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


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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ROCHA, Rafael; OLIVEIRA, Ewerton; CARVALHO, Eduardo; TEDESCHI, Renata; COSTA, Claudia; FERREIRA, Douglas; ALVES, Ronnie. Assessing U-Net Model Performance in Weather Radar-Based Precipitation Nowcasting: A Reflectivity Threshold Analysis. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 782-793. ISSN 2763-9061. DOI: