Assessing U-Net Model Performance in Weather Radar-Based Precipitation Nowcasting: A Reflectivity Threshold Analysis

• Rafael Rocha Instituto Tecnológico Vale / Universidade Federal do Pará
• Ewerton Oliveira Instituto Tecnológico Vale / Universidade Federal do Pará
• Eduardo Carvalho Instituto Tecnológico Vale
• Renata Tedeschi Instituto Tecnológico Vale
• Claudia Costa Instituto Tecnológico Vale
• Douglas Ferreira Instituto Tecnológico Vale
• Ronnie Alves Instituto Tecnológico Vale

DOI: https://doi.org/10.5753/eniac.2023.234448

Resumo

Severe weather events pose a global threat, causing property damage and endangering lives. Weather-related disasters account for 50% of all natural and technological disasters. The development of accurate prediction systems are crucial for early warnings and mitigation. The present study evaluates the effectiveness of the U-Net model in weather radar-based precipitation nowcasting, considering reflectivity thresholds. Visual comparison and evaluation metrics are used to assess observed and predicted reflectivity. The 10 dBZ threshold achieved a prominent result, accurately predicting over 75% of values above the reflectivity threshold. Results contribute to improving severe weather prediction and decision-making.

Palavras-chave: Deep Learning, Precipitation Nowcasting, Weather Radar

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