Analysis and Forecast of Landfalling Typhoons with Direct Assimilation of Radar Reflectivity: OSSE and Real Case

Our previous study has explored the predictability of typhoon intensity, demonstrating the importance of inner-core data assimilation. Radar is one of few observations capable of observing TC inner-core structure and circulation at high spatial and temporal resolution. Compared to radial velocity, reflectivity is more difficult to directly assimilate. Most efforts have used simple reflectivity operator based on single-moment (SM) microphysics. In this study, Doppler radar reflectivity data were assimilated directly in EnKF with a complex operator based on double-moment (DM) microphysics (Thompson scheme) and non-Rayleigh scattering calculations.

Numerical experiments were conducted to assimilate simulated and real reflectivity data for typhoon ‘In-fa’ (2021) and ‘Lekima’ (2019) cases every 12 minutes for 5 cycles by using a convective-scale (3km) EnKF-WRF system. The correlation analysis shows that radar reflectivity data have the greatest correlation with hydrometers, and also significant correlation with variables not directly related to the reflectivity operator as well, such as temperature and vertical velocity. Directly assimilating radar reflectivity with EnKF is able to develop reliable multivariate covariance among microphysical and other large-scale variables. Therefore EnKF updates well not only hydrometers but also other cross-variables (u,v,t,w) fields, resulting in obvious improvement in both structure analysis and typhoon forecast skills of intensity, and precipitation. If without updating the cross-variables, the positive impact of reflectivity assimilation vanishes quickly during the forecast process.

In addition, comparative experiments for using reflectivity operators based on SM (Lin scheme) microphysics were conducted. The observational operator based on SM is likely to introduce errors in the forward simulation, and the inconsistency between assimilation (SM operator) and forecast model (DM) will further aggravate the analysis and forecast performance.

Key Words: Radar Reflectivity; Direct Assimilation; Typhoon; Ensemble Kalman Filter