Sensitivity Of Tropical Cyclone Structure To Double-Moment Microphysics In A Dynamically Calibrated WRF Framework

Diabatic heating and microphysical processes remain major sources of uncertainty in tropical cyclone simulations. This study evaluates the sensitivity of cyclone structure and intensity to microphysical assumptions using a dynamically calibrated WRF-ARW framework applied to Extremely Severe Cyclonic Storm (ESCS) Fani (2019). Track and timing errors were first reduced via grid nudging in the outer domain, coupled with the Multi-Scale Kain–Fritsch (MSKF) cumulus scheme, YSU boundary layer physics, RRTMG radiation, and an ocean mixed-layer model. This configuration improved landfall timing accuracy from ~11 to ~2 hours and reduced spatial error to ~60–70 km. Initial results indicate that ice-inclusive physics enhance vortex strength and structural realism compared to warm-rain schemes, albeit with a reduced translation speed. Building on this setup, we compare several double-moment microphysics schemes that prognose both hydrometeor mass and number concentration. Simulated radar reflectivity fields are generated using a physically consistent forward operator, which incorporates hydrometeor-specific reflectivity (dBZ) retrievals and liquid-equivalent scattering assumptions. These fields are evaluated against Doppler Weather Radar (for eyewall and convective structure), GPM DPR (for vertical hydrometeor and melting-layer profiles), and GPM IMERG (for surface rainfall distribution). A phase-locked evaluation strategy enables a structural comparison across schemes despite differences in landfall timing. The results highlight how microphysics choices modulate convective organisation and precipitation features in high-resolution simulations over the Bay of Bengal, offering guidance for improving microphysical representations in cyclone forecasting models.