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Open AccessArticle

Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model

1
Laboratory for Climatology and Remote Sensing (LCRS), Faculty of Geography, University of Marburg, D-35032 Marburg, Germany
2
Departamento de Recursos Hídricos y Ciencias Ambientales, Universidad de Cuenca, Cuenca EC010207, Ecuador
3
Facultad de Ingeniería, Universidad de Cuenca, Cuenca EC010207, Ecuador
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1632; https://doi.org/10.3390/rs11141632
Received: 30 May 2019 / Revised: 3 July 2019 / Accepted: 5 July 2019 / Published: 10 July 2019
(This article belongs to the Special Issue Radar Meteorology)
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Abstract

Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars. View Full-Text
Keywords: radar; rainfall retrieval; machine-learning; mountain region; Andes; X-band radar; rainfall retrieval; machine-learning; mountain region; Andes; X-band
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Orellana-Alvear, J.; Célleri, R.; Rollenbeck, R.; Bendix, J. Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model. Remote Sens. 2019, 11, 1632.

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