Assessing the environmental costs of multi-scale recurrent neural networks for sustainable extreme rainfall nowcasting

The nowcasting of extreme rainfall poses significant daily challenges on a global scale, especially in vulnerable regions of the Global South. Conventional Numerical Weather Prediction models often fail to deliver accurate and timely forecasts for extreme weather events, exacerbating socioeconomic inequalities and increasing climate vulnerability. Deep learning approaches present a promising opportunity to uncover more precise predictive patterns; however, their application remains constrained by the high computational costs associated to their large parameter spaces. This study evaluates the effectiveness of the MS-RNN framework for improving computational efficiency and predictive accuracy in extreme precipitation nowcasting, using real weather radar data from the TAASRAD19 and Rio de Janeiro datasets. While the framework has been extensively validated both theoretically and experimentally in other scenarios, this work examines its application to real radar data. Metrics related to sustainability, such as energy consumption, [Formula: see text] emissions, and water usage, have not been calculated in this specific context and are rarely addressed in current literature. Our findings demonstrate the potential of the solution to enhance computational efficiency maintaining predictive performance when applied to real weather radar data, supporting sustainable and accessible AI solutions for climate resilience in resource-limited regions.