Comment on gmd-2022-30(response to reviewers)

Abstract

Satellite visible (VIS) radiance data contain rich cloud information that are increasingly assimilated for improving cloud and precipitation forecasting of numerical weather prediction models. Recently, the Data Assimilation Research Testbed (DART), a widely used data assimilation resource that supports the Weather Research and Forecasting (WRF) model, was facilitated with an interface for the Radiative Transfer for TOVS (RTTOV), which supports radiance assimilation from visible (VIS) to microwave wavelength channels. This study evaluates the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for assimilating the radiance data of channel 2 (0.55~0.75 μm) of the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4. Observing System Simulation Experiments (OSSEs) were performed for a cyclone case. The results indicate that assimilating VIS radiance data improves cloud forecast skills in general. Best results were achieved for the data assimilation (DA) experiment with dense observations and high updating frequency. The best results could capture the “eye” structure of the cyclone system and significantly improves cloud water path and cloud coverage simulations. Nevertheless, three main problems were revealed. The first is its inability to improve cloud vertical distribution such as layered structures and cloud phases; The second is the its inability to influence atmosphere thermodynamic state variables positively; The third is a waste of up to 50 % observations during the filtering processes.