Assimilation of Polarimetric Radar Observation With GSI Cloud Analysis for the Prediction of a Squall Line

Dual‐polarization radars can provide rich three‐dimensional (3D) information on cloud precipitation structure. To utilize the existing polarimetric radar network in operational data assimilation systems, a new polarimetric radar‐based cloud analysis method is introduced. New features include the employment of fuzzy‐logic hydrometeor classification, improved estimation of liquid and ice regions, and newly‐added number concentration estimation. The new scheme is evaluated with a typical squall line case. Results show that the 3D cloud precipitation structure and short‐term precipitation forecast is consistently improved. With extra information from the polarimetric observations, the new scheme is able to produce reasonable polarimetric signatures for analysis. More supercooled water results in more latent heat release which enhances the updraft, leading to stronger convection in the subsequent forecast. This study emphasizes the importance of correct initial liquid and ice particle condition for the prediction of deep convection. Plain Language Summary Severe convective storms have always been one of the most the disastrous types of weather. However, due to their relatively small scale and rapid evolution, such storms are difficult to predict. Polarimetric radar networks have been established for nearly a decade and have been widely used in the monitoring and short‐term forecast of severe weather. Yet, this extra polarimetric information has not been used in the operational data assimilation system, which is key to a successful forecast. A new dual‐polarization (dual‐pol) radar‐based cloud analysis aiming to improve short‐term forecasts is therefore proposed. Results show that the new scheme is able to capture the 3D in‐cloud signature of deep convection. Consistent improvements are found for the prediction of 3D structure of deep convection and as well as for the quantitative precipitation forecast. Key Points A modified cloud analysis model is proposed for polarimetric radar data assimilation A fuzzy‐logic method combined with ZDP is used to classify hydrometeor particles, followed by a partially double‐moment retrieval scheme Experiments with polarimetric‐data‐based cloud analysis show consistent improvement on analysis and forecast