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%0 Journal Article
%4 urlib.net/www/2022/02.14.11.08
%2 urlib.net/www/2022/02.14.11.08.47
%@issn 2073-4433
%T South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach
%D 2022
%8 Feb.
%9 journal article
%A Monego, Vinicius Schmidt,
%A Anochi, Juliana Aparecida,
%A Campos Velho, Haroldo Fraga de,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress vinicius.monego@inpe.br
%@electronicmailaddress juliana.anochi@gmail.com
%@electronicmailaddress haroldo.camposelho@inpe.br
%B Atmosphere
%V 13
%N 2
%P e243
%K Gradient boosting, Machine learning, Precipitation, Seasonal climate prediction.
%X 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.
%@language en
%3 atmosphere-13-00243-v2.pdf


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