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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil
Author
Verdelho, Fernanda F 1   VIAFID ORCID Logo  ; Beneti, Cesar 2   VIAFID ORCID Logo  ; PavamJr, Luis G 1   VIAFID ORCID Logo  ; Calvetti, Leonardo 3   VIAFID ORCID Logo  ; Oliveira, Luiz E S 4   VIAFID ORCID Logo  ; Zanata Alves, Marco A 4   VIAFID ORCID Logo 

 Parana Environmental Technology and Monitoring System—SIMEPAR, Curitiba 81530-900, Brazil; [email protected] (C.B.); ; Department of Computer Science, Polytechnic Center, Federal University of Paraná—UFPR, Curitiba 81530-000, Brazil; [email protected] (L.E.S.O.); [email protected] (M.A.Z.A.) 
 Parana Environmental Technology and Monitoring System—SIMEPAR, Curitiba 81530-900, Brazil; [email protected] (C.B.); 
 Department of Meteorology, Federal University of Pelotas—UFPEL, Pelotas 96010-610, Brazil; [email protected] 
 Department of Computer Science, Polytechnic Center, Federal University of Paraná—UFPR, Curitiba 81530-000, Brazil; [email protected] (L.E.S.O.); [email protected] (M.A.Z.A.) 
First page
1971
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3067436077
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.