Forward modeling of spaceborne radar observations

Abstract

Accurate forward models, particularly radiative transfer models, are essential for the assimilation of both passive and active satellite observations in modern data assimilation frameworks. The Community Radiative Transfer Model (CRTM), widely used in the assimilation of satellite observations within numerical weather prediction systems, especially in the United States, has recently been expanded to include a radar module. This study assesses the new module across multiple radar frequencies using observations from the Earth Clouds, Aerosols and Radiation Explorer Cloud Profiling Radar (EarthCARE CPR), the CloudSat CPR, and the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR). Simulated radar reflectivities were compared with the spaceborne measurements to evaluate the impacts of hydrometeor profiles, particle size distributions (PSDs), and frozen hydrometeor habits. The results indicate that both PSD selection and particle shape largely influence the simulated reflectivities, with snow particle habits introducing differences of up to 4 dBZ in W-band comparisons. For the GPM DPR, reflectivities simulated using the Thompson PSD showed closer agreement with the observations than those using the Abel PSD; this agreement should be interpreted in the context of the limited independence between the observations and the retrievals used as input to the CRTM, which themselves rely on PSD-related assumptions. The sensitivity of forward radar simulations to microphysical assumptions, underscores their importance in the assimilation of radar observations in numerical weather forecast models.