge values and probabilistic maps of inundation areas can be computed and presented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system integrates ensemble forecasts with different spatial resolution and different lead times. The hydrological models are controlled in an adaptive way, mainly depending on the lead time of the forecast, the expected magnitude of the flood event and the availability of measured data.

    The aforementioned flood forecast techniques have been applied to a case study. The Mulde River Basin (South-Eastern Germany, Czech Republic has often been affected by severe flood events including local flash floods. Hindcasts for the large scale extreme flood in August 2002 have been computed using meteorological predictions from both the COSMO-LEPS ensemble prediction system and the deterministic COSMO-DE local model. The temporal evolution of a the meteorological forecast uncertainty and b the probability of exceeding flood alert levels is discussed. Results from the hindcast simulations demonstrate, that the systems would have predicted a high probability of an extreme flood event, if they would already have been operational in 2002. COSMO-LEPS showed a reasonably good

  12. An Ensemble Ocean Wave Forecast System and Preliminary Experiment%一个集合海浪预报系统及其初步试验

    Institute of Scientific and Technical Information of China (English)

    孙明华

    2011-01-01

    The basic methods of ensemble wave prediction at home and abroad and an ensemble wave numerical forecast system are introduced. The system is established by running the operational wave model WAV-EWATCH H using wind data from the 15 members' ensemble weather forecast system, which is based on the T213L31 model and operational running in China National Meteorological Center. The ensemble model calculates 15 members wave field, including one control forecast and 14 perturbation members forecast. The model computes the waves over all the oceans up to 10 days at 12; 00 with 1° by 1° resolution. Based on hot initialization, the model uses the 12 h wave hindcast as its initial field. Through the information extraction and processing from the 15 member's wave fields, the system produces the ensemble wave products such as ensemble mean and spread, spaghetti charts and probability charts. Forecast experiment for the period from 1 Sep to 31 Oct in 2007 is done using the system and buoys data in the Pacific and Atlantic at the same period. The main verifying indexes are bias and root mean square error. For the mean monthly bias of 10 m wind speed and significant wave height, the performance of the ensemble mean is better than the control forecast, however both of them have a relatively low system deviation and one cause is that the spin up time of the system may not be long enough. For the monthly root mean square error, the ensemble mean of 10 m wind speed is lower than the control forecast by 3-12 percentage for the different forecast periods, meanwhile the ensemble mean of significant wave height is lower than the control forecast by 3- 17 percentage with consistent variation trend. Comparing 15 ensemble members' forecast to the Buoy_ 46006 data, it shows that the ensemble members can follow the changes of the observation well and cover the variation range of the observation other than the deterministic forecast. In summary, the verification results show that the

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

    Science.gov (United States)

    Yang, H.

    2015-12-01

    used to create a multi-parameter and multi-physics ensemble. The ensemble forecast system is implemented operationally for San Diego Gas & Electric Company to improve system operations.

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

    Science.gov (United States)

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

    2014-01-01

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

  15. Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method

    Directory of Open Access Journals (Sweden)

    Yukun Bao

    2012-01-01

    Full Text Available With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition (EEMD based support vector machines (SVMs modeling framework incorporating a slope-based method to restrain the end effect issue occurring during the shifting process of EEMD, which is abbreviated as EEMD-Slope-SVMs. Real monthly air passenger traffic series including six selected airlines in USA and UK were collected to test the effectiveness of the proposed approach. Empirical results demonstrate that the proposed decomposition and ensemble modeling framework outperform the selected counterparts such as single SVMs (straightforward application of SVMs, Holt-Winters, and ARIMA in terms of RMSE, MAPE, GMRAE, and DS. Additional evidence is also shown to highlight the improved performance while compared with EEMD-SVM model not restraining the end effect.

  16. A centralized real-time controller for the reservoir's management on the Seine River using ensemble weather forecasting

    Science.gov (United States)

    Ficchi, Andrea; Raso, Luciano; Jay-Allemand, Maxime; Dorchies, David; Malaterre, Pierre-Olivier; Pianosi, Francesca; Van Overloop, Peter-Jules

    2013-04-01

    The reservoirs on the Seine River, upstream of Paris, are regulated with the objective of reducing floods and supporting low flows. The current management of these reservoirs is empirical, reactive, and decentralized, mainly based on filling curves, constructed from an analysis of historical floods and low flows. When inflows are significantly different from their seasonal average, this management strategy proves inefficient. Climate change is also a challenge, for the possible modification of future hydrologic conditions. To improve such management strategy, in this study we investigate the use of Tree-Based Model Predictive Control (TB-MPC), a proactive and centralized method that uses all the information available in real-time, including ensemble weather forecasting. In TB-MPC, a tree is generated from an ensemble of weather forecast. The tree structure summarizes the information contained in the ensemble, specifying the time, along the optimization horizon, when forecast trajectories diverge and thus uncertainty is expected to be resolved. This information is then used in the model predictive control framework. The TB-MPC controller is implemented in combination with the integrated model of the water system, including a semi-distributed hydrologic model of the watershed, a simplified hydraulic model of the river network, and the four reservoir models. Optimization takes into account the cost associated to floods and low-flows, and a penalty cost based on the final reservoir storages. The performances of the TB-MPC controller will be simulated and compared with those of deterministic MPC and with the actual management performances. This work is part of the Climaware European project (2010-2013) set up to develop and to assess measures for sustainable water resources management regarding adaptation to climate change.

  17. Validation of precipitation over Japan during 1985-2004 simulated by three regional climate models and two multi-model ensemble means

    Energy Technology Data Exchange (ETDEWEB)

    Ishizaki, Yasuhiro [Meteorological Research Institute, Tsukuba (Japan); National Institute for Environmental Studies, Tsukuba (Japan); Nakaegawa, Toshiyuki; Takayabu, Izuru [Meteorological Research Institute, Tsukuba (Japan)

    2012-07-15

    We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate. (orig.)

  18. Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts

    Science.gov (United States)

    2017-01-01

    Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species’ distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species’ distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel’s quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence

  19. 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.

  20. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    Science.gov (United States)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  1. Comparison of four ensemble methods combining regional climate simulations over Asia

    Science.gov (United States)

    Feng, Jinming; Lee, Dong-Kyou; Fu, Congbin; Tang, Jianping; Sato, Yasuo; Kato, Hisashi; McGregor, John L.; Mabuchi, Kazuo

    2011-02-01

    A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.

  2. SWIFT2: Software for continuous ensemble short-term streamflow forecasting for use in research and operations

    Science.gov (United States)

    Perraud, Jean-Michel; Bennett, James C.; Bridgart, Robert; Robertson, David E.

    2016-04-01

    Research undertaken through the Water Information Research and Development Alliance (WIRADA) has laid the foundations for continuous deterministic and ensemble short-term forecasting services. One output of this research is the software Short-term Water Information Forecasting Tools version 2 (SWIFT2). SWIFT2 is developed for use in research on short term streamflow forecasting techniques as well as operational forecasting services at the Australian Bureau of Meteorology. The variety of uses in research and operations requires a modular software system whose components can be arranged in applications that are fit for each particular purpose, without unnecessary software duplication. SWIFT2 modelling structures consist of sub-areas of hydrologic models, nodes and links with in-stream routing and reservoirs. While this modelling structure is customary, SWIFT2 is built from the ground up for computational and data intensive applications such as ensemble forecasts necessary for the estimation of the uncertainty in forecasts. Support for parallel computation on multiple processors or on a compute cluster is a primary use case. A convention is defined to store large multi-dimensional forecasting data and its metadata using the netCDF library. SWIFT2 is written in modern C++ with state of the art software engineering techniques and practices. A salient technical feature is a well-defined application programming interface (API) to facilitate access from different applications and technologies. SWIFT2 is already seamlessly accessible on Windows and Linux via packages in R, Python, Matlab and .NET languages such as C# and F#. Command line or graphical front-end applications are also feasible. This poster gives an overview of the technology stack, and illustrates the resulting features of SWIFT2 for users. Research and operational uses share the same common core C++ modelling shell for consistency, but augmented by different software modules suitable for each context. The

  3. Using NCAR Yellowstone for PhotoVoltaic Power Forecasts with Artificial Neural Networks and an Analog Ensemble

    Science.gov (United States)

    Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Delle Monache, L.

    2016-12-01

    A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plant located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4,450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4,450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

  4. Importance of ensembles in projecting regional climate trends

    Science.gov (United States)

    Arritt, Raymond; Daniel, Ariele; Groisman, Pavel

    2016-04-01

    We have performed an ensemble of simulations using RegCM4 to examine the ability to reproduce observed trends in precipitation intensity and to project future changes through the 21st century for the central United States. We created a matrix of simulations over the CORDEX North America domain for 1950-2099 by driving the regional model with two different global models (HadGEM2-ES and GFDL-ESM2M, both for RCP8.5), by performing simulations at both 50 km and 25 km grid spacing, and by using three different convective parameterizations. The result is a set of 12 simulations (two GCMs by two resolutions by three convective parameterizations) that can be used to systematically evaluate the influence of simulation design on predicted precipitation. The two global models were selected to bracket the range of climate sensitivity in the CMIP5 models: HadGEM2-ES has the highest ECS of the CMIP5 models, while GFDL-ESM2M has one of the lowestt. Our evaluation metrics differ from many other RCM studies in that we focus on the skill of the models in reproducing past trends rather than the mean climate state. Trends in frequency of extreme precipitation (defined as amounts exceeding 76.2 mm/day) for most simulations are similar to the observed trend but with notable variations depending on RegCM4 configuration and on the driving GCM. There are complex interactions among resolution, choice of convective parameterization, and the driving GCM that carry over into the future climate projections. We also note that biases in the current climate do not correspond to biases in trends. As an example of these points the Emanuel scheme is consistently "wet" (positive bias in precipitation) yet it produced the smallest precipitation increase of the three convective parameterizations when used in simulations driven by HadGEM2-ES. However, it produced the largest increase when driven by GFDL-ESM2M. These findings reiterate that ensembles using multiple RCM configurations and driving GCMs are

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

    Science.gov (United States)

    Zheng, Fei; Zhu, Jiang

    2016-12-01

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

  6. Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign.

  7. 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.

  8. 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.

  9. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    NARCIS (Netherlands)

    Candogan Yossef, Naze; Beek, Van Rens; Weerts, Albrecht; Winsemius, Hessel; Bierkens, Marc F.P.

    2017-01-01

    In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS).

  10. 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.

  11. 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.

  12. 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

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

    DEFF Research Database (Denmark)

    Courdent, V.; Grum, M.; Munk-Nielsen, T.

    2017-01-01

    extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide...... insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework......Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs...

  14. How to pose the question matters: Behavioural Economics concepts in decision making on the basis of ensemble forecasts

    Science.gov (United States)

    Alfonso, Leonardo; van Andel, Schalk Jan

    2014-05-01

    Part of recent research in ensemble and probabilistic hydro-meteorological forecasting analyses which probabilistic information is required by decision makers and how it can be most effectively visualised. This work, in addition, analyses if decision making in flood early warning is also influenced by the way the decision question is posed. For this purpose, the decision-making game "Do probabilistic forecasts lead to better decisions?", which Ramos et al (2012) conducted at the EGU General Assembly 2012 in the city of Vienna, has been repeated with a small group and expanded. In that game decision makers had to decide whether or not to open a flood release gate, on the basis of flood forecasts, with and without uncertainty information. A conclusion of that game was that, in the absence of uncertainty information, decision makers are compelled towards a more risk-averse attitude. In order to explore to what extent the answers were driven by the way the questions were framed, in addition to the original experiment, a second variant was introduced where participants were asked to choose between a sure value (for either loosing or winning with a giving probability) and a gamble. This set-up is based on Kahneman and Tversky (1979). Results indicate that the way how the questions are posed may play an important role in decision making and that Prospect Theory provides promising concepts to further understand how this works.

  15. 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

  16. FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID EC-ONOMETRIC-AI ENSEMBLE LEARNING APPROACH

    Institute of Scientific and Technical Information of China (English)

    Lean YU; Shouyang WANG; Kin Keung LAI

    2008-01-01

    Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal-ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en-semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic-tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.

  17. Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xiaogu; WU Guocan; ZHANG Shupeng; LIANG Xiao; DAI Yongjiu; LI Yong

    2013-01-01

    Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme.If it is not correctly estimated,the assimilated states could be far from the true states.A popular method to address this problem is error covariance matrix inflation.That is,to multiply the forecast error covariance matrix by an appropriate factor.In this paper,analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed.The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model,both of which are associated with spatially correlated observational systems.The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure,the assimilation results were further improved.

  18. 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.

  19. Ensemble-based analysis of Front Range severe convection on 6-7 June 2012: Forecast uncertainty and communication of weather information to Front Range decision-makers

    Science.gov (United States)

    Vincente, Vanessa

    The variation of topography in Colorado not only adds to the beauty of its landscape, but also tests our ability to predict warm season severe convection. Deficient radar coverage and limited observations make quantitative precipitation forecasting quite a challenge. Past studies have suggested that greater forecast skill of mesoscale convection initiation and precipitation characteristics are achievable considering an ensemble with explicitly predicted convection compared to one that has parameterized convection. The range of uncertainty and probabilities in these forecasts can help forecasters in their precipitation predictions and communication of weather information to emergency managers (EMs). EMs serve an integral role in informing and protecting communities in anticipation of hazardous weather. An example of such an event occurred on the evening of 6 June 2012, where areas to the lee of the Rocky Mountain Front Range were impacted by flash-flood-producing severe convection that included heavy rain and copious amounts of hail. Despite the discrepancy in the timing, location and evolution of convection, the convection-allowing ensemble forecasts generally outperformed those of the convection-parameterized ensemble in representing the mesoscale processes responsible for the 6-7 June severe convective event. Key features sufficiently reproduced by several of the convection-allowing ensemble members resembled the observations: 1) general location of a convergence boundary east of Denver, 2) convective initiation along the boundary, 3) general location of a weak cold front near the Wyoming/Nebraska border, and 4) cold pools and moist upslope characteristics that contributed to the backbuilding of convection. Members from the convection-parameterized ensemble that failed to reproduce these results displaced the convergence boundary, produced a cold front that moved southeast too quickly, and used the cold front for convective initiation. The convection

  20. Higher precision estimates of regional polar warming by ensemble regression of climate model projections

    Energy Technology Data Exchange (ETDEWEB)

    Bracegirdle, Thomas J. [British Antarctic Survey, Cambridge (United Kingdom); Stephenson, David B. [University of Exeter, Mathematics Research Institute, Exeter (United Kingdom); NCAS-Climate, Reading (United Kingdom)

    2012-12-15

    This study presents projections of twenty-first century wintertime surface temperature changes over the high-latitude regions based on the third Coupled Model Inter-comparison Project (CMIP3) multi-model ensemble. The state-dependence of the climate change response on the present day mean state is captured using a simple yet robust ensemble linear regression model. The ensemble regression approach gives different and more precise estimated mean responses compared to the ensemble mean approach. Over the Arctic in January, ensemble regression gives less warming than the ensemble mean along the boundary between sea ice and open ocean (sea ice edge). Most notably, the results show 3 C less warming over the Barents Sea ({proportional_to} 7 C compared to {proportional_to} 10 C). In addition, the ensemble regression method gives projections that are 30 % more precise over the Sea of Okhostk, Bering Sea and Labrador Sea. For the Antarctic in winter (July) the ensemble regression method gives 2 C more warming over the Southern Ocean close to the Greenwich Meridian ({proportional_to} 7 C compared to {proportional_to} 5 C). Projection uncertainty was almost half that of the ensemble mean uncertainty over the Southern Ocean between 30 W to 90 E and 30 % less over the northern Antarctic Peninsula. The ensemble regression model avoids the need for explicit ad hoc weighting of models and exploits the whole ensemble to objectively identify overly influential outlier models. Bootstrap resampling shows that maximum precision over the Southern Ocean can be obtained with ensembles having as few as only six climate models. (orig.)

  1. Comparison between genetic programming and an ensemble Kalman filter as data assimilation techniques for probabilistic flood forecasting

    Science.gov (United States)

    Mediero, L.; Garrote, L.; Requena, A.; Chávez, A.

    2012-04-01

    Flood events are among the natural disasters that cause most economic and social damages in Europe. Information and Communication Technology (ICT) developments in last years have enabled hydrometeorological observations available in real-time. High performance computing promises the improvement of real-time flood forecasting systems and makes the use of post processing techniques easier. This is the case of data assimilation techniques, which are used to develop an adaptive forecast model. In this paper, a real-time framework for probabilistic flood forecasting is presented and two data assimilation techniques are compared. The first data assimilation technique uses genetic programming to adapt the model to the observations as new information is available, updating the estimation of the probability distribution of the model parameters. The second data assimilation technique uses an ensemble Kalman filter to quantify errors in both hydrologic model and observations, updating estimates of system states. Both forecast models take the result of the hydrologic model calibration as a starting point and adapts the individuals of this first population to the new observations in each operation time step. Data assimilation techniques have great potential when are used in hydrological distributed models. The distributed RIBS (Real-time Interactive Basin Simulator) rainfall-runoff model was selected to simulate the hydrological process in the basin. The RIBS model is deterministic, but it is run in a probabilistic way through Monte Carlo simulations over the probability distribution functions that best characterise the most relevant model parameters, which were identified by a probabilistic multi-objective calibration developed in a previous work. The Manzanares River basin was selected as a case study. Data assimilation processes are computationally intensive. Therefore, they are well suited to test the applicability of the potential of the Grid technology to

  2. Evaluation of ensemble atmospheric simulations in oil dispersion models at Itaguai Port region; Avaliacao do uso de resultados numericos de previsao atmosferica por conjunto na modelagem da dispersao de oleo na regiao do Porto de Itaguai

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Renato Goncalves dos; Silva, Mariana P.R.; Silva, Ricardo Marcelo da; Torres Junior, Audalio R. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Lab. de Modelagem de Processos Marinhos e Atmosfericos (LAMMA); Landau, Luiz [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Lab. de Metodos Computacinais em Engenharia (LAMCE); Sa, Reginaldo Ventura de; Hochleitner, Fabio; Correa, Eduardo Barbosa [AQUAMET Meteorologia e Projeto de Sistemas, Rio de Janeiro, RJ (Brazil)

    2008-07-01

    This work discusses the use of numerical prediction using ensemble as boundary condition in pollutants dispersion models, applied in a hypothetical case of an oil spill occurrence in Itaguai Port. The Princeton Ocean Model (POM) has been used to simulate hydrodynamics and NICOIL Eulerian model to forecast oil spill dispersion, and ensemble wind forecast from Global Forecast System (GFS), aiming to assess the importance of this parameter variability in oil dispersion at sea. The wind scenarios using ensemble members has showed significant dispersion when compared to control simulation, demonstrating that the uncertainty in the atmospheric modeling can generate considerable variations in the placement of the final spot of oil. The region of interest was the Sepetiba Bay, located on the southern coast of the Rio de Janeiro state; because of port operations carried out around the Port of Itaguai where they can, eventually, oil leaks occur. (author)

  3. Statistical Analysis of Ensemble Forecasts of Tropical Cyclone Tracks over the North Atlantic

    Science.gov (United States)

    2012-06-01

    generally best suited for the extratropics such that no special perturbations are applied specific to individual tropical cyclones ( UCAR 2012...Available from http://edocs.nps.edu/]. 63 UCAR , cited 2012: Guide to GFS Ensemble Track Plots. [Available online at http://www.ral.ucar.edu/guidance

  4. Comparison of Ensemble Mean and Deterministic Forecasts for Long-Range Airlift Fuel Planning

    Science.gov (United States)

    2014-03-27

    methods it uses. An example of this would be to use grid-point differencing versus spectral differencing . A third method to perturb the model is to... fraction of ensemble members that exceed a chosen threshold. The highest probabilities are depicted in red. The locations with the highest probabilities

  5. Comparison of the performance and reliability of 18 lumped hydrological models driven by ECMWF rainfall ensemble forecasts: a case study on 29 French catchments

    Science.gov (United States)

    Velázquez, Juan Alberto; Anctil, François; Ramos, Maria-Helena; Perrin, Charles

    2010-05-01

    An ensemble forecasting system seeks to assess and to communicate the uncertainty of hydrological predictions by proposing, at each time step, an ensemble of forecasts from which one can estimate the probability distribution of the predictant (the probabilistic forecast), in contrast with a single estimate of the flow, for which no distribution is obtainable (the deterministic forecast). In the past years, efforts towards the development of probabilistic hydrological prediction systems were made with the adoption of ensembles of numerical weather predictions (NWPs). The additional information provided by the different available Ensemble Prediction Systems (EPS) was evaluated in a hydrological context on various case studies (see the review by Cloke and Pappenberger, 2009). For example, the European ECMWF-EPS was explored in case studies by Roulin et al. (2005), Bartholmes et al. (2005), Jaun et al. (2008), and Renner et al. (2009). The Canadian EC-EPS was also evaluated by Velázquez et al. (2009). Most of these case studies investigate the ensemble predictions of a given hydrological model, set up over a limited number of catchments. Uncertainty from weather predictions is assessed through the use of meteorological ensembles. However, uncertainty from the tested hydrological model and statistical robustness of the forecasting system when coping with different hydro-meteorological conditions are less frequently evaluated. The aim of this study is to evaluate and compare the performance and the reliability of 18 lumped hydrological models applied to a large number of catchments in an operational ensemble forecasting context. Some of these models were evaluated in a previous study (Perrin et al. 2001) for their ability to simulate streamflow. Results demonstrated that very simple models can achieve a level of performance almost as high (sometimes higher) as models with more parameters. In the present study, we focus on the ability of the hydrological models to

  6. Comparison of OMI NO2 tropospheric columns with an ensemble of global and European regional air quality models

    Directory of Open Access Journals (Sweden)

    D. Zyryanov

    2010-04-01

    Full Text Available We present a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ models, and an intercomparison of the contributing RAQ models and two global models for the period July 2008–June 2009 over Europe. The model forecasts were produced routinely on a daily basis in the context of the European GEMS ("Global and regional Earth-system (atmosphere Monitoring using Satellite and in-situ data" project. The tropospheric vertical column of the RAQ ensemble median shows a spatial distribution which agrees well with the OMI NO2 observations, with a correlation r=0.8. This is higher than the correlations from any one of the individual RAQ models, which supports the use of a model ensemble approach for regional air pollution forecasting. The global models show high correlations compared to OMI, but with significantly less spatial detail, due to their coarser resolution. Deviations in the tropospheric NO2 columns of individual RAQ models from the mean were in the range of 20–34% in winter and 40–62% in summer, suggesting that the RAQ ensemble prediction is relatively more uncertain in the summer months. The ensemble median shows a stronger seasonal cycle of NO2 columns than OMI, and the ensemble is on average 50% below the OMI observations in summer, whereas in winter the bias is small. On the other hand the ensemble median shows a somewhat weaker seasonal cycle than NO2 surface observations from the Dutch Air Quality Network, and on average a negative bias of 14%. Full profile information was available for two RAQ models and for the global models. For these models the retrieval averaging kernel was applied. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of replacing the a priori profiles by the RAQ model profiles is on average small. However, the contrast between major hotspots and rural areas is stronger for the direct

  7. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    V S Prasad; C J Johny

    2016-04-01

    Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global ForecastSystem) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) ofresolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013and forecast skills over different spatial domains are compared with respect to mean analysis state.Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF.Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3DVar. Hybrid experiment made significant improvement in wind forecasts in all the regions on verificationagainst mean analysis. The verification of forecasts with radiosonde observations also show improvementin wind forecasts with the hybrid assimilation. On verification against observations, hybrid experimentshows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013.

  8. Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

    Science.gov (United States)

    Shedd, R.; Reed, S. M.; Porter, J. H.

    2015-12-01

    The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional

  9. Predictability during active break phases of Indian summer monsoon in an ensemble prediction system using climate forecast system

    Science.gov (United States)

    Abhilash, S.; Sahai, A. K.; Pattnaik, S.; De, S.

    2013-08-01

    This study examines the phase dependant temporal and spatial error evolution and prediction of active break spells of Indian summer monsoon rainfall in an ensemble prediction system (EPS) on a pentad time scale using climate forecast system (CFS). The EPS system shows systematic wet bias (overestimation) over west coast over the Arabian Sea and Myanmar coast and dry bias (underestimation) over Indian land mass even at pentad 1 lead and these biases consistently increase up to 4 pentad lead and saturate thereafter. Irrespective of the phases of the monsoon, the lower bound of predictability is 2 pentads, while upper bound of predictability for initial conditions starting from active phase saturates at 3 pentads and for break and transition phases predictability error saturates at a later stage at about 5 pentad. Initial conditions started from transition phase shows higher potential predictability followed by break phase and then active phase.

  10. Regional interdependency of precipitation indices across Denmark in two ensembles of high-resolution RCMs

    DEFF Research Database (Denmark)

    Sunyer Pinya, Maria Antonia; Madsen, Henrik; Rosbjerg, Dan

    2013-01-01

    on the method and precipitation index considered. The results also show that the main cause of interdependency in the ensemble is the use of the same RCMdriven by different GCMs. This study shows that the precipitation outputs from the RCMs in the ENSEMBLES project cannot be considered independent....... Daily precipitation indices from an ensemble of RCMs driven by the 40-yrECMWFRe-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles....... If the interdependency between RCMs is not taken into account, the uncertainty in theRCMsimulations of current regional climatemay be underestimated. This will in turn lead to an underestimation of the uncertainty in future precipitation projections. © 2013 American Meteorological Society....

  11. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    Science.gov (United States)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  12. Multi-ensemble regional simulation of Indian monsoon during contrasting rainfall years: role of convective schemes and nested domain

    Science.gov (United States)

    Devanand, Anjana; Ghosh, Subimal; Paul, Supantha; Karmakar, Subhankar; Niyogi, Dev

    2017-08-01

    Regional simulations of the seasonal Indian summer monsoon rainfall (ISMR) require an understanding of the model sensitivities to physics and resolution, and its effect on the model uncertainties. It is also important to quantify the added value in the simulated sub-regional precipitation characteristics by a regional climate model (RCM), when compared to coarse resolution rainfall products. This study presents regional model simulations of ISMR at seasonal scale using the Weather Research and Forecasting (WRF) model with the synoptic scale forcing from ERA-interim reanalysis, for three contrasting monsoon seasons, 1994 (excess), 2002 (deficit) and 2010 (normal). Impact of four cumulus schemes, viz., Kain-Fritsch (KF), Betts-Janjić-Miller, Grell 3D and modified Kain-Fritsch (KFm), and two micro physical parameterization schemes, viz., WRF Single Moment Class 5 scheme and Lin et al. scheme (LIN), with eight different possible combinations are analyzed. The impact of spectral nudging on model sensitivity is also studied. In WRF simulations using spectral nudging, improvement in model rainfall appears to be consistent in regions with topographic variability such as Central Northeast and Konkan Western Ghat sub-regions. However the results are also dependent on choice of cumulus scheme used, with KF and KFm providing relatively good performance and the eight member ensemble mean showing better results for these sub-regions. There is no consistent improvement noted in Northeast and Peninsular Indian monsoon regions. Results indicate that the regional simulations using nested domains can provide some improvements on ISMR simulations. Spectral nudging is found to improve upon the model simulations in terms of reducing the intra ensemble spread and hence the uncertainty in the model simulated precipitation. The results provide important insights regarding the need for further improvements in the regional climate simulations of ISMR for various sub-regions and contribute

  13. Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California

    Science.gov (United States)

    Delaney, C.; Hartman, R. K.; Mendoza, J.; Evans, K. M.; Evett, S.

    2016-12-01

    Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation or flow forecasts to inform the flood operations of reservoirs. Previous research and modeling for flood control reservoirs has shown that FIRO can reduce flood risk and increase water supply for many reservoirs. The risk-based method of FIRO presents a unique approach that incorporates flow forecasts made by NOAA's California-Nevada River Forecast Center (CNRFC) to model and assess risk of meeting or exceeding identified management targets or thresholds. Forecasted risk is evaluated against set risk tolerances to set reservoir flood releases. A water management model was developed for Lake Mendocino, a 116,500 acre-foot reservoir located near Ukiah, California. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United State Army Corps of Engineers and is operated by the Sonoma County Water Agency for water supply. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has been plagued with water supply reliability issues since 2007. FIRO is applied to Lake Mendocino by simulating daily hydrologic conditions from 1985 to 2010 in the Upper Russian River from Lake Mendocino to the City of Healdsburg approximately 50 miles downstream. The risk-based method is simulated using a 15-day, 61 member streamflow hindcast by the CNRFC. Model simulation results of risk-based flood operations demonstrate a 23% increase in average end of water year (September 30) storage levels over current operations. Model results show no increase in occurrence of flood damages for points downstream of Lake Mendocino. This investigation demonstrates that FIRO may be a viable flood control operations approach for Lake Mendocino and warrants further investigation through additional modeling and analysis.

  14. 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.

  15. 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.

  16. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation

    Science.gov (United States)

    Meng, Zhiyong

    This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a mesoscale model in increasingly realistic contexts from under a perfect model assumption and in the presence of significant model error with synthetic observations to real-world data assimilation in comparison to the three-dimensional variational (3DVar) method via both case study and month-long experiments. The EnKF is shown to be promising for future application in operational data assimilation practice. The EnKF with synthetic observations, which is implemented in the mesoscale model MM5, is very effective in keeping the analysis close to the truth under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the presence of significant model errors from physical parameterization schemes, the EnKF performs reasonably well though sometimes it can be significantly degraded compared to its performance under the perfect model assumption. Using a combination of different physical parameterization schemes in the ensemble (the so-called "multi-scheme" ensemble) can significantly improve filter performance due to the resulting better background error covariance and a smaller ensemble bias. The EnKF performs differently for different flow regimes possibly due to scale- and flow-dependent error growth dynamics and predictability. Real-data (including soundings, profilers and surface observations) are assimilated by directly comparing the EnKF and 3DVar and both are implemented in the Weather Research and Forecasting model. A case study and month-long experiments show that the EnKF is efficient in tracking observations in terms of both prior forecast and posterior analysis. The EnKF performs consistently better than 3DVar for the time period of interest due to the benefit of the EnKF from both using ensemble mean for state estimation and using a flow

  17. 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

  18. On the reliability of seasonal climate forecasts.

    Science.gov (United States)

    Weisheimer, A; Palmer, T N

    2014-07-06

    Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.

  19. 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

    channels. For example, those information have been directly assimilated by modifying the water vapour profile in the initial conditions of the WRF model in California using GOES satellite imagery. In Europe, the assimilation of cloud-top height and relative humidity has been performed in an indirect approach using an ensemble Kalman filter. In this case Meteosat SEVIRI cloud information has been assimilated in the COSMO model. Although such methods generally provide improved cloud cover forecasts in mid-latitudes, the major limitation is that only clear-sky or completely cloudy cases can be considered. Indeed, fractional clouds cause a measured signal mixing cold clouds and warmer Earth surface. If the model's initial state is directly forced by cloud properties observed by satellite, the changed model fields have to be smoothed in order to avoid numerical instability. Other crucial aspects which influence forecast quality in the case of satellite radiance assimilation are channel selection, bias and error treatment. The overall promising satellite data assimilation methods in regional NWP have not yet been explicitly applied and tested under tropical conditions. Therefore, a deeper understanding on the benefits of such methods is necessary to improve irradiance forecast schemes.

  20. The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

    Energy Technology Data Exchange (ETDEWEB)

    Deque, M.; Somot, S. [Meteo-France, Centre National de Recherches Meteorologiques, CNRS/GAME, Toulouse Cedex 01 (France); Sanchez-Gomez, E. [Cerfacs/CNRS, SUC URA1875, Toulouse Cedex 01 (France); Goodess, C.M. [University of East Anglia, Climatic Research Unit, Norwich (United Kingdom); Jacob, D. [Max Planck Institute for Meteorology, Hamburg (Germany); Lenderink, G. [KNMI, Postbus 201, De Bilt (Netherlands); Christensen, O.B. [Danish Meteorological Institute, Copenhagen Oe (Denmark)

    2012-03-15

    Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021-2050 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM x GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021-2050 response which shows a similar pattern to the one obtained for 2071-2100 in PRUDENCE. The uncertainty

  1. Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

    Science.gov (United States)

    da Silva, Arlindo

    2010-01-01

    A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of

  2. 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.

  3. 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...

  4. 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,...

  5. 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)...

  6. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  7. 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.

  8. Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

    Science.gov (United States)

    Li, Yu; Giuliani, Matteo; Castelletti, Andrea

    2016-04-01

    Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in

  9. BMA Probabilistic Forecasting for the 24-h TIGGE Multi-model Ensemble Forecasts of Surface Air Temperature%基于TIGGE多模式集合的24小时气温BMA概率预报

    Institute of Scientific and Technical Information of China (English)

    刘建国; 谢正辉; 赵琳娜; 贾炳浩

    2013-01-01

    利用TIGGE (THORPEX Interactive Grand Global Ensemble)单中心集合预报系统(ECMWF、United Kingdom Meteorological Office、China Meteorological Administration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesian model averaging,BMA)参数,从而建立地面日均气温BMA概率预报模型.由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好.多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好.它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%.基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义.%Bayesian model averaging (BMA) probability forecast models were established through calibration of their parameters using 24-h ensemble forecasts of average daily surface air temperature provided by single-center ensemble prediction systems (EPSs) from the following agencies: the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Meteorological Office (UKMO), the China Meteorological Administration (CMA), and the United States National Center for Environmental Prediction (NCEP) and its multi-center model grand-ensemble (GE) EPSs in the THORPEX Interactive Grand Global Ensemble (TIGGE), and observations in the Huaihe basin. The BMA probability forecasts of average daily surface air temperature for different EPSs were assessed by comparison with observations in the Huaihe basin. The results suggest that performance was better in the BMA predictive models than

  10. Impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot (2009) using WRF-based ensemble Kalman filter data assimilation

    Science.gov (United States)

    Yue, Jian; Meng, Zhiyong; Yu, Cheng-Ku; Cheng, Lin-Wen

    2017-01-01

    This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot (2009) using a WRF-based ensemble Kalman filter (EnKF) data assimilation (DA) system. The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone (TC). It was found that assimilating radial velocity (Vr) data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall. The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled. Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment. Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line. However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts. Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.

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

    Science.gov (United States)

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

    2017-05-01

    Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken

  12. 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

  13. 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.

  14. Regional winter wheat yield forecasting based on assimilation of remote sensing data and crop growth model with Ensemble Kalman method%遥感信息与作物模型集合卡尔曼滤波同化的区域冬小麦产量预测

    Institute of Scientific and Technical Information of China (English)

    黄健熙; 武思杰; 刘兴权; 马冠南; 马鸿元; 吴文斌; 邹金秋

    2012-01-01

    Regional crop production prediction is a significant component of national food security assessment. Remote sensing has the advantage of acquiring soil surface and crop canopy radiation information, however it is hard to reveal the inherence mechanism of crop growth and yield formation. Crop growth models based on the crop photosynthesis, transpiration, respiration, nutrition are successfully applicable for yield forecasting in simple point scale, however, they are hampered by the deriving of regional crop key input parameters. Data assimilation method which combines crop growth model and remotely sensed data has been proved the most potential approach in regional yield estimation. Hengshui district was taken as the study area. Based on the calibration and regional of WOFOST, the WOFOST model had been used to express the characteristic of time series LAI in crop growth season. To solve the system errors of MODIS-LAI due to the mixed pixels effect, the corrected MODIS-LAI was implemented by combining the field measured LAI data and the MODIS-LAI temporal trend information. Time-series LAI was assimilated through combined corrected MODIS-LAI and WOFOST simulated LAI from green-up to heading stage with EnKF algorithm. The assimilated optimal LAI was used to drive the WOFOST model per-pixel to estimate the regional yield. The results indicated that the precision of yield forecasting was obviously improved with EnKF assimilation, compared with the statistical yield, the coefficient of determination was improved from 0.10 to 0.45 and RMSE was reduced from 2 480 kg/hm2 to 860kg/hm2. The results showed that assimilation of the remotely sensed data into crop growth model with EnKF can provide a reliable approach for prediction regional crop yield and had great potential in agricultural applications. The research can provide an important reference value for the regional crop production estimation.%区域作物产量预测是国家粮食安全评估的重要内容.遥感虽

  15. Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

    Institute of Scientific and Technical Information of China (English)

    ZHU Congwen; Chung-Kyu PARK; Woo-Sung LEE; Won-Tae YUN

    2008-01-01

    The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific(Asia-Pacific)summer monsoon region(0°-50°N,100°-150°E)were evaluated in nine different AGCM,used in the Asia-Pacific Economic Cooperation Climate Center(APCCl multi.model ensemble seasonal prediction system.The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially difierent from the observed counterparts in this region.but the summer monsoon circulations are reasonably predicted.For example,all models can well produce the interannual variability of the western North Pacific monsoon index(WNPMI)defined by 850 hPa winds,but they failed to predict the relationship between WNPMI and precipitation anomalies.The interannual variability of the 500 hPa geopotential height(GPH)can be well predicted by the models in contrast to precipitation anomalies.On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies.we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly Oll the basis of EOF and singular value decomposition(SVD).In this scheme,the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation.respectively.Then. the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field,which is assembled by the forecasted expansion toeffcients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period.The cross-validated forecasts suggest that this dowuscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea,western North Pacific and the East Asia Pacific regions

  16. An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

    Directory of Open Access Journals (Sweden)

    Jonathan T. Barge

    2016-06-01

    Full Text Available This study focused on employing Linear Genetic Programming (LGP, Ensemble Empirical Mode Decomposition (EEMD, and the Self-Organizing Map (SOM in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was utilized to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN results over the same dataset, posting an average absolute relative error (AARE of 17.118 and Nash-Sutcliff (E of 0.937. Utilizing EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalized mean biased error (NMBE of −2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Utilizing the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of utilizing EEMD and an SOM in conjunction with LGP for streamflow forecasting.

  17. 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)

  18. A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

    Science.gov (United States)

    2012-03-01

    Multivariate ENSO Index METOC Meteorological and oceanographic MJO Madden Julian Oscillation mm millimeter NAO North Atlantic Oscillation NCAR...region remains the largest and most militarized territorial dispute in the world, with Pakistan, India, and China laying claim to the area (CIA...the connection between the AO in late spring and summer monsoon rainfall in China and found a significant correlation. When the AO index is positive

  19. Method for Water Management Considering Long-term Probabilistic Forecasts

    Science.gov (United States)

    Hwang, J.; Kang, J.; Suh, A. S.

    2015-12-01

    This research is aimed at predicting the monthly inflow of the Andong-dam basin in South Korea using long-term probabilistic forecasts to apply long-term forecasts to water management. Forecasted Cumulative Distribution Functions (CDFs) of monthly precipitation are plotted by combining the range of monthly precipitation based on proper Probability Density Function (PDF) in past data with probabilistic forecasts in each category. Ensembles of inflow are estimated by entering generated ensembles of precipitation based on the CDFs into the 'abcd' water budget model. The bias and RMSE between averages in past data and observed inflow are compared to them in forecasted ensembles. In our results, the bias and RMSE of average precipitation in the forecasted ensemble are bigger than in past data, whereas the average inflow in the forecasted ensemble is smaller than in past data. This result could be used for reference data to apply long-term forecasts to water management, because of the limit in the number of forecasted data for verification and differences between the Andong-dam basin and the forecasted regions. This research has significance by suggesting a method of applying probabilistic information in climate variables from long-term forecasts to water management in Korea. Original data of a climate model, which produces long-term probabilistic forecasts should be verified directly as input data of a water budget model in the future, so that a more scientific response in water management against uncertainty of climate change could be reached.

  20. 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

  1. Seasonal precipitation forecasts for selected regions in West Africa using circulation type classifications in combination with further statistical approaches - Conceptual framework and first results

    Science.gov (United States)

    Bliefernicht, Jan; Laux, Patrik; Waongo, Moussa; Kunstmann, Harald

    2015-04-01

    precipitation forecasts of a global ensemble prediction system, the Climate Forecast System Version 2. In addition, an event-based comparison is performed for the year 2013 using the precipitation forecasts from a regional ensemble prediction system which refines the CFS2 forecasts by using the Weather and Research Forecasting model. In this poster presentation a detailed overview about the various techniques is given including first outcomes of this investigation.

  2. The ensemble forecasting technique of the thunderstorm and its application%雷暴云的集合预报技术及其应用

    Institute of Scientific and Technical Information of China (English)

    王佳; 智协飞; 陈钰文; 商兆堂; 白卡娃

    2012-01-01

    A thunderstorm ensemble forecasting approach is perti~rmed by the ensemble analysis of the cloud model' s forecast results according to the initial condition aggregation provided by the grid fore-cast results of the mesoscale non-hydrostatic WRF model. The approach is applied to forecast the char-acteristics of thunderstorm around Nanjing one day in advance and is verified by the Doppler radar data of the thunderstorms in summer in Nanjing. Results show that the intensity and distribution of thunder-storms over the research area is reasonably predicted one day in advance. It is particularly good to fore-cast the distribution of the strong thunderstorms by using the ensemble forecasting technique. Moreover, the application of the PDF(probability density function)of thunderstorms' duration into the probability forecasting of the influenced area of thunderstorms improves the radar' s warning and monitoring capac-ity of local thunderstorms.%以中尺度非静力WRF模式的格点预报结果作为云模式的初值集合,经云模式的多初值雷暴预报及预报结果的集合分析,建立了雷暴云的集合预报方法。将该方法应用于南京周边地区未来一天雷暴天气的特征预报,并利用南京夏季9个雷暴天气的多普勒雷达资料(SCIT,storm cell identification and tracking)进行预报效果的检验。结果表明,雷暴云的集合预报对研究区域内未来一天雷暴强度、分布预报效果较好,尤其对强雷暴的分布有较强的预警预测能力。此外,雷暴持续时间概率密度分布的集合预报产品,在雷暴影响范围概率预报上的应用,提高了雷达对雷暴的预警监测能力。

  3. Dynamical downscaling of regional climate over eastern China using RSM with multiple physics scheme ensembles

    Science.gov (United States)

    Peishu, Zong; Jianping, Tang; Shuyu, Wang; Lingyun, Xie; Jianwei, Yu; Yunqian, Zhu; Xiaorui, Niu; Chao, Li

    2017-08-01

    The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble.

  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. Arctic climate change in an ensemble of regional CORDEX simulations

    Directory of Open Access Journals (Sweden)

    Torben Koenigk

    2015-03-01

    Full Text Available Fifth phase Climate Model Intercomparison Project historical and scenario simulations from four global climate models (GCMs using the Representative Concentration Pathways greenhouse gas concentration trajectories RCP4.5 and RCP8.5 are downscaled over the Arctic with the regional Rossby Centre Atmosphere model (RCA. The regional model simulations largely reflect the circulation bias patterns of the driving global models in the historical period, indicating the importance of lateral and lower boundary conditions. However, local differences occur as a reduced winter 2-m air temperature bias over the Arctic Ocean and increased cold biases over land areas in RCA. The projected changes are dominated by a strong warming in the Arctic, exceeding 15°K in autumn and winter over the Arctic Ocean in RCP8.5, strongly increased precipitation and reduced sea-level pressure. Near-surface temperature and precipitation are linearly related in the Arctic. The wintertime inversion strength is reduced, leading to a less stable stratification of the Arctic atmosphere. The diurnal temperature range is reduced in all seasons. The large-scale change patterns are dominated by the surface and lateral boundary conditions so future response is similar in RCA and the driving global models. However, the warming over the Arctic Ocean is smaller in RCA; the warming over land is larger in winter and spring but smaller in summer. The future response of winter cloud cover is opposite in RCA and the GCMs. Precipitation changes in RCA are much larger during summer than in the global models and more small-scale change patterns occur.

  6. 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.

  7. Air Quality Multi-model Ensemble Forecast System Based on GIS%基于GIS的空气质量多模式集合预报系统

    Institute of Scientific and Technical Information of China (English)

    谢涛; 姚新; 孙世友

    2011-01-01

    空气质量多模式集合预报是研究大气污染演变规律、进行空气质量预报预警,提供大气污染控制管理决策的重要工具.本文介绍了地理信息系统(GIS)与多模式集合预报系统进行集成的设计思路,并对GIS在系统中起到的作用进行了探讨.将GIS技术应用于多模式环境空气质量预测预报系统中,可提供空间数据和相关属性数据的快速存取和管理功能,使环境基础信息与环境管理信息以及模型分析结果以可视化的图形、图像方式呈现给决策管理人员,并能充分发挥GIS的空间分析能力,为辅助决策提供依据.该系统在广州市环境空气质量保障的应用中取得了较好效果.%Air quality multi-model ensemble forecasting is an important tool to study the evolution of air pollution rules, air quality forecasting and warning, and to provide air pollution control management decision-making. In this paper, the design of the integration of geographic information system (GIS) and multi-model ensemble forecasting system was introduced, and the important roles of GIS in this system were discussed. Using GIS technology in multi-model ensemble forecast system, which provides the functions for fast accessing and management of spatial data and attributes data, can make the basic environmental information, environmental management information and modeling results visible to the manager through graphics or images. Meanwhile, the spatial analysis function of GIS is helpful for assistant decision support The GIS based multi-model ensemble forecast system used in Guangzhou City has played an effective role on air quality protection.

  8. Calibration and parameterization of a semi-distributed hydrological model to support sub-daily ensemble flood forecasting; a watershed in southeast Brazil

    Science.gov (United States)

    de Almeida Bressiani, D.; Srinivasan, R.; Mendiondo, E. M.

    2013-12-01

    The use of distributed or semi-distributed models to represent the processes and dynamics of a watershed in the last few years has increased. These models are important tools to predict and forecast the hydrological responses of the watersheds, and they can subside disaster risk management and planning. However they usually have a lot of parameters, of which, due to the spatial and temporal variability of the processes, are not known, specially in developing countries; therefore a robust and sensible calibration is very important. This study conduced a sub-daily calibration and parameterization of the Soil & Water Assessment Tool (SWAT) for a 12,600 km2 watershed in southeast Brazil, and uses ensemble forecasts to evaluate if the model can be used as a tool for flood forecasting. The Piracicaba Watershed, in São Paulo State, is mainly rural, but has about 4 million of population in highly relevant urban areas, and three cities in the list of critical cities of the National Center for Natural Disasters Monitoring and Alerts. For calibration: the watershed was divided in areas with similar hydrological characteristics, for each of these areas one gauge station was chosen for calibration; this procedure was performed to evaluate the effectiveness of calibrating in fewer places, since areas with the same group of groundwater, soil, land use and slope characteristics should have similar parameters; making calibration a less time-consuming task. The sensibility analysis and calibration were performed on the software SWAT-CUP with the optimization algorithm: Sequential Uncertainly Fitting Version 2 (SUFI-2), which uses Latin hypercube sampling scheme in an iterative process. The performance of the models to evaluate the calibration and validation was done with: Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (r2), root mean square error (RMSE), and percent bias (PBIAS), with monthly average values of NSE around 0.70, r2 of 0.9, normalized RMSE of 0

  9. 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.

  10. 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

  11. 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

  12. 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

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

    Science.gov (United States)

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

    2015-04-01

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

  14. 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.

  15. 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.

  16. 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.

  17. 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;...

  18. A seasonal hydrologic ensemble prediction system for water resource management

    Science.gov (United States)

    Luo, L.; Wood, E. F.

    2006-12-01

    A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

  19. 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

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

    Science.gov (United States)

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

    2015-12-01

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

  1. Future trends of snowfall days in northern Spain from ENSEMBLES regional climate projections

    Science.gov (United States)

    Pons, M. R.; Herrera, S.; Gutiérrez, J. M.

    2016-06-01

    In a previous study Pons et al. (Clim Res 54(3):197-207, 2010. doi: 10.3354/cr01117g) reported a significant decreasing trend of snowfall occurrence in the Northern Iberian Peninsula since the mid 70s. The study was based on observations of annual snowfall frequency (measured as the annual number of snowfall days NSD) from a network of 33 stations ranging from 60 to 1350 m. In the present work we analyze the skill of Regional Climate Models (RCMs) to reproduce this trend for the period 1961-2000 (using both reanalysis- and historical GCM-driven boundary conditions) and the trend and the associated uncertainty of the regional future projections obtained under the A1B scenario for the first half of the twenty-first century. In particular, we consider the regional simulation dataset from the EU-funded ENSEMBLES project, consisting of thirteen state-of-the-art RCMs run at 25 km resolution over Europe. While ERA40 severely underestimates both the mean NSD and its observed trend (-2.2 days/decade), the corresponding RCM simulations driven by the reanalysis appropriately capture the interannual variability and trends of the observed NSD (trends ranging from -3.4 to -0.7, -2.1 days/decade for the ensemble mean). The results driven by the GCM historical runs are quite variable, with trends ranging from -8.5 to 0.2 days/decade (-1.5 days/decade for the ensemble mean), and the greatest uncertainty by far being associated with the particular GCM used. Finally, the trends for the future 2011-2050 A1B runs are more consistent and significant, ranging in this case from -3.7 to -0.5 days/decade (-2.0 days/decade for the ensemble mean), indicating a future significant decreasing trend. These trends are mainly determined by the increasing temperatures, as indicated by the interannual correlation between temperature and NSD (-0.63 in the observations), which is preserved in both ERA40- and GCM-driven simulations.

  2. 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.

  3. Future changes in South American temperature and precipitation in an ensemble of CORDEX regional climate model simulations

    Science.gov (United States)

    Kjellström, Erik; Nikulin, Grigory; Rana, Arun; Fuentes Franco, Ramón

    2017-04-01

    In this study we investigate possible changes in temperature and precipitation on a regional scale over South America from 1961 to 2100. We use data from two ensembles of climate simulations, one global and one regional, over the South America CORDEX domain. The global ensemble includes ten coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CSIRO-Mk3, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M, MPI-ESM-LR and NorESM1-M. In the regional ensemble all 10 AOGCMs are downscaled at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Three forcing scenarios are considered: RCP2.6 (five out of ten AOGCMs); RCP4.5 and RCP8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, taking both AOGCM and RCM ensembles and focusing on seasonal mean temperature and precipitation over South America we i) evaluate the ability of the ensembles and their individual members to simulate the observed climatology in South America, ii) analyse similarities and differences in future climate projections between the two ensembles and iii) assess how both ensembles capture the spread of the grand CMIP5 ensemble. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.

  4. Optimized Flood Forecasts Using a Statistical Enemble

    Science.gov (United States)

    Silver, Micha; Fredj, Erick

    2016-04-01

    The method presented here assembles an optimized flood forecast from a set of consecutive WRF-Hydro simulations by applying coefficients which we derive from straightforward statistical procedures. Several government and research institutions that produce climate data offer ensemble forecasts, which merge predictions from different models to gain a more accurate fit to observed data. Existing ensemble forecasts present climate and weather predictions only. In this research we propose a novel approach to constructing hydrological ensembles for flood forecasting. The ensemble flood forecast is created by combining predictions from the same model, but initiated at different times. An operative flood forecasting system, run by the Israeli Hydrological Service, produces flood forecasts twice daily with a 72 hour forecast period. By collating the output from consecutive simulation runs we have access to multiple overlapping forecasts. We then apply two statistical procedures to blend these consecutive forecasts, resulting in a very close fit to observed flood runoff. We first employ cross-correlation with a time lag to determine a time shift for each of the original, consecutive forecasts. This shift corrects for two possible sources of error: slow or fast moving weather fronts in the base climate data; and mis-calibrations of the WRF-Hydro model in determining the rate of flow of surface runoff and in channels. We apply this time shift to all consecutive forecasts, then run a linear regression with the observed runoff data as the dependent variable and all shifted forecasts as the predictor variables. The solution to the linear regression equation is a set of coefficients that corrects the amplitude errors in the forecasts. These resulting regression coefficients are then applied to the consecutive forecasts producing a statistical ensemble which, by design, closely matches the observed runoff. After performing this procedure over many storm events in the Negev region

  5. Effect of Considering Sub-Grid Scale Uncertainties on the Forecasts of a High-Resolution Limited Area Ensemble Prediction System

    Science.gov (United States)

    Kim, SeHyun; Kim, Hyun Mee

    2017-03-01

    The ensemble prediction system (EPS) is widely used in research and at operation center because it can represent the uncertainty of predicted atmospheric state and provide information of probabilities. The high-resolution (so-called "convection-permitting") limited area EPS can represent the convection and turbulence related to precipitation phenomena in more detail, but it is also much sensitive to small-scale or sub-grid scale processes. The convection and turbulence are represented using physical processes in the model and model errors occur due to sub-grid scale processes that were not resolved. This study examined the effect of considering sub-grid scale uncertainties using the high-resolution limited area EPS of the Korea Meteorological Administration (KMA). The developed EPS has horizontal resolution of 3 km and 12 ensemble members. The initial and boundary conditions were provided by the global model. The Random Parameters (RP) scheme was used to represent sub-grid scale uncertainties. The EPSs with and without the RP scheme were developed and the results were compared. During the one month period of July, 2013, a significant difference was shown in the spread of 1.5 m temperature and the Root Mean Square Error and spread of 10 m zonal wind due to application of the RP scheme. For precipitation forecast, the precipitation tended to be overestimated relative to the observation when the RP scheme was applied. Moreover, the forecast became more accurate for heavy precipitations and the longer forecast lead times. For two heavy rainfall cases occurred during the research period, the higher Equitable Threat Score was observed for heavy precipitations in the system with the RP scheme compared to the one without, demonstrating consistency with the statistical results for the research period. Therefore, the predictability for heavy precipitation phenomena that affected the Korean Peninsula increases if the RP scheme is used to consider sub-grid scale uncertainties

  6. 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.

  7. Skill and reliability of experimental GEFS ensemble forecast guidance designed to inform decision-making in reservoir management in California

    Science.gov (United States)

    Scheuerer, Michael; Webb, Robert S.; Hamill, Thomas M.

    2017-04-01

    Many reservoirs operated by the U.S. Army Corps of Engineers (Corps) in California provide flood control as well as water supply, recreation and stream flow regulation. Operations for flood control follow seasonally specified elevations for an upper volume of reservoir storage with unused storage capacity designated for flood risk management and thus not available for water supply storage. In the flood control operation of these reservoirs, runoff is captured during rain events and then released soon after at rates that do not result in downstream flooding (typically over a 5 to 8 day period), resulting in evacuated storage space to capture runoff from the next potential storm. As part of the Forecast-Informed Reservoir Operations (FIRO) partnership to more effectively balance flood and drought risks, we developed an experimental California medium-range precipitation forecast system based on NCEP GEFS reforecasts and Climatology-Calibrated Precipitation Analysis (CCPA). We have applied this experimental forecast system to predict the probability of day 5-10 precipitation accumulations at each CCPA grid point within California to exceed certain pre-specified thresholds. Discussions with flood and water supply managers indicate that forecast guidance for the very low risk of extreme precipitation for watersheds above reservoirs can be valuable for decision making. In this study, we assess the skill and reliability of this experimental forecast system to predict low probabilities of precipitation extreme events for select watersheds during recent winter precipitation seasons. Our analysis indicate there may be sufficient reliability in forecasts guidance for low probabilities of heavy precipitation events to inform decision making in reservoir management in select California river basins to manage flood risk while increasing water supply for consumptive use and ecosystem services.

  8. 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

  9. 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

  10. Meteorological Drought Prediction Using a Multi-Model Ensemble Approach

    Science.gov (United States)

    Chen, L.; Mo, K. C.; Zhang, Q.; Huang, J.

    2013-12-01

    In the United States, drought is among the costliest natural hazards, with an annual average of 6 billion dollars in damage. Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Started in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the National Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the meteorological drought predictability using the retrospective NMME forecasts for the period from 1982 to 2010. Before predicting SPI, monthly-mean precipitation (P) forecasts from each model were bias corrected and spatially downscaled (BCSD) to regional grids of 0.5-degree resolution over the contiguous United States based on the probability distribution functions derived from the hindcasts. The corrected P forecasts were then appended to the CPC Unified Precipitation Analysis to form a P time series for computing 3-month and 6-month SPIs. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation and root-mean-square errors against the observations, are used to evaluate forecast skill. For P forecasts, errors vary among models and skill generally is low after the second month. All model P forecasts have higher skill in winter and lower skill in summer. In wintertime, BCSD improves both P and SPI forecast skill. Most improvements are over the western mountainous regions and along the Great Lake. Overall, SPI predictive skill is regionally and seasonally dependent. The six-month SPI forecasts are skillful out to four months. For

  11. 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.

  12. 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

  13. The use of different ensemble forecasting systems for wind power prediction on a real case in the South of Italy

    DEFF Research Database (Denmark)

    Alessandrini, Stefano; Sperati, Simone; Pinson, Pierre

    Short-term forecasting applied to wind energy is becoming increasingly important due to the constant growth of this renewable source, whose uncertainty requires a constant effort to meet the needs of the national electrical systems and their operators. Regarding to this, the probabilistic approac...

  14. The use of different ensemble forecasting systems for wind power prediction on a real case in the South of Italy

    DEFF Research Database (Denmark)

    Alessandrini, Stefano; Sperati, Simone; Pinson, Pierre

    2012-01-01

    Short-term forecasting applied to wind energy is becoming increasingly important due to the constant growth of this renewable source, whose uncertainty requires a constant effort to meet the needs of the national electrical systems and their operators. Regarding to this, the probabilistic approac...

  15. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.;

    2011-01-01

    The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear ...

  16. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.

    2011-01-01

    The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear ...

  17. ESPC Coupled Global Ensemble Design

    Science.gov (United States)

    2014-09-30

    coupled system infrastructure and forecasting capabilities. Initial operational capability is targeted for 2018. APPROACH 1. It is recognized...provided will be the probability distribution function (PDF) of environmental conditions. It is expected that this distribution will have skill. To...system would be the initial capability for ensemble forecasts . Extensions to fully coupled ensembles would be the next step. 2. Develop an extended

  18. Probabilistic Forecast for 21st Century Climate Based on an Ensemble of Simulations using a Business-As-Usual Scenario

    Science.gov (United States)

    Scott, J. R.; Forest, C. E.; Sokolov, A. P.; Dutkiewicz, S.

    2011-12-01

    The behavior of the climate system is examined in an ensemble of runs using an Earth System Model of intermediate complexity. Climate "parameters" varied are the climate sensitivity, the aerosol forcing, and the strength of ocean heat uptake. Variations in the latter were accomplished by changing the strength of the oceans' background vertical mixing. While climate sensitivity and aerosol forcing can be varied over rather wide ranges, it is more difficult to create such variation in heat uptake while maintaining a realistic overturning ocean circulation. Therefore, separate ensembles were carried out for a few values of the vertical diffusion coefficient. Joint probability distributions for climate sensitivity and aerosol forcing are constructed by comparing results from 20th century simulations with available observational data. These distributions are then used to generate ensembles of 21st century simulations; results allow us to construct probabilistic distributions for changes in important climate change variables such as surface air temperature, sea level rise, and magnitude of the AMOC. Changes in the rate of air-sea flux of CO2 and the export of carbon into the deep ocean are also examined.

  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. Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

    Science.gov (United States)

    Heinrich, Georg; Gobiet, Andreas; Mendlik, Thomas

    2014-01-01

    This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.

  1. 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.

  2. 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