The Hydrologic Ensemble Prediction Experiment (HEPEX)
01 Dec 2012-Vol. 2012
TL;DR: The Hydrologic Ensemble Prediction Experiment (HEPEXPERiment) as discussed by the authors has been formed to develop and demonstrate new hydrologic forecasting technologies, and to facilitate the implementation of beneficial technologies into the operational environment.
Abstract: Abstract. Users of hydrologic predictions need reliable, quantitative forecast information, including estimates of uncertainty, for lead times ranging from less than an hour during flash flooding events to more than a year for long-term water management. To meet this need, operational agencies are developing hydrological ensemble forecast techniques to account for sources of uncertainty such as future precipitation, initial hydrological conditions, and hydrological model limitations including uncertain model parameters. Research advances in areas such as hydrologic modeling, data assimilation, ensemble prediction, and forecast verification need to be incorporated into operational forecasting systems to assure that the state-of-the-art products are reaching the forecast user community. The Hydrologic Ensemble Prediction EXperiment (HEPEX) has been formed to develop and demonstrate new hydrologic forecasting technologies, and to facilitate the implementation of beneficial technologies into the operational environment.
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01 May 2010
TL;DR: The scientific drivers of this shift towards ‘ensemble flood forecasting’ and the literature evidence of the ‘added value’ of flood forecasts based on EPS are reviewed.
Abstract: Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.
132 citations
DOI•
01 Jan 2018
TL;DR: In this paper, the authors present a Table of Table of Contents ( Table 1.1) and Table 2 ( Table 3.2) for a survey of the authors' work.
Abstract: ......................................................................................................................................... ii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of
4 citations
24 Sep 2014
3 citations
DOI•
14 Jun 2022
TL;DR: In this paper , the authors provide an evaluation of two widely used short-range reforecast products during summer in the mainland of China, including reforecasts from the Integrated Forecasting System of the European Center for Medium-Range Weather Forecasts (ECMWF), and re-casts from Global Ensemble Forecast System version 12 (GEFSv12) of the National Centers for Environmental Prediction.
Abstract: Raw weather forecasts from numerical prediction models usually suffer from systematic bias, which can be removed by statistical post‐processing methods to achieve accurate and reliable ensemble forecasts. The post‐processing of precipitation forecasts often requires reforecasts with long historical archives to ensure sufficient sample size. In this work, we provide an evaluation of two widely used short‐range reforecast products during summer in the mainland of China, including reforecasts from the Integrated Forecasting System of the European Center for Medium‐Range Weather Forecasts (ECMWF), and reforecasts from Global Ensemble Forecast System version 12 (GEFSv12) of the National Centers for Environmental Prediction. The results suggest that ECMWF reforecasts outperform GEFSv12 reforecasts in accuracy and discrimination in most regions of China, especially for heavy rain. On the other hand, the post‐processed GEFSv12 reforecasts are better than post‐processed ECMWF reforecasts in reliability for light rain in several dry regions. Moreover, we combine ECMWF and GEFSv12 reforecasts by Bayesian model averaging (BMA). The results show that BMA is able to combine the advantages of two reforecasts. Post‐processed forecasts from BMA perform as well as ECMWF reforecasts in accuracy and discrimination skill for heavy rain in wet regions of southern China. BMA results are also as reliable as post‐processed GEFSv12 reforecasts in dry regions of northern China. The evaluation results in this study could serve as a useful guide for further applications of those reforecast products in China.
1 citations
TL;DR: In this article , the authors present the latest developments in flood characterization and modeling, identify challenges in understanding flood processes, associated uncertainties and risks in coupled hydrologic and hydrodynamic modeling for forecasting and inundation mapping, and the potential use of state-of-the-art data assimilation and machine learning to tackle the complexities involved in transitioning such developments to operation.
Abstract: Over the past decades, the scientific community has made significant efforts to simulate flooding conditions using a variety of complex physically based models. Despite all advances, these models still fall short in accuracy and reliability and are often considered computationally intensive to be fully operational. This could be attributed to insufficient comprehension of the causative mechanisms of flood processes, assumptions in model development and inadequate consideration of uncertainties. We suggest adopting an approach that accounts for the influence of human activities, soil saturation, snow processes, topography, river morphology, and land‐use type to enhance our understanding of flood generating mechanisms. We also recommend a transition to the development of innovative earth system modeling frameworks where the interaction among all components of the earth system are simultaneously modeled. Additionally, more nonselective and rigorous studies should be conducted to provide a detailed comparison of physical models and simplified methods for flood inundation mapping. Linking process‐based models with data‐driven/statistical methods offers a variety of opportunities that are yet to be explored and conveyed to researchers and emergency managers. The main contribution of this paper is to notify scientists and practitioners of the latest developments in flood characterization and modeling, identify challenges in understanding flood processes, associated uncertainties and risks in coupled hydrologic and hydrodynamic modeling for forecasting and inundation mapping, and the potential use of state‐of‐the‐art data assimilation and machine learning to tackle the complexities involved in transitioning such developments to operation.
References
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TL;DR: The European Flood Alert System (EFAS) as discussed by the authors aims at increasing preparedness for floods in transnational European river basins by providing local water authorities with medium-range and probabilistic flood forecasting information 3 to 10 days in advance.
Abstract: . This paper presents the development of the European Flood Alert System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with medium-range and probabilistic flood forecasting information 3 to 10 days in advance. The EFAS research project started in 2003 with the development of a prototype at the European Commission Joint Research Centre (JRC), in close collaboration with the national hydrological and meteorological services. The prototype covers the whole of Europe on a 5 km grid. In parallel, different high-resolution data sets have been collected for the Elbe and Danube river basins, allowing the potential of the system under optimum conditions and on a higher resolution to be assessed. Flood warning lead-times of 3–10 days are achieved through the incorporation of medium-range weather forecasts from the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF), comprising a full set of 51 probabilistic forecasts from the Ensemble Prediction System (EPS) provided by ECMWF. The ensemble of different hydrographs is analysed and combined to produce early flood warning information, which is disseminated to the hydrological services that have agreed to participate in the development of the system. In Part 1 of this paper, the scientific approach adopted in the development of the system is presented. The rational of the project, the system�s set-up, its underlying components, basic principles and products are described. In Part 2, results of a detailed statistical analysis of the performance of the system are shown, with regard to both probabilistic and deterministic forecasts.
443 citations
University of Maryland, College Park1, Goddard Space Flight Center2, National Center for Atmospheric Research3, Forschungszentrum Jülich4, Science Applications International Corporation5, Portland State University6, University of Texas at Arlington7, University of Duisburg-Essen8, Lancaster University9, Australian National University10, Delft University of Technology11, Silver Spring Networks12, University Corporation for Atmospheric Research13, Kyoto University14, Wageningen University and Research Centre15, North Central College16
TL;DR: It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologics modellers, DA developers, and operational forecasters.
Abstract: Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.
392 citations
TL;DR: In this paper, numerical weather prediction (NWP) models are used to generate quantitative precipitation forecasts (QPF) to reduce the uncertainty for streamflow forecast, which is the dominant source of uncertainty for many streamflow forecasts.
Abstract: Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagge...
213 citations
TL;DR: A comprehensive review of the commonly used statistical postprocessing methods for both meteorological and hydrological forecasts is presented in this article, where methods to generate ensemble members that maintain the observed spatio-temporal and intervariable dependency are reviewed.
Abstract: Computer simulation models have been widely used to generate hydrometeorological forecasts. As the raw forecasts contain uncertainties arising from various sources, including model inputs and outputs, model initial and boundary conditions, model structure, and model parameters, it is necessary to apply statistical postprocessing methods to quantify and reduce those uncertainties. Different postprocessing methods have been developed for meteorological forecasts (e.g., precipitation) and for hydrological forecasts (e.g., streamflow) due to their different statistical properties. In this paper, we conduct a comprehensive review of the commonly used statistical postprocessing methods for both meteorological and hydrological forecasts. Moreover, methods to generate ensemble members that maintain the observed spatiotemporal and intervariable dependency are reviewed. Finally, some perspectives on the further development of statistical postprocessing methods for hydrometeorological ensemble forecasting are provided. WIREs Water 2017, 4:e1246. doi: 10.1002/wat2.1246
For further resources related to this article, please visit the WIREs website.
136 citations
01 May 2010
TL;DR: The scientific drivers of this shift towards ‘ensemble flood forecasting’ and the literature evidence of the ‘added value’ of flood forecasts based on EPS are reviewed.
Abstract: Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.
132 citations