COMPARISON OF WPMM VERSUS REGRESSION FOR EVALUATING Z-R RELATIONSHIPS

The accuracy of the estimation of Z-R relationships is evaluated for t he Window Probability Matching Method (WPMM) and regression methods. T he evaluation is based on experiments of random subsampling of disdrom eter-obtained 1-min reflectivity Z and rain-rate R pairs. The simulati on of the disparity between the radar and the rain gauge measurement v olumes was done by 3-min time averaging of the reflectivity data. Geom etrical mismatch and synchronization inaccuracies between the radar an d rain gauges are simulated by desynchronization of dt minutes, that i s, shifting the R and Z time series with respect to each other by dt m inutes. The WPMM and bias-corrected regression methods have similar sk ill in estimating rainfall accumulation even when geometrical and sync hronization errors are introduced. However, the WPMM has significant a dvantage in estimating the rain intensities when geometrical and synch ronization errors are introduced to the radar-gauge-measured Z-R pairs for simulating real-world radar and rain gauge comparisons. Regressio n-based Z-R relationships tend to overestimate the low rain intensitie s and underestimate the high rain intensities with the crossover at th e estimated median rain volume intensity. This trend becomes more seve re with the increased desynchronization. This reduction of the dynamic range of R does not occur when using WPMM. Although rain gauge bias c orrection may render the overall rain accumulation insensitive to the power of the Z-R law, its appropriate selection has a major effect on the partition of rainfall amounts between weak and strong intensities or the partition between convective and stratiform rainfall.