Evaluating Past Horizons for U-Net-Based Precipitation Nowcasting with Radar Data in Southeastern Pará, Brazil - Rocha, Rafael; Carvalho, Eduardo; Oliveira, Ewerton; Viademonte, Sergio; Ferreira, Douglas; Alves, Ronnie (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244469, 2024)
Severe weather events significantly impact daily life, especially during emergencies, affecting human lives and the economy. Decision-making in such events, like heavy rainfall, strong winds, and flash floods, is challenging due to the rapid changes and strong interconnections between the variables involved. Nowcasting models use real-time data, commonly from weather radars, to forecast short-term rain up to 6 hours ahead, supporting decision-making in severe weather situations. These models provide early warnings and precise information about the location, intensity, and duration of these events. Recently, machine learning models for precipitation forecasting have gained prominence due to their ability to learn from data and offer reliable and fast predictions. This study explores precipitation nowcasting using weather radar data in the southeast of Pará, Brazil, focusing on a one-hour forecast horizon utilizing the U-Net architecture. Four models based on U-Net algorithm, investigating past horizons of 30, 60, 90, and 120 minutes, are evaluated using categorical and continuous metrics, and a visual comparison of the 60-minute forecast horizon. The results demonstrate that the model with a past horizon of 120 minutes outperforms the other models in all evaluated metrics, achieving 37.96 and 0.5476 scores in continuous and categorical metrics, respectively, improving the forecast of severe events and decision making up to a 60-minute forecast horizon.