Precipitation forecasting: from geophysical aspects to machine learning applications

Ewerton Cristhian Lima de Oliveira, Antônio Vasconcelos Nogueira Neto, Ana Paula Paes dos Santos, Claudia Priscila Wanzeler da Costa, Julio Cezar Gonçalves de Freitas, Pedro Walfir Martins e Souza Filho, Rafael de Lima Rocha, Ronnie Cley de Oliveira Alves, Vânia dos Santos Franco, Eduardo Costa de Carvalho, Renata Gonçalves Tedeschi

Frontiers in Climate · 19 October 2023

Paper

In one sentence. Reviews precipitation forecasting, covering rainfall physics and the growing role of machine learning methods, with discussion of computational challenges.

Abstract

Intense precipitation events pose a significant threat to human life. Mathematical and computational models have been developed to simulate atmospheric dynamics to predict and understand these climates and weather events. However, recent advancements in artificial intelligence (AI) algorithms, particularly in machine learning (ML) techniques, coupled with increasing computer processing power and meteorological data availability, have enabled the development of more cost-effective and robust computational models that are capable of predicting precipitation types and aiding decision-making to mitigate damage. In this paper, we provide a comprehensive overview of the state-of-the-art in predicting precipitation events, addressing issues and foundations, physical origins of rainfall, potential use of AI as a predictive tool for forecasting, and computational challenges in this area of research. Through this review, we aim to contribute to a deeper understanding of precipitation formation and forecasting aided by ML algorithms.

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