Cite as:
Orellana-Alvear, J.; Celleri, R.; Rollenbeck, R. &amp; Bendix, J. (2019): <b>Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model</b>. <i>Remote Sensing</i> <b>11</b>(14), 1632.

Resource Description

Title: Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model
FOR816dw ID: 338
Publication Date: 2019-01-01
License and Usage Rights:
Resource Owner(s):
Individual: Johanna Orellana-Alvear
Individual: Rolando Celleri
Individual: Rütger Rollenbeck
Individual: Jörg Bendix
Despite many eorts of the radar community, quantitative precipitation estimation (QPE)<br/> from weather radar data remains a challenging topic. The high resolution of X-band radar imagery<br/> in space and time comes with an intricate correction process of reflectivity. The steep and high<br/> mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall<br/> derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF)<br/> model and single Plan Position Indicator (PPI) scans. The performance of the RFmodel was evaluated<br/> in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a<br/> site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and<br/> hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an<br/> improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer)<br/> Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coecient<br/> < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in<br/> all testing locations and on dierent rainfall events (correlation coecient up to 0.83; slope = 1.04).<br/> The results are promising and unveil a dierent approach to overcome the high attenuation issues<br/> inherent to X-band radars.
| Andes | South Ecuador | Random forests | Radar |
Literature type specific fields:
Journal: Remote Sensing
Volume: 11
Issue: 14
Page Range: 1632
Metadata Provider:
Individual: Jörg Bendix
Online Distribution:
Download File:

Quick search

  • Publications:
  • Datasets: