Next Article in Journal
Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets
Next Article in Special Issue
A Study of Vertical Structures and Microphysical Characteristics of Different Convective Cloud–Precipitation Types Using Ka-Band Millimeter Wave Radar Measurements
Previous Article in Journal
Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea
Previous Article in Special Issue
Decoupling between Precipitation Processes and Mountain Wave Induced Circulations Observed with a Vertically Pointing K-Band Doppler Radar
Open AccessArticle

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

Laboratory for Climatology and Remote Sensing (LCRS), Faculty of Geography, University of Marburg, D-35032 Marburg, Germany
Departamento de Recursos Hídricos y Ciencias Ambientales, Universidad de Cuenca, Cuenca EC010207, Ecuador
Facultad de Ingeniería, Universidad de Cuenca, Cuenca EC010207, Ecuador
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1632;
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop