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Statistical Postprocessing Of Ensemble Forecasts

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Statistical Postprocessing Of Ensemble Forecasts

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Author : Stéphane Vannitsem
language : en
Publisher: Elsevier
Release Date : 2018-05-17

Statistical Postprocessing Of Ensemble Forecasts written by Stéphane Vannitsem and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-17 with Science categories.

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place Provides real-world examples of methods used to formulate forecasts Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

Statistical Postprocessing Of Ensemble Forecasts

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Author : Stéphane Vannitsem
language : en
Publisher: Elsevier
Release Date : 2018-05-22

Advanced Statistical Post Processing Of Ensemble Weather Forecasts

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Author : S. Allen
language : en
Publisher:
Release Date : 2021

Advanced Statistical Post Processing Of Ensemble Weather Forecasts written by S. Allen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

A Spatial Gamma Gamma Model For The Statistical Postprocessing Of Ensemble Weather Forecasts

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Author : Lambert De Monte
language : en
Publisher:
Release Date : 2022

A Spatial Gamma Gamma Model For The Statistical Postprocessing Of Ensemble Weather Forecasts written by Lambert De Monte and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

"A spatial Gamma-Gamma hierarchical model is proposed for the statistical postprocessing of ensemble weather forecasts in the context of probabilistic forecasting of extreme meteorological events. Its flexible marginal properties allow for the modelling of exceedances of potentially sub-asymptotic thresholds while preserving heavy-tail behaviour. To be consistent with the asymptotically independent nature of extremes from meteorological phenomena generally observed at distinct geographical locations, the spatial dependence structure of the model is governed by a Gaussian copula whose correlation level is a function of the distance between sites. A simulation study is performed and it is shown that the model recovers parameter values and fits the data well. The model is then applied to the statistical postprocessing of rain precipitation ensemble weather forecasts at five watersheds of the Manicouagan hydroelectric complex managed by Hydro-Québec. This project was realized in collaboration with the Institut de recherche d'Hydro-Québec (IREQ) and is in keeping with this public utility company's strategy for reducing network damages and constraints imposed by meteorological hazards"--

Statistical Methods For Post Processing Ensemble Weather Forecasts

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Author : Robin Mark Williams
language : en
Publisher:
Release Date : 2016

Statistical Methods For Post Processing Ensemble Weather Forecasts written by Robin Mark Williams and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Statistical Post Processing Of Ensemble Wrf Forecasts For Microclimatic Regions In The U S Northeast

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Author : Marc James Alessi
language : en
Publisher:
Release Date : 2020

Statistical Post Processing Of Ensemble Wrf Forecasts For Microclimatic Regions In The U S Northeast written by Marc James Alessi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

This study utilizes the Weather Research and Forecasting model (WRF) to produce 9 km and 3 km resolution forecasts from the Global Forecast System (GFS) model for microclimatic, agricultural regions in the U.S. Northeast. These forecasts are then statistically post-processed to generate probabilistic forecasts for temperature, specific humidity, incoming solar radiation, and precipitation. A comparison of forecast skill was conducted between these post-processed forecasts, the raw WRF output, the GFS forecasts, and forecasts from the National Weather Service's National Digital Forecast Database (NDFD). Overall, significant improvement was observed in post-processed WRF forecasts over all other methods for all regions and variables. Furthermore, 9 km post-processed WRF had the same forecast skill as 3 km post-processed WRF, rendering 3 km WRF unnecessary if observational data is available. NDFD was found to be competitive with raw WRF for temperature, so that if observational data is unavailable for post-processing, the NDFD forecast method should be selected over running high resolution ensemble WRF.

Statistical Post Processing Methods And Their Implementation On The Ensemble Prediction Systems For Forecasting Temperature In The Use Of The French Electric Consumption

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Author : Adriana Geanina Gogonel
language : en
Publisher:
Release Date : 2012

Statistical Post Processing Methods And Their Implementation On The Ensemble Prediction Systems For Forecasting Temperature In The Use Of The French Electric Consumption written by Adriana Geanina Gogonel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

The thesis has for objective to study new statistical methods to correct temperature predictionsthat may be implemented on the ensemble prediction system (EPS) of Meteo France so toimprove its use for the electric system management, at EDF France. The EPS of Meteo Francewe are working on contains 51 members (forecasts by time-step) and gives the temperaturepredictions for 14 days. The thesis contains three parts: in the first one we present the EPSand we implement two statistical methods improving the accuracy or the spread of the EPS andwe introduce criteria for comparing results. In the second part we introduce the extreme valuetheory and the mixture models we use to combine the model we build in the first part withmodels for fitting the distributions tails. In the third part we introduce the quantile regressionas another way of studying the tails of the distribution.

Statistical Methods In The Atmospheric Sciences

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Author : Daniel S. Wilks
language : en
Publisher: Academic Press
Release Date : 2011-07-04

Statistical Methods In The Atmospheric Sciences written by Daniel S. Wilks and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-04 with Science categories.

Statistical Methods in the Atmospheric Sciences, Third Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revised and expanded text is intended to help students understand and communicate what their data sets have to say, or to make sense of the scientific literature in meteorology, climatology, and related disciplines. In this new edition, what was a single chapter on multivariate statistics has been expanded to a full six chapters on this important topic. Other chapters have also been revised and cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, and time series analysis. There is now an expanded treatment of resampling tests and key analysis techniques, an updated discussion on ensemble forecasting, and a detailed chapter on forecast verification. In addition, the book includes new sections on maximum likelihood and on statistical simulation and contains current references to original research. Students will benefit from pedagogical features including worked examples, end-of-chapter exercises with separate solutions, and numerous illustrations and equations. This book will be of interest to researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines. Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting Many worked examples End-of-chapter exercises, with answers provided

Combining Spatial Statistical And Ensemble Information In Probabilistic Weather Forecasts

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Author :
language : en
Publisher:
Release Date : 2006

Combining Spatial Statistical And Ensemble Information In Probabilistic Weather Forecasts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.

Forecast ensembles typically show a spread-skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the Spatial BMA technique, which combines BMA and the geostatistical output perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather fields simultaneously, rather than just weather events at individual locations. At any site individually, Spatial BMA reduces to the original BMA technique. The Spatial BMA method provides statistical ensembles of weather field forecasts that take the spatial structure of observed fields into account and honor the flow-dependent information contained in the dynamical ensemble. The members of the Spatial BMA ensemble are obtained by dressing the weather field forecasts from the dynamical ensemble with simulated spatially correlated error fields, in proportions that correspond to the BMA weights for the member models in the dynamical ensemble. Statistical ensembles of any size can be generated at minimal computational costs. The Spatial BMA technique was applied to 48-h forecasts of surface temperature over the North American Pacific Northwest in 2004, using the University of Washington mesoscale ensemble. The Spatial BMA ensemble generally outperformed the BMA and GOP ensembles and showed much better verification results than the raw ensemble, both at individual sites, for weather field forecasts, and for forecasts of composite quantities, such as average temperature in National Weather Service forecast zones and minimum temperature along the Interstate 90 Mountains to Sound Greenway.

Statistical Methods In The Atmospheric Sciences

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Author : Daniel S. Wilks
language : en
Publisher: Academic Press
Release Date : 2006

Praise for the First Edition:""I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences.""--BAMS (Bulletin of the American Meteorological Society)Fundamentally, statistics is concerned with managing data and making inferences and forecasts in the face of uncertainty. It should not be surprising, therefore, that statistical methods have a key role to play in the atmospheric sciences. It is the uncertainty in atmospheric behavior that continues to move res.

Statistical Postprocessing Of Ensemble Forecasts

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