Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results

Fabio Andre Machado Porto, Mariza Ferro, Eduardo Ogasawara, Thiago Moeda, Claudio D. T. Barros, Anderson Chaves da Silva, Rocío Zorrilla, Rafael Silva Pereira, Rafaela Castro, João Victor Silva, Rebecca Pontes Salles, Augusto J. M. da Fonseca et al.

Supercomputing Frontiers and Innovations · 01 March 2022

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In one sentence. Ongoing project builds ML models for extreme urban rainfall forecasts in Rio de Janeiro, reporting early results and open challenges.

Abstract

Weather forecast services in urban areas face an increasingly hard task of alerting the population on extreme weather events. The hardness of the problem is due to the dynamics of the phenomenon, which challenges numerical weather prediction models and opens an opportunity for Machine Learning (ML) based models that may learn complex mappings between input-output from data. In this paper, we present an ongoing research project which aims at building ML predictive models for extreme precipitation forecast in urban areas, in particular in the Rio de Janeiro City. We present the techniques that we have been developing to improve rainfall prediction and extreme rainfall forecast, along with some initial experimental results. Finally, we discuss some challenges that remain to be tackled in this project.

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