WorldWideScience

Sample records for regional ensemble forecasts

  1. Regionalization of post-processed ensemble runoff forecasts

    Directory of Open Access Journals (Sweden)

    J. O. Skøien

    2016-05-01

    Full Text Available For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather, and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather. The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS (Gneiting et al., 2005. Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu, where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014. We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

  2. Wave ensemble forecast system for tropical cyclones in the Australian region

    Science.gov (United States)

    Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.

    2018-05-01

    Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.

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

    Science.gov (United States)

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

    2017-12-01

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

  4. 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

  5. Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

    Science.gov (United States)

    Aminyavari, Saleh; Saghafian, Bahram; Delavar, Majid

    2018-04-01

    The application of numerical weather prediction (NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous (yes/no), and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.

  6. 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...

  7. Ensemble forecasting of species distributions.

    Science.gov (United States)

    Araújo, Miguel B; New, Mark

    2007-01-01

    Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

  8. Using ensemble forecasting for wind power

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L.; Badger, J. [Risoe National Lab., Roskilde (Denmark); Sattler, K.

    2003-07-01

    Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and

  9. Urban runoff forecasting with ensemble weather predictions

    DEFF Research Database (Denmark)

    Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

    This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....

  10. 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.

  11. Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

    Science.gov (United States)

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

    2009-04-01

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

  12. 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.

  13. 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.

  14. Ensemble methods for seasonal limited area forecasts

    DEFF Research Database (Denmark)

    Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.

    2004-01-01

    The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged-average...

  15. Skill prediction of local weather forecasts based on the ECMWF ensemble

    Directory of Open Access Journals (Sweden)

    C. Ziehmann

    2001-01-01

    Full Text Available Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.

  16. 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...

  17. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    forecasts where a dampening of the differences of precipitation quality occurs. Seasonal meteorological forecasts are possible due to changes of large scale patterns of the ocean and land, such as el Niño, that evolve at a much slower pace than the atmosphere, which can have an impact on its evolution later...

  18. Uncertainty in dispersion forecasts using meteorological ensembles

    International Nuclear Information System (INIS)

    Chin, H N; Leach, M J

    1999-01-01

    The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. However, there is uncertainty in any meteorological forecast, arising from both errors in the data (both initial conditions and boundary conditions) and from errors in the model. We use ensemble forecasts to estimate the uncertainty in the forecasts and the range of possible outcomes

  19. On the forecast skill of a convection-permitting ensemble

    Science.gov (United States)

    Schellander-Gorgas, Theresa; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Kann, Alexander

    2017-01-01

    The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale - Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational - Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

  20. Ensemble forecasts of road surface temperatures

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr, jr.; Pešice, Petr; Škuthan, M.

    2017-01-01

    Roč. 187, 1 May (2017), s. 33-41 ISSN 0169-8095 R&D Projects: GA ČR GA13-34856S; GA TA ČR(CZ) TA01031509 Institutional support: RVO:68378289 Keywords : ensemble prediction * road surface temperature * road weather forecast Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 3.778, year: 2016 http://www.sciencedirect.com/science/article/pii/S0169809516307311

  1. Multicomponent ensemble models to forecast induced seismicity

    Science.gov (United States)

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

    2018-01-01

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

  2. Ensemble-based Probabilistic Forecasting at Horns Rev

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2009-01-01

    forecasting methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. This modelemploys local polynomial regression, and is adoptively estimated with an orthogonal fitting method. The obtained ensemble forecasts of wind power...

  3. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

    Science.gov (United States)

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2018-01-01

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  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. 3-D visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Science.gov (United States)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-02-01

    We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

  6. ENSEMBLE methods to reconcile disparate national long range dispersion forecasts

    OpenAIRE

    Mikkelsen, Torben; Galmarini, S.; Bianconi, R.; French, S.

    2003-01-01

    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 disparatenational 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 a...

  7. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting in the Tehachapi Region Winter Season

    Energy Technology Data Exchange (ETDEWEB)

    Zack, John [AWS Truepower, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truepower, LLC, Albany, NY (United States); Young, Steve [AWS Truepower, LLC, Albany, NY (United States); Van Knowe, Glenn [AWS Truepower, LLC, Albany, NY (United States); Waight, Ken [AWS Truepower, LLC, Albany, NY (United States); Manobainco, John [AWS Truepower, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2010-10-20

    This study extends the wind power forecast sensitivity work done by Zack et al. (2010a, b) in two prior research efforts. Zack et al. (2010a, b) investigated the relative predictive value and optimal combination of different variables/locations from correlated sensitivity patterns. Their work involved developing the Multiple Observation Optimization Algorithm (MOOA) and applying the algorithm to the results obtained from the Ensemble Sensitivity Analysis (ESA) method (Ancell and Hakim 2007; Torn and Hakim 2008).

  8. 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.

  9. 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

  10. Monthly ENSO Forecast Skill and Lagged Ensemble Size

    Science.gov (United States)

    Trenary, L.; DelSole, T.; Tippett, M. K.; Pegion, K.

    2018-04-01

    The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

  11. 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

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

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    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...... of translation and dilation factors are discussed. Copyright © 2012 Royal Meteorological Society...

  13. 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.

  14. 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

    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, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product...

  15. 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).

  16. 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

  17. Ensemble forecasting using sequential aggregation for photovoltaic power applications

    International Nuclear Information System (INIS)

    Thorey, Jean

    2017-01-01

    Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts. (author) [fr

  18. 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.

  19. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  20. Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

    Science.gov (United States)

    Delle Monache, L.; Shahriari, M.; Cervone, G.

    2017-12-01

    We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

  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. 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...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

  3. Skill forecasting from different wind power ensemble prediction methods

    International Nuclear Information System (INIS)

    Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

    2007-01-01

    This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving 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 dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

  4. A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

    Science.gov (United States)

    Li, Xiang; He, Hongrang; Chen, Chaohui; Miao, Ziqing; Bai, Shigang

    2017-10-01

    A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.

  5. Short-term ensemble radar rainfall forecasts for hydrological applications

    Science.gov (United States)

    Codo de Oliveira, M.; Rico-Ramirez, M. A.

    2016-12-01

    Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

  6. Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA

    Directory of Open Access Journals (Sweden)

    Arturo I. Quintanar

    2012-02-01

    Full Text Available This study investigated the potential impact of soil moisture perturbations on the statistical spread of an ensemble forecast for three different synoptic events during the summer of 2006. Soil moisture was perturbed from a control simulation to generate a 12 member ensemble with six drier and six moister soils. The impacts on the near-surface atmospheric conditions and on precipitation were analysed. It was found, as previous studies have confirmed, that soil moisture can change the spatial and temporal distribution of precipitation and of the overlying circulation. It was found that regardless of the conditions in synoptic forcing, temperature, relative humidity and horizontal wind field exhibited a spatial correlation coefficient (R close to one with respect to the control simulation. Vertical velocity, however, showed a marked decrease in R down to 0.4 as the precipitation activity increased. For vertical velocity, however, this quantity grew to near 1.0 consistent with R near zero and standard deviations very close to that of the control. These results suggested a more complex picture in which soil moisture perturbations played a major role in modifying precipitation and the near-surface circulation but did not broaden the statistical spread of trajectories in phase space of all variables.

  7. Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

    Science.gov (United States)

    Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

    1998-11-01

    Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.

  8. Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

    Science.gov (United States)

    Keane, R. J.; Plant, R. S.; Tennant, W. J.

    2015-12-01

    The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

  9. 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)

  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. Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

    Science.gov (United States)

    Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

    2017-12-01

    A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

  12. ENSEMBLE methods to reconcile disparate national long range dispersion forecasts

    DEFF Research Database (Denmark)

    Mikkelsen, Torben; Galmarini, S.; Bianconi, R.

    2003-01-01

    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 disparatenational forecasts for long-range dispersion...... emergency and meteorological forecasting centres, which may choose to integrate them directly intooperational emergency information systems, or possibly use them as a basis for future system development.......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 disparatenational 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 accidentalatmospheric release of radioactive material. A series of new decision-making “ENSEMBLE” procedures...

  13. 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.

  14. Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Science.gov (United States)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-07-01

    We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

  15. Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present the application of interactive three-dimensional (3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off.

  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. Spatio-temporal behaviour of medium-range ensemble forecasts

    Science.gov (United States)

    Kipling, Zak; Primo, Cristina; Charlton-Perez, Andrew

    2010-05-01

    Using the recently-developed mean-variance of logarithms (MVL) diagram, together with the TIGGE archive of medium-range ensemble forecasts from nine different centres, we present an analysis of the spatio-temporal dynamics of their perturbations, and show how the differences between models and perturbation techniques can explain the shape of their characteristic MVL curves. We also consider the use of the MVL diagram to compare the growth of perturbations within the ensemble with the growth of the forecast error, showing that there is a much closer correspondence for some models than others. We conclude by looking at how the MVL technique might assist in selecting models for inclusion in a multi-model ensemble, and suggest an experiment to test its potential in this context.

  18. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    Science.gov (United States)

    Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

    2014-12-01

    Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

  19. 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

  20. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Bijay Neupane

    2017-01-01

    Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.

  1. 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

  2. Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

    Directory of Open Access Journals (Sweden)

    Federico Divina

    2018-04-01

    Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

  3. 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...

  4. 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.

  5. 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

  6. 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

  7. 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

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

    Science.gov (United States)

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

    2015-04-01

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

  9. 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.

  10. Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

    Science.gov (United States)

    Huang, Ling; Luo, Yali

    2017-08-01

    Based on The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set, this study evaluates the ability of global ensemble prediction systems (EPSs) from the European Centre for Medium-Range Weather Forecasts (ECMWF), U.S. National Centers for Environmental Prediction, Japan Meteorological Agency (JMA), Korean Meteorological Administration, and China Meteorological Administration (CMA) to predict presummer rainy season (April-June) precipitation in south China. Evaluation of 5 day forecasts in three seasons (2013-2015) demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light-to-heavy rainfall (0.1 to 30 mm/12 h) and underestimate heavier rainfall (>30 mm/12 h), with JMA being the worst. By analyzing the synoptic situations predicted by the identified more skillful (ECMWF) and less skillful (JMA and CMA) EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm-moist air into south China by the low-level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast. The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall. A slight improvement in the performance is obtained by shortening the forecast lead time from 30-36 h to 18-24 h to 6-12 h for the cases with large-scale forcing, but not for the locally produced cases.

  11. 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.

  12. Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

    Science.gov (United States)

    Breivik, Øyvind; Aarnes, Ole Johan; Bidlot, Jean-Raymond; Carrasco, Ana; Saetra, Øyvind

    2013-10-01

    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 annual mean and variance over the 11-year archived period exhibit no significant departures from stationarity compared with a recent reforecast, i.e., there is no spurious trend due to model upgrades. EPS yields significantly higher return values than ERA-40 and ERA-Interim and is in good agreement with the high-resolution hindcast NORA10, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it is biased low. Confidence intervals are half the width of those found for ERA-Interim due to the magnitude of the data set.

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

    OpenAIRE

    Jewson, Stephen; Rowlands, Dan

    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.

  14. Simulation of regional day-ahead PV power forecast scenarios

    DEFF Research Database (Denmark)

    Nuno, Edgar; Koivisto, Matti Juhani; Cutululis, Nicolaos Antonio

    2017-01-01

    Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often...... at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional...... PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records....

  15. A note on the multi model super ensemble technique for reducing forecast errors

    International Nuclear Information System (INIS)

    Kantha, L.; Carniel, S.; Sclavo, M.

    2008-01-01

    The multi model super ensemble (S E) technique has been used with considerable success to improve meteorological forecasts and is now being applied to ocean models. Although the technique has been shown to produce deterministic forecasts that can be superior to the individual models in the ensemble or a simple multi model ensemble forecast, there is a clear need to understand its strengths and limitations. This paper is an attempt to do so in simple, easily understood contexts. The results demonstrate that the S E forecast is almost always better than the simple ensemble forecast, the degree of improvement depending on the properties of the models in the ensemble. However, the skill of the S E forecast with respect to the true forecast depends on a number of factors, principal among which is the skill of the models in the ensemble. As can be expected, if the ensemble consists of models with poor skill, the S E forecast will also be poor, although better than the ensemble forecast. On the other hand, the inclusion of even a single skillful model in the ensemble increases the forecast skill significantly.

  16. Singular vectors, predictability and ensemble forecasting for weather and climate

    International Nuclear Information System (INIS)

    Palmer, T N; Zanna, Laure

    2013-01-01

    The local instabilities of a nonlinear dynamical system can be characterized by the leading singular vectors of its linearized operator. The leading singular vectors are perturbations with the greatest linear growth and are therefore key in assessing the system’s predictability. In this paper, the analysis of singular vectors for the predictability of weather and climate and ensemble forecasting is discussed. An overview of the role of singular vectors in informing about the error growth rate in numerical models of the atmosphere is given. This is followed by their use in the initialization of ensemble weather forecasts. Singular vectors for the ocean and coupled ocean–atmosphere system in order to understand the predictability of climate phenomena such as ENSO and meridional overturning circulation are reviewed and their potential use to initialize seasonal and decadal forecasts is considered. As stochastic parameterizations are being implemented, some speculations are made about the future of singular vectors for the predictability of weather and climate for theoretical applications and at the operational level. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (review)

  17. 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.

  18. A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

    Science.gov (United States)

    Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

    2017-12-01

    The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

  19. Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

    Science.gov (United States)

    Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

    2017-12-01

    This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

  20. Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

    Science.gov (United States)

    Jin, Y.; Rong, X.; Liu, Z.

    2017-12-01

    This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

  1. Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

    Science.gov (United States)

    Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

    2017-11-01

    Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity 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 the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

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

    KAUST Repository

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

    2014-01-01

    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.

  3. 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.

  4. 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.

  5. Assessing the potential for improving S2S forecast skill through multimodel ensembling

    Science.gov (United States)

    Vigaud, N.; Robertson, A. W.; Tippett, M. K.; Wang, L.; Bell, M. J.

    2016-12-01

    Non-linear logistic regression is well suited to probability forecasting and has been successfully applied in the past to ensemble weather and climate predictions, providing access to the full probabilities distribution without any Gaussian assumption. However, little work has been done at sub-monthly lead times where relatively small re-forecast ensembles and lengths represent new challenges for which post-processing avenues have yet to be investigated. A promising approach consists in extending the definition of non-linear logistic regression by including the quantile of the forecast distribution as one of the predictors. So-called Extended Logistic Regression (ELR), which enables mutually consistent individual threshold probabilities, is here applied to ECMWF, CFSv2 and CMA re-forecasts from the S2S database in order to produce rainfall probabilities at weekly resolution. The ELR model is trained on seasonally-varying tercile categories computed for lead times of 1 to 4 weeks. It is then tested in a cross-validated manner, i.e. allowing real-time predictability applications, to produce rainfall tercile probabilities from individual weekly hindcasts that are finally combined by equal pooling. Results will be discussed over a broader North American region, where individual and MME forecasts generated out to 4 weeks lead are characterized by good probabilistic reliability but low sharpness, exhibiting systematically more skill in winter than summer.

  6. Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy

    Directory of Open Access Journals (Sweden)

    Justin A. Schulte

    2017-12-01

    Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.

  7. 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.

  8. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

    Science.gov (United States)

    Sharma, Sanjib; Siddique, Ridwan; Reed, Seann; Ahnert, Peter; Mendoza, Pablo; Mejia, Alfonso

    2018-03-01

    The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases

  9. 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.

  10. 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.

  11. Constraining the ensemble Kalman filter for improved streamflow forecasting

    Science.gov (United States)

    Maxwell, Deborah H.; Jackson, Bethanna M.; McGregor, James

    2018-05-01

    Data assimilation techniques such as the Ensemble Kalman Filter (EnKF) are often applied to hydrological models with minimal state volume/capacity constraints enforced during ensemble generation. 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 paper, we investigate the effect of constraining the EnKF on forecast performance. A "free run" in which no assimilation is applied is compared to a completely unconstrained EnKF implementation, 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 to a more tightly constrained implementation where flux as well as mass constraints are imposed to force the rate of water movement to/from ensemble states to be within physically consistent boundaries. A three year period (2008-2010) was selected from the available data record (1976-2010). This was specifically chosen as it had no significant data gaps and represented well the range of flows observed in the longer dataset. 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. Mass constraints alone did little to improve forecast performance; in fact, several were significantly degraded compared to the free run. In contrast, the combined use of mass and flux constraints significantly improved forecast performance in six events relative to all other implementations, while the remaining two events showed no significant difference in performance. Placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state estimation and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also

  12. Multimodel hydrological ensemble forecasts for the Baskatong catchment in Canada using the TIGGE database.

    Science.gov (United States)

    Tito Arandia Martinez, Fabian

    2014-05-01

    Adequate uncertainty assessment is an important issue in hydrological modelling. An important issue for hydropower producers is to obtain ensemble forecasts which truly grasp the uncertainty linked to upcoming streamflows. If properly assessed, this uncertainty can lead to optimal reservoir management and energy production (ex. [1]). The meteorological inputs to the hydrological model accounts for an important part of the total uncertainty in streamflow forecasting. Since the creation of the THORPEX initiative and the TIGGE database, access to meteorological ensemble forecasts from nine agencies throughout the world have been made available. This allows for hydrological ensemble forecasts based on multiple meteorological ensemble forecasts. Consequently, both the uncertainty linked to the architecture of the meteorological model and the uncertainty linked to the initial condition of the atmosphere can be accounted for. The main objective of this work is to show that a weighted combination of meteorological ensemble forecasts based on different atmospheric models can lead to improved hydrological ensemble forecasts, for horizons from one to ten days. This experiment is performed for the Baskatong watershed, a head subcatchment of the Gatineau watershed in the province of Quebec, in Canada. Baskatong watershed is of great importance for hydro-power production, as it comprises the main reservoir for the Gatineau watershed, on which there are six hydropower plants managed by Hydro-Québec. Since the 70's, they have been using pseudo ensemble forecast based on deterministic meteorological forecasts to which variability derived from past forecasting errors is added. We use a combination of meteorological ensemble forecasts from different models (precipitation and temperature) as the main inputs for hydrological model HSAMI ([2]). The meteorological ensembles from eight of the nine agencies available through TIGGE are weighted according to their individual performance and

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

    Science.gov (United States)

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

    2013-04-01

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

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

    KAUST Repository

    Raboudi, Naila

    2016-01-01

    KF-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

  15. An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

    Science.gov (United States)

    Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

    2017-12-01

    Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware

  16. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

    International Nuclear Information System (INIS)

    Tang, Ling; Yu, Lean; Wang, Shuai; Li, Jianping; Wang, Shouyang

    2012-01-01

    Highlights: ► A hybrid ensemble learning paradigm integrating EEMD and LSSVR is proposed. ► The hybrid ensemble method is useful to predict time series with high volatility. ► The ensemble method can be used for both one-step and multi-step ahead forecasting. - Abstract: In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. 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...

  3. Predicting Power Outages Using Multi-Model Ensemble Forecasts

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

    2017-12-01

    Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

  4. 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.

  5. 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.

  6. 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

  7. 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......-oriented view of verification, for instance with the wind power applications in mind. The verification framework is specifically applied to the case of ECMWF ensemble forecasts and over Europe. Dynamic climatologies are derived at the various stations, serving as a benchmark. The impact of observational...... uncertainty on scores and diagnostic tools is also considered. The interest of this framework is demonstrated from its application to the routine evaluation of ensemble forecasts and to the assessment of the quality improvements brought in by the recent change in horizontal resolution of the ECMWF ensemble...

  8. 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.

  9. 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.

  10. Development of a regional ensemble prediction method for probabilistic weather prediction

    International Nuclear Information System (INIS)

    Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

    2015-01-01

    A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

  11. 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.

  12. Ensemble seasonal forecast of extreme water inflow into a large reservoir

    Directory of Open Access Journals (Sweden)

    A. N. Gelfan

    2015-06-01

    Full Text Available An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study. Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2 located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories.

  13. Forecasting Global Rainfall for Points Using ECMWF's Global Ensemble and Its Applications in Flood Forecasting

    Science.gov (United States)

    Pillosu, F. M.; Hewson, T.; Mazzetti, C.

    2017-12-01

    Prediction of local extreme rainfall has historically been the remit of nowcasting and high resolution limited area modelling, which represent only limited areas, may not be spatially accurate, give reasonable results only for limited lead times (based statistical post-processing software ("ecPoint-Rainfall, ecPR", operational in 2017) that uses ECMWF Ensemble (ENS) output to deliver global probabilistic rainfall forecasts for points up to day 10. Firstly, ecPR applies a new notion of "remote calibration", which 1) allows us to replicate a multi-centennial training period using only one year of data, and 2) provides forecasts for anywhere in the world. Secondly, the software applies an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals, and of where biases in the model can be improved upon. A long-term verification has shown that the post-processed rainfall has better reliability and resolution at every lead time if compared with ENS, and for large totals, ecPR outputs have the same skill at day 5 that the raw ENS has at day 1 (ROC area metric). ecPR could be used as input for hydrological models if its probabilistic output is modified accordingly to the inputs requirements for hydrological models. Indeed, ecPR does not provide information on where the highest total is likely to occur inside the gridbox, nor on the spatial distribution of rainfall values nearby. "Scenario forecasts" could be a solution. They are derived from locating the rainfall peak in sensitive positions (e.g. urban areas), and then redistributing the remaining quantities in the gridbox modifying traditional spatial correlation characterization methodologies (e.g. variogram analysis) in order to take account, for instance, of the type of rainfall forecast (stratiform, convective). Such an approach could be a turning point in the field of medium-range global real-time riverine flood forecasts. This presentation will

  14. Ensemble dispersion forecasting - Part 2. Application and evaluation

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Addis, R.

    2004-01-01

    of the dispersion of ETEX release 1 and the model ensemble is compared with the monitoring data. The scope of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results and represents a superior analysis of the process evolution. (C) 2004 Elsevier...

  15. Counteracting structural errors in ensemble forecast of influenza outbreaks.

    Science.gov (United States)

    Pei, Sen; Shaman, Jeffrey

    2017-10-13

    For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

  16. Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

    Directory of Open Access Journals (Sweden)

    Jianzhong Zhou

    2018-05-01

    Full Text Available This paper introduces three artificial neural network (ANN architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT, were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.

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

    DEFF Research Database (Denmark)

    Lundholm, Sisse Camilla

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

  18. Post-processing of multi-model ensemble river discharge forecasts using censored EMOS

    Science.gov (United States)

    Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

    2014-05-01

    When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a

  19. Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

    Science.gov (United States)

    Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

    2017-12-01

    Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

  20. Improving volcanic ash forecasts with ensemble-based data assimilation

    NARCIS (Netherlands)

    Fu, Guangliang

    2017-01-01

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

  1. Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

    Science.gov (United States)

    Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

    2017-12-01

    The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

  2. A novel hybrid ensemble learning paradigm for tourism forecasting

    Science.gov (United States)

    Shabri, Ani

    2015-02-01

    In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

  3. Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

    Directory of Open Access Journals (Sweden)

    S. K. Jha

    2018-03-01

    Full Text Available Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP, developed in Australia (Robertson et al., 2013; Shrestha et al., 2015, has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS, from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

  4. Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

    Science.gov (United States)

    Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

    2018-03-01

    Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

  5. 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.

  6. A Novel Ensemble Learning Approach for Corporate Financial Distress Forecasting in Fashion and Textiles Supply Chains

    Directory of Open Access Journals (Sweden)

    Gang Xie

    2013-01-01

    Full Text Available This paper proposes a novel ensemble learning approach based on logistic regression (LR and artificial intelligence tool, that is, support vector machine (SVM and back-propagation neural networks (BPNN, for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM, and BPNN are introduced. Then, the forecasting results by LR are introduced into the SVM and BPNN techniques which can recognize the forecasting errors in fitness by LR. Moreover, empirical analysis of Chinese listed companies in fashion and textile sector is implemented for the comparison of the methods, and some related issues are discussed. The results suggest that the proposed novel ensemble learning approach can achieve higher forecasting performance than those of individual models.

  7. An application of ensemble/multi model approach for wind power production forecast.

    Science.gov (United States)

    Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

    2010-09-01

    model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

  8. 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

    Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources of uncer......Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources...... 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......), and emission scenarios. The greatest source of uncertainty in forecasts of species range shifts arises from using alternative methods for niche modeling, followed by AOGCM, and their interaction. Our results concur with previous studies that discovered that projections from alternative models can be extremely...

  9. 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.

  10. An application of ensemble/multi model approach for wind power production forecasting

    Science.gov (United States)

    Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

    2011-02-01

    The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

  11. Regional-seasonal weather forecasting

    Energy Technology Data Exchange (ETDEWEB)

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

    1980-08-01

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

  12. Environmentally-driven ensemble forecasts of dengue fever

    Science.gov (United States)

    Yamana, T. K.; Shaman, J. L.

    2017-12-01

    Dengue fever is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas where dengue is found, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission remains a significant public health challenge. Recently, we developed a framework for forecasting dengue incidence using an dynamical model of disease transmission coupled with observational data of dengue cases using data-assimilation methods. Here, we investigate the use of environmental data to drive the disease transmission model. We produce retrospective forecasts of the timing and severity of dengue outbreaks, and quantify forecast predictive accuracy.

  13. Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

    Science.gov (United States)

    Henley, E. M.; Pope, E. C. D.

    2017-12-01

    This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

  14. 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

  15. 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

  16. 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

    Directory of Open Access Journals (Sweden)

    J. I. Rubin

    2016-03-01

    Full Text Available 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

  17. 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.

  18. 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

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

    KAUST Repository

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

    2017-01-01

    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. 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.

  1. Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

    Science.gov (United States)

    Gelfan, Alexander; Moreydo, Vsevolod; Motovilov, Yury; Solomatine, Dimitri P.

    2018-04-01

    A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April-June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

  2. Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

    Directory of Open Access Journals (Sweden)

    A. Gelfan

    2018-04-01

    Full Text Available A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia, is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast, and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast. We have studied the following: (1 whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2 whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form outperform the operational forecasts of the April–June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

  3. Major Risks, Uncertain Outcomes: Making Ensemble Forecasts Work for Multiple Audiences

    Science.gov (United States)

    Semmens, K. A.; Montz, B.; Carr, R. H.; Maxfield, K.; Ahnert, P.; Shedd, R.; Elliott, J.

    2017-12-01

    When extreme river levels are possible in a community, effective communication of weather and hydrologic forecasts is critical to protect life and property. Residents, emergency personnel, and water resource managers need to make timely decisions about how and when to prepare. Uncertainty in forecasting is a critical component of this decision-making, but often poses a confounding factor for public and professional understanding of forecast products. In 2016 and 2017, building on previous research about the use of uncertainty forecast products, and with funding from NOAA's CSTAR program, East Carolina University and Nurture Nature Center (a non-profit organization with a focus on flooding issues, based in Easton, PA) conducted a research project to understand how various audiences use and interpret ensemble forecasts showing a range of hydrologic forecast possibilities. These audiences include community residents, emergency managers and water resource managers. The research team held focus groups in Jefferson County, WV and Frederick County, MD, to test a new suite of products from the National Weather Service's Hydrologic Ensemble Forecast System (HEFS). HEFS is an ensemble system that provides short and long-range forecasts, ranging from 6 hours to 1 year, showing uncertainty in hydrologic forecasts. The goal of the study was to assess the utility of the HEFS products, identify the barriers to proper understanding of the products, and suggest modifications to product design that could improve the understandability and accessibility for residential, emergency managers, and water resource managers. The research team worked with the Sterling, VA Weather Forecast Office and the Middle Atlantic River Forecast center to develop a weather scenario as the basis of the focus group discussions, which also included pre and post session surveys. This presentation shares the findings from those focus group discussions and surveys, including recommendations for revisions to

  4. Verification of an ensemble prediction system for storm surge forecast in the Adriatic Sea

    Science.gov (United States)

    Mel, Riccardo; Lionello, Piero

    2014-12-01

    In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level forecast is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction system) for operational forecasting of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather Forecasts) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. Forecast uncertainty increases with the predicted value of the storm surge and with the forecast lead time. The EMF (ensemble mean forecast) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic forecast), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the forecast and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the forecast. For large lead times the EPS spread and the forecast error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the forecast and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability forecast) of the EPS

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

    Science.gov (United States)

    Kann, A.

    2012-05-01

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

  6. 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.

  7. Evaluation of the NMC regional ensemble prediction system during the Beijing 2008 Olympic Games

    Science.gov (United States)

    Li, Xiaoli; Tian, Hua; Deng, Guo

    2011-10-01

    Based on the B08RDP (Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project) that was launched by the World Weather Research Programme (WWRP) in 2004, a regional ensemble prediction system (REPS) at a 15-km horizontal resolution was developed at the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). Supplementing to the forecasters' subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games (BOG), this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period. By use of a set of advanced probabilistic verification scores, the value of the REPS compared to the quasi-operational global ensemble prediction system (GEPS) is assessed for a 36-day period (21 July-24 August 2008). The evaluation here involves different aspects of the REPS and GEPS, including their general forecast skills, specific attributes (reliability and resolution), and related economic values. The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS, and for light to heavy rainfall events, the REPS provides more skillful forecasts for accumulated 6- and 24-h precipitation. By further identifying the performance of the REPS through the attribute-focused measures, it is found that the advantages of the REPS over the GEPS come from better reliability (smaller biases and better dispersion) and increased resolution. Also, evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model (the control run) forecasts, especially for the heavy rainfall events.

  8. 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

  9. Remote and Local Influences in Forecasting Pacific SST: a Linear Inverse Model and a Multimodel Ensemble Study

    Science.gov (United States)

    Faggiani Dias, D.; Subramanian, A. C.; Zanna, L.; Miller, A. J.

    2017-12-01

    Sea surface temperature (SST) in the Pacific sector is well known to vary on time scales from seasonal to decadal, and the ability to predict these SST fluctuations has many societal and economical benefits. Therefore, we use a suite of statistical linear inverse models (LIMs) to understand the remote and local SST variability that influences SST predictions over the North Pacific region and further improve our understanding on how the long-observed SST record can help better guide multi-model ensemble forecasts. Observed monthly SST anomalies in the Pacific sector (between 15oS and 60oN) are used to construct different regional LIMs for seasonal to decadal prediction. The forecast skills of the LIMs are compared to that from two operational forecast systems in the North American Multi-Model Ensemble (NMME) revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The forecast skill with LIM is also influenced by the verification period utilized to make the predictions, likely due to the changing character of El Niño in the 20th century. The North Pacific seems to be a source of predictability for the Tropics on seasonal to interannual time scales, while the Tropics act to worsen the skill for the forecast in the North Pacific. The data were also bandpassed into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. For the decadal component, this coupling occurs the other way around: Tropics seem to be a source of predictability for the Extratropics, but the Extratropics don't improve the predictability for the Tropics. These results indicate the importance of temporal

  10. 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

  11. Ensemble averaging and stacking of ARIMA and GSTAR model for rainfall forecasting

    Science.gov (United States)

    Anggraeni, D.; Kurnia, I. F.; Hadi, A. F.

    2018-04-01

    Unpredictable rainfall changes can affect human activities, such as in agriculture, aviation, shipping which depend on weather forecasts. Therefore, we need forecasting tools with high accuracy in predicting the rainfall in the future. This research focus on local forcasting of the rainfall at Jember in 2005 until 2016, from 77 rainfall stations. The rainfall here was not only related to the occurrence of the previous of its stations, but also related to others, it’s called the spatial effect. The aim of this research is to apply the GSTAR model, to determine whether there are some correlations of spatial effect between one to another stations. The GSTAR model is an expansion of the space-time model that combines the time-related effects, the locations (stations) in a time series effects, and also the location it self. The GSTAR model will also be compared to the ARIMA model that completely ignores the independent variables. The forcested value of the ARIMA and of the GSTAR models then being combined using the ensemble forecasting technique. The averaging and stacking method of ensemble forecasting method here provide us the best model with higher acuracy model that has the smaller RMSE (Root Mean Square Error) value. Finally, with the best model we can offer a better local rainfall forecasting in Jember for the future.

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

    Science.gov (United States)

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

    2017-12-01

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

  13. 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

  14. Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Xiao

    2013-02-01

    Full Text Available Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN and wavelet neural network (WNN are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO. Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

  15. High resolution ensemble forecasting for the Gulf of Mexico eddies and fronts

    Science.gov (United States)

    Counillon, F.; Bertino, L.

    2007-05-01

    As oil production moves further into deeper waters, the costs related to strong current hazards are increasing accordingly, and accurate three-dimensional forecasts of currents are urgently needed. To be useful, models have to locate eddies and fronts to an accuracy of 30 km at a nowcast stage, which is almost impossible to accomplish with the use of satellite data of the same accuracy. The use of stochastic forecast allows us to give confidence of our prediction. We are using a nested configuration of the Hybrid coordinate ocean model (HYCOM), where the TOPAZ system, which covers the Atlantic and the Artic, gives lateral boundary condition to a high-resolution (5km) model of the Gulf of Mexico (GOM). TOPAZ is a real-time forecasting coupled ocean-ice model, which assimilates sea level anomaly (SLA), sea surface temperature, and sea ice concentration, with the ensemble Kalman filter. The high- resolution model assimilates SLA using the ensemble optimal interpolation, which updates accordingly the currents, salinity, temperature, and layer interface at all depths. Here, we evaluate the ensemble forecast capabilities of our high-resolution model, for eddy Extreme that has been observed from altimeters around the 15th of July. We run 6 successive ensemble runs composed of 10 members of equal likelihood. Members differ by perturbations of the initial state, of the lateral boundary conditions, and of the atmospheric boundary conditions. We have started the experiment 1 month prior to the shedding event, because it was the time necessary for perturbation of boundary conditions to spread uniformly and reach a significant level across the GOM. The ensemble reproduces well the dynamics of the eddy shedding and produces a significant spread at the boundary of the eddy, but underestimates the RMS error of the SLA. Prior to the shedding time, the error growth increase, induced by the highly non-linear growth of cyclonic eddies at the boundary of the Loop Current. Additionally

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

    KAUST Repository

    Hollt, Thomas; Altaf, Muhammad; Mandli, Kyle T.; Hadwiger, Markus; Dawson, Clint N.; Hoteit, Ibrahim

    2015-01-01

    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

  17. 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.

  18. Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China.

    Science.gov (United States)

    Zhai, Binxu; Chen, Jianguo

    2018-04-18

    A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of

  19. On the incidence of meteorological and hydrological processors: Effect of resolution, sharpness and reliability of hydrological ensemble forecasts

    Science.gov (United States)

    Abaza, Mabrouk; Anctil, François; Fortin, Vincent; Perreault, Luc

    2017-12-01

    Meteorological and hydrological ensemble prediction systems are imperfect. Their outputs could often be improved through the use of a statistical processor, opening up the question of the necessity of using both processors (meteorological and hydrological), only one of them, or none. This experiment compares the predictive distributions from four hydrological ensemble prediction systems (H-EPS) utilising the Ensemble Kalman filter (EnKF) probabilistic sequential data assimilation scheme. They differ in the inclusion or not of the Distribution Based Scaling (DBS) method for post-processing meteorological forecasts and the ensemble Bayesian Model Averaging (ensemble BMA) method for hydrological forecast post-processing. The experiment is implemented on three large watersheds and relies on the combination of two meteorological reforecast products: the 4-member Canadian reforecasts from the Canadian Centre for Meteorological and Environmental Prediction (CCMEP) and the 10-member American reforecasts from the National Oceanic and Atmospheric Administration (NOAA), leading to 14 members at each time step. Results show that all four tested H-EPS lead to resolution and sharpness values that are quite similar, with an advantage to DBS + EnKF. The ensemble BMA is unable to compensate for any bias left in the precipitation ensemble forecasts. On the other hand, it succeeds in calibrating ensemble members that are otherwise under-dispersed. If reliability is preferred over resolution and sharpness, DBS + EnKF + ensemble BMA performs best, making use of both processors in the H-EPS system. Conversely, for enhanced resolution and sharpness, DBS is the preferred method.

  20. An analog ensemble for short-term probabilistic solar power forecast

    International Nuclear Information System (INIS)

    Alessandrini, S.; Delle Monache, L.; Sperati, S.; Cervone, G.

    2015-01-01

    Highlights: • A novel method for solar power probabilistic forecasting is proposed. • The forecast accuracy does not depend on the nominal power. • The impact of climatology on forecast accuracy is evaluated. - Abstract: The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the solar elevation and the solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the solar radiation within the troposphere is the main meteorological factor influencing the solar power production, as a fraction of short wave solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on solar photovoltaic power prediction. This lack of predictability makes the solar energy integration into the grid challenging. A cost-effective utilization of solar energy over a grid strongly depends on the accuracy and reliability of the power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring solar power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic solar power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model

  1. Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

    Directory of Open Access Journals (Sweden)

    Hossein Foroozand

    2018-03-01

    Full Text Available Recently, the Entropy Ensemble Filter (EEF method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size.

  2. 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

  3. 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.

  4. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    International Nuclear Information System (INIS)

    Yu, Lean; Wang, Shouyang; Lai, Kin Keung

    2008-01-01

    In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

  5. Evaluations of Extended-Range tropical Cyclone Forecasts in the Western North Pacific by using the Ensemble Reforecasts: Preliminary Results

    Science.gov (United States)

    Tsai, Hsiao-Chung; Chen, Pang-Cheng; Elsberry, Russell L.

    2017-04-01

    The objective of this study is to evaluate the predictability of the extended-range forecasts of tropical cyclone (TC) in the western North Pacific using reforecasts from National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) during 1996-2015, and from the Climate Forecast System (CFS) during 1999-2010. Tsai and Elsberry have demonstrated that an opportunity exists to support hydrological operations by using the extended-range TC formation and track forecasts in the western North Pacific from the ECMWF 32-day ensemble. To demonstrate this potential for the decision-making processes regarding water resource management and hydrological operation in Taiwan reservoir watershed areas, special attention is given to the skill of the NCEP GEFS and CFS models in predicting the TCs affecting the Taiwan area. The first objective of this study is to analyze the skill of NCEP GEFS and CFS TC forecasts and quantify the forecast uncertainties via verifications of categorical binary forecasts and probabilistic forecasts. The second objective is to investigate the relationships among the large-scale environmental factors [e.g., El Niño Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), etc.] and the model forecast errors by using the reforecasts. Preliminary results are indicating that the skill of the TC activity forecasts based on the raw forecasts can be further improved if the model biases are minimized by utilizing these reforecasts.

  6. Advances in sequential data assimilation and numerical weather forecasting: An Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic ensemble square root filters, and a test of a new time stepping scheme in an atmospheric model

    Science.gov (United States)

    Amezcua, Javier

    This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion

  7. Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

    Science.gov (United States)

    Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

    2012-12-01

    Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

  8. A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data

    DEFF Research Database (Denmark)

    Alessandrini, S.; Sperati, S.; Pinson, Pierre

    2013-01-01

    together with a single forecast power value for each future time horizon. A comparison between two different ensemble forecasting models, ECMWF EPS (Ensemble Prediction System in use at the European Centre for Medium-Range Weather Forecasts) and COSMO-LEPS (Limited-area Ensemble Prediction System developed...... ahead forecast horizon. A statistical calibration of the ensemble wind speed members based on the use of past wind speed measurements is explained. The two models are compared using common verification indices and diagrams. The higher horizontal resolution model (COSMO-LEPS) shows slightly better...

  9. 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.

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

    KAUST Repository

    Altaf, Muhammad; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, Ibrahim

    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 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.

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

    Science.gov (United States)

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

    2017-12-01

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

  12. A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

    Science.gov (United States)

    Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

    2018-05-01

    A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

  13. Evaluation of the Plant-Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

    Science.gov (United States)

    Keane, Richard J.; Plant, Robert S.; Tennant, Warren J.

    2016-05-01

    The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

  14. A hydro-meteorological ensemble prediction system for real-time flood forecasting purposes in the Milano area

    Science.gov (United States)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco

    2015-04-01

    Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area 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. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and

  15. 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.

  16. Observation-based Quantitative Uncertainty Estimation for Realtime Tsunami Inundation Forecast using ABIC and Ensemble Simulation

    Science.gov (United States)

    Takagawa, T.

    2016-12-01

    An ensemble forecasting scheme for tsunami inundation is presented. The scheme consists of three elemental methods. The first is a hierarchical Bayesian inversion using Akaike's Bayesian Information Criterion (ABIC). The second is Montecarlo sampling from a probability density function of multidimensional normal distribution. The third is ensamble analysis of tsunami inundation simulations with multiple tsunami sources. Simulation based validation of the model was conducted. A tsunami scenario of M9.1 Nankai earthquake was chosen as a target of validation. Tsunami inundation around Nagoya Port was estimated by using synthetic tsunami waveforms at offshore GPS buoys. The error of estimation of tsunami inundation area was about 10% even if we used only ten minutes observation data. The estimation accuracy of waveforms on/off land and spatial distribution of maximum tsunami inundation depth is demonstrated.

  17. Forecasting the consequences of accidental releases of radionuclides in the atmosphere from ensemble dispersion modelling

    International Nuclear Information System (INIS)

    Galmarini, S.; Bianconi, R.; Bellasio, R.; Graziani, G.

    2001-01-01

    The RTMOD system is presented as a tool for the intercomparison of long-range dispersion models as well as a system for support of decision making. RTMOD is an internet-based procedure that collects the results of more than 20 models used around the world to predict the transport and deposition of radioactive releases in the atmosphere. It allows the real-time acquisition of model results and their intercomparison. Taking advantage of the availability of several model results, the system can also be used as a tool to support decision making in case of emergency. The new concept of ensemble dispersion modelling is introduced which is the basis for the decision-making application of RTMOD. New statistical parameters are presented that allow gathering the results of several models to produce a single dispersion forecast. The devised parameters are presented and tested on the results of RTMOD exercises

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. A study on reducing update frequency of the forecast samples in the ensemble-based 4DVar data assimilation method

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Aimei; Xu, Daosheng [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province; Chinese Academy of Meteorological Sciences, Beijing (China). State Key Lab. of Severe Weather; Qiu, Xiaobin [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province; Tianjin Institute of Meteorological Science (China); Qiu, Chongjian [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province

    2013-02-15

    In the ensemble-based four dimensional variational assimilation method (SVD-En4DVar), a singular value decomposition (SVD) technique is used to select the leading eigenvectors and the analysis variables are expressed as the orthogonal bases expansion of the eigenvectors. The experiments with a two-dimensional shallow-water equation model and simulated observations show that the truncation error and rejection of observed signals due to the reduced-dimensional reconstruction of the analysis variable are the major factors that damage the analysis when the ensemble size is not large enough. However, a larger-sized ensemble is daunting computational burden. Experiments with a shallow-water equation model also show that the forecast error covariances remain relatively constant over time. For that reason, we propose an approach that increases the members of the forecast ensemble while reducing the update frequency of the forecast error covariance in order to increase analysis accuracy and to reduce the computational cost. A series of experiments were conducted with the shallow-water equation model to test the efficiency of this approach. The experimental results indicate that this approach is promising. Further experiments with the WRF model show that this approach is also suitable for the real atmospheric data assimilation problem, but the update frequency of the forecast error covariances should not be too low. (orig.)

  3. Ensemble empirical model decomposition and neuro-fuzzy conjunction model for middle and long-term runoff forecast

    Science.gov (United States)

    Tan, Q.

    2017-12-01

    Forecasting the runoff over longer periods, such as months and years, is one of the important tasks for hydrologists and water resource managers to maximize the potential of the limited water. However, due to the nonlinear and nonstationary characteristic of the natural runoff, it is hard to forecast the middle and long-term runoff with a satisfactory accuracy. It has been proven that the forecast performance can be improved by using signal decomposition techniques to product more cleaner signals as model inputs. In this study, a new conjunction model (EEMD-neuro-fuzzy) with adaptive ability is proposed. The ensemble empirical model decomposition (EEMD) is used to decompose the runoff time series into several components, which are with different frequencies and more cleaner than the original time series. Then the neuro-fuzzy model is developed for each component. The final forecast results can be obtained by summing the outputs of all neuro-fuzzy models. Unlike the conventional forecast model, the decomposition and forecast models in this study are adjusted adaptively as long as new runoff information is added. The proposed models are applied to forecast the monthly runoff of Yichang station, located in Yangtze River of China. The results show that the performance of adaptive forecast model we proposed outperforms than the conventional forecast model, the Nash-Sutcliffe efficiency coefficient can reach to 0.9392. Due to its ability to process the nonstationary data, the forecast accuracy, especially in flood season, is improved significantly.

  4. 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. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

  5. Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

    Science.gov (United States)

    Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

    2017-11-01

    Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

  6. Very short-term rainfall forecasting by effectively using the ensemble outputs of numerical weather prediction models

    Science.gov (United States)

    Wu, Ming-Chang; Lin, Gwo-Fong; Feng, Lei; Hwang, Gong-Do

    2017-04-01

    In Taiwan, heavy rainfall brought by typhoons often causes serious disasters and leads to loss of life and property. In order to reduce the impact of these disasters, accurate rainfall forecasts are always important for civil protection authorities to prepare proper measures in advance. In this study, a methodology is proposed for providing very short-term (1- to 6-h ahead) rainfall forecasts in a basin-scale area. The proposed methodology is developed based on the use of analogy reasoning approach to effectively integrate the ensemble precipitation forecasts from a numerical weather prediction system in Taiwan. To demonstrate the potential of the proposed methodology, an application to a basin-scale area (the Choshui River basin located in west-central Taiwan) during five typhoons is conducted. The results indicate that the proposed methodology yields more accurate hourly rainfall forecasts, especially the forecasts with a lead time of 1 to 3 hours. On average, improvement of the Nash-Sutcliffe efficiency coefficient is about 14% due to the effective use of the ensemble forecasts through the proposed methodology. The proposed methodology is expected to be useful for providing accurate very short-term rainfall forecasts during typhoons.

  7. 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.)

  8. Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

    Science.gov (United States)

    Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

    2018-03-01

    In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

  9. Real­-Time Ensemble Forecasting of Coronal Mass Ejections Using the Wsa-Enlil+Cone Model

    Science.gov (United States)

    Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; Odstrcil, D.; MacNeice, P. J.; Rastaetter, L.; LaSota, J. A.

    2014-12-01

    Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions. Real-time ensemble modeling of CME propagation is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL+cone model available at the Community Coordinated Modeling Center (CCMC). To estimate the effect of uncertainties in determining CME input parameters on arrival time predictions, a distribution of n (routinely n=48) CME input parameter sets are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest, including a probability distribution of CME arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). We present the results of ensemble simulations for a total of 38 CME events in 2013-2014. For 28 of the ensemble runs containing hits, the observed CME arrival was within the range of ensemble arrival time predictions for 14 runs (half). The average arrival time prediction was computed for each of the 28 ensembles predicting hits and using the actual arrival time, an average absolute error of 10.0 hours (RMSE=11.4 hours) was found for all 28 ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this still allows the ruling out of prediction errors caused by tested CME input parameters. Prediction errors can also arise from ambient model parameters such as the accuracy of the solar wind background, and other limitations. Additionally the ensemble modeling sysem was used to

  10. Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

    Science.gov (United States)

    Ouyang, Yicun; Yin, Hujun

    2018-05-01

    Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

  11. 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)

  12. A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay

    Science.gov (United States)

    Saleh, F.; Ramaswamy, V.; Wang, Y.; Georgas, N.; Blumberg, A.; Pullen, J.

    2017-12-01

    Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).

  13. 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.)

  14. Comparison of surface freshwater fluxes from different climate forecasts produced through different ensemble generation schemes.

    Science.gov (United States)

    Romanova, Vanya; Hense, Andreas; Wahl, Sabrina; Brune, Sebastian; Baehr, Johanna

    2016-04-01

    The decadal variability and its predictability of the surface net freshwater fluxes is compared in a set of retrospective predictions, all using the same model setup, and only differing in the implemented ocean initialisation method and ensemble generation method. The basic aim is to deduce the differences between the initialization/ensemble generation methods in view of the uncertainty of the verifying observational data sets. The analysis will give an approximation of the uncertainties of the net freshwater fluxes, which up to now appear to be one of the most uncertain products in observational data and model outputs. All ensemble generation methods are implemented into the MPI-ESM earth system model in the framework of the ongoing MiKlip project (www.fona-miklip.de). Hindcast experiments are initialised annually between 2000-2004, and from each start year 10 ensemble members are initialized for 5 years each. Four different ensemble generation methods are compared: (i) a method based on the Anomaly Transform method (Romanova and Hense, 2015) in which the initial oceanic perturbations represent orthogonal and balanced anomaly structures in space and time and between the variables taken from a control run, (ii) one-day-lagged ocean states from the MPI-ESM-LR baseline system (iii) one-day-lagged of ocean and atmospheric states with preceding full-field nudging to re-analysis in both the atmospheric and the oceanic component of the system - the baseline one MPI-ESM-LR system, (iv) an Ensemble Kalman Filter (EnKF) implemented into oceanic part of MPI-ESM (Brune et al. 2015), assimilating monthly subsurface oceanic temperature and salinity (EN3) using the Parallel Data Assimilation Framework (PDAF). The hindcasts are evaluated probabilistically using fresh water flux data sets from four different reanalysis data sets: MERRA, NCEP-R1, GFDL ocean reanalysis and GECCO2. The assessments show no clear differences in the evaluations scores on regional scales. However, on the

  15. Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Pai, Ping-Feng [Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan (China)

    2006-09-15

    Because of the privatization of electricity in many countries, load forecasting has become one of the most crucial issues in the planning and operations of electric utilities. In addition, accurate regional load forecasting can provide the transmission and distribution operators with more information. The hybrid ellipsoidal fuzzy system was originally designed to solve control and pattern recognition problems. The main objective of this investigation is to develop a hybrid ellipsoidal fuzzy system for time series forecasting (HEFST) and apply the proposed model to forecast regional electricity loads in Taiwan. Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model. Subsequently, numerical data taken from the existing literature is used to demonstrate the forecasting performance of the HEFST model. Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model. Thus, the HEFST model is a valid and promising alternative for forecasting regional electricity loads. (author)

  16. Sensitivity of monthly streamflow forecasts to the quality of rainfall forcing: When do dynamical climate forecasts outperform the Ensemble Streamflow Prediction (ESP) method?

    Science.gov (United States)

    Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.

    2017-12-01

    Forecasting hydrological extremes is challenging, especially at lead times over 1 month for catchments with limited hydrological memory and variable climates. One simple way to derive monthly or seasonal hydrological forecasts is to use historical climate data to drive hydrological models using the Ensemble Streamflow Prediction (ESP) method. This gives a range of possible future streamflow given known initial hydrologic conditions alone. The degree of skill of ESP depends highly on the forecast initialisation month and catchment type. Using dynamic rainfall forecasts as driving data instead of historical data could potentially improve streamflow predictions. A lot of effort is being invested within the meteorological community to improve these forecasts. However, while recent progress shows promise (e.g. NAO in winter), the skill of these forecasts at monthly to seasonal timescales is generally still limited, and the extent to which they might lead to improved hydrological forecasts is an area of active research. Additionally, these meteorological forecasts are currently being produced at 1 month or seasonal time-steps in the UK, whereas hydrological models require forcings at daily or sub-daily time-steps. Keeping in mind these limitations of available rainfall forecasts, the objectives of this study are to find out (i) how accurate monthly dynamical rainfall forecasts need to be to outperform ESP, and (ii) how the method used to disaggregate monthly rainfall forecasts into daily rainfall time series affects results. For the first objective, synthetic rainfall time series were created by increasingly degrading observed data (proxy for a `perfect forecast') from 0 % to +/-50 % error. For the second objective, three different methods were used to disaggregate monthly rainfall data into daily time series. These were used to force a simple lumped hydrological model (GR4J) to generate streamflow predictions at a one-month lead time for over 300 catchments

  17. 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

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

    Science.gov (United States)

    Owens, Mathew J.; Riley, Pete

    2017-11-01

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

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

    Science.gov (United States)

    Owens, Mathew J; Riley, Pete

    2017-11-01

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

  20. Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts.

    Science.gov (United States)

    Luo, Jing-Jia; Masson, Sebastien; Behera, Swadhin; Shingu, Satoru; Yamagata, Toshio

    2005-11-01

    Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured.Hindcast results for the period 1982 2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9 12-month lead times.In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7 12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

  1. 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...... by a recalibration procedure that allowed obtaining a more uniform distribution among the 51 intervals, making the ensemble spread large enough to include the observations. After that it was observed that the EPS power spread seemed to have enough correlation with the error calculated on the deterministic forecast...

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

    Science.gov (United States)

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

    2017-12-01

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

  3. An assessment of the ECMWF tropical cyclone ensemble forecasting system and its use for insurance loss predictions

    Science.gov (United States)

    Aemisegger, F.; Martius, O.; Wüest, M.

    2010-09-01

    Tropical cyclones (TC) are amongst the most impressive and destructive weather systems of Earth's atmosphere. The costs related to such intense natural disasters have been rising in recent years and may potentially continue to increase in the near future due to changes in magnitude, timing, duration or location of tropical storms. This is a challenging situation for numerical weather prediction, which should provide a decision basis for short term protective measures through high quality medium range forecasts on the one hand. On the other hand, the insurance system bears great responsibility in elaborating proactive plans in order to face these extreme events that individuals cannot manage independently. Real-time prediction and early warning systems are needed in the insurance sector in order to face an imminent hazard and minimise losses. Early loss estimates are important in order to allocate capital and to communicate to investors. The ECMWF TC identification algorithm delivers information on the track and intensity of storms based on the ensemble forecasting system. This provides a physically based framework to assess the uncertainty in the forecast of a specific event. The performance of the ECMWF TC ensemble forecasts is evaluated in terms of cyclone intensity and location in this study and the value of such a physically-based quantification of uncertainty in the meteorological forecast for the estimation of insurance losses is assessed. An evaluation of track and intensity forecasts of hurricanes in the North Atlantic during the years 2005 to 2009 is carried out. Various effects are studied like the differences in forecasts over land or sea, as well as links between storm intensity and forecast error statistics. The value of the ECMWF TC forecasting system for the global re-insurer Swiss Re was assessed by performing insurance loss predictions using their in-house loss model for several case studies of particularly devastating events. The generally known

  4. 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

  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. 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.

  7. Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

    Science.gov (United States)

    Williams, John L.; Maxwell, Reed M.; Monache, Luca Delle

    2013-12-01

    Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

  8. 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.

  9. Ensemble hydrological forecast efficiency evolution over various issue dates and lead-time: case study for the Cheboksary reservoir (Volga River)

    Science.gov (United States)

    Gelfan, Alexander; Moreido, Vsevolod

    2017-04-01

    Ensemble hydrological forecasting allows for describing uncertainty caused by variability of meteorological conditions in the river basin for the forecast lead-time. At the same time, in snowmelt-dependent river basins another significant source of uncertainty relates to variability of initial conditions of the basin (snow water equivalent, soil moisture content, etc.) prior to forecast issue. Accurate long-term hydrological forecast is most crucial for large water management systems, such as the Cheboksary reservoir (the catchment area is 374 000 sq.km) located in the Middle Volga river in Russia. Accurate forecasts of water inflow volume, maximum discharge and other flow characteristics are of great value for this basin, especially before the beginning of the spring freshet season that lasts here from April to June. The semi-distributed hydrological model ECOMAG was used to develop long-term ensemble forecast of daily water inflow into the Cheboksary reservoir. To describe variability of the meteorological conditions and construct ensemble of possible weather scenarios for the lead-time of the forecast, two approaches were applied. The first one utilizes 50 weather scenarios observed in the previous years (similar to the ensemble streamflow prediction (ESP) procedure), the second one uses 1000 synthetic scenarios simulated by a stochastic weather generator. We investigated the evolution of forecast uncertainty reduction, expressed as forecast efficiency, over various consequent forecast issue dates and lead time. We analyzed the Nash-Sutcliffe efficiency of inflow hindcasts for the period 1982 to 2016 starting from 1st of March with 15 days frequency for lead-time of 1 to 6 months. This resulted in the forecast efficiency matrix with issue dates versus lead-time that allows for predictability identification of the basin. The matrix was constructed separately for observed and synthetic weather ensembles.

  10. Reproducing multi-model ensemble average with Ensemble-averaged Reconstructed Forcings (ERF) in regional climate modeling

    Science.gov (United States)

    Erfanian, A.; Fomenko, L.; Wang, G.

    2016-12-01

    Multi-model ensemble (MME) average is considered the most reliable for simulating both present-day and future climates. It has been a primary reference for making conclusions in major coordinated studies i.e. IPCC Assessment Reports and CORDEX. The biases of individual models cancel out each other in MME average, enabling the ensemble mean to outperform individual members in simulating the mean climate. This enhancement however comes with tremendous computational cost, which is especially inhibiting for regional climate modeling as model uncertainties can originate from both RCMs and the driving GCMs. Here we propose the Ensemble-based Reconstructed Forcings (ERF) approach to regional climate modeling that achieves a similar level of bias reduction at a fraction of cost compared with the conventional MME approach. The new method constructs a single set of initial and boundary conditions (IBCs) by averaging the IBCs of multiple GCMs, and drives the RCM with this ensemble average of IBCs to conduct a single run. Using a regional climate model (RegCM4.3.4-CLM4.5), we tested the method over West Africa for multiple combination of (up to six) GCMs. Our results indicate that the performance of the ERF method is comparable to that of the MME average in simulating the mean climate. The bias reduction seen in ERF simulations is achieved by using more realistic IBCs in solving the system of equations underlying the RCM physics and dynamics. This endows the new method with a theoretical advantage in addition to reducing computational cost. The ERF output is an unaltered solution of the RCM as opposed to a climate state that might not be physically plausible due to the averaging of multiple solutions with the conventional MME approach. The ERF approach should be considered for use in major international efforts such as CORDEX. Key words: Multi-model ensemble, ensemble analysis, ERF, regional climate modeling

  11. 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

  12. Application of Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region

    Science.gov (United States)

    Jayasinghe, S.; Dutta, R.; Basnayake, S. B.; Granger, S. L.; Andreadis, K. M.; Das, N.; Markert, K. N.; Cutter, P. G.; Towashiraporn, P.; Anderson, E.

    2017-12-01

    The Lower Mekong Region has been experiencing frequent and prolonged droughts resulting in severe damage to agricultural production leading to food insecurity and impacts on livelihoods of the farming communities. Climate variability further complicates the situation by making drought harder to forecast. The Regional Drought and Crop Yield Information System (RDCYIS), developed by SERVIR-Mekong, helps decision makers to take effective measures through monitoring, analyzing and forecasting of drought conditions and providing early warnings to farmers to make adjustments to cropping calendars. The RDCYIS is built on regionally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. The RHEAS allows ingestion of numerus freely available earth observation and ground observation data to generate and customize drought related indices, variables and crop yield information for better decision making. The Lower Mekong region has experienced severe drought in 2016 encompassing the region's worst drought in 90 years. This paper presents the simulation of the 2016 drought event using RDCYIS based on its hindcast and forecast capabilities. The regionally calibrated RDCYIS can help capture salient features of drought through a variety of drought indices, soil variables, energy balance variables and water balance variables. The RDCYIS is capable of assimilating soil moisture data from different satellite products and perform ensemble runs to further reduce the uncertainty of it outputs. The calibrated results have correlation coefficient around 0.73 and NSE between 0.4-0.5. Based on the acceptable results of the retrospective runs, the system has the potential to generate reliable drought monitoring and forecasting information to improve decision-makings at operational, technological and

  13. Application of the North American Multi-Model Ensemble to seasonal water supply forecasting in the Great Lakes basin through the use of the Great Lakes Seasonal Climate Forecast Tool

    Science.gov (United States)

    Gronewold, A.; Apps, D.; Fry, L. M.; Bolinger, R.

    2017-12-01

    The U.S. Army Corps of Engineers (USACE) contribution to the internationally coordinated 6-month forecast of Great Lakes water levels relies on several water supply models, including a regression model relating a coming month's water supply to past water supplies, previous months' precipitation and temperature, and forecasted precipitation and temperature. Probabilistic forecasts of precipitation and temperature depicted in the Climate Prediction Center's seasonal outlook maps are considered to be standard for use in operational forecasting for seasonal time horizons, and have provided the basis for computing a coming month's precipitation and temperature for use in the USACE water supply regression models. The CPC outlook maps are a useful forecast product offering insight into interpretation of climate models through the prognostic discussion and graphical forecasts. However, recent evolution of USACE forecast procedures to accommodate automated data transfer and manipulation offers a new opportunity for direct incorporation of ensemble climate forecast data into probabilistic outlooks of water supply using existing models that have previously been implemented in a deterministic fashion. We will present results from a study investigating the potential for applying data from the North American Multi-Model Ensemble to operational water supply forecasts. The use of NMME forecasts is facilitated by a new, publicly available, Great Lakes Seasonal Climate Forecast Tool that provides operational forecasts of monthly average temperatures and monthly total precipitation summarized for each lake basin.

  14. 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.)

  15. Towards uncertainty estimation for operational forecast products - a multi-model-ensemble approach for the North Sea and the Baltic Sea

    Science.gov (United States)

    Golbeck, Inga; Li, Xin; Janssen, Frank

    2014-05-01

    Several independent operational ocean models provide forecasts of the ocean state (e.g. sea level, temperature, salinity and ice cover) in the North Sea and the Baltic Sea on a daily basis. These forecasts are the primary source of information for a variety of information and emergency response systems used e.g. to issue sea level warnings or carry out oil drift forecast. The forecasts are of course highly valuable as such, but often suffer from a lack of information on their uncertainty. With the aim of augmenting the existing operational ocean forecasts in the North Sea and the Baltic Sea by a measure of uncertainty a multi-model-ensemble (MME) system for sea surface temperature (SST), sea surface salinity (SSS) and water transports has been set up in the framework of the MyOcean-2 project. Members of MyOcean-2, the NOOS² and HIROMB/BOOS³ communities provide 48h-forecasts serving as inputs. Different variables are processed separately due to their different physical characteristics. Based on the so far collected daily MME products of SST and SSS, a statistical method, Empirical Orthogonal Function (EOF) analysis is applied to assess their spatial and temporal variability. For sea surface currents, progressive vector diagrams at specific points are consulted to estimate the performance of the circulation models especially in hydrodynamic important areas, e.g. inflow/outflow of the Baltic Sea, Norwegian trench and English Channel. For further versions of the MME system, it is planned to extend the MME to other variables like e.g. sea level, ocean currents or ice cover based on the needs of the model providers and their customers. It is also planned to include in-situ data to augment the uncertainty information and for validation purposes. Additionally, weighting methods will be implemented into the MME system to develop more complex uncertainty measures. The methodology used to create the MME will be outlined and different ensemble products will be presented. In

  16. 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.

  17. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

    Science.gov (United States)

    Liu, P.

    2013-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  18. 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

  19. 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

  20. Applying Multimodel Ensemble from Regional Climate Models for Improving Runoff Projections on Semiarid Regions of Spain

    Science.gov (United States)

    Garcia Galiano, S. G.; Olmos, P.; Giraldo Osorio, J. D.

    2015-12-01

    In the Mediterranean area, significant changes on temperature and precipitation are expected throughout the century. These trends could exacerbate the existing conditions in regions already vulnerable to climatic variability, reducing the water availability. Improving knowledge about plausible impacts of climate change on water cycle processes at basin scale, is an important step for building adaptive capacity to the impacts in this region, where severe water shortages are expected for the next decades. RCMs ensemble in combination with distributed hydrological models with few parameters, constitutes a valid and robust methodology to increase the reliability of climate and hydrological projections. For reaching this objective, a novel methodology for building Regional Climate Models (RCMs) ensembles of meteorological variables (rainfall an temperatures), was applied. RCMs ensembles are justified for increasing the reliability of climate and hydrological projections. The evaluation of RCMs goodness-of-fit to build the ensemble is based on empirical probability density functions (PDF) extracted from both RCMs dataset and a highly resolution gridded observational dataset, for the time period 1961-1990. The applied method is considering the seasonal and annual variability of the rainfall and temperatures. The RCMs ensembles constitute the input to a distributed hydrological model at basin scale, for assessing the runoff projections. The selected hydrological model is presenting few parameters in order to reduce the uncertainties involved. The study basin corresponds to a head basin of Segura River Basin, located in the South East of Spain. The impacts on runoff and its trend from observational dataset and climate projections, were assessed. Considering the control period 1961-1990, plausible significant decreases in runoff for the time period 2021-2050, were identified.

  1. Uncertainty estimation and ensemble forecast with a chemistry-transport model - Application to air-quality modeling and simulation

    International Nuclear Information System (INIS)

    Mallet, Vivien

    2005-01-01

    The thesis deals with the evaluation of a chemistry-transport model, not primarily with classical comparisons to observations, but through the estimation of its a priori uncertainties due to input data, model formulation and numerical approximations. These three uncertainty sources are studied respectively on the basis of Monte Carlos simulations, multi-models simulations and numerical schemes inter-comparisons. A high uncertainty is found, in output ozone concentrations. In order to overtake the limitations due to the uncertainty, a solution is ensemble forecast. Through combinations of several models (up to forty-eight models) on the basis of past observations, the forecast can be significantly improved. The achievement of this work has also led to develop the innovative modelling-system Polyphemus. (author) [fr

  2. 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.

  3. Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

    Science.gov (United States)

    Akperov, Mirseid; Rinke, Annette; Mokhov, Igor I.; Matthes, Heidrun; Semenov, Vladimir A.; Adakudlu, Muralidhar; Cassano, John; Christensen, Jens H.; Dembitskaya, Mariya A.; Dethloff, Klaus; Fettweis, Xavier; Glisan, Justin; Gutjahr, Oliver; Heinemann, Günther; Koenigk, Torben; Koldunov, Nikolay V.; Laprise, René; Mottram, Ruth; Nikiéma, Oumarou; Scinocca, John F.; Sein, Dmitry; Sobolowski, Stefan; Winger, Katja; Zhang, Wenxin

    2018-03-01

    The ability of state-of-the-art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA-Interim, National Centers for Environmental Prediction-Climate Forecast System Reanalysis, National Aeronautics and Space Administration-Modern-Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency-Japanese 55-year reanalysis) in winter and summer for 1981-2010 period. In addition, we compare cyclone statistics between ERA-Interim and the Arctic System Reanalysis reanalyses for 2000-2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large-scale spectral nudging show a better agreement with ERA-Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables.

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

    Science.gov (United States)

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

    2017-12-01

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

  5. 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.

  6. Regional corrections and checking the reliability of geomagnetic forecasts

    International Nuclear Information System (INIS)

    Afanas'eva, V.I.; Shevnin, A.D.

    1978-01-01

    Regional corrections of the K index mark estimate with respect to the Moskva observatory are reviewed in order to improve the short-range forecast of the geomagnetic activity and to promote it within the aqua area. The forecasts of the storms of all categories and weak perturbations have been verified for the predominant days in the catalogue of the magnetic storms family. It is shown that the adopted methods of forecasts yield considerably good results for weak perturbations as well as for weak and moderate magnetic storms. Strong and very strong storms are less predictable

  7. 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

    all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark....... 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....... These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending...

  8. Pollen Dispersion Forecast At Regional Scale

    Science.gov (United States)

    Mangin, A.; Asthma Forecast System Team

    The forecast of the pollen concentration is generally based on an identification of sim- ilar coincidence of measured pollen at given points and meteorological data that is searched in an archive and which, with the help of experts, allows building a predicted value. This may be classified under the family of statistical approaches for forecast- ing. While palynologists make these methods more and more accurate with the help of innovative techniques of regression against empirical rules and/or evolving mathe- matical structures (e.g. neural networks), the spatial dispersion of the pollen is not or poorly considered, mainly because it requires a lot of means and technique that are not familiar to this scientific discipline. The research on pollen forecasts are presently mainly focused on the problematic of modeling the behavior of pollen trends and sea- sons at one location regardless of the topography, the locations of emitters, the relative strengths of emitter, in one word the Sspatial backgroundT. This research work was a & cedil;successful attempt to go a step further combining this SlocalT approach with a trans- & cedil;port/dispersion modeling allowing the access to mapping of concentration. The areas of interest that were selected for the demonstration of feasibility were 200x200km zones centered on Cordoba, Barcelona and Bologna and four pollen types were ex- amined, namely: Cupressaceae, Olea europaea, Poaceae and Parietaria. At the end of this three-year European project in December 2001, the system was fully deployed and validated. The multidisciplinary team will present the original methodologies that were derived for modeling the numerous aspects of this problem and also some con- clusions regarding potential extent to other areas and taxa.

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

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2011-01-01

    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...

  10. Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal

    Science.gov (United States)

    Kutty, Govindan; Muraleedharan, Rohit; Kesarkar, Amit P.

    2018-03-01

    Uncertainties in the numerical weather prediction models are generally not well-represented in ensemble-based data assimilation (DA) systems. The performance of an ensemble-based DA system becomes suboptimal, if the sources of error are undersampled in the forecast system. The present study examines the effect of accounting for model error treatments in the hybrid ensemble transform Kalman filter—three-dimensional variational (3DVAR) DA system (hybrid) in the track forecast of two tropical cyclones viz. Hudhud and Thane, formed over the Bay of Bengal, using Advanced Research Weather Research and Forecasting (ARW-WRF) model. We investigated the effect of two types of model error treatment schemes and their combination on the hybrid DA system; (i) multiphysics approach, which uses different combination of cumulus, microphysics and planetary boundary layer schemes, (ii) stochastic kinetic energy backscatter (SKEB) scheme, which perturbs the horizontal wind and potential temperature tendencies, (iii) a combination of both multiphysics and SKEB scheme. Substantial improvements are noticed in the track positions of both the cyclones, when flow-dependent ensemble covariance is used in 3DVAR framework. Explicit model error representation is found to be beneficial in treating the underdispersive ensembles. Among the model error schemes used in this study, a combination of multiphysics and SKEB schemes has outperformed the other two schemes with improved track forecast for both the tropical cyclones.

  11. Short-term forecasting model for aggregated regional hydropower generation

    International Nuclear Information System (INIS)

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  12. Improved ensemble-mean forecast skills of ENSO events by a zero-mean stochastic model-error model of an intermediate coupled model

    Science.gov (United States)

    Zheng, F.; Zhu, J.

    2015-12-01

    To perform an ensemble-based ENSO probabilistic forecast, the crucial issue is to design a reliable ensemble prediction strategy that should include the major uncertainties of a forecast system. In this study, we developed a new general ensemble perturbation technique to improve the ensemble-mean predictive skill of forecasting ENSO using an intermediate coupled model (ICM). The model uncertainties are first estimated and analyzed from EnKF analysis results through assimilating observed SST. Then, based on the pre-analyzed properties of the model errors, a zero-mean stochastic model-error model is developed to mainly represent the model uncertainties induced by some important physical processes missed in the coupled model (i.e., stochastic atmospheric forcing/MJO, extra-tropical cooling and warming, Indian Ocean Dipole mode, etc.). Each member of an ensemble forecast is perturbed by the stochastic model-error model at each step during the 12-month forecast process, and the stochastical perturbations are added into the modeled physical fields to mimic the presence of these high-frequency stochastic noises and model biases and their effect on the predictability of the coupled system. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr retrospective forecast experiments. The two forecast schemes are differentiated by whether they considered the model stochastic perturbations, with both initialized by the ensemble-mean analysis states from EnKF. The comparison results suggest that the stochastic model-error perturbations have significant and positive impacts on improving the ensemble-mean prediction skills during the entire 12-month forecast process. Because the nonlinear feature of the coupled model can induce the nonlinear growth of the added stochastic model errors with model integration, especially through the nonlinear heating mechanism with the vertical advection term of the model, the

  13. Toward Sub-seasonal to Seasonal Arctic Sea Ice Forecasting Using the Regional Arctic System Model (RASM)

    Science.gov (United States)

    Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.

    2017-12-01

    The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of sea ice forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, sea ice, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook (SIO) of the Sea Ice Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September sea ice extent, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan sea ice extent, daily and monthly mean pan-Arctic maps of sea ice probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and sea ice components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).

  14. 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)

  15. 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 approach...... calibration performed on the wind speed EPS members allows an improvement from an over-confident situation observable from the rank histograms (in which the measurements fell quite always outside the bounds of the probability distribution) to a consistent ensemble spread. After that it is possible to convert...... the data to wind energy: the spread calculated on wind power can then be used as an accuracy predictor due to its level of correlation with the deterministic WPF error. In this presentation we investigate the performances for both wind power and accuracy prediction of the new EPS used at the ECMWF, whose...

  16. 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

    2018-06-01

    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

  17. 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

  18. 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.

  19. The WASCAL high-resolution regional climate simulation ensemble for West Africa: concept, dissemination and assessment

    Science.gov (United States)

    Heinzeller, Dominikus; Dieng, Diarra; Smiatek, Gerhard; Olusegun, Christiana; Klein, Cornelia; Hamann, Ilse; Salack, Seyni; Bliefernicht, Jan; Kunstmann, Harald

    2018-04-01

    Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change scenarios for the greater West African region is provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations, as well as detailed information on the experimental design, and provides information on the format and dissemination of the available data. All data are made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512" target="_blank">https://doi.pangaea.de/10.1594/PANGAEA.880512). A brief assessment of the data are presented to provide guidance for future users. Regional climate projections are generated at high (12 km) and intermediate (60 km) resolution using the Weather Research and Forecasting Model (WRF). The simulations cover the validation period 1980-2010 and the two future periods 2020-2050 and 2070-2100. A brief comparison to observations and two climate change scenarios from the Coordinated Regional Downscaling Experiment (CORDEX) initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the results suggest an increase in temperature by 1.5 °C at the coast of Guinea and by up to 3 °C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300 mm per year along the coast of Guinea, by up to 150 mm per year in the Soudano region adjacent in the north and

  20. 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.

  1. An ensemble prediction approach to weekly Dengue cases forecasting based on climatic and terrain conditions

    Directory of Open Access Journals (Sweden)

    Sougata Deb

    2017-11-01

    Full Text Available Introduction: Dengue fever has been one of the most concerning endemic diseases of recent times. Every year, 50-100 million people get infected by the dengue virus across the world. Historically, it has been most prevalent in Southeast Asia and the Pacific Islands. In recent years, frequent dengue epidemics have started occurring in Latin America as well. This study focused on assessing the impact of different short and long-term lagged climatic predictors on dengue cases. Additionally, it assessed the impact of building an ensemble model using multiple time series and regression models, in improving prediction accuracy. Materials and Methods: Experimental data were based on two Latin American cities, viz. San Juan (Puerto Rico and Iquitos (Peru. Due to weather and geographic differences, San Juan recorded higher dengue incidences than Iquitos. Using lagged cross-correlations, this study confirmed the impact of temperature and vegetation on the number of dengue cases for both cities, though in varied degrees and time lags. An ensemble of multiple predictive models using an elaborate set of derived predictors was built and validated. Results: The proposed ensemble prediction achieved a mean absolute error of 21.55, 4.26 points lower than the 25.81 obtained by a standard negative binomial model. Changes in climatic conditions and urbanization were found to be strong predictors as established empirically in other researches. Some of the predictors were new and informative, which have not been explored in any other relevant studies yet. Discussion and Conclusions: Two original contributions were made in this research. Firstly, a focused and extensive feature engineering aligned with the mosquito lifecycle. Secondly, a novel covariate pattern-matching based prediction approach using past time series trend of the predictor variables. Increased accuracy of the proposed model over the benchmark model proved the appropriateness of the analytical approach

  2. 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

    Global Real Time Control (RTC) of urban drainage system is increasingly seen as cost-effective solution in order to respond to increasing performance demand (e.g. reduction of Combined Sewer Overflow, protection of sensitive areas as bathing water etc.). The Dynamic Overflow Risk Assessment (DORA......) 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...

  3. Short-range ensemble predictions based on convection perturbations in the Eta Model for the Serra do Mar region in Brazil

    Science.gov (United States)

    Bustamante, J. F. F.; Chou, S. C.; Gomes, J. L.

    2009-04-01

    The Southeast Brazil, in the coastal and mountain region called Serra do Mar, between Sao Paulo and Rio de Janeiro, is subject to frequent events of landslides and floods. The Eta Model has been producing good quality forecasts over South America at about 40-km horizontal resolution. For that type of hazards, however, more detailed and probabilistic information on the risks should be provided with the forecasts. Thus, a short-range ensemble prediction system (SREPS) based on the Eta Model is being constructed. Ensemble members derived from perturbed initial and lateral boundary conditions did not provide enough spread for the forecasts. Members with model physics perturbation are being included and tested. The objective of this work is to construct more members for the Eta SREPS by adding physics perturbed members. The Eta Model is configured at 10-km resolution and 38 layers in the vertical. The domain covered is most of Southeast Brazil, centered over the Serra do Mar region. The constructed members comprise variations of the cumulus parameterization Betts-Miller-Janjic (BMJ) and Kain-Fritsch (KF) schemes. Three members were constructed from the BMJ scheme by varying the deficit of saturation pressure profile over land and sea, and 2 members of the KF scheme were included using the standard KF and a momentum flux added to KF scheme version. One of the runs with BMJ scheme is the control run as it was used for the initial condition perturbation SREPS. The forecasts were tested for 6 cases of South America Convergence Zone (SACZ) events. The SACZ is a common summer season feature of Southern Hemisphere that causes persistent rain for a few days over the Southeast Brazil and it frequently organizes over Serra do Mar region. These events are particularly interesting because of the persistent rains that can accumulate large amounts and cause generalized landslides and death. With respect to precipitation, the KF scheme versions have shown to be able to reach the

  4. A multi-stage intelligent approach based on an ensemble of two-way interaction model for forecasting the global horizontal radiation of India

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao; Xiao, Ling

    2017-01-01

    Highlights: • Ensemble learning system is proposed to forecast the global solar radiation. • LASSO is utilized as feature selection method for subset model. • GSO is used to select the weight vector aggregating the response of subset model. • A simple and efficient algorithm is designed based on thresholding function. • Theoretical analysis focusing on error rate is provided. - Abstract: Forecasting of effective solar irradiation has developed a huge interest in recent decades, mainly due to its various applications in grid connect photovoltaic installations. This paper develops and investigates an ensemble learning based multistage intelligent approach to forecast 5 days global horizontal radiation at four given locations of India. The two-way interaction model is considered with purpose of detecting the associated correlation between the features. The main structure of the novel method is the ensemble learning, which is based on Divide and Conquer principle, is applied to enhance the forecasting accuracy and model stability. An efficient feature selection method LASSO is performed in the input space with the regularization parameter selected by Cross-Validation. A weight vector which best represents the importance of each individual model in ensemble system is provided by glowworm swarm optimization. The combination of feature selection and parameter selection are helpful in creating the diversity of the ensemble learning. In order to illustrate the validity of the proposed method, the datasets at four different locations of the India are split into training and test datasets. The results of the real data experiments demonstrate the efficiency and efficacy of the proposed method comparing with other competitors.

  5. 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

  6. 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

    niche models, four AOGCMs and two emission scenarios. Combinations of these effects (50 cross-validations for each of the 15 subsets of the environmental variables) were used to estimate and map the occurrence frequencies (EOF) across all analyses. A three-way anova was used to partition and map...... the sources of variation. 3. The projections for 2080 show that the range edges of the species are likely to remain approximately constant, but shifts in maximum EOF are forecasted. Suitable climatic conditions tend to disappear from central areas of Amazon, although this depends on the AOGCM and the niche...

  7. 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.

  8. 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

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

    Science.gov (United States)

    Riley, Pete

    2017-01-01

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

  10. On the reliability of seasonal climate forecasts

    Science.gov (United States)

    Weisheimer, A.; Palmer, T. N.

    2014-01-01

    Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal 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. PMID:24789559

  11. Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Shuyu Dai

    2018-01-01

    Full Text Available Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm, SVM (Support Vector

  12. Combining super-ensembles and statistical emulation to improve a regional climate and vegetation model

    Science.gov (United States)

    Hawkins, L. R.; Rupp, D. E.; Li, S.; Sarah, S.; McNeall, D. J.; Mote, P.; Betts, R. A.; Wallom, D.

    2017-12-01

    Changing regional patterns of surface temperature, precipitation, and humidity may cause ecosystem-scale changes in vegetation, altering the distribution of trees, shrubs, and grasses. A changing vegetation distribution, in turn, alters the albedo, latent heat flux, and carbon exchanged with the atmosphere with resulting feedbacks onto the regional climate. However, a wide range of earth-system processes that affect the carbon, energy, and hydrologic cycles occur at sub grid scales in climate models and must be parameterized. The appropriate parameter values in such parameterizations are often poorly constrained, leading to uncertainty in predictions of how the ecosystem will respond to changes in forcing. To better understand the sensitivity of regional climate to parameter selection and to improve regional climate and vegetation simulations, we used a large perturbed physics ensemble and a suite of statistical emulators. We dynamically downscaled a super-ensemble (multiple parameter sets and multiple initial conditions) of global climate simulations using a 25-km resolution regional climate model HadRM3p with the land-surface scheme MOSES2 and dynamic vegetation module TRIFFID. We simultaneously perturbed land surface parameters relating to the exchange of carbon, water, and energy between the land surface and atmosphere in a large super-ensemble of regional climate simulations over the western US. Statistical emulation was used as a computationally cost-effective tool to explore uncertainties in interactions. Regions of parameter space that did not satisfy observational constraints were eliminated and an ensemble of parameter sets that reduce regional biases and span a range of plausible interactions among earth system processes were selected. This study demonstrated that by combining super-ensemble simulations with statistical emulation, simulations of regional climate could be improved while simultaneously accounting for a range of plausible land

  13. From wind ensembles to probabilistic information about future wind power production - results from an actual application

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2006-01-01

    on the wind power ensemble forecasts. Given measurements of power production, representing a region or a single wind farm, we have developed methods applicable for these two steps. While (ii) should in principle be a simple task we found that the probabilistic information contained in the wind power ensembles...... horizon we aim at supplying quantiles of the wind power production conditional on the information available at the time at which the forecast is generated. This involves: (i) transformation of meteorological ensemble forecasts into wind power ensemble forecasts and (ii) calculation of quantiles based....... The application use ECMWF-ensembles. One setup corresponds to an offshore wind farm (Nysted, Denmark) and one corresponds to regional forecasting (Western Denmark). In the paper we analyze the results obtained from 8 months of actual operation of this system. It is concluded that the demo-application produce...

  14. 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.

  15. Impact of streamflow data assimilation and length of the verification period on the quality of short-term ensemble hydrologic forecasts

    Science.gov (United States)

    Randrianasolo, A.; Thirel, G.; Ramos, M. H.; Martin, E.

    2014-11-01

    Data assimilation has gained wide recognition in hydrologic forecasting due mainly to its capacity to improve the quality of short-term forecasts. In this study, a comparative analysis is conducted to assess the impact of discharge data assimilation on the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response. The sensitivity of the performance metrics to the length of the verification period is also investigated. The hydrological modeling approaches are: the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU, a distributed model with a data assimilation procedure that uses streamflow measurements to assess the initial state of soil water content that optimizes discharge simulations, and the lumped soil moisture-accounting type rainfall-runoff model GRP, which assimilates directly the last observed discharge to update the state of the routing store. The models are driven by the weather ensemble prediction system PEARP of Météo-France, which is based on the global spectral ARPEGE model zoomed over France. It runs 11 perturbed members for a forecast range of 60 h. Forecast and observed data are available for 86 catchments over a 17-month period (March 2005-July 2006) for both models and for 82 catchments over a 52-month period (April 2005-July 2009) for the GRP model. The first dataset is used to investigate the impact of streamflow data assimilation on forecast quality, while the second is used to evaluate the impact of the length of the verification period on the assessment of forecast quality. Forecasts are compared to daily observed discharges and scores are computed for lead times 24 h and 48 h. Results indicate an overall good performance of both hydrological models forced by the PEARP ensemble predictions when the models are run with their data assimilation procedures. In general, when data assimilation is performed, the quality of the forecasts increases: median differences between

  16. 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,...

  17. 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)...

  18. Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006

    Science.gov (United States)

    Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories...

  19. 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

  20. 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

  1. Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

    Science.gov (United States)

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

    2018-04-01

    Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble prediction systems (EPSs) relative to climatology-based forecasts for 1 to 5-day accumulated precipitation based on the monsoon seasons 2007-2014 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), and verify against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated, unreliable, and underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. Differences between raw ensemble and climatological forecasts are large, and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles, but - somewhat disappointingly - typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007-2014, but overall have little added value compared to climatology. We suspect that the parametrization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

  2. Evaluation of an ensemble of Arctic regional climate models

    DEFF Research Database (Denmark)

    Rinke, A.; Dethloff, K.; Cassano, J. J.

    2006-01-01

    Simulations of eight different regional climate models (RCMs) have been performed for the period September 1997-September 1998, which coincides with the Surface Heat Budget of the Arctic Ocean (SHEBA) project period. Each of the models employed approximately the same domain covering the western......, temperature, cloud cover, and long-/shortwave downward radiation between the individual model simulations are investigated. With this work, we quantify the scatter among the models and therefore the magnitude of disagreement and unreliability of current Arctic RCM simulations. Even with the relatively...... constrained experimental design we notice a considerable scatter among the different RCMs. We found the largest across-model scatter in the 2 m temperature over land, in the surface radiation fluxes, and in the cloud cover which implies a reduced confidence level for these variables....

  3. 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.

  4. Ensemble Assimilation Using Three First-Principles Thermospheric Models as a Tool for 72-hour Density and Satellite Drag Forecasts

    Science.gov (United States)

    Hunton, D.; Pilinski, M.; Crowley, G.; Azeem, I.; Fuller-Rowell, T. J.; Matsuo, T.; Fedrizzi, M.; Solomon, S. C.; Qian, L.; Thayer, J. P.; Codrescu, M.

    2014-12-01

    Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by variability in the density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. As the population of satellites in Earth orbit grows, higher space-weather prediction accuracy is required for critical missions, such as accurate catalog maintenance, collision avoidance for manned and unmanned space flight, reentry prediction, satellite lifetime prediction, defining on-board fuel requirements, and satellite attitude dynamics. We describe ongoing work to build a comprehensive nowcast and forecast system for neutral density, winds, temperature, composition, and satellite drag. This modeling tool will be called the Atmospheric Density Assimilation Model (ADAM). It will be based on three state-of-the-art coupled models of the thermosphere-ionosphere running in real-time, using assimilative techniques to produce a thermospheric nowcast. It will also produce, in realtime, 72-hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition. We will review the requirements for the ADAM system, the underlying full-physics models, the plethora of input options available to drive the models, a feasibility study showing the performance of first-principles models as it pertains to satellite-drag operational needs, and review challenges in designing an assimilative space-weather prediction model. The performance of the ensemble assimilative model is expected to exceed the performance of current empirical and assimilative density models.

  5. 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

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

    International Nuclear Information System (INIS)

    Ying, L.-C.; Pan, M.-C.

    2008-01-01

    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

  7. 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)

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

    Directory of Open Access Journals (Sweden)

    Korkmaz Seher

    2010-05-01

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

  9. Impacts of using an ensemble Kalman filter on air quality simulations along the California-Mexico border region during Cal-Mex 2010 field campaign.

    Science.gov (United States)

    Bei, Naifang; Li, Guohui; Meng, Zhiyong; Weng, Yonghui; Zavala, Miguel; Molina, L T

    2014-11-15

    The purpose of this study is to investigate the impact of using an ensemble Kalman filter (EnKF) on air quality simulations in the California-Mexico border region on two days (May 30 and June 04, 2010) during Cal-Mex 2010. The uncertainties in ozone (O3) and aerosol simulations in the border area due to the meteorological initial uncertainties were examined through ensemble simulations. The ensemble spread of surface O3 averaged over the coastal region was less than 10ppb. The spreads in the nitrate and ammonium aerosols are substantial on both days, mostly caused by the large uncertainties in the surface temperature and humidity simulations. In general, the forecast initialized with the EnKF analysis (EnKF) improved the simulation of meteorological fields to some degree in the border region compared to the reference forecast initialized with NCEP analysis data (FCST) and the simulation with observation nudging (FDDA), which in turn leading to reasonable air quality simulations. The simulated surface O3 distributions by EnKF were consistently better than FCST and FDDA on both days. EnKF usually produced more reasonable simulations of nitrate and ammonium aerosols compared to the observations, but still have difficulties in improving the simulations of organic and sulfate aerosols. However, discrepancies between the EnKF simulations and the measurements were still considerably large, particularly for sulfate and organic aerosols, indicating that there are still ample rooms for improvement in the present data assimilation and/or the modeling systems. Copyright © 2014 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2017-12-01

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

  11. Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

    Directory of Open Access Journals (Sweden)

    Shuyu Dai

    2018-04-01

    Full Text Available For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm, is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product, industrial structure (the proportion of the second industry added value, energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an

  12. A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

    Science.gov (United States)

    Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

    2017-12-01

    Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    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 < 0.001). It retrieves 94% of the total PMCH released and substantially reduces transport error (>80% average reduction of the normalized mean square error). - Highlights: • EnKF is augmented for estimating emission and improving dispersion forecast. • The improved system retrieves 94% of the actual total tracer release in ETEX. • The system substantially improves the 3-h forecast of the tracer dispersion. • The method is robust and insensitive to the first-guess emissions. • The meteorological uncertainties exert strong influence on the performance

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

    International Nuclear Information System (INIS)

    Shukla, Shraddhanand; Funk, Christopher; 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. (letter)

  15. Ensemble Sensitivity Analysis of a Severe Downslope Windstorm in Complex Terrain: Implications for Forecast Predictability Scales and Targeted Observing Networks

    Science.gov (United States)

    2013-09-01

    observations, linear regression finds the straight line that explains the linear relationship of the sample. This line is given by the equation y = mx + b...SENSITIVITY ANALYSIS OF A SEVERE DOWNSLOPE WINDSTORM IN COMPLEX TERRAIN: IMPLICATIONS FOR FORECAST PREDICTABILITY SCALES AND TARGETED OBSERVING...SENSITIVITY ANALYSIS OF A SEVERE DOWNSLOPE WINDSTORM IN COMPLEX TERRAIN: IMPLICATIONS FOR FORECAST PREDICTABILITY SCALES AND TARGETED OBSERVING NETWORKS

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

    Science.gov (United States)

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

    2012-12-01

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

  17. Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

    Science.gov (United States)

    Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.

    2018-07-01

    Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.

  18. Macroeconomic Forecast in Regional Strategies: The Example of Khabarovsk Territory

    Directory of Open Access Journals (Sweden)

    Natalia Gennadyevna Zakharchenko

    2016-06-01

    Full Text Available The article discusses the methodological and instrumental problems of forecasting medium-term dynamics of a region’s socio-economic development as part of constructing the region’s socio-economic development strategy. The authors present results of constructing the system of models of different classes integrated in a single model complex. The model complex is based on the synthesis of methods of intersectoral analysis and dynamic optimization and consists of four The article was supported by the FEB RAS Program of Fundamental Research ‘Far East’ (Subprogram 8. predictive-analytical modules: a aggregate demand generation, b estimation of intersectoral interactions, c resource constraints testing d estimation of standard of living indicators. The modules are interconnected with horizontal communication channels activated within one time cycle (step or year and vertical communication channels activated between different time cycles. The authors test the model complex on the data of Khabarovsk Territory. The results of the study include quantitative assessments of macroeconomic and social parameters projections in the context of stages and scenarios of socio-economic development of the region

  19. The WASCAL high-resolution regional climate simulation ensemble for West Africa: concept, dissemination and assessment

    Directory of Open Access Journals (Sweden)

    D. Heinzeller

    2018-04-01

    Full Text Available Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL, an ensemble of high-resolution regional climate change scenarios for the greater West African region is provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations, as well as detailed information on the experimental design, and provides information on the format and dissemination of the available data. All data are made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512. A brief assessment of the data are presented to provide guidance for future users. Regional climate projections are generated at high (12 km and intermediate (60 km resolution using the Weather Research and Forecasting Model (WRF. The simulations cover the validation period 1980–2010 and the two future periods 2020–2050 and 2070–2100. A brief comparison to observations and two climate change scenarios from the Coordinated Regional Downscaling Experiment (CORDEX initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 (Representative Concentration Pathway 4.5 scenario, the results suggest an increase in temperature by 1.5 °C at the coast of Guinea and by up to 3 °C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300 mm per year along the coast of Guinea, by up to 150 mm per year in the Soudano region adjacent in the north and almost no change in

  20. The Challenge of Forecasting Metropolitan Growth: Urban Characteristics Based Models versus Regional Dummy Based Models

    OpenAIRE

    NA

    2005-01-01

    This paper presents a study of errors in forecasting the population of Metropolitan Statistical Areas and the Primary MSAs of Consolidated Metropolitan Statistical Areas and New England MAs. The forecasts are for the year 2000 and are based on a semi-structural model estimated by Mills and Lubelle using 1970 to 1990 census data on population, employment and relative real wages. This model allows the testing of regional effects on population and employment growth. The year 2000 forecasts are f...

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

    Directory of Open Access Journals (Sweden)

    O. Rakovec

    2012-09-01

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

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

  2. Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

    DEFF Research Database (Denmark)

    Bremnes, John Bjørnar; Giebel, Gregor

    2017-01-01

    resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

  3. REAL - Ensemble radar precipitation estimation for hydrology in a mountainous region

    OpenAIRE

    Germann, Urs; Berenguer Ferrer, Marc; Sempere Torres, Daniel; Zappa, Massimiliano

    2009-01-01

    An elegant solution to characterise the residual errors in radar precipitation estimates is to generate an ensemble of precipitation fields. The paper proposes a radar ensemble generator designed for usage in the Alps using LU decomposition (REAL), and presents first results from a real-time implementation coupling the radar ensemble with a semi-distributed rainfall–runoff model for flash flood modelling in a steep Alpine catchment. Each member of the radar ensemble is a possible realisati...

  4. Enhanced regional forecasting considering single wind farm distribution for upscaling

    International Nuclear Information System (INIS)

    Bremen, Lueder von; Saleck, Nadja; Heinemann, Detlev

    2007-01-01

    With increasing wind power penetration the need for more accurate wind power forecasts increases to raise the market value of wind power. State-of-the-art wind power forecasting tools are considered either statistical or physical. Fundamentally new techniques are rare, thus it is tried to establish a new approach. The spatial decomposition of wind power generation in Germany can be done with principle component analysis to extract the main pattern of variability. They have a physical meaning when linked with typical weather situation. The first four eigenvectors explain about 94 % of the observed variance. The time-evolving principle components are linked with the total wind power feed-in in Germany and are used for its estimation. A new wind power forecasting model has been implemented with this approach and shows very good results that are comparable with state-of-the-art commercial wind power forecast models. The day-ahead forecast error for a common intercomparison period Jan-Jul 2006 is 4.4 %. The suggested approach offers wide ranges for future developments (e.g. several NWP models), because it is computationally very cheap to run

  5. 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

  6. Ensembl 2017

    OpenAIRE

    Aken, Bronwen L.; Achuthan, Premanand; Akanni, Wasiu; Amode, M. Ridwan; Bernsdorff, Friederike; Bhai, Jyothish; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Gil, Laurent; Gir?n, Carlos Garc?a; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.

    2016-01-01

    Ensembl (www.ensembl.org) is a database and genome browser for enabling research on vertebrate genomes. We import, analyse, curate and integrate a diverse collection of large-scale reference data to create a more comprehensive view of genome biology than would be possible from any individual dataset. Our extensive data resources include evidence-based gene and regulatory region annotation, genome variation and gene trees. An accompanying suite of tools, infrastructure and programmatic access ...

  7. Handbook for Forecasters in the Mediterranean. Part 2. Regional Forecasting Aids for the Mediterranean Basin.

    Science.gov (United States)

    1980-12-01

    important role in modifying the poniente. Strong northwesterly flow crossing the Sierra Nevada (see Figure 1-1) causes a lee trough to develop along...the coast of southern France. *Comprises British forecast sea areas Lions, Unicorn , Bougie; see Figure lb in the Introduction. II-I 0) (1) C- C.0 CDC L...Alps are most important features insofar as they play a significant weather role in terms of planetary waves, cyclogenesis and fronts. 1 .2 SEASONAL

  8. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2018-01-01

    In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

  9. Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

    Directory of Open Access Journals (Sweden)

    Waseem Muhammad

    2018-04-01

    Full Text Available Satellite-based precipitation products (e.g., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG and its predecessor, Tropical Rainfall Measuring Mission (TRMM are a critical source of precipitation estimation, particularly for a region with less, or no, hydrometric networking. However, the inconsistency in the performance of these products has been observed in different climatic and topographic diverse regions, timescales, and precipitation intensities and there is still room for improvement. Hence, using a projected ensemble algorithm, the regional precipitation estimate (RP is introduced here. The RP concept is mainly based on the regional performance weights derived from the Mean Square Error (MSE and the precipitation estimate from the TRMM product, that is, TRMM 3B42 (TR, real-time (late (IT and the research (post-real-time (IR products of IMERG. The overall results of the selected contingency table (e.g., Probability of detection (POD and statistical indices (e.g., Correlation Coefficient (CC signposted that the proposed RP product has shown an overall better potential to capture the gauge observations compared with the TR, IR, and IT in five different climatic regions of Pakistan from January 2015 to December 2016, at a diurnal time scale. The current study could be the first research providing preliminary feedback from Pakistan for global precipitation measurement researchers by highlighting the need for refinement in the IMERG.

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

    Science.gov (United States)

    Fernández-Vázquez, Esteban; Moreno, Blanca

    2017-10-01

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

  11. Projections of Heat Waves Events in the Intra-Americas Region Using Multimodel Ensemble

    Directory of Open Access Journals (Sweden)

    Moises Angeles-Malaspina

    2018-01-01

    Full Text Available Significant accelerated warming of the Sea Surface Temperature of 0.15°C per decade (1982–2012 was recently detected, which motivated the research for the present consequences and future projections on the heat index and heat waves in the intra-Americas region. Present records every six hours are retrieved from NCEP reanalysis (1948–2015 to calculate heat waves changes. Heat index intensification has been detected in the region since 1998 and driven by surface pressure changes, sinking air enhancement, and warm/weaker cold advection. This regional warmer atmosphere leads to heat waves intensification with changes in both frequency and maximum amplitude distribution. Future projections using a multimodel ensemble mean for five global circulation models were used to project heat waves in the future under two scenarios: RCP4.5 and RCP8.5. Massive heat waves events were projected at the end of the 21st century, particularly in the RCP8.5 scenario. Consequently, the regional climate change in the current time and in the future will require special attention to mitigate the more intense and frequent heat waves impacts on human health, countries’ economies, and energy demands in the IAR.

  12. 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.

  13. Perturbations of modeling and forecast of karachi coastal region seawater

    International Nuclear Information System (INIS)

    Hussain, M.A.; Abbas, S.; Ansari, M.R.K.; Zaffar, A.

    2013-01-01

    Global warming is now a stark reality affecting the humanity in many hazardous ways. Continuous floods in Pakistan in past two years are an eye opener in this regard. A great loss of property, agriculture and life as a result of these floods suggests for an intelligent monitoring of the future projections of climate change and global warming. This is necessary because the harmful impacts of natural hazards can be coped and alleviated with a good planning in advance. This monitoring demands for enhanced forecasting capabilities, use of better analytical techniques and a clear determination and study of the controlling factors. Karachi is a coastal city which is also the industrial hub of Pakistan. Moreover, it is among one of the largest metropolitans of the world. So expectedly is most suitable for the study of high level of complex natural and anthropogenic activities. It is peculiar in the sense that it has two summer seasons, a situation scarcely observable on the globe. Here, summer season seawater temperature fluctuations are studied with the help of Seasonal Autoregressive Integrated Moving Average (SARIMA) models and short- and long-term forecasts are made. Our short-term forecasts determine months for the summer wise temperature extremes. It appears that the months of May, June, July and August are the months of extreme temperature for the first summer and October is the month of extreme temperature for the second summer. The long-term forecasts predict that 2014, 2016, 2018, and 2019 will be the years of warm summers. The analysis appearing here would be useful for coastal-urban planners in emphasizing the impact of seawater extreme temperatures on urban industrial activities, etc. (author)

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

    Science.gov (United States)

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

    2015-12-01

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

  15. Decadal climate predictions improved by ocean ensemble dispersion filtering

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-06-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

  16. Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

    Science.gov (United States)

    Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

    2016-04-01

    Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

  17. Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

    Science.gov (United States)

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

    2009-04-01

    observations and several years of archived forecasts, overall empirical error distributions termed 'overall error' were for each gauge derived for a range of relevant forecast lead times. b) The error distributions vary strongly with the hydrometeorological situation, therefore a subdivision into the hydrological cases 'low flow, 'rising flood', 'flood', flood recession' was introduced. c) For the sake of numerical compression, theoretical distributions were fitted to the empirical distributions using the method of moments. Here, the normal distribution was generally best suited. d) Further data compression was achieved by representing the distribution parameters as a function (second-order polynome) of lead time. In general, the 'overall error' obtained from the above procedure is most useful in regions where large human impact occurs and where the influence of the meteorological forecast is limited. In upstream regions however, forecast uncertainty is strongly dependent on the current predictability of the atmosphere, which is contained in the spread of an ensemble forecast. Including this dynamically in the hydrological forecast uncertainty estimation requires prior elimination of the contribution of the weather forecast to the 'overall error'. This was achieved by calculating long series of hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The resulting error distribution is termed 'model error' and can be applied on hydrological ensemble forecasts, where ensemble rainfall forecasts are used as forcing. The concept will be illustrated by examples (good and bad ones) covering a wide range of catchment sizes, hydrometeorological regimes and quality of hydrological model calibration. The methodology to combine the static and dynamic shares of uncertainty will be presented in part II of this study.

  18. Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

    Science.gov (United States)

    Dairaku, K.

    2017-12-01

    The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.

  19. Spatial and temporal characteristics of heat waves over Central Europe in an ensemble of regional climate model simulations

    Czech Academy of Sciences Publication Activity Database

    Lhotka, Ondřej; Kyselý, Jan

    2015-01-01

    Roč. 45, č. 9 (2015), s. 2351-2366 ISSN 0930-7575 R&D Projects: GA ČR GAP209/10/2265 EU Projects: European Commission(XE) 505539 - ENSEMBLES Program:FP6 Institutional support: RVO:68378289 Keywords : heat waves * regional climate models * land–atmosphere coupling * spatial characteristics * interannual variability * ENSEMBLES project Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 4.708, year: 2015 http://link.springer.com/article/10.1007%2Fs00382-015-2475-7

  20. MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe

    NARCIS (Netherlands)

    Sofiev, M.; Berger, U.; Prank, M.; Vira, J.; Arteta, J.; Belmonte, J.; Bergmann, K.C.; Chéroux, F.; Elbern, H.; Friese, E.; Galan, C.; Gehrig, R.; Khvorostyanov, D.; Kranenburg, R.; Kumar, U.; Marécal, V.; Meleux, F.; Menut, L.; Pessi, A.M.; Robertson, L.; Ritenberga, O.; Rodinkova, V.; Saarto, A.; Segers, A.; Severova, E.; Sauliene, I.; Siljamo, P.; Steensen, B.M.; Teinemaa, E.; Thibaudon, M.; Peuch, V.H.

    2015-01-01

    This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with

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

    Science.gov (United States)

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

    2014-01-01

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

  2. Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

    Science.gov (United States)

    Borah, Nabanita; Sukumarpillai, Abhilash; Sahai, Atul Kumar; Chattopadhyay, Rajib; Joseph, Susmitha; De, Soumyendu; Nath Goswami, Bhupendra; Kumar, Arun

    2014-05-01

    An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

  3. Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts

    NARCIS (Netherlands)

    Wit, de A.J.W.; Boogaard, H.L.; Diepen, van C.A.

    2005-01-01

    This paper explores the effect of uncertainty in precipitation and radiation on crop simulation results at local (50 × 50 km grids) and regional scale (NUTS1 regions) and on the crop yield forecasts for Germany and France. Two experiments were carried out where crop yields for winter-wheat and grain

  4. Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)

    Science.gov (United States)

    Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

    2016-01-01

    Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast SystemVersion 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

  5. Near-surface wind variability over the broader Adriatic region: insights from an ensemble of regional climate models

    Science.gov (United States)

    Belušić, Andreina; Prtenjak, Maja Telišman; Güttler, Ivan; Ban, Nikolina; Leutwyler, David; Schär, Christoph

    2018-06-01

    Over the past few decades the horizontal resolution of regional climate models (RCMs) has steadily increased, leading to a better representation of small-scale topographic features and more details in simulating dynamical aspects, especially in coastal regions and over complex terrain. Due to its complex terrain, the broader Adriatic region represents a major challenge to state-of-the-art RCMs in simulating local wind systems realistically. The objective of this study is to identify the added value in near-surface wind due to the refined grid spacing of RCMs. For this purpose, we use a multi-model ensemble composed of CORDEX regional climate simulations at 0.11° and 0.44° grid spacing, forced by the ERA-Interim reanalysis, a COSMO convection-parameterizing simulation at 0.11° and a COSMO convection-resolving simulation at 0.02° grid spacing. Surface station observations from this region and satellite QuikSCAT data over the Adriatic Sea have been compared against daily output obtained from the available simulations. Both day-to-day wind and its frequency distribution are examined. The results indicate that the 0.44° RCMs rarely outperform ERA-Interim reanalysis, while the performance of the high-resolution simulations surpasses that of ERA-Interim. We also disclose that refining the grid spacing to a few km is needed to properly capture the small-scale wind systems. Finally, we show that the simulations frequently yield the accurate angle of local wind regimes, such as for the Bora flow, but overestimate the associated wind magnitude. Finally, spectral analysis shows good agreement between measurements and simulations, indicating the correct temporal variability of the wind speed.

  6. Risk assessments of regional climate change over Europe: generation of probabilistic ensemble and uncertainty assessment for EURO-CODEX

    Science.gov (United States)

    Yuan, J.; Kopp, R. E.

    2017-12-01

    Quantitative risk analysis of regional climate change is crucial for risk management and impact assessment of climate change. Two major challenges to assessing the risks of climate change are: CMIP5 model runs, which drive EURO-CODEX downscaling runs, do not cover the full range of uncertainty of future projections; Climate models may underestimate the probability of tail risks (i.e. extreme events). To overcome the difficulties, this study offers a viable avenue, where a set of probabilistic climate ensemble is generated using the Surrogate/Model Mixed Ensemble (SMME) method. The probabilistic ensembles for temperature and precipitation are used to assess the range of uncertainty covered by five bias-corrected simulations from the high-resolution (0.11º) EURO-CODEX database, which are selected by the PESETA (The Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis) III project. Results show that the distribution of SMME ensemble is notably wider than both distribution of raw ensemble of GCMs and the spread of the five EURO-CORDEX in RCP8.5. Tail risks are well presented by the SMME ensemble. Both SMME ensemble and EURO-CORDEX projections are aggregated to administrative level, and are integrated into impact functions of PESETA III to assess climate risks in Europe. To further evaluate the uncertainties introduced by the downscaling process, we compare the 5 runs from EURO-CORDEX with runs from the corresponding GCMs. Time series of regional mean, spatial patterns, and climate indices are examined for the future climate (2080-2099) deviating from the present climate (1981-2010). The downscaling processes do not appear to be trend-preserving, e.g. the increase in regional mean temperature from EURO-CORDEX is slower than that from the corresponding GCM. The spatial pattern comparison reveals that the differences between each pair of GCM and EURO-CORDEX are small in winter. In summer, the temperatures of EURO

  7. Analysis of the regional MiKlip decadal prediction system over Europe: skill, added value of regionalization, and ensemble size dependeny

    Science.gov (United States)

    Reyers, Mark; Moemken, Julia; Pinto, Joaquim; Feldmann, Hendrik; Kottmeier, Christoph; MiKlip Module-C Team

    2017-04-01

    Decadal climate predictions can provide a useful basis for decision making support systems for the public and private sectors. Several generations of decadal hindcasts and predictions have been generated throughout the German research program MiKlip. Together with the global climate predictions computed with MPI-ESM, the regional climate model (RCM) COSMO-CLM is used for regional downscaling by MiKlip Module-C. The RCMs provide climate information on spatial and temporal scales closer to the needs of potential users. In this study, two downscaled hindcast generations are analysed (named b0 and b1). The respective global generations are both initialized by nudging them towards different reanalysis anomaly fields. An ensemble of five starting years (1961, 1971, 1981, 1991, and 2001), each comprising ten ensemble members, is used for both generations in order to quantify the regional decadal prediction skill for precipitation and near-surface temperature and wind speed over Europe. All datasets (including hindcasts, observations, reanalysis, and historical MPI-ESM runs) are pre-processed in an analogue manner by (i) removing the long-term trend and (ii) re-gridding to a common grid. Our analysis shows that there is potential for skillful decadal predictions over Europe in the regional MiKlip ensemble, but the skill is not systematic and depends on the PRUDENCE region and the variable. Further, the differences between the two hindcast generations are mostly small. As we used detrended time series, the predictive skill found in our study can probably attributed to reasonable predictions of anomalies which are associated with the natural climate variability. In a sensitivity study, it is shown that the results may strongly change when the long-term trend is kept in the datasets, as here the skill of predicting the long-term trend (e.g. for temperature) also plays a major role. The regionalization of the global ensemble provides an added value for decadal predictions for

  8. Forecasting the daily electricity consumption in the Moscow region using artificial neural networks

    Science.gov (United States)

    Ivanov, V. V.; Kryanev, A. V.; Osetrov, E. S.

    2017-07-01

    In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.

  9. Attributing anthropogenic impact on regional heat wave events using CAM5 model large ensemble simulations

    Science.gov (United States)

    Lo, S. H.; Chen, C. T.

    2017-12-01

    Extreme heat waves have serious impacts on society. It was argued that the anthropogenic forcing might substantially increase the risk of extreme heat wave events (e.g. over western Europe in 2003 and over Russia in 2010). However, the regional dependence of such anthropogenic impact and the sensitivity of the attributed risk to the definition of heat wave still require further studies. In our research framework, the change in the frequency and severity of a heat wave event under current conditions is calculated and compared with the probability and magnitude of the event if the effects of particular external forcing, such as due to human influence, had been absent. In our research, we use the CAM5 large ensemble simulation from the CLIVAR C20C+ Detection and Attribution project (http://portal.nersc.gov/c20c/main.html, Folland et al. 2014) to detect the heat wave events occurred in both historical all forcing run and natural forcing only run. The heat wave events are identified by partial duration series method (Huth et al., 2000). We test the sensitivity of heat wave thresholds from daily maximum temperature (Tmax) in warm season (from May to September) between 1959 and 2013. We consider the anthropogenic effect on the later period (2000-2013) when the warming due to human impact is more evident. Using Taiwan and surrounding area as our preliminary research target, We found the anthropogenic effect will increase the heat wave day per year from 30 days to 75 days and make the mean starting(ending) day for heat waves events about 15-30 days earlier(later). Using the Fraction of Attribution Risk analysis to estimate the risk of frequency of heat wave day, our results show the anthropogenic forcing very likely increase the heat wave days over Taiwan by more than 50%. Further regional differences and sensitivity of the attributed risk to the definition of heat wave will be compared and discussed.

  10. The Santos Basin Ocean Observing System: From R&D to Operational Regional Forecasts

    Science.gov (United States)

    Da Rocha Fragoso, M.; Moore, A. M.; dos Santos, F. A.; Marques Da Cruz, L.; Carvalho, G. V.; Soares, F.

    2016-02-01

    Santos Basin is located on the Southwestern Brazilian Ocean Basin and comprises the main offshore oil reserves of Brazil. The exploration and production activities on its ocean are growing in accelerated pace, which means that oil spill contingency and search & rescue operations are likely to be more frequent. Therefore, ocean current reliable nowcasts and forecasts has become even more important for this region. The Santos Basin Ocean Observing System was designed as an R&D project and its main objective was to establish and maintain a systematic oceanographic data collection for this region in order to study its ocean dynamics and improve regional ocean forecast through data assimilation. In the first three years of the project surface drifters, profiling floats and gliders were deployed to measure and monitor mainly the Brazil Current Western Boundary System, a highly unstable baroclinic current system, that present several meanders and mesoscale eddies activities. Throughout the development of the project, the team involved was able to learn how to operate the equipment, treat the collected data and use it to assimilate on the Regional Ocean Modeling System (ROMS). After performing a one-year 4DVAR assimilation cycle (Fragoso et al., 2015) in which the forecasting skill was assessed, the system was considered mature enough to start producing ocean circulation forecasts for Santos Basin. It is the first time in Brazil that a regional ocean model using a 4DVAR data assimilation scheme was used to produce high resolution operational ocean current forecasts. This paper describes all the components of this forecasting system, its main results and discoveries with special focus on the Brazil Current System Transport and mesocale eddies dynamics and statistics.

  11. MAG4 versus alternative techniques for forecasting active region flare productivity

    Science.gov (United States)

    Falconer, David A; Moore, Ronald L; Barghouty, Abdulnasser F; Khazanov, Igor

    2014-01-01

    MAG4 is a technique of forecasting an active region's rate of production of major flares in the coming few days from a free magnetic energy proxy. We present a statistical method of measuring the difference in performance between MAG4 and comparable alternative techniques that forecast an active region's major-flare productivity from alternative observed aspects of the active region. We demonstrate the method by measuring the difference in performance between the “Present MAG4” technique and each of three alternative techniques, called “McIntosh Active-Region Class,” “Total Magnetic Flux,” and “Next MAG4.” We do this by using (1) the MAG4 database of magnetograms and major flare histories of sunspot active regions, (2) the NOAA table of the major-flare productivity of each of 60 McIntosh active-region classes of sunspot active regions, and (3) five technique performance metrics (Heidke Skill Score, True Skill Score, Percent Correct, Probability of Detection, and False Alarm Rate) evaluated from 2000 random two-by-two contingency tables obtained from the databases. We find that (1) Present MAG4 far outperforms both McIntosh Active-Region Class and Total Magnetic Flux, (2) Next MAG4 significantly outperforms Present MAG4, (3) the performance of Next MAG4 is insensitive to the forward and backward temporal windows used, in the range of one to a few days, and (4) forecasting from the free-energy proxy in combination with either any broad category of McIntosh active-region classes or any Mount Wilson active-region class gives no significant performance improvement over forecasting from the free-energy proxy alone (Present MAG4). Key Points Quantitative comparison of performance of pairs of forecasting techniques Next MAG4 forecasts major flares more accurately than Present MAG4 Present MAG4 forecast outperforms McIntosh AR Class and total magnetic flux PMID:26213517

  12. Multi-model Ensemble Regional Climate Projection of the Maritime Continent using the MIT Regional Climate Model

    Science.gov (United States)

    Kang, S.; IM, E. S.; Eltahir, E. A. B.

    2016-12-01

    In this study, the future change in precipitation due to global warming is investigated over the Maritime Continent using the MIT Regional Climate Model (MRCM). A total of nine 30-year projections under multi-GCMs (CCSM, MPI, ACCESS) and multi-scenarios of emissions (Control, RCP4.5, RCP8.5) are dynamically downscaled using the MRCM with 12km horizontal resolution. Since downscaled results tend to systematically overestimate the precipitation regardless of GCM used as lateral boundary conditions, the Parametric Quantile Mapping (PQM) is applied to reduce this wet bias. The cross validation for the control simulation shows that the PQM method seems to retain the spatial pattern and temporal variability of raw simulation, however it effectively reduce the wet bias. Based on ensemble projections produced by dynamical downscaling and statistical bias correction, a reduction of future precipitation is discernible, in particular during dry season (June-July-August). For example, intense precipitation in Singapore is expected to be reduced in RCP8.5 projection compared to control simulation. However, the geographical patterns and magnitude of changes still remain uncertain, suffering from statistical insignificance and a lack of model agreement. Acknowledgements This research is supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise programme. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology

  13. Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

    Science.gov (United States)

    Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

    2017-12-01

    The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

  14. Improving the principles of short-term electric load forecasting of the Irkutsk region

    Directory of Open Access Journals (Sweden)

    Kornilov Vladimir

    2017-01-01

    Full Text Available Forecasting of electric load (EL is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region.

  15. Incorporating regional growth into forecasts of greenhouse gas emissions from project-level residential and commercial development

    International Nuclear Information System (INIS)

    Rowangould, Dana; Eldridge, Melody; Niemeier, Deb

    2013-01-01

    To better understand the greenhouse gas (GHG) implications of land use planning decisions, regional planning organizations have developed tools to forecast the emissions from project-level residential and commercial development. This paper reviews the state of GHG emissions forecasting methods for project-level development. We argue that when forecasting changes in regional emissions it is important to make explicit what is assumed about a project′s effect on the population of residents and businesses in the region. We present five regional growth assumptions capturing the range of ways that project-level development might influence (i) construction and occupancy of similar developments elsewhere in a region and (ii) relocation of the initial activities that occur on-site before the project is built. We show that current forecasting tools inconsistently address the latter when they are interpreted as forecasted changes in regional emissions. Using a case study in Yolo County, California we demonstrate that forecasted changes in regional emissions are greatly affected by the regional growth assumption. In the absence of information about which regional growth assumption is accurate, we provide guidelines for selection of a conservative regional growth assumption. - Highlights: • Current tools inconsistently forecast GHG emissions from project-level development. • We outline five assumptions about how projects may affect regional growth. • Our assumptions capture a range of economic and population effects of projects. • Our case study shows that growth assumptions greatly affect regional GHG estimates. • We provide guidelines for selecting a conservative regional growth assumption

  16. Future hydroclimatological changes in South America based on an ensemble of regional climate models

    Science.gov (United States)

    Zaninelli, Pablo G.; Menéndez, Claudio G.; Falco, Magdalena; López-Franca, Noelia; Carril, Andrea F.

    2018-05-01

    Changes between two time slices (1961-1990 and 2071-2100) in hydroclimatological conditions for South America have been examined using an ensemble of regional climate models. Annual mean precipitation (P), evapotranspiration (E) and potential evapotranspiration (EP) are jointly considered through the balances of land water and energy. Drying or wetting conditions, associated with changes in land water availability and atmospheric demand, are analysed in the Budyko space. The water supply limit (E limited by P) is exceeded at about 2% of the grid points, while the energy limit to evapotranspiration (E = EP) is overall valid. Most of the continent, except for the southeast and some coastal areas, presents a shift toward drier conditions related to a decrease in water availability (the evaporation rate E/P increases) and, mostly over much of Brazil, to an increase in the aridity index (V = EP/P). These changes suggest less humid conditions with decreasing surface runoff over Amazonia and the Brazilian Highlands. In contrast, Argentina and the coasts of Ecuador and Peru are characterized by a tendency toward wetter conditions associated with an increase of water availability and a decrease of aridity index, primarily due to P increasing faster than both E and EP. This trend towards wetter soil conditions suggest that the chances of having larger periods of flooding and enhanced river discharges would increase over parts of southeastern South America. Interannual variability increases with V (for a given time slice) and with climate change (for a given aridity regimen). There are opposite interannual variability responses to the cliamte change in Argentina and Brazil by which the variability increases over the Brazilian Highlands and decreases in central-eastern Argentina.

  17. Low-wave number analysis of observations and ensemble forecasts to develop metrics for the selection of most realistic members to study multi-scale interactions between the environment and the convective organization of hurricanes: Focus on Rapid Intensification

    Science.gov (United States)

    Hristova-Veleva, S. M.; Chen, H.; Gopalakrishnan, S.; Haddad, Z. S.

    2017-12-01

    Tropical cyclones (TCs) are the product of complex multi-scale processes and interactions. The role of the environment has long been recognized. However, recent research has shown that convective-scale processes in the hurricane core might also play a crucial role in determining TCs intensity and size. Several studies have linked Rapid Intensification to the characteristics of the convective clouds (shallow versus deep), their organization (isolated versus wide-spread) and their location with respect to dynamical controls (the vertical shear, the radius of maximum wind). Yet a third set of controls signifies the interaction between the storm-scale and large-scale processes. Our goal is to use observations and models to advance the still-lacking understanding of these processes. Recently, hurricane models have improved significantly. However, deterministic forecasts have limitations due to the uncertainty in the representation of the physical processes and initial conditions. A crucial step forward is the use of high-resolution ensembles. We adopt the following approach: i) generate a high resolution ensemble forecast using HWRF; ii) produce synthetic data (e.g. brightness temperature) from the model fields for direct comparison to satellite observations; iii) develop metrics to allow us to sub-select the realistic members of the ensemble, based on objective measures of the similarity between observed and forecasted structures; iv) for these most-realistic members, determine the skill in forecasting TCs to provide"guidance on guidance"; v) use the members with the best predictive skill to untangle the complex multi-scale interactions. We will report on the first three goals of our research, using forecasts and observations of hurricane Edouard (2014), focusing on RI. We will focus on describing the metrics for the selection of the most appropriate ensemble members, based on applying low-wave number analysis (WNA - Hristova-Veleva et al., 2016) to the observed and

  18. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Xike Zhang

    2018-05-01

    Full Text Available Daily land surface temperature (LST forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD coupled with Machine Learning (ML algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs and a single residue item. Then, the Partial Autocorrelation Function (PACF is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE, Mean Absolute Error (MAE, Mean Absolute Percentage Error (MAPE, Root Mean Square Error (RMSE, Pearson Correlation Coefficient (CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE. To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN, LSTM and Empirical Mode Decomposition (EMD coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other

  19. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

    Science.gov (United States)

    Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

    2018-05-21

    Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five

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

    Science.gov (United States)

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

    2017-06-01

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

  1. Ensemble simulations to study the impact of land use change of Atlanta to regional climate

    Science.gov (United States)

    Liu, P.; Hu, Y.; Stone, B.; Vargo, J.; Nenes, A.; Russell, A.; Trail, M.; Tsimpidi, A.

    2012-12-01

    area within 30 miles of the center of Atlanta is replaced with asphalt. This choice is made to maximize the potential effects and scales of impact. To make the two cases different as much as possible, a constant green vegetation fraction of 1.0 is assigned to the forest over the Atlanta; while 0.0 is assigned to the asphalt. To test the significance of the impact of land use change, 5 ensemble members were generated for each land use case using different initial conditions. The results of student's t test found that the impact of land use change in Atlanta city has a very local impact. This finding indicates that using WRF, applied at continental and regional scales, with BCs from the GCM and with spectral nudging, is appropriate. Although our results showed the impact is very local, results may change when meteorological conditions change or the area where land use changes is increased. Therefore, when investigating the land use change relevant issues, similar testing is suggested in order to demonstrate that the domain is large enough so that downscaling by RCMs is an appropriate approach. References: Rosenzweig, C., W. Solecki, S.A. Hammer, and S. Mehrotra, 2010: Cities lead the way in climate-change action. Nature, 467, 909-911, doi:10.1038/467909a

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

    Science.gov (United States)

    Tefera Diro, Gulilat; Sushama, Laxmi

    2017-04-01

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

  3. Regional forecasting with global atmospheric models; Final report

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-05-01

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

  4. PSO-Ensemble Demo Application

    DEFF Research Database (Denmark)

    2004-01-01

    Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output...

  5. Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2016-11-01

    Full Text Available In recent years, the Weather Research and Forecast (WRF model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI, but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS over this region.

  6. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Directory of Open Access Journals (Sweden)

    S. Zhang

    2018-03-01

    Full Text Available Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  7. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Science.gov (United States)

    Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

    2018-03-01

    Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  8. Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

    Science.gov (United States)

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

    2010-01-01

    Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation

  9. Anomaly transform methods based on total energy and ocean heat content norms for generating ocean dynamic disturbances for ensemble climate forecasts

    Science.gov (United States)

    Romanova, Vanya; Hense, Andreas

    2017-08-01

    In our study we use the anomaly transform, a special case of ensemble transform method, in which a selected set of initial oceanic anomalies in space, time and variables are defined and orthogonalized. The resulting orthogonal perturbation patterns are designed such that they pick up typical balanced anomaly structures in space and time and between variables. The metric used to set up the eigen problem is taken either as the weighted total energy with its zonal, meridional kinetic and available potential energy terms having equal contributions, or the weighted ocean heat content in which a disturbance is applied only to the initial temperature fields. The choices of a reference state for defining the initial anomalies are such that either perturbations on seasonal timescales and or on interannual timescales are constructed. These project a-priori only the slow modes of the ocean physical processes, such that the disturbances grow mainly in the Western Boundary Currents, in the Antarctic Circumpolar Current and the El Nino Southern Oscillation regions. An additional set of initial conditions is designed to fit in a least square sense data from global ocean reanalysis. Applying the AT produced sets of disturbances to oceanic initial conditions initialized by observations of the MPIOM-ESM coupled model on T63L47/GR15 resolution, four ensemble and one hind-cast experiments were performed. The weighted total energy norm is used to monitor the amplitudes and rates of the fastest growing error modes. The results showed minor dependence of the instabilities or error growth on the selected metric but considerable change due to the magnitude of the scaling amplitudes of the perturbation patterns. In contrast to similar atmospheric applications, we find an energy conversion from kinetic to available potential energy, which suggests a different source of uncertainty generation in the ocean than in the atmosphere mainly associated with changes in the density field.

  10. Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

    2009-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

  11. a system approach to the long term forecasting of the climat data in baikal region

    Science.gov (United States)

    Abasov, N.; Berezhnykh, T.

    2003-04-01

    The Angara river running from Baikal with a cascade of hydropower plants built on it plays a peculiar role in economy of the region. With view of high variability of water inflow into the rivers and lakes (long-term low water periods and catastrophic floods) that is due to climatic peculiarities of the water resource formation, a long-term forecasting is developed and applied for risk decreasing at hydropower plants. Methodology and methods of long-term forecasting of natural-climatic processes employs some ideas of the research schools by Academician I.P.Druzhinin and Prof. A.P.Reznikhov and consists in detailed investigation of cause-effect relations, finding out physical analogs and their application to formalized methods of long-term forecasting. They are divided into qualitative (background method; method of analogs based on solar activity), probabilistic and approximative methods (analog-similarity relations; discrete-continuous model). These forecasting methods have been implemented in the form of analytical aids of the information-forecasting software "GIPSAR" that provides for some elements of artificial intelligence. Background forecasts of the runoff of the Ob, the Yenisei, the Angara Rivers in the south of Siberia are based on space-time regularities that were revealed on taking account of the phase shifts in occurrence of secular maxima and minima on integral-difference curves of many-year hydrological processes in objects compared. Solar activity plays an essential role in investigations of global variations of climatic processes. Its consideration in the method of superimposed epochs has allowed a conclusion to be made on the higher probability of the low-water period in the actual inflow to Lake Baikal that takes place on the increasing branch of solar activity of its 11-year cycle. The higher probability of a high-water period is observed on the decreasing branch of solar activity from the 2nd to the 5th year after its maximum. Probabilistic method

  12. Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.

    Science.gov (United States)

    Kelly, David; Majda, Andrew J; Tong, Xin T

    2015-08-25

    The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.

  13. A Bayesian posterior predictive framework for weighting ensemble regional climate models

    Directory of Open Access Journals (Sweden)

    Y. Fan

    2017-06-01

    Full Text Available We present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles in order to create probabilistic projections. The weight of each climate model is obtained by weighting the current day observed data under the posterior distribution admitted under competing climate models. We use a linear model to describe the model output and observations. The approach accounts for uncertainty in model bias, trend and internal variability, including error in the observations used. Our framework is general, requires very little problem-specific input, and works well with default priors. We carry out cross-validation checks that confirm that the method produces the correct coverage.

  14. Links between circulation indices and precipitation in the Mediterranean in an ensemble of regional climate models

    Czech Academy of Sciences Publication Activity Database

    Beranová, Romana; Kyselý, Jan

    2016-01-01

    Roč. 123, č. 3 (2016), s. 693-701 ISSN 0177-798X R&D Projects: GA ČR GAP209/10/2265 EU Projects: European Commission(XE) 505539 - ENSEMBLES Program:FP6 Institutional support: RVO:68378289 Keywords : atmospheric sciences climatology * atmospheric protection * air quality control * air pollution * waste water technology * water pollution control * water management * aquatic pollution Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.640, year: 2016 http://link.springer.com/article/10.1007%2Fs00704-015-1381-6

  15. Consistency and Main Differences Between European Regional Climate Downscaling Intercomparison Results; From PRUDENCE and ENSEMBLES to CORDEX

    Science.gov (United States)

    Christensen, J. H.; Larsen, M. A. D.; Christensen, O. B.; Drews, M.

    2017-12-01

    For more than 20 years, coordinated efforts to apply regional climate models to downscale GCM simulations for Europe have been pursued by an ever increasing group of scientists. This endeavor showed its first results during EU framework supported projects such as RACCS and MERCURE. Here, the foundation for today's advanced worldwide CORDEX approach was laid out by a core of six research teams, who conducted some of the first coordinated RCM simulations with the aim to assess regional climate change for Europe. However, it was realized at this stage that model bias in GCMs as well as RCMs made this task very challenging. As an immediate outcome, the idea was conceived to make an even more coordinated effort by constructing a well-defined and structured set of common simulations; this lead to the PRUDENCE project (2001-2004). Additional coordinated efforts involving ever increasing numbers of GCMs and RCMs followed in ENSEMBLES (2004-2009) and the ongoing Euro-CORDEX (officially commenced 2011) efforts. Along with the overall coordination, simulations have increased their standard resolution from 50km (PRUDENCE) to about 12km (Euro-CORDEX) and from time slice simulations (PRUDENCE) to transient experiments (ENSEMBLES and CORDEX); from one driving model and emission scenario (PRUDENCE) to several (Euro-CORDEX). So far, this wealth of simulations have been used to assess the potential impacts of future climate change in Europe providing a baseline change as defined by a multi-model mean change with associated uncertainties calculated from model spread in the ensemble. But how has the overall picture of state-of-the-art regional climate change projections changed over this period of almost two decades? Here we compare across scenarios, model resolutions and model vintage the results from PRUDENCE, ENSEMBLES and Euro-CORDEX. By appropriate scaling we identify robust findings about the projected future of European climate expressed by temperature and precipitation changes

  16. Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

    Science.gov (United States)

    Exbrayat, Jean-François; Bloom, A. Anthony; Falloon, Pete; Ito, Akihiko; Smallman, T. Luke; Williams, Mathew

    2018-02-01

    Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) business as usual emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095-2099) compared to 2001-2005, which is 2-3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y-1. Using REA also leads to a 45-68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.

  17. Development of a forecasting method of a region`s electric power demand. 1. Forecasting economic and social indexes; Chiikibetsu denryoku juyo yosoku shuhono kaihatsu ni tsuite. 1. Keizai shakai shihyo no yosoku

    Energy Technology Data Exchange (ETDEWEB)

    Minato, Y. [Shikoku Research Institute Inc., Kagawa (Japan); Yokoi, Y. [The University of Tokushima, Tokushima (Japan)

    1996-01-20

    This paper relates to the forecasting method of the electric power demands (kWh and kW) of a region, approached by not only time series analysis but economic and social indexes. Those indexes, based on historical statistics such as census and establishment statistics, are rearranged from an administrative division to a managerial division of the electric power company, and applied as fundamental information for forecasting the area`s kWh and also sales promotion. This method of forecasting the area`s kWh is based on the concept that area`s kWh is strongly connected with the population their lifestyle and their activity within the region. In the paper, the framework of the computational model system and forecast result are discussed. The population, number of households and their members, and number of employed persons, are all evaluated. The forecasting method of the area`s population proposed here is based on the concept that the transition of population consists of both natural growth and immigration. By estimating both factors, the future area`s population can be easily forecasted. The information of whether the population is increasing or decreasing is useful for forecasting the region`s kWh and required sales promotion. 8 refs., 8 figs., 3 tabs.

  18. Forecasting world and regional aviation jet fuel demands to the mid-term (2025)

    International Nuclear Information System (INIS)

    Cheze, Benoit; Gastineau, Pascal; Chevallier, Julien

    2011-01-01

    This article provides jet fuel demand projections at the worldwide level and for eight geographical zones until 2025. Air traffic forecasts are performed using dynamic panel-data econometrics. Then, the conversion of air traffic projections into quantities of jet fuel is accomplished by using a complementary approach to the 'Traffic Efficiency' method developed previously by the UK Department of Trade and Industry to support the Intergovernmental Panel on Climate Change (). According to our main scenario, air traffic should increase by about 100% between 2008 and 2025 at the world level, corresponding to a yearly average growth rate of 4.7%. World jet fuel demand is expected to increase by about 38% during the same period, corresponding to a yearly average growth rate of 1.9% per year. According to these results, energy efficiency improvements allow reducing the effect of air traffic rise on the increase in jet fuel demand, but do not annihilate it. Jet fuel demand is thus unlikely to diminish unless there is a radical technological shift, or air travel demand is restricted. - Highlights: → Jet fuel demand is forecasted at the worldwide and regional level until 2025. → Regional heterogeneity must be considered when forecasting jet fuel demand. → World air traffic should increase by about 100% between 2008 and 2025. → World jet fuel demand is expected to increase by about 38% during the same period. → Technological progress will not be enough to decrease the world jet fuel demand.

  19. The wind forecasting improvement project. Description and results from the Southern study region

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey [AWS Truepower LLC, Albany, NY (United States); Benjamin, Stan; Wilczak, James [National Oceanic and Atmospheric Administration, Washington, DC and Boulder, CO (United States)] [and others

    2012-07-01

    The Wind Forecasting Improvement Project (WFIP) is a multi-year U.S. Department of Energy (DOE)/National Oceanographic and Atmospheric Administration (NOAA) sponsored study whose main purpose is to demonstrate the scientific and economic benefits of additional atmospheric observations and model enhancements on wind energy production forecasts. WFIP covers two geographical regions of the U.S.: (1) the upper Great Plains, or Northern Study Area, and (2) most of Texas-the Southern Study Area. The Southern campaign is being led by AWS Truepower LLC, and includes a team of private, government, and academic partners with collective experience and expertise in all facets required to ensure a successful completion of the project. In addition presenting a summary of the state-of-the-art forecasting techniques used and phenomena-based analysis mentioned above, a brief synopsis of how ''lessons learned'' from the WFIP Southern Study Area can be articulated and applied to other wind resource regions will be described. (orig.)

  20. Short-range solar radiation forecasts over Sweden

    Directory of Open Access Journals (Sweden)

    T. Landelius

    2018-04-01

    Full Text Available In this article the performance for short-range solar radiation forecasts by the global deterministic and ensemble models from the European Centre for Medium-Range Weather Forecasts (ECMWF is compared with an ensemble of the regional mesoscale model HARMONIE-AROME used by the national meteorological services in Sweden, Norway and Finland. Note however that only the control members and the ensemble means are included in the comparison. The models resolution differs considerably with 18 km for the ECMWF ensemble, 9 km for the ECMWF deterministic model, and 2.5 km for the HARMONIE-AROME ensemble.The models share the same radiation code. It turns out that they all underestimate systematically the Direct Normal Irradiance (DNI for clear-sky conditions. Except for this shortcoming, the HARMONIE-AROME ensemble model shows the best agreement with the distribution of observed Global Horizontal Irradiance (GHI and DNI values. During mid-day the HARMONIE-AROME ensemble mean performs best. The control member of the HARMONIE-AROME ensemble also scores better than the global deterministic ECMWF model. This is an interesting result since mesoscale models have so far not shown good results when compared to the ECMWF models.Three days with clear, mixed and cloudy skies are used to illustrate the possible added value of a probabilistic forecast. It is shown that in these cases the mesoscale ensemble could provide decision support to a grid operator in terms of forecasts of both the amount of solar power and its probabilities.

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

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-06-01

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

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

    International Nuclear Information System (INIS)

    Ekman, Annica

    2000-02-01

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

  3. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    Science.gov (United States)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  4. Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

    Science.gov (United States)

    Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

    2017-12-01

    Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

  5. Forecasting of Energy and Petroleum Consumption by Motor Transport in the Regions of the Russian Federation

    Directory of Open Access Journals (Sweden)

    Leontiy Viktorovich Eder

    2017-09-01

    Full Text Available The paper offers the directions for the improvement of methodological approach to forecasting the energy consumption in transport, taking into account special features of Russian regions. The authors developed a multivariate model allowing to predict the motor vehicle rate specified for the regions of the Russian Federation depending on the economic, social and institutional features. We formalized the dynamic (trend model for predicting the effectiveness of energy consumption per unit of the vehicle in Russia with details on Federal districts. In the study, in predicting the number of motor transport, the authors applied the methods of economic and mathematical simulation modelling based on the results of the econometric analysis for the calculation of the population having motor transport. In determining the potential specific energy consumption, we have aggregated trending patterns and convergence. The study has shown that by 2040, the number of passenger cars in Russia will grow to 57.1 million, and the total number of all types of road transport will grow by 14.9 million units to 66.2 million. The highest growth rates are predicted in the Central regions of Russia and in some areas of Siberia. The smallest growth rates are expected in the Chukotka Autonomous District, Kamchatka and Primorsky regions. Energy efficiency in transport and active introduction of alternative motor fuels, primarily methane, will reduce the consumption of gasoline and diesel fuel by motor transport. Thus, in the forecast period of 2018–2040, the consumption of petroleum products by motor transport will be reduced by 8.9 million tons: from 61,9 million tons of oil to 51.7 million tons of oil. The results of the study can be applied for the formulation of proposals on the creation of scientific and methodological apparatus to predict the development of transport sector and oil products supply in of the regions of Russia.

  6. Operational forecasting of daily temperatures in the Valencia Region. Part II: minimum temperatures in winter.

    Science.gov (United States)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of minimum temperatures during winter is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, winter minimum temperatures are considered a parameter of interest and concern since persistent cold-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict cold-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that low temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily minimum temperatures during winter over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the winter forecast period from 1 December 2007 - 31 March 2008. The results obtained are encouraging and indicate a good agreement between the observed and simulated minimum temperatures. Moreover, the model captures quite well the temperatures in the extreme cold episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia

  7. Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer.

    Science.gov (United States)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of summer maximum temperatures is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, summer maximum daily temperatures are considered a parameter of interest and concern since persistent heat-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict heat-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily maximum temperatures during summer over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the summer forecast period of 1 June - 30 September, 2007. The results obtained are encouraging and indicate a good agreement between the observed and simulated maximum temperatures. Moreover, the model captures quite well the temperatures in the extreme heat episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

  8. Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-12-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http://www.fona-miklip.de/decadal-forecast

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

    Science.gov (United States)

    Zheng, Guizhou; Chao, Yi; Xu, Hongwen

    2008-10-01

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

  10. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2017-09-01

    Full Text Available Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts from the GloSea5 model (1996 to 2009 are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region. Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 % in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows, whereas for the 3-month ahead lead time, GloSea5 forecasts account for  ∼ 70

  11. Forecasting Precipitation over the MENA Region: A Data Mining and Remote Sensing Based Approach

    Science.gov (United States)

    Elkadiri, R.; Sultan, M.; Elbayoumi, T.; Chouinard, K.

    2015-12-01

    We developed and applied an integrated approach to construct predictive tools with lead times of 1 to 12 months to forecast precipitation amounts over the Middle East and North Africa (MENA) region. The following steps were conducted: (1) acquire and analyze temporal remote sensing-based precipitation datasets (i.e. Tropical Rainfall Measuring Mission [TRMM]) over five main water source regions in the MENA area (i.e. Atlas Mountains in Morocco, Southern Sudan, Red Sea Hills of Yemen, and Blue Nile and White Nile source areas) throughout the investigation period (1998 to 2015), (2) acquire and extract monthly values for all of the climatic indices that are likely to influence the climatic patterns over the MENA region (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); and (3) apply data mining methods to extract relationships between the observed precipitation and the controlling factors (climatic indices) and use predictive tools to forecast monthly precipitation over each of the identified pilot study areas. Preliminary results indicate that by using the period from January 1998 until August 2012 for model training and the period from September 2012 to January 2015 for testing, precipitation can be successfully predicted with a three-months lead over South West Yemen, Atlas Mountains in Morocco, Southern Sudan, Blue Nile sources and White Nile sources with confidence (Pearson correlation coefficient: 0.911, 0.823, 0.807, 0.801 and 0.895 respectively). Future work will focus on applying this technique for prediction of precipitation over each of the climatically contiguous areas of the MENA region. If our efforts are successful, our findings will lead the way to the development and implementation of sound water management scenarios for the MENA countries.

  12. Forecasting of future earthquakes in the northeast region of India considering energy released concept

    Science.gov (United States)

    Zarola, Amit; Sil, Arjun

    2018-04-01

    This study presents the forecasting of time and magnitude size of the next earthquake in the northeast India, using four probability distribution models (Gamma, Lognormal, Weibull and Log-logistic) considering updated earthquake catalog of magnitude Mw ≥ 6.0 that occurred from year 1737-2015 in the study area. On the basis of past seismicity of the region, two types of conditional probabilities have been estimated using their best fit model and respective model parameters. The first conditional probability is the probability of seismic energy (e × 1020 ergs), which is expected to release in the future earthquake, exceeding a certain level of seismic energy (E × 1020 ergs). And the second conditional probability is the probability of seismic energy (a × 1020 ergs/year), which is expected to release per year, exceeding a certain level of seismic energy per year (A × 1020 ergs/year). The logarithm likelihood functions (ln L) were also estimated for all four probability distribution models. A higher value of ln L suggests a better model and a lower value shows a worse model. The time of the future earthquake is forecasted by dividing the total seismic energy expected to release in the future earthquake with the total seismic energy expected to release per year. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in zone Z.12, zone Z.16 and zone Z.15, respectively and that are the identified seismic source zones in the study area which show that the proposed techniques and models yield good forecasting accuracy.

  13. Regional Data Assimilation Using a Stretched-Grid Approach and Ensemble Calculations

    Science.gov (United States)

    Fox-Rabinovitz, M. S.; Takacs, L. L.; Govindaraju, R. C.; Atlas, Robert (Technical Monitor)

    2002-01-01

    The global variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) incorporating the GEOS SG-GCM (Fox-Rabinovitz 2000, Fox-Rabinovitz et al. 2001a,b), has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The major area of interest with enhanced regional resolution used in different SG-DAS experiments includes a rectangle over the U.S. with 50 or 60 km horizontal resolution. The analyses and diagnostics are produced for all mandatory levels from the surface to 0.2 hPa. The assimilated regional mesoscale products are consistent with global scale circulation characteristics due to using the SG-approach. Both the stretched grid and basic uniform grid DASs use the same amount of global grid-points and are compared in terms of regional product quality.

  14. Noodles: a tool for visualization of numerical weather model ensemble uncertainty.

    Science.gov (United States)

    Sanyal, Jibonananda; Zhang, Song; Dyer, Jamie; Mercer, Andrew; Amburn, Philip; Moorhead, Robert J

    2010-01-01

    Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single mid-troposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.

  15. Forecasting Hurricane Tracks Using a Complex Adaptive System

    National Research Council Canada - National Science Library

    Lear, Matthew R

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that consists of linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual...

  16. A Complex Adaptive System Approach to Forecasting Hurricane Tracks

    National Research Council Canada - National Science Library

    Lear, Matthew R

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that consists of linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-02-01

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

  18. Methodological Aspects in Forecasting Innovation Development of Dairy Cattle Breeding in the Region

    Directory of Open Access Journals (Sweden)

    Natal’ya Aleksandrovna Medvedeva

    2016-07-01

    Full Text Available Due to the fact that Russia is now a member of the World Trade Organization, long-term forecasting becomes an objectively necessary condition that helps choose an effective science-based long-term strategy for development of dairy cattle breeding that would take into consideration intellectual and innovative characteristics. Current structure of available statistical information does not meet modern challenges of innovation development and does not reflect adequately the trends of ongoing changes. The paper suggests a system of indicators to analyze the status, development and prospects of dairy cattle breeding in the region; this system provides timely identification of emerging risks and threats of deviation from the specified parameters. The system included indicators contained in the current statistical reporting and new indicators of innovation development of the industry, the quality of human capital and the level of government support. When designing the system of indicators, we used several methodological aspects of the Oslo Manual, which the Federal State Statistics Service considers to be an official methodological document concerning the collection of information about innovation activities. A structured system of indicators shifts the emphasis in the analysis of the final results to the conditions and prerequisites that help achieve forecast performance indicators in the functioning of Russia’s economy under WTO rules and make substantiated management decisions

  19. Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

    Science.gov (United States)

    Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

    2013-02-01

    Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

  20. Forecasting the development of regional economy on the basis of input — output tables

    Directory of Open Access Journals (Sweden)

    Yury Konstantinovich Mashunin

    2014-06-01

    Full Text Available The article presents a practical technology of forecasting the development of the regional economy, including the statement of the problem, the construction of a mathematical model, and its implementation. At the constructing of a model, the standard statistical data for the previous period (2011, built on the basis of the table “input — output” are used. A unit of output of final demand, resulting from investments is added. As a result, a model of the regional economy made in the form of a vector mathematical programming problem that takes into account the investment processes in a region is obtained. Its purpose is to maximize the production of final demand in a region (all industries in a region within the constraints of the input-output balance, investments, resource costs and capacities. For solving linear programming problems of vector, methods, based on the principle of normalization criteria and guaranteed result are used. Vector dynamics problem is solved in a specified number of years. The factors taking into account the rate of growth: gross volumes (resources, final demand, investment in every sector of the region are introduced. Numerical implementation of the prediction is shown in the test case economic modeling of Primorsky Krai, including fifteen branches of a three-year period in accordance with the requirements of the Budget Code. Results of the solution include the major economic indicators for a region: gross, gross regional product (GRP, investments (including broken by industry, as well as payroll taxes and other. All these economic indicators are the basis for the formation of budget revenues in a region.

  1. Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

    Czech Academy of Sciences Publication Activity Database

    Gaál, Ľ.; Beranová, R.; Hlavčová, K.; Kyselý, Jan

    2014-01-01

    Roč. 2014, č. 943487 (2014), s. 1-14 ISSN 1687-9309 Institutional support: RVO:67179843 ; RVO:68378289 Keywords : precipitation extremes * regional climate models * climate change Subject RIV: EH - Ecology, Behaviour Impact factor: 0.946, year: 2014

  2. Visualization of uncertainty and ensemble data: Exploration of climate modeling and weather forecast data with integrated ViSUS-CDAT systems

    International Nuclear Information System (INIS)

    Potter, Kristin; Pascucci, Valerio; Johhson, Chris; Wilson, Andrew; Bremer, Peer-Timo; Williams, Dean; Doutriaux, Charles

    2009-01-01

    Climate scientists and meteorologists are working towards a better understanding of atmospheric conditions and global climate change. To explore the relationships present in numerical predictions of the atmosphere, ensemble datasets are produced that combine time- and spatially-varying simulations generated using multiple numeric models, sampled input conditions, and perturbed parameters. These data sets mitigate as well as describe the uncertainty present in the data by providing insight into the effects of parameter perturbation, sensitivity to initial conditions, and inconsistencies in model outcomes. As such, massive amounts of data are produced, creating challenges both in data analysis and in visualization. This work presents an approach to understanding ensembles by using a collection of statistical descriptors to summarize the data, and displaying these descriptors using variety of visualization techniques which are familiar to domain experts. The resulting techniques are integrated into the ViSUS/Climate Data and Analysis Tools (CDAT) system designed to provide a directly accessible, complex visualization framework to atmospheric researchers.

  3. Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model

    Science.gov (United States)

    Li, S.; Rupp, D. E.; Hawkins, L.; Mote, P.; McNeall, D. J.; Sarah, S.; Wallom, D.; Betts, R. A.

    2017-12-01

    This study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office's atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. The bias-reduction process is done through a series of steps: 1) Generation of perturbed physics ensemble (PPE) through the volunteer computing network weather@home; 2) Using machine learning to train "cheap" and fast statistical emulators of climate model, to rule out regions of parameter spaces that lead to model variants that do not satisfy observational constraints, where the observational constraints (e.g., top-of-atmosphere energy flux, magnitude of annual temperature cycle, summer/winter temperature and precipitation) are introduced sequentially; 3) Designing a new PPE by "pre-filtering" using the emulator results. Steps 1) through 3) are repeated until results are considered to be satisfactory (3 times in our case). The process includes a sensitivity analysis to find dominant parameters for various model output metrics, which reduces the number of parameters to be perturbed with each new PPE. Relative to observational uncertainty, we achieve regional improvements without introducing large biases in other parts of the globe. Our results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate model, in combination with PPEs in systematic model improvement.

  4. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  5. The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

    Science.gov (United States)

    Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

    2015-10-01

    The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

  6. Ensemble atmospheric dispersion modeling for emergency response consequence assessments

    International Nuclear Information System (INIS)

    Addis, R.P.; Buckley, R.L.

    2003-01-01

    models. This provides a better understanding of the atmosphere and plume behavior than would a single model output. Atmospheric models often give the impression of greater accuracy than the science is capable of delivering. The ensemble approach is a powerful way to reassert the concept of having a family of equally valid solutions, while enabling outliers to be identified. The U.S. Department of Energy's Savannah River Technology Center (SRTC) has participated in RTMOD and ENSEMBLE. SRTC uses the Regional Atmospheric Modeling System (RAMS) and Lagrangian Particle Dispersion Model (LPDM) to provide plume forecasts in real-time for the European grid as described in the figure. The NOAA northern hemispheric model, Global Forecast System (a combination of the medium range forecast and aviation forecast models), is used to provide the initial and boundary conditions for RAMS. The model plume forecast data are sent to the ENSEMBLE WEB page in real-time where they may be compared with other model outputs. SRTC has participated in all the ENSEMBLE exercises in real-time. An example of the ensemble output is shown in the figure, which shows an overlay of the SRTC (crosshatched) initial 60-hour forecast for the plume overlaid on an ensemble of 5 other model outputs. The plume shadings show the level of consensus for a minimum threshold, enabling modelers to determine consensus between models and identify possible outliers. The traditional approach to provide atmospheric consequence assessment tools to aid decision-makers in response to a release from a nuclear facility is to provide a plume output from a particular model. However, the non-unique nature of solutions to the non-linear equations that govern the atmosphere, and the sensitivity of such equations to perturbations in the initial and boundary conditions, results in any single model output being simply one of many viable solutions. As such, the traditional approach does a disservice to decision-makers by inferring greater

  7. Ensemble Methods

    Science.gov (United States)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

  8. Climate change scenarios of precipitation extremes in the Carpathian region based on an ENSEMBLE of regional climate models

    Czech Academy of Sciences Publication Activity Database

    Gaál, Ladislav; Beranová, Romana; Hlavčová, K.; Kyselý, Jan

    2014-01-01

    Roč. 2014, č. 943487 (2014), s. 1-14 ISSN 1687-9309 R&D Projects: GA ČR(CZ) GA14-18675S Institutional support: RVO:68378289 Keywords : precipitation extremes * regional climate models * climate change Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.946, year: 2014 http://www.hindawi.com/journals/amete/2014/943487/

  9. Seasonal forecasting of lightning and thunderstorm activity in tropical and temperate regions of the world.

    Science.gov (United States)

    Dowdy, Andrew J

    2016-02-11

    Thunderstorms are convective systems characterised by the occurrence of lightning. Lightning and thunderstorm activity has been increasingly studied in recent years in relation to the El Niño/Southern Oscillation (ENSO) and various other large-scale modes of atmospheric and oceanic variability. Large-scale modes of variability can sometimes be predictable several months in advance, suggesting potential for seasonal forecasting of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, seasonal lightning activity in the world's tropical and temperate regions is examined here in relation to numerous different large-scale modes of variability. Of the seven modes of variability examined, ENSO has the strongest relationship with lightning activity during each individual season, with relatively little relationship for the other modes of variability. A measure of ENSO variability (the NINO3.4 index) is significantly correlated to local lightning activity at 53% of locations for one or more seasons throughout the year. Variations in atmospheric parameters commonly associated with thunderstorm activity are found to provide a plausible physical explanation for the variations in lightning activity associated with ENSO. It is demonstrated that there is potential for accurately predicting lightning and thunderstorm activity several months in advance in various regions throughout the world.

  10. Using Seasonal Forecasting Data for Vessel Routing

    Science.gov (United States)

    Bell, Ray; Kirtman, Ben

    2017-04-01

    We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

  11. A Simple Ensemble Simulation Technique for Assessment of Future Variations in Specific High-Impact Weather Events

    Science.gov (United States)

    Taniguchi, Kenji

    2018-04-01

    To investigate future variations in high-impact weather events, numerous samples are required. For the detailed assessment in a specific region, a high spatial resolution is also required. A simple ensemble simulation technique is proposed in this paper. In the proposed technique, new ensemble members were generated from one basic state vector and two perturbation vectors, which were obtained by lagged average forecasting simulations. Sensitivity experiments with different numbers of ensemble members, different simulation lengths, and different perturbation magnitudes were performed. Experimental application to a global warming study was also implemented for a typhoon event. Ensemble-mean results and ensemble spreads of total precipitation, atmospheric conditions showed similar characteristics across the sensitivity experiments. The frequencies of the maximum total and hourly precipitation also showed similar distributions. These results indicate the robustness of the proposed technique. On the other hand, considerable ensemble spread was found in each ensemble experiment. In addition, the results of the application to a global warming study showed possible variations in the future. These results indicate that the proposed technique is useful for investigating various meteorological phenomena and the impacts of global warming. The results of the ensemble simulations also enable the stochastic evaluation of differences in high-impact weather events. In addition, the impacts of a spectral nudging technique were also examined. The tracks of a typhoon were quite different between cases with and without spectral nudging; however, the ranges of the tracks among ensemble members were comparable. It indicates that spectral nudging does not necessarily suppress ensemble spread.

  12. 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

    ). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance......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...

  13. Ensemble using different Planetary Boundary Layer schemes in WRF model for wind speed and direction prediction over Apulia region

    Science.gov (United States)

    Tateo, Andrea; Marcello Miglietta, Mario; Fedele, Francesca; Menegotto, Micaela; Monaco, Alfonso; Bellotti, Roberto

    2017-04-01

    The Weather Research and Forecasting mesoscale model (WRF) was used to simulate hourly 10 m wind speed and direction over the city of Taranto, Apulia region (south-eastern Italy). This area is characterized by a large industrial complex including the largest European steel plant and is subject to a Regional Air Quality Recovery Plan. This plan constrains industries in the area to reduce by 10 % the mean daily emissions by diffuse and point sources during specific meteorological conditions named wind days. According to the Recovery Plan, the Regional Environmental Agency ARPA-PUGLIA is responsible for forecasting these specific meteorological conditions with 72 h in advance and possibly issue the early warning. In particular, an accurate wind simulation is required. Unfortunately, numerical weather prediction models suffer from errors, especially for what concerns near-surface fields. These errors depend primarily on uncertainties in the initial and boundary conditions provided by global models and secondly on the model formulation, in particular the physical parametrizations used to represent processes such as turbulence, radiation exchange, cumulus and microphysics. In our work, we tried to compensate for the latter limitation by using different Planetary Boundary Layer (PBL) parameterization schemes. Five combinations of PBL and Surface Layer (SL) schemes were considered. Simulations are implemented in a real-time configuration since our intention is to analyze the same configuration implemented by ARPA-PUGLIA for operational runs; the validation is focused over a time range extending from 49 to 72 h with hourly time resolution. The assessment of the performance was computed by comparing the WRF model output with ground data measured at a weather monitoring station in Taranto, near the steel plant. After the analysis of the simulations performed with different PBL schemes, both simple (e.g. average) and more complex post-processing methods (e.g. weighted average

  14. NYYD Ensemble

    Index Scriptorium Estoniae

    2002-01-01

    NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx

  15. iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

    Science.gov (United States)

    Sumi, S. J.; Ferreira, C.

    2017-12-01

    Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system

  16. Forecasting Forest Type and Age Classes in the Appalachian-Cumberland Subregion of the Central Hardwood Region

    Science.gov (United States)

    David N. Wear; Robert Huggett

    2011-01-01

    This chapter describes how forest type and age distributions might be expected to change in the Appalachian-Cumberland portions of the Central Hardwood Region over the next 50 years. Forecasting forest conditions requires accounting for a number of biophysical and socioeconomic dynamics within an internally consistent modeling framework. We used the US Forest...

  17. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  18. Forecasting electric demand of distribution system planing in rural and sparsely populated regions

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Buri, M.J. [ABB Automated Distribution Div., Raleigh, NC (United States); Finley, L.A. [Snohomish County PUD, Everett, WA (United States)

    1995-11-01

    Modern computerized distribution load forecasting methods, although accurate when applied to urban areas, give somewhat less satisfactory results when forecasting load growth in sparsely populated rural areas. This paper examines the differences between rural and urban load growth histories, identifying a major difference in the observed behavior of load growth. This difference is exploited in a new simulation forecasting algorithm. Tests show the new method is as accurate in forecasting rural load growth and as useful for analyzing DSM impacts than past methods, while requiring considerably lower computer resources and data than other simulation methods of comparable accuracy.

  19. Using an ensemble of regional climate models to assess climate change impacts on water scarcity in European river basins.

    Science.gov (United States)

    Gampe, David; Nikulin, Grigory; Ludwig, Ralf

    2016-12-15

    Climate change will likely increase pressure on the water balances of Mediterranean basins due to decreasing precipitation and rising temperatures. To overcome the issue of data scarcity the hydrological relevant variables total runoff, surface evaporation, precipitation and air temperature are taken from climate model simulations. The ensemble applied in this study consists of 22 simulations, derived from different combinations of four General Circulation Models (GCMs) forcing different Regional Climate Models (RCMs) and two Representative Concentration Pathways (RCPs) at ~12km horizontal resolution provided through the EURO-CORDEX initiative. Four river basins (Adige, Ebro, Evrotas and Sava) are selected and climate change signals for the future period 2035-2065 as compared to the reference period 1981-2010 are investigated. Decreased runoff and evaporation indicate increased water scarcity over the Ebro and the Evrotas, as well as the southern parts of the Adige and the Sava, resulting from a temperature increase of 1-3° and precipitation decrease of up to 30%. Most severe changes are projected for the summer months indicating further pressure on the river basins already at least partly characterized by flow intermittency. The widely used Falkenmark indicator is presented and confirms this tendency and shows the necessity for spatially distributed analysis and high resolution projections. Related uncertainties are addressed by the means of a variance decomposition and model agreement to determine the robustness of the projections. The study highlights the importance of high resolution climate projections and represents a feasible approach to assess climate impacts on water scarcity also in regions that suffer from data scarcity. Copyright © 2016. Published by Elsevier B.V.

  20. The Development of Storm Surge Ensemble Prediction System and Case Study of Typhoon Meranti in 2016

    Science.gov (United States)

    Tsai, Y. L.; Wu, T. R.; Terng, C. T.; Chu, C. H.

    2017-12-01

    Taiwan is under the threat of storm surge and associated inundation, which is located at a potentially severe storm generation zone. The use of ensemble prediction can help forecasters to know the characteristic of storm surge under the uncertainty of track and intensity. In addition, it can help the deterministic forecasting. In this study, the kernel of ensemble prediction system is based on COMCOT-SURGE (COrnell Multi-grid COupled Tsunami Model - Storm Surge). COMCOT-SURGE solves nonlinear shallow water equations in Open Ocean and coastal regions with the nested-grid scheme and adopts wet-dry-cell treatment to calculate potential inundation area. In order to consider tide-surge interaction, the global TPXO 7.1 tide model provides the tidal boundary conditions. After a series of validations and case studies, COMCOT-SURGE has become an official operating system of Central Weather Bureau (CWB) in Taiwan. In this study, the strongest typhoon in 2016, Typhoon Meranti, is chosen as a case study. We adopt twenty ensemble members from CWB WRF Ensemble Prediction System (CWB WEPS), which differs from parameters of microphysics, boundary layer, cumulus, and surface. From box-and-whisker results, maximum observed storm surges were located in the interval of the first and third quartile at more than 70 % gauge locations, e.g. Toucheng, Chengkung, and Jiangjyun. In conclusion, the ensemble prediction can effectively help forecasters to predict storm surge especially under the uncertainty of storm track and intensity

  1. Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

    Directory of Open Access Journals (Sweden)

    Ramona Magno

    2018-02-01

    Full Text Available A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets are integrated through an open-source, interoperable SDI (spatial data infrastructure based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

  2. Mapping Distribution and Forecasting Invasion of Prosopis juliflora in Ethiopia's Afar Region

    Science.gov (United States)

    West, A. M.; Wakie, T.; Luizza, M.; Evangelista, P.

    2014-12-01

    Invasion of non-native species is among the most critical threats to natural ecosystems and economies world-wide. Mesquite (which includes some 45 species) is an invasive deciduous tree which is known to have an array of negative impacts on ecosystems and rural livelihoods in arid and semi-arid regions around the world, dominating millions of hectares of land in Asia, Africa, Australia and the Americas. In Ethiopia, Prosopis juliflora (the only reported mesquite) is the most pervasive plant invader, threatening local livelihoods and the country's unique biodiversity. Due to its rapid spread and persistence, P. juliflora has been ranked as one of the leading threats to traditional land use, exceeded only by drought and conflict. This project utilized NASA's Earth Observing System (EOS) data and species distribution modeling to map current infestations of P. juliflora in the Afar region of northeastern Ethiopia, and forecast its suitable habitat across the entire country. This project provided a time and cost-effective strategy for conducting risk assessments of invasive mesquite and subsequent monitoring and mitigation efforts by land managers and local communities.

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

    Energy Technology Data Exchange (ETDEWEB)

    Parhizgari, A M

    1978-09-25

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

  4. An Operational Short-Term Forecasting System for Regional Hydropower Management

    Science.gov (United States)

    Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

    2017-12-01

    The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

  5. Application of a regional hurricane wind risk forecasting model for wood-frame houses.

    Science.gov (United States)

    Jain, Vineet Kumar; Davidson, Rachel Ann

    2007-02-01

    Hurricane wind risk in a region changes over time due to changes in the number, type, locations, vulnerability, and value of buildings. A model was developed to quantitatively estimate changes over time in hurricane wind risk to wood-frame houses (defined in terms of potential for direct economic loss), and to estimate how different factors, such as building code changes and population growth, contribute to that change. The model, which is implemented in a simulation, produces a probability distribution of direct economic losses for each census tract in the study region at each time step in the specified time horizon. By changing parameter values and rerunning the analysis, the effects of different changes in the built environment on the hurricane risk trends can be estimated and the relative effectiveness of hypothetical mitigation strategies can be evaluated. Using a case study application for wood-frame houses in selected counties in North Carolina from 2000 to 2020, this article demonstrates how the hurricane wind risk forecasting model can be used: (1) to provide insight into the dynamics of regional hurricane wind risk-the total change in risk over time and the relative contribution of different factors to that change, and (2) to support mitigation planning. Insights from the case study include, for example, that the many factors contributing to hurricane wind risk for wood-frame houses interact in a way that is difficult to predict a priori, and that in the case study, the reduction in hurricane losses due to vulnerability changes (e.g., building code changes) is approximately equal to the increase in losses due to building inventory growth. The potential for the model to support risk communication is also discussed.

  6. Shipping emission forecasts and cost-benefit analysis of China ports and key regions' control.

    Science.gov (United States)

    Liu, Huan; Meng, Zhi-Hang; Shang, Yi; Lv, Zhao-Feng; Jin, Xin-Xin; Fu, Ming-Liang; He, Ke-Bin

    2018-05-01

    China established Domestic Emission Control Area (DECA) for sulphur since 2015 to constrain the increasing shipping emissions. However, future DECA policy-makings are not supported due to a lack of quantitive evaluations. To investigate the effects of current and possible Chinese DECAs policies, a model is presented for the forecast of shipping emissions and evaluation of potential costs and benefits of an DECA policy package set in 2020. It includes a port-level and regional-level projection accounting for shipping trade volume growth, share of ship types, and fuel consumption. The results show that without control measures, both SO 2 and particulate matter (PM) emissions are expected to increase by 15.3-61.2% in Jing-Jin-Ji, the Yangtze River Delta, and the Pearl River Delta from 2013 to 2020. However, most emissions can be reduced annually by the establishment of a DECA that depends on the size of the control area and the fuel sulphur content limit. Costs range from 0.667 to 1.561 billion dollars (control regional shipping emissions) based on current fuel price. A social cost method shows the regional control scenarios benefit-cost ratios vary from 4.3 to 5.1 with large uncertainty. Chemical transportation model combined with health model method is used to get the monetary health benefits and then compared with the results from social cost method. This study suggests that Chinese DECAs will reduce the projected emissions at a favorable benefit-cost ratio, and furthermore proposes policy combinations that provide high cost-effective benefits as a reference for future policy-making. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

  7. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

    2017-01-01

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation

  8. Skilful seasonal forecasts of streamflow over Europe?

    Science.gov (United States)

    Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

    2018-04-01

    This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate

  9. Ensembl 2004.

    Science.gov (United States)

    Birney, E; Andrews, D; Bevan, P; Caccamo, M; Cameron, G; Chen, Y; Clarke, L; Coates, G; Cox, T; Cuff, J; Curwen, V; Cutts, T; Down, T; Durbin, R; Eyras, E; Fernandez-Suarez, X M; Gane, P; Gibbins, B; Gilbert, J; Hammond, M; Hotz, H; Iyer, V; Kahari, A; Jekosch, K; Kasprzyk, A; Keefe, D; Keenan, S; Lehvaslaiho, H; McVicker, G; Melsopp, C; Meidl, P; Mongin, E; Pettett, R; Potter, S; Proctor, G; Rae, M; Searle, S; Slater, G; Smedley, D; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Storey, R; Ureta-Vidal, A; Woodwark, C; Clamp, M; Hubbard, T

    2004-01-01

    The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organize biology around the sequences of large genomes. It is a comprehensive and integrated source of annotation of large genome sequences, available via interactive website, web services or flat files. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. The facilities of the system range from sequence analysis to data storage and visualization and installations exist around the world both in companies and at academic sites. With a total of nine genome sequences available from Ensembl and more genomes to follow, recent developments have focused mainly on closer integration between genomes and external data.

  10. Probabilistic Near and Far-Future Climate Scenarios of Precipitation and Surface Temperature for the North American Monsoon Region Under a Weighted CMIP5-GCM Ensemble Approach.

    Science.gov (United States)

    Montero-Martinez, M. J.; Colorado, G.; Diaz-Gutierrez, D. E.; Salinas-Prieto, J. A.

    2017-12-01

    It is well known the North American Monsoon (NAM) region is already a very dry region which is under a lot of stress due to the lack of water resources on multiple locations of the area. However, it is very interesting that even under those conditions, the Mexican part of the NAM region is certainly the most productive in Mexico from the agricultural point of view. Thus, it is very important to have realistic climate scenarios for climate variables such as temperature, precipitation, relative humidity, radiation, etc. This study tries to tackle that problem by generating probabilistic climate scenarios using a weighted CMIP5-GCM ensemble approach based on the Xu et al. (2010) technique which is on itself an improved method from the better known Reliability Ensemble Averaging algorithm of Giorgi and Mearns (2002). In addition, it is compared the 20-plus GCMs individual performances and the weighted ensemble versus observed data (CRU TS2.1) by using different metrics and Taylor diagrams. This study focuses on probabilistic results reaching a certain threshold given the fact that those types of products could be of potential use for agricultural applications.

  11. Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

    Science.gov (United States)

    Cawiding, Olive R.; Natividad, Gina May R.; Bato, Crisostomo V.; Addawe, Rizavel C.

    2017-11-01

    The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.

  12. Estimating the sensitivity of a regional atmospheric model to a sea state dependent roughness using ensemble calculations

    Energy Technology Data Exchange (ETDEWEB)

    Weisse, R.; Heyen, H.; Storch, H. von [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Hydrophysik

    1999-07-01

    The sensitivity of an atmospheric high resolution limited area model to a sea state dependent roughness is examined. Two sets of Monte Carlo experiments are compared with each other, one set with a sea state dependent roughness, the other with a roughness parameterized by the standard Charnock relation. From the climatic point of view, i.e., on time scales of months and longer, the differences of mean and standard deviation between the two sets are small and exceed, for instance in winter, where the differences are largest, rarely 0.4 hPa. From the forecasters' point of view, i.e., locally and on a daily time scale, differences of several hPa may occur, but generally differences are smaller than 1 hPa, too. It is concluded that on longer time scales the Charnock relation is a reasonable parameterization in global and regional atmospheric climate models. It is illustrated, too, that the comparison of single realizations, a common practice in regional sensitivity studies, can be misleading. Large differences were found between individual realizations, suggesting a considerable impact of the sea state dependent roughness on the atmosphere. However, it is shown that these differences are to a large fraction caused by internal variability of the atmospheric model itself. (orig.) [German] Die Sensitivitaet eines atmosphaerischen Regionalmodells bezueglich einer seegangsabhaengigen Rauhigkeit wird mittels Ensemblerechnungen untersucht. Dazu werden eine Reihe von Monte-Carlo-Experimenten mit und ohne seegangsabhaengige Rauhigkeit verglichen. Im letzten Fall wird die Charnock-Relation zur Bestimmung der Rauhigkeit verwendet. Auf Zeitskalen von Monaten und laenger sind die Unterschiede zwischen den Experimenten mit und denen ohne seegangsabhaengige Rauhigkeit gering. Auf der lokalen und der taeglichen Skala koennen jedoch Unterschiede von einigen hPa im Bodenluftdruckfeld auftreten. Es wird gezeigt, dass diese Unterschiede jedoch weniger eine Reaktion des

  13. Ensemble Sampling

    OpenAIRE

    Lu, Xiuyuan; Van Roy, Benjamin

    2017-01-01

    Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems. In its basic form, the algorithm requires computing and sampling from a posterior distribution over models, which is tractable only for simple special cases. This paper develops ensemble sampling, which aims to approximate Thompson sampling while maintaining tractability even in the face of complex models such as neural networks. Ensemble sampling dramatically expands on the range of applica...

  14. The role of ensemble-based statistics in variational assimilation of cloud-affected observations from infrared imagers

    Science.gov (United States)

    Hacker, Joshua; Vandenberghe, Francois; Jung, Byoung-Jo; Snyder, Chris

    2017-04-01

    Effective assimilation of cloud-affected radiance observations from space-borne imagers, with the aim of improving cloud analysis and forecasting, has proven to be difficult. Large observation biases, nonlinear observation operators, and non-Gaussian innovation statistics present many challenges. Ensemble-variational data assimilation (EnVar) systems offer the benefits of flow-dependent background error statistics from an ensemble, and the ability of variational minimization to handle nonlinearity. The specific benefits of ensemble statistics, relative to static background errors more commonly used in variational systems, have not been quantified for the problem of assimilating cloudy radiances. A simple experiment framework is constructed with a regional NWP model and operational variational data assimilation system, to provide the basis understanding the importance of ensemble statistics in cloudy radiance assimilation. Restricting the observations to those corresponding to clouds in the background forecast leads to innovations that are more Gaussian. The number of large innovations is reduced compared to the more general case of all observations, but not eliminated. The Huber norm is investigated to handle the fat tails of the distributions, and allow more observations to be assimilated without the need for strict background checks that eliminate them. Comparing assimilation using only ensemble background error statistics with assimilation using only static background error statistics elucidates the importance of the ensemble statistics. Although the cost functions in both experiments converge to similar values after sufficient outer-loop iterations, the resulting cloud water, ice, and snow content are greater in the ensemble-based analysis. The subsequent forecasts from the ensemble-based analysis also retain more condensed water species, indicating that the local environment is more supportive of clouds. In this presentation we provide details that explain the

  15. Classification of rainfall events for weather forecasting purposes in andean region of Colombia

    Science.gov (United States)

    Suárez Hincapié, Joan Nathalie; Romo Melo, Liliana; Vélez Upegui, Jorge Julian; Chang, Philippe

    2016-04-01

    This work presents a comparative analysis of the results of applying different methodologies for the identification and classification of rainfall events of different duration in meteorological records of the Colombian Andean region. In this study the work area is the urban and rural area of Manizales that counts with a monitoring hydro-meteorological network. This network is composed of forty-five (45) strategically located stations, this network is composed of forty-five (45) strategically located stations where automatic weather stations record seven climate variables: air temperature, relative humidity, wind speed and direction, rainfall, solar radiation and barometric pressure. All this information is sent wirelessly every five (5) minutes to a data warehouse located at the Institute of Environmental Studies-IDEA. With obtaining the series of rainfall recorded by the hydrometeorological station Palogrande operated by the National University of Colombia in Manizales (http://froac.manizales.unal.edu.co/bodegaIdea/); it is with this information that we proceed to perform behavior analysis of other meteorological variables, monitored at surface level and that influence the occurrence of such rainfall events. To classify rainfall events different methodologies were used: The first according to Monjo (2009) where the index n of the heavy rainfall was calculated through which various types of precipitation are defined according to the intensity variability. A second methodology that permitted to produce a classification in terms of a parameter β introduced by Rice and Holmberg (1973) and adapted by Llasat and Puigcerver, (1985, 1997) and the last one where a rainfall classification is performed according to the value of its intensity following the issues raised by Linsley (1977) where the rains can be considered light, moderate and strong fall rates to 2.5 mm / h; from 2.5 to 7.6 mm / h and above this value respectively for the previous classifications. The main

  16. Impacto da utilização de previsões "defasadas" no sistema de previsão de tempo por conjunto do CPTEC/INPE The impact of using lagged forecasts on the CPTEC/INPE ensemble prediction system

    Directory of Open Access Journals (Sweden)

    Lúcia Helena Ribas Machado

    2010-03-01

    Full Text Available Neste trabalho é descrita a aplicação da técnica de previsões defasadas no sistema de previsão de tempo por conjuntos do Centro de Previsão de Tempo e Estudos Climáticos (EPS-CPTEC/INPE. Os dados do CPTEC/INPE consistem em uma amostra de dois meses com previsões de 15 dias para as variáveis: altura geopotencial em 500 hPa, temperatura do ar no nível de 850 hPa, e pressão atmosférica ao nível médio do mar. O estudo consiste em investigar: 1 o desempenho do EPS-CPTEC/INPE utilizando a técnica de previsões defasadas comparado àquele do conjunto operacional; 2 a relação entre o espalhamento e o desempenho da previsão, a fim de avaliar o uso da dispersão como preditor do desempenho. Os resultados indicam que a utilização de previsões defasadas em 12h, melhora o desempenho do conjunto operacional, contribuindo para aumentar o espalhamento do conjunto e, conseqüentemente, reduzir a sub-dispersão do sistema. Também foi observado que o conjunto defasado tem desempenho comparável àquele do conjunto operacional e que há uma tendência de desempenho alto quando o espalhamento é baixo, para os prazos de 5 e 7 dias de previsão. Estes resultados servem como base para a implementação operacional desta técnica, que apresenta baixo custo computacional, e contribui para a utilização mais eficiente das previsões por conjunto do CPTEC/INPE.In this work we report the application of the lagged average forecasting technique to CPTEC/INPE ensemble forecast. The CPTEC/INPE data consist of two months samples of 15 days forecast for the variables: geopotential height at 500 hPa, air temperature at 850 hPa and mean sea level atmospheric pressure. We focus on the following: 1 Does the lagged averaged ensemble forecast improve forecast skill compared to the CPTEC/INPE operational ensemble? 2 Is the dispersion of the ensemble useful in predicting forecast skill? The results indicate that the utilization of 12h-lagged average forecasts

  17. Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961-2000

    Science.gov (United States)

    Sanchez-Gomez, Emilia; Somot, S.; Déqué, M.

    2009-10-01

    One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961-2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation.

  18. Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961-2000

    Energy Technology Data Exchange (ETDEWEB)

    Somot, S.; Deque, M. [Meteo-France CNRM/GMGEC CNRS/GAME, Toulouse (France); Sanchez-Gomez, Emilia

    2009-10-15

    One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961-2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation. (orig.)

  19. Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling

    International Nuclear Information System (INIS)

    Dyson, Brian; Chang, N.-B.

    2005-01-01

    Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach - system dynamics modeling - for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool - Stella[reg]. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues

  20. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib

    2017-05-26

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  1. Forecasting the regional distribution and sufficiency of physicians in Japan with a coupled system dynamics-geographic information system model.

    Science.gov (United States)

    Ishikawa, Tomoki; Fujiwara, Kensuke; Ohba, Hisateru; Suzuki, Teppei; Ogasawara, Katsuhiko

    2017-09-12

    In Japan, the shortage of physicians has been recognized as a major medical issue. In our previous study, we reported that the absolute shortage will be resolved in the long term, but maldistribution among specialties will persist. To address regional shortage, several Japanese medical schools increased existing quota and established "regional quotas." This study aims to assist policy makers in designing effective policies; we built a model for forecasting physician numbers by region to evaluate future physician supply-demand balances. For our case study, we selected Hokkaido Prefecture in Japan, a region displaying disparities in healthcare services availability between urban and rural areas. We combined a system dynamics (SD) model with geographic information system (GIS) technology to analyze the dynamic change in spatial distribution of indicators. For Hokkaido overall and for each secondary medical service area (SMSA) within the prefecture, we analyzed the total number of practicing physicians. For evaluating absolute shortage and maldistribution, we calculated sufficiency levels and Gini coefficient. Our study covered the period 2010-2030 in 5-year increments. According to our forecast, physician shortage in Hokkaido Prefecture will largely be resolved by 2020. Based on current policies, we forecast that four SMSAs in Hokkaido will continue to experience physician shortages past that date, but only one SMSA would still be understaffed in 2030. The results show the possibility that diminishing imbalances between SMSAs would not necessarily mean that regional maldistribution would be eliminated, as seen from the sufficiency levels of the various SMSAs. Urgent steps should be taken to place doctors in areas where our forecasting model predicts that physician shortages could occur in the future.

  2. Future changes in peak river flows across northern Eurasia as inferred from an ensemble of regional climate projections under the IPCC RCP8.5 scenario

    Science.gov (United States)

    Shkolnik, Igor; Pavlova, Tatiana; Efimov, Sergey; Zhuravlev, Sergey

    2018-01-01

    Climate change simulation based on 30-member ensemble of Voeikov Main Geophysical Observatory RCM (resolution 25 km) for northern Eurasia is used to drive hydrological model CaMa-Flood. Using this modeling framework, we evaluate the uncertainties in the future projection of the peak river discharge and flood hazard by 2050-2059 relative to 1990-1999 under IPCC RCP8.5 scenario. Large ensemble size, along with reasonably high modeling resolution, allows one to efficiently sample natural climate variability and increase our ability to predict future changes in the hydrological extremes. It has been shown that the annual maximum river discharge can almost double by the mid-XXI century in the outlets of major Siberian rivers. In the western regions, there is a weak signal in the river discharge and flood hazard, hardly discernible above climate variability. Annual maximum flood area is projected to increase across Siberia mostly by 2-5% relative to the baseline period. A contribution of natural climate variability at different temporal scales to the uncertainty of ensemble prediction is discussed. The analysis shows that there expected considerable changes in the extreme river discharge probability at locations of the key hydropower facilities. This suggests that the extensive impact studies are required to develop recommendations for maintaining regional energy security.

  3. Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

    Science.gov (United States)

    So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

    2018-05-01

    Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode

  4. The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada

    Science.gov (United States)

    Gagnon, Patrick; Rousseau, Alain N.; Charron, Dominique; Fortin, Vincent; Audet, René

    2017-11-01

    Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada's (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Québec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.

  5. Quantile forecast discrimination ability and value

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Pinson, Pierre; Friederichs, Petra

    2015-01-01

    While probabilistic forecast verification for categorical forecasts is well established, some of the existing concepts and methods have not found their equivalent for the case of continuous variables. New tools dedicated to the assessment of forecast discrimination ability and forecast value are ...... is illustrated based on synthetic datasets, as well as for the case of global radiation forecasts from the high resolution ensemble COSMO-DE-EPS of the German Weather Service....

  6. Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

    Science.gov (United States)

    Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

    2018-02-01

    A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

  7. Modeling the wind-fields of accidental releases with an operational regional forecast model

    International Nuclear Information System (INIS)

    Albritton, J.R.; Lee, R.L.; Sugiyama, G.

    1995-01-01

    The Atmospheric Release Advisory Capability (ARAC) is an operational emergency preparedness and response organization supported primarily by the Departments of Energy and Defense. ARAC can provide real-time assessments of atmospheric releases of radioactive materials at any location in the world. ARAC uses robust three-dimensional atmospheric transport and dispersion models, extensive geophysical and dose-factor databases, meteorological data-acquisition systems, and an experienced staff. Although it was originally conceived and developed as an emergency response and assessment service for nuclear accidents, the ARAC system has been adapted to also simulate non-radiological hazardous releases. For example, in 1991 ARAC responded to three major events: the oil fires in Kuwait, the eruption of Mt. Pinatubo in the Philippines, and the herbicide spill into the upper Sacramento River in California. ARAC's operational simulation system, includes two three-dimensional finite-difference models: a diagnostic wind-field scheme, and a Lagrangian particle-in-cell transport and dispersion scheme. The meteorological component of ARAC's real-time response system employs models using real-time data from all available stations near the accident site to generate a wind-field for input to the transport and dispersion model. Here we report on simulation studies of past and potential release sites to show that even in the absence of local meteorological observational data, readily available gridded analysis and forecast data and a prognostic model, the Navy Operational Regional Atmospheric Prediction System, applied at an appropriate grid resolution can successfully simulate complex local flows

  8. Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

    Science.gov (United States)

    Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

    2017-04-01

    The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV

  9. Forecasting the future of biodiversity

    DEFF Research Database (Denmark)

    Fitzpatrick, M. C.; Sanders, Nate; Ferrier, Simon

    2011-01-01

    , but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single-species models to those from a multi-species modeling approach, Generalized Dissimilarity Modeling (GDM...... climate change impacts on biodiversity....

  10. Bias correction of daily precipitation projected by the CORDEX-Africa ensemble for a sparsely gauged region in West Africa with regionalized distribution parameters

    Science.gov (United States)

    Lorenz, Manuel; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald

    2017-04-01

    Reliable estimates of future climatic conditions are indispensable for the sustainable planning of agricultural activities in West Africa. Precipitation time series of regional climate models (RCMs) typically exhibit a bias in the distribution of both rainfall intensities and wet day frequencies. Furthermore, the annual and monthly sums of precipitation may remarkably vary from the observations in this region. As West Africa experiences a distinct rainy season, sowing dates are oftentimes planned based on the beginning of this rainfall period. A biased representation of the annual cycle of precipitation in the uncorrected RCMs can therefore lead to crop failure. The precipitation ensemble, obtained from the Coordinated Downscaling Experiment CORDEX-Africa, was bias-corrected for the study region in West Africa (extending approximately 343,358 km2) which covers large parts of Burkina Faso, Ghana and Benin. In oder to debias the RCM precipitation simulations, a Quantile-Mapping method was applied to the historical period 1950-2005. For the RCM future projections (2006-2100), the Double-Quantile-Mapping procedure was chosen. This method makes use of the shift in the distribution function of the future precipitation values which allows to incorporate the climate change signal of the RCM projections into the bias correction. As large areas of the study region are ungauged, the assignment of the information from the nearest station to the ungauged location would lead to sharp changes in the estimated statistics from one location to another. Thus, the distribution parameters needed for the Quantile-Mapping were estimated by Kriging the distribution parameters of the available measurement stations. This way it is possible to obtain reasonable estimates of the expected distribution of precipitation at ungauged locations. The presentation will illustrate some aspects and trade-offs in the distribution parameter interpolation as well as an analysis of the uncertainties of the

  11. MAG4 Versus Alternative Techniques for Forecasting Active-Region Flare Productivity

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2014-01-01

    MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events. MAG4 does not forecast that a flare will occur at a particular time in the next 24 or 48 hours; rather the probability of one occurring.

  12. Application of probabilistic precipitation forecasts from a ...

    African Journals Online (AJOL)

    Application of probabilistic precipitation forecasts from a deterministic model towards increasing the lead-time of flash flood forecasts in South Africa. ... The procedure is applied to a real flash flood event and the ensemble-based rainfall forecasts are verified against rainfall estimated by the SAFFG system. The approach ...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01-01 to 2007-09-05

    Data.gov (United States)

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

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

    Czech Academy of Sciences Publication Activity Database

    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, Kryštof

    2012-01-01

    Roč. 12, - (2012), s. 1-87 ISSN 1680-7316 Institutional research plan: CEZ:AV0Z10300504 Keywords : chemical weather * numerical models * operational forecasting * air Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 5.510, year: 2012

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-12-15

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

  16. A hierarchical Bayesian spatio-temporal model to forecast trapped particle fluxes over the SAA region

    Czech Academy of Sciences Publication Activity Database

    Suparta, W.; Gusrizal, G.; Kudela, Karel; Isa, Z.

    2017-01-01

    Roč. 28, č. 3 (2017), s. 357-370 ISSN 1017-0839 R&D Projects: GA MŠk EF15_003/0000481 Institutional support: RVO:61389005 Keywords : trapped particle * spatio-temporal * hierarchical Bayesian * forecasting Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 0.752, year: 2016

  17. Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support

    NARCIS (Netherlands)

    C.G.H. Diks (Cees); V. Panchenko (Valentyn); O. Sokolinskiy (Oleg); D.J.C. van Dijk (Dick)

    2013-01-01

    textabstractThis paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample)

  18. Comparing the accuracy of copula-based multivariate density forecasts in selected regions of support

    NARCIS (Netherlands)

    Diks, C.; Panchenko, V.; Sokolinskiy, O.; van Dijk, D.

    2013-01-01

    This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional

  19. Seasonal regional forecast of the minimum sea ice extent in the LapteV Sea

    Science.gov (United States)

    Tremblay, B.; Brunette, C.; Newton, R.

    2017-12-01

    Late winter anomaly of sea ice export from the peripheral seas of the Atctic Ocean was found to be a useful predictor for the minimum sea ice extent (SIE) in the Arctic Ocean (Williams et al., 2017). In the following, we present a proof of concept for a regional seasonal forecast of the min SIE for the Laptev Sea based on late winter coastal divergence quantified using a Lagrangian Ice Tracking System (LITS) forced with satellite derived sea-ice drifts from the Polar Pathfinder. Following Nikolaeva and Sesterikov (1970), we track an imaginary line just offshore of coastal polynyas in the Laptev Sea from December of the previous year to May 1 of the following year using LITS. Results show that coastal divergence in the Laptev Sea between February 1st and May 1st is best correlated (r = -0.61) with the following September minimum SIE in accord with previous results from Krumpen et al. (2013, for the Laptev Sea) and Williams et a. (2017, for the pan-Arctic). This gives a maximum seasonal predictability of Laptev Sea min SIE anomalies from observations of approximately 40%. Coastal ice divergence leads to formation of thinner ice that melts earlier in early summer, hence creating areas of open water that have a lower albedo and trigger an ice-albedo feedback. In the Laptev Sea, we find that anomalies of coastal divergence in late winter are amplified threefold to result in the September SIE. We also find a correlation coefficient r = 0.49 between February-March-April (FMA) anomalies of coastal divergence with the FMA averaged AO index. Interestingly, the correlation is stronger, r = 0.61, when comparing the FMA coastal divergence anomalies to the DJFMA averaged AO index. It is hypothesized that the AO index at the beginning of the winter (and the associated anomalous sea ice export) also contains information that impact the magnitude of coastal divergence opening later in the winter. Our approach differs from previous approaches (e.g. Krumpen et al and Williams et al

  20. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

  1. Regional Differences in Striatal Neuronal Ensemble Excitability Following Cocaine and Extinction Memory Retrieval in Fos-GFP Mice.

    Science.gov (United States)

    Ziminski, Joseph J; Sieburg, Meike C; Margetts-Smith, Gabriella; Crombag, Hans S; Koya, Eisuke

    2018-03-01

    Learned associations between drugs of abuse and the drug administration environment have an important role in addiction. In rodents, exposure to a drug-associated environment elicits conditioned psychomotor activation, which may be weakened following extinction (EXT) learning. Although widespread drug-induced changes in neuronal excitability have been observed, little is known about specific changes within neuronal ensembles activated during the recall of drug-environment associations. Using a cocaine-conditioned locomotion (CL) procedure, the present study assessed the excitability of neuronal ensembles in the nucleus accumbens core and shell (NAc core and NAc shell ), and dorsal striatum (DS) following cocaine conditioning and EXT in Fos-GFP mice that express green fluorescent protein (GFP) in activated neurons (GFP+). During conditioning, mice received repeated cocaine injections (20 mg/kg) paired with a locomotor activity chamber (Paired) or home cage (Unpaired). Seven to 13 days later, both groups were re-exposed to the activity chamber under drug-free conditions and Paired, but not Unpaired, mice exhibited CL. In a separate group of mice, CL was extinguished by repeatedly exposing mice to the activity chamber under drug-free conditions. Following the expression and EXT of CL, GFP+ neurons in the NAc core (but not NAc shell and DS) displayed greater firing capacity compared to surrounding GFP- neurons. This difference in excitability was due to a generalized decrease in GFP- excitability following CL and a selective increase in GFP+ excitability following its EXT. These results suggest a role for both widespread and ensemble-specific changes in neuronal excitability following recall of drug-environment associations.

  2. Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

    Directory of Open Access Journals (Sweden)

    Hugo Carrão

    2018-06-01

    Full Text Available Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated. Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin

  3. Development of a methodology for monthly forecasting of surface fires of Colombia's vegetation cover, an application to north Andean region

    International Nuclear Information System (INIS)

    Gonzalez Hernandez, Yolanda; Rangel CH, Jesus Orlando

    2004-01-01

    In the present article a methodology is presented for the forecasting of the monthly risk of surface fires of the vegetation cover in Colombia, based on the analysis of meteorological components and variables of climatic and anthropic variability involved in fire risks of the north Andean region. The methodology enables one to regionalize the country, with fire prediction purposes in mind, into ten sub-regions, in each one of which seven height levels are defined to make up separate regions of study. For each of these, a database is built to feed both the logistic regression models and the Poisson models, which identify the variables independent from, and/or associated with the presence or absence of fires

  4. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

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

    Science.gov (United States)

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

    2016-10-01

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

  6. Reliability of windstorm predictions in the ECMWF ensemble prediction system

    Science.gov (United States)

    Becker, Nico; Ulbrich, Uwe

    2016-04-01

    Windstorms caused by extratropical cyclones are one of the most dangerous natural hazards in the European region. Therefore, reliable predictions of such storm events are needed. Case studies have shown that ensemble prediction systems (EPS) are able to provide useful information about windstorms between two and five days prior to the event. In this work, ensemble predictions with the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS are evaluated in a four year period. Within the 50 ensemble members, which are initialized every 12 hours and are run for 10 days, windstorms are identified and tracked in time and space. By using a clustering approach, different predictions of the same storm are identified in the different ensemble members and compared to reanalysis data. The occurrence probability of the predicted storms is estimated by fitting a bivariate normal distribution to the storm track positions. Our results show, for example, that predicted storm clusters with occurrence probabilities of more than 50% have a matching observed storm in 80% of all cases at a lead time of two days. The predicted occurrence probabilities are reliable up to 3 days lead time. At longer lead times the occurrence probabilities are overestimated by the EPS.

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

    Science.gov (United States)

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

    2016-04-01

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

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

    Directory of Open Access Journals (Sweden)

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