Precipitation forecasting: from geophysical aspects to machine learning applications

Frontiers in Climate

Climate

Eduardo Costa de Carvalho¹  Ronnie Cley Alves²  Rafael de Lima Rocha²  Ewerton Cristhian Lima de Oliveira³  Renata Gonçalves Tedeschi⁴  Ana Paula Paes dos Santos⁴  Antonio Vasconcelos Nogueira Neto⁴  Claudia Priscila Wanzeler da Costa⁴  Pedro Walfir Martins Souza-Filho⁵  Vânia dos Santos Franco⁶  Julio Cezar Gonçalves de Freitas⁷ 

[1] Grupo de Ciência de Dados, Instituto Tecnológico Vale, Belém, Pará, Brazil;Grupo de Ciência de Dados, Instituto Tecnológico Vale, Belém, Pará, Brazil;Programa de Pós-graduação em Ciência da Computação (PPGCC), Universidade Federal do Pará, Belém, Pará, Brazil;Grupo de Ciência de Dados, Instituto Tecnológico Vale, Belém, Pará, Brazil;Programa de Pós-graduação em Engenharia Elétrica (PPGEE), Universidade Federal do Pará, Belém, Pará, Brazil;Grupo de Tecnologia Ambiental, Instituto Tecnológico Vale, Belém, Pará, Brazil;Grupo de Tecnologia Ambiental, Instituto Tecnológico Vale, Belém, Pará, Brazil;Instituto de Geociências, Universidade Federal do Pará, Belém, Pará, Brazil;Grupo de Tecnologia Ambiental, Instituto Tecnológico Vale, Belém, Pará, Brazil;Programa de Pós-graduação em Ciência Ambientais (PPGCA), Universidade Federal do Pará, Belém, Pará, Brazil;Programa de Pós-Graduação em Computação Aplicada (PPCA), Universidade Federal do Pará, Tucuruí, Pará, Brazil;

关键词: precipitation prediction;    dynamic models;    precipitation formation;    machine learning;    artificial intelligence;   

DOI  :  10.3389/fclim.2023.1250201

 received in 2023-06-29, accepted in 2023-09-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

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Copyright © 2023 Oliveira, Nogueira Neto, Santos, da Costa, Freitas, Souza-Filho, Rocha, Alves, Franco, Carvalho and Tedeschi.