Quantitative precipitation estimation using the characteristics of raindrop size distribution based on the maximum diameter

The raindrop size distribution (DSD) plays an important role in determining the radar measurements. From the radar perspective, the accurate parameterization of the tail of the DSD is important because the radar parameters are strongly influenced by the concentration of the large drops or on the tail of the DSD (Bringi and Chandrasekar, 2001). In the issue of rainfall estimation by radar through the necessary relationship between radar measurement and rain rate, the main limitation is attributed to the variability of the radar rainfall relationship. And it is well known that the sensitivity to natural variation in DSD is a substantial source of the relationship's variability (Maki et al., 2005). The raindrop number and size at ground are being the resultant of various microphysical processes, a suitable approach would need to analyze their variability in relation to that of rainfall estimation relationship.

In this study, an effect of DSD variability on radar measurements according to the maximum diameter was investigated for the purpose of determining the variability of rainfall estimation relations. The shape of averaged drop size distribution was produced as a function of the maximum diameter (D_max): The shape of small D_max had upward vortex shape but it was changed to linear shape as D_max increased. The D_max played a role as a fingerprint to present the variability of DSD: shape m, slope Λ, and intercept parameter N₀ generally decreased with increasing maximum diameter and median volume diameter D₀ and mass weighted mean diameter D_m values are linearly increased with D_max. The characteristics of DSD variation according to the D_max were affected to the rain rate estimator. On the Z-R relation (Z=AR^b), the coefficient A increased as D_max increased whereas the exponent b decreased. On the R-K_DP relation (R=aK_DP^b), coefficient a decreased and exponent β increased as D_max increased.

Based on the analysis of implication of DSD variability in regards to rainfall estimation relationship variability, rain rate estimation algorithm was developed considering DSD variability to make optimized empirical relationships of coefficient and exponent on the rain rate estimator with D_max. The new rain rate estimation algorithm reduced RMSE (root mean square error) and normalized error of the results as compared with conventional rain rate estimator.