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- Title
Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil.
- Authors
Verdelho, Fernanda F.; Beneti, Cesar; Pavam Jr., Luis G.; Calvetti, Leonardo; Oliveira, Luiz E. S.; Zanata Alves, Marco A.
- Abstract
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.
- Subjects
BRAZIL; MACHINE learning; RADAR meteorology; FLOOD forecasting; RANDOM forest algorithms; RAINFALL; WEATHER
- Publication
Remote Sensing, 2024, Vol 16, Issue 11, p1971
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16111971