Machine Learning for Climate Precipitation Prediction Modeling over South America

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-07, Vol.13 (13), p.2468
• Main Authors: Anochi, Juliana Aparecida, de Almeida, Vinícius Albuquerque, de Campos Velho, Haroldo Fraga
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Atmospheric circulation; Atmospheric models; Climate models; climate precipitation prediction; Climate prediction; Computer applications; deep learning; Differential equations; El Nino; Fault diagnosis; Learning algorithms; Machine learning; Natural disasters; Neural networks; Numerical weather forecasting; optimal neural architecture; Optimization; Partial differential equations; Precipitation; Prediction models; Remote sensing; Satellite observation; Space research; Support vector machines; Weather forecasting
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13132468

Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models have been developed to tackle different issues in society, but there is still a gap in applications to applied physics. Here, different machine learning models are evaluated for precipitation prediction over South America. Currently, numerical weather prediction models are unable to precisely reproduce the precipitation patterns in South America due to many factors such as the lack of region-specific parametrizations and data availability. The results are compared to the general circulation atmospheric model currently used operationally in the National Institute for Space Research (INPE: Instituto Nacional de Pesquisas Espaciais), Brazil. Machine learning models are able to produce predictions with errors under 2 mm in most of the continent in comparison to satellite-observed precipitation patterns for different climate seasons, and also outperform INPE’s model for some regions (e.g., reduction of errors from 8 to 2 mm in central South America in winter). Another advantage is the computational performance from machine learning models, running faster with much lower computer resources than models based on differential equations currently used in operational centers. Therefore, it is important to consider machine learning models for precipitation forecasts in operational centers as a way to improve forecast quality and to reduce computation costs.