Dual-Polarization Radar Data Assimilation Based on Hydrometeor Classification and Its Impact on Severe Weather Prediction

Abstract

The indirect radar reflectivity assimilation method, which assimilates retrieved hydrometeors from radar reflectivity data, is simple and efficient in severe weather forecasting applications. However, it suffers from retrieval errors due to the uncertainties in discerning multiple hydrometeor types based solely on reflectivity observations. To mitigate these inaccuracies, dual-polarization radar data are incorporated into the background-dependent indirect reflectivity assimilation method in this study. First, the contribution of multiple hydrometeor species to the whole reflectivity is estimated using the observed reflectivity and background microphysical information; then, the hydrometeor classification algorithm (HCA) product from dual-polarization radar observations is introduced to correct the dominant hydrometeor type if in error; and finally, the contribution factors are adjusted and used to retrieve multiple hydrometeor species from reflectivity data. Through a single squall line case, it is demonstrated that the incorporation of the HCA product from dual-polarization radar data leads to more reasonable hydrometeor identification, with more supercooled rainwater above the melting layer and more graupel at low levels, thereby refining the hydrometeor analysis. With the 15-min rapid update cycling configuration, the changes in the analysis field enable more cold rain processes, resulting in more intense latent heat release at higher levels and stronger cooling near the surface in the forecast. This in turn strengthens updraft motion and cold pools in the convective regions, thereby improving the reflectivity and precipitation forecasts. Four cases' quantitative evaluations of the 0–3-hr reflectivity and precipitation forecasts further validate the effectiveness of incorporating dual-polarization radar data in the assimilation process.