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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16111971