Alternate identifier:
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Related identifier:
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Creator/Author:
Schulz, Benedikt https://orcid.org/0000-0003-1367-1543 [Institut für Stochastik]

Lerch, Sebastian https://orcid.org/0000-0002-3467-4375 [Institut für Statistik]
Contributors:
(Data Curator)
Redl, Robert [Redl, Robert]

(Data Curator)
Hess, Reinhold [Hess, Reinhold]
Title:
Machine Learning Methods for Postprocessing Ensemble Forecasts of Wind Gusts: Data
Additional titles:
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Description:
(Abstract) Datensatz zu Schulz und Lerch (2022): "Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison", Monthly Weather Review, 150 (1), 235-257, https://doi.org/10.1175/MWR-D-21-0150.1. Die Daten beinhalten die NWV-Vorhersagen und dazugehörigen Beobachtungen im gesamten Zeitraum von 2010 bis 2016 sowie die nachbearbeiteten Vorhersagen im Testzeitraum 2016. Weiter sind noch die Stationsdaten sowie die berechneten Scores im Testzeitraum enthalten. Die NWV-Vorhersagen beinhalten die einzelnen Ensemble-Member der Windböen-Vorhersage sowie die Mittelwerte und Standardabweichungen der anderen Ensemble-Variablen. Zusätzlich zu den nachbearbeiteten Vorhersagen sind auch Informationen zu den trainierten Modellen verfügbar (bspw. geschätzte Parameter).
(Abstract) Data set for Schulz and Lerch (2022): "Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison", Monthly Weather Review, 150 (1), 235-257, https://doi.org/10.1175/MWR-D-21-0150.1. The data includes the NWP forecasts and corresponding observations for the entire period from 2010 to 2016 as well as the postprocessed forecasts for the test period 2016. The station data and the calculated scores for the test period are also included. The NWP forecasts contain the individual ensemble members of the wind gust forecast as well as the ensemble mean and standard deviation values of the other ensemble variables. In addition to the postprocessed forecasts, information on the trained models is also available (e.g., estimated parameters).
(Technical Remarks) A detailed description of the data is given on the corresponding Github-page (https://github.com/benediktschulz/paper_pp_wind_gusts).
Keywords:
Statistical postprocessing
wind gusts
data set
machine learning
ensemble forecasts
numerical weather prediction
Related information:
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Language:
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Production year:
Subject areas:
Mathematics
Resource type:
Dataset
Data source:
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Software used:
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Data processing:
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Publication year:
Rights holders:
Deutscher Wetterdienst
Funding:
-
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2024-05-28
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