Abstract:
Despite many e orts 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 RFmodel 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 coecient
< 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in
all testing locations and on di erent rainfall events (correlation coecient up to 0.83; slope = 1.04).
The results are promising and unveil a di erent approach to overcome the high attenuation issues
inherent to X-band radars.
