Optimizing Radar-Based Rainfall Estimators Using Machine Learning Modles

Weather radar research has produced numerous radar-based rainfall estimators based on climate, rainfall intensity, a variety of ground-truthing instruments and sensors (e.g., rain gauges, disdrometers), and techniques. Although each research direction gives improvement, their collective application in an operational sense still yields uncertainty in rainfall estimation at different times. This study aims to explore the concept of implementing Machine Learning (ML) models in choosing the optimal radar-based rainfall estimator from a group of estimators at each bin of a radar scan.  

The Canadian King City C-Band radar was used with a GEONOR T-200B rain gauge, a total of 263 sample points, to establish a group of polarimetric-based rainfall estimators (R(Z), R(Z, ZDR), R(KDP)). The estimators were used to train three ML models, namely Decision Tree, Random Forest, and Gradient Boost, to choose the optimal rainfall estimators based on radar variables (Z, ZDR, KDP). Data from the Canadian Exeter C-Band radar and a Texas Electronics TE525 tipping bucket gauge at a different location were used to verify the ML models and compare their results to the classic Marshall-Gunn (1952) Z-R relation and the composite estimator produced by Bringi et al. (2011). The results show promising results for the ML models, specifically the Gradient Boost model. These encouraging results need to be further explored with more sample points to further refine the ML mod