An Approach for Assimilating FY4 Lightning and Cloud Top Height Data Using 3DVAR

Abstract

The vertical distribution of water vapor affects the intensity of the updraft, downdraft and cold pool in convection, so how to adjust lightning proxy-humidity in the vertical direction is very important for convective scale numerical weather prediction (NWP). In this study, a data assimilation approach is presented that uses information from FengYun4 (FY4) lightning data with cloud top height (CTH) data. Specifically, the FY4 CTH is used to locate the upper boundary of the relative humidity adjustment. This method can effectively determine the vertical distribution of water vapor and obtain accurate pseudo-observations. Two severe convection events with different characteristics were studied to evaluate the data assimilation approach for short-term precipitation forecast. For comparison, two other relative humidity adjustment schemes with different vertical ranges were performed. One scheme adjusted the relative humidity between two isothermal layers and introduced the smallest water vapor increments compared with the other two data assimilation experiments and showed a slight improvement on precipitation forecast. The other scheme adjusted the relative humidity between the lifting condensation level (LCL) and a fixed height and introduced the maximum water vapor increments and exhibited better precipitation forecast based on Equitable Threat Scores (ETSs). The adjustment between LCL and CTH introduced appropriate amounts of water vapor and was adaptable for various convection developments and effectively avoided producing spurious convection in the particular case. Assimilation of FY4 lightning and CTH data improves the short-term precipitation forecast and provides the best forecast skill in a particular forecast period.