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		<doi>10.3390/atmos13020243</doi>
		<issn>2073-4433</issn>
		<citationkey>MonegoAnocCamp:2022:SoAmSe</citationkey>
		<title>South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach</title>
		<year>2022</year>
		<month>Feb.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Monego, Vinicius Schmidt,</author>
		<author>Anochi, Juliana Aparecida,</author>
		<author>Campos Velho, Haroldo Fraga de,</author>
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		<orcid>0000-0003-0769-9750</orcid>
		<orcid>0000-0003-4968-5330</orcid>
		<group>CAP-COMP-DIPGR-INPE-MCTI-GOV-BR</group>
		<group>DIPTC-CGCT-INPE-MCTI-GOV-BR</group>
		<group>COPDT-CGIP-INPE-MCTI-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>vinicius.monego@inpe.br</electronicmailaddress>
		<electronicmailaddress>juliana.anochi@gmail.com</electronicmailaddress>
		<electronicmailaddress>haroldo.camposelho@inpe.br</electronicmailaddress>
		<journal>Atmosphere</journal>
		<volume>13</volume>
		<number>2</number>
		<pages>e243</pages>
		<secondarymark>B3_ENGENHARIAS_III B3_ENGENHARIAS_I B3_CIÊNCIAS_AMBIENTAIS B4_ENGENHARIAS_II B5_GEOCIÊNCIAS</secondarymark>
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		<contenttype>External Contribution</contenttype>
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		<keywords>Gradient boosting, Machine learning, Precipitation, Seasonal climate prediction.</keywords>
		<abstract>Machine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning techniquea formulation of gradient-boosted treesis applied to climate seasonal precipitation prediction over South America. The Optuna framework, based on Bayesian optimization, was employed to determine the optimal hyperparameters for the gradient-boosting scheme. A comparison between seasonal precipitation forecasting among the numerical atmospheric models used by the National Institute for Space Research (INPE, Brazil) as an operational procedure for weather/climate forecasting, gradient boosting, and deep-learning techniques is made regarding observation, with some showing better performance for the boosting scheme.</abstract>
		<area>COMP</area>
		<language>en</language>
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