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.
Highlights
Accurate rainfall estimations are needed for meteorological and hydrological applications
Evaluation was performed by a comparison of the estimated and observed rainfall rates for the test dataset
The goodness of fit between observations and predictions was measured by means of the mean squared error (MSE), Pearson’s coefficient correlation (CC) and the slope of the linear regression
Summary
Accurate rainfall estimations are needed for meteorological and hydrological applications. Rain gauge and disdrometer observations are frequently considered as ground truth data. Their reliability and spatial representation are limited. Weather radar technology provides far better coverage in space and in time. Even though radar provides spatially distributed reflectivity data, they still need to be converted to rainfall rates. Due to the spatio-temporal variability in precipitation (i.e., drop size distribution) as well as signal attenuation through rain cells, it is very difficult to find an adequate relationship to transform reflectivity measurements to rainfall rate [1]. Radar rainfall retrieval has become a field of major interest and development in the research community
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