Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images

Abstract. The Radiative Transfer for TOVS (RTTOV) is a commonly used forward operator software package for the Data Assimilation (DA) of satellite visible reflectance data. However, a wide choice of Cloud Optical Parameterizations (COPs) in RTTOV poses challenges in discerning the optimal configuration. In this study, the performance of different COPs was evaluated by comparing the observed and synthetic visible satellite images. Observed images (O) were provided by Fengyun (FY)-4B and Himawari-9, two operational geostationary meteorological satellites covering East Asia. Synthetic images (B) were generated by RTTOV with the Discrete Ordinate Method (DOM) and the Method for FAst Satellite Image Simulation (MFASIS). The inputs to RTTOV were provided by the 3-h forecasts of the China Meteorological Administration Mesoscale (CMA-MESO) model and the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data. On the domain average, B was smaller than O, especially in cloudy situations. The minimum O-B bias was revealed for the COP of liquid water clouds in terms of effective diameter (Deff) in combination with the COP of ice clouds developed by the Space Science and Engineering Center (SSEC), with the Deff for ice clouds parameterized in terms of ice water content and temperature. Compared with the O-B biases, the standard deviations of the O-B departure were less sensitive to COPs. In addition, histogram analysis of reflectance indicated that the synthetic images with the minimum O-B bias resembled best with the observed images. Therefore, the optimal cloud optical parameterization was proposed to be the “Deff” + “SSEC” suite.