Incorporating geostationary lightning data into a radar reflectivity based hydrometeor retrieval method: An observing system simulation experiment

Abstract

A retrieval method for deriving the hydrometeor mixing ratio within mesoscale convective system (MCS) is presented in this study. The hydrometeor retrieval method was designed to incorporate the flash extent densities (FED) data from the Feng-Yun-4 geostationary satellite into the S-band radar reflectivity (Zh) and ambient temperature (T) data-based hydrometeor retrieval method. Total lightning data are utilized to better discern regions containing graupel in clouds. In the quantitative estimation of rain mixing ratio, different intercept parameters are used for different ranges of Zh and different estimated precursors of raindrop in cold-cloud microphysical processes (i.e., graupel and snow aggregate). The hydrometeor retrieval method was evaluated through an observing system simulation experiment (OSSE) in which the pseudo-input-data for the hydrometeor retrieval (i.e., the FED, Zh and T data) were obtained from the cloud-scale (1-km) simulation of an MCS using explicit electrification implemented within the Weather Research and Forecasting model. By incorporating the FED data as an additional input data source into the Zh and T-based hydrometeor retrieval method, the hydrometeor retrieval accuracy was improved. The hydrometeor retrievals were then assimilated into the model using the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system. Assimilating more accurate hydrometeor fields slightly improved the analyses and forecasts of convective precipitation in the test MCS case. The improvement could be due to the more accurate hydrometeor analysis, which further affected the strength of the cold pool and gust front.