Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-06, Vol.16 (11), p.1971
• Main Authors: Verdelho, Fernanda F., Beneti, Cesar, Pavam, Luis G., Calvetti, Leonardo, Oliveira, Luiz E. S., Zanata Alves, Marco A.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Calibration; Comparative analysis; Data collection; Datasets; Environmental aspects; Flash floods; Flood forecasting; Flood management; Gauges; gradient boosting; Hydroelectric power; Hydroelectric power generation; Learning algorithms; Machine learning; Measurement; Meteorological radar; Precipitation; Precipitation (Meteorology); precipitation estimation; quantitative precipitation estimation; Radar; Radar data; Radar systems; Rain; Rainfall; random forest; Research methodology; Variables; Weather; Weather forecasting
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16111971

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method.