Development of the TCWA2 Bulk Cloud Microphysics Scheme and Its Integration with a Dual-Polarization Radar Operator for Forecasting Applications

Abstract. This study presents the development and evaluation of TCWA2, a double-moment bulk cloud microphysics scheme designed for weather forecasting that incorporates radar observations at the Taiwan Central Weather Administration. By simplifying the triple-moment NTU microphysics scheme, TCWA2 retains a gamma-type particle size distribution with variable spectral parameters, diagnoses hydrometeor-associated physical properties, revises number sinks due to evaporation loss, and implements theoretically based fall-speed formulations that account for particle density and aspect ratio. To connect bulk microphysics parameterizations with radar-based diagnostics, TCWA2 is coupled with a customized bulk dual-polarization radar operator derived from offline bin-based scattering calculations under the Rayleigh approximation. This integrated microphysics–radar system provides an internally consistent representation linking particle-size distribution characteristics, hydrometeor morphology, sedimentation processes, and bulk radar observables. The intrinsic behavior of TCWA2 is first examined through two-dimensional idealized squall-line simulations in the WRF model, which reveal realistic microphysical structures and coherent polarimetric radar signatures. The scheme is further assessed through a real-case simulation of an afternoon convective event using the MPAS model, with validation against observed dual-polarization radar data. The joint distributions of radar reflectivity and polarimetric variables show strong agreement with observations, with pattern correlations exceeding 0.9 across three altitude layers, indicating that TCWA2 effectively captures the dominant microphysical features in radar signatures. Therefore, TCWA2 offers a physically consistent and computationally efficient framework for integrating bulk cloud microphysics with dual-polarization radar operators across platforms, with potential for future radar-based forecasting.