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South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach

2022; Multidisciplinary Digital Publishing Institute; Volume: 13; Issue: 2 Linguagem: Inglês

10.3390/atmos13020243

2073-4433

Vinicius Schmidt Monego, Juliana Aparecida Anochi, Haroldo Fraga de Campos Velho,

Precipitation Measurement and Analysis

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 technique—a formulation of gradient-boosted trees—is 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.