Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts

Seasonal forecasting skill in machine learning methods that are trained on large climate model ensembles can compete with, or out-compete, existing dynamical models, while retaining physical interpretability.

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Autores principales:	Peter B. Gibson, William E. Chapman, Alphan Altinok, Luca Delle Monache, Michael J. DeFlorio, Duane E. Waliser
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Geology QE1-996.5 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/91735fc8166e455f88e2a5b2a1307447
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spelling	oai:doaj.org-article:91735fc8166e455f88e2a5b2a13074472021-12-02T16:28:50ZTraining machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts10.1038/s43247-021-00225-42662-4435https://doaj.org/article/91735fc8166e455f88e2a5b2a13074472021-08-01T00:00:00Zhttps://doi.org/10.1038/s43247-021-00225-4https://doaj.org/toc/2662-4435Seasonal forecasting skill in machine learning methods that are trained on large climate model ensembles can compete with, or out-compete, existing dynamical models, while retaining physical interpretability.Peter B. GibsonWilliam E. ChapmanAlphan AltinokLuca Delle MonacheMichael J. DeFlorioDuane E. WaliserNature PortfolioarticleGeologyQE1-996.5Environmental sciencesGE1-350ENCommunications Earth & Environment, Vol 2, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Geology QE1-996.5 Environmental sciences GE1-350
spellingShingle	Geology QE1-996.5 Environmental sciences GE1-350 Peter B. Gibson William E. Chapman Alphan Altinok Luca Delle Monache Michael J. DeFlorio Duane E. Waliser Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
description	Seasonal forecasting skill in machine learning methods that are trained on large climate model ensembles can compete with, or out-compete, existing dynamical models, while retaining physical interpretability.
format	article
author	Peter B. Gibson William E. Chapman Alphan Altinok Luca Delle Monache Michael J. DeFlorio Duane E. Waliser
author_facet	Peter B. Gibson William E. Chapman Alphan Altinok Luca Delle Monache Michael J. DeFlorio Duane E. Waliser
author_sort	Peter B. Gibson
title	Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
title_short	Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
title_full	Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
title_fullStr	Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
title_full_unstemmed	Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
title_sort	training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/91735fc8166e455f88e2a5b2a1307447
work_keys_str_mv	AT peterbgibson trainingmachinelearningmodelsonclimatemodeloutputyieldsskillfulinterpretableseasonalprecipitationforecasts AT williamechapman trainingmachinelearningmodelsonclimatemodeloutputyieldsskillfulinterpretableseasonalprecipitationforecasts AT alphanaltinok trainingmachinelearningmodelsonclimatemodeloutputyieldsskillfulinterpretableseasonalprecipitationforecasts AT lucadellemonache trainingmachinelearningmodelsonclimatemodeloutputyieldsskillfulinterpretableseasonalprecipitationforecasts AT michaeljdeflorio trainingmachinelearningmodelsonclimatemodeloutputyieldsskillfulinterpretableseasonalprecipitationforecasts AT duaneewaliser trainingmachinelearningmodelsonclimatemodeloutputyieldsskillfulinterpretableseasonalprecipitationforecasts
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Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts

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