Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach

Atmosphere

Rodolfo Rodriguez¹  Simon Macalupu¹  Pool Nolasco¹  Johanna Orellana-Alvear²  Rütger Rollenbeck² 

[1] Facultad de Ingeneria, Universidad de Piura, Piura 20009, Peru;Laboratory for Climatology and Remote Sensing (LCRS), Faculty of Geography, University of Marburg, D-35032 Frankfurt, Germany;

关键词: weather radar;    quantitative precipitation estimate;    random forest;    machine learning;    extreme events;    tropical desert;   

DOI  :  10.3390/atmos12121561

来源: DOAJ

【 摘 要 】

Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitivity and resolution, which makes them well suited for complex precipitation patterns. The first horizontal scanning weather radar in Peru was installed in Piura in 2019, after the devastating impact of the 2017 coastal El Niño. To obtain a calibrated rain rate from radar reflectivity, we employ a modified empirical approach and draw a direct comparison to a well-established machine learning technique used for radar QPE. For both methods, preprocessing steps are required, such as clutter and noise elimination, atmospheric, geometric, and precipitation-induced attenuation correction, and hardware variations. For the new empirical approach, the corrected reflectivity is related to rain gauge observations, and a spatially and temporally variable parameter set is iteratively determined. The machine learning approach uses a set of features mainly derived from the radar data. The random forest (RF) algorithm employed here learns from the features and builds decision trees to obtain quantitative precipitation estimates for each bin of detected reflectivity. Both methods capture the spatial variability of rainfall quite well. Validating the empirical approach, it performed better with an overall linear regression slope of 0.65 and r of 0.82. The RF approach had limitations with the quantitative representation (slope = 0.44 and r = 0.65), but it more closely matches the reflectivity distribution, and it is independent of real-time rain-gauge data. Possibly, a weighted mean of both approaches can be used operationally on a daily basis.

【 授权许可】

Unknown