Direct Assimilation of Radar Reflectivity Data Using Ensemble Kalman Filter Based on a Two-Moment Microphysics Scheme for the Analysis and Forecast of Typhoon Lekima (2019)

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF assimilation (DA) framework and WRF model for landfall typhoon Lekima (2019). Observations from single operational coastal Doppler are quality-controlled assimilated. Compared with baseline experiment initialized by GFS analysis, (Z-DA) resulted obvious improvement both structural analysis forecast skills terms intensity, precipitation, track. Sensitivity experiments were conducted to evaluate ability EnKF update certain state variables considering that degree freedom analytical increased DM MP scheme. When either total number concentration or other large-scale not linked observations via observation operator updated, tendency RMSIs PS be imbalanced is significantly during DA cycles compared those Z-DA updating full set variables, resulting intensity track errors. These results indicate reliable covariance could handle underconstraint issue associated scheme, helps obtaining more physically balanced fields.