Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

Measurement(s) radiation components at the surface Technology Type(s) machine learning Factor Type(s) radiation Sample Characteristic - Environment climate Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15090...

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Autores principales:	T. C. Chakraborty, Xuhui Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/dcc9c77c48794b95a7f267838e8a27d1
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spelling	oai:doaj.org-article:dcc9c77c48794b95a7f267838e8a27d12021-12-02T18:50:49ZUsing supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset10.1038/s41597-021-01016-42052-4463https://doaj.org/article/dcc9c77c48794b95a7f267838e8a27d12021-09-01T00:00:00Zhttps://doi.org/10.1038/s41597-021-01016-4https://doaj.org/toc/2052-4463Measurement(s) radiation components at the surface Technology Type(s) machine learning Factor Type(s) radiation Sample Characteristic - Environment climate Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15090462T. C. ChakrabortyXuhui LeeNature PortfolioarticleScienceQENScientific Data, Vol 8, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q T. C. Chakraborty Xuhui Lee Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
description	Measurement(s) radiation components at the surface Technology Type(s) machine learning Factor Type(s) radiation Sample Characteristic - Environment climate Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15090462
format	article
author	T. C. Chakraborty Xuhui Lee
author_facet	T. C. Chakraborty Xuhui Lee
author_sort	T. C. Chakraborty
title	Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
title_short	Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
title_full	Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
title_fullStr	Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
title_full_unstemmed	Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
title_sort	using supervised learning to develop barad, a 40-year monthly bias-adjusted global gridded radiation dataset
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dcc9c77c48794b95a7f267838e8a27d1
work_keys_str_mv	AT tcchakraborty usingsupervisedlearningtodevelopbarada40yearmonthlybiasadjustedglobalgriddedradiationdataset AT xuhuilee usingsupervisedlearningtodevelopbarada40yearmonthlybiasadjustedglobalgriddedradiationdataset
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Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

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