All-Sky Assimilation of FY-4B AGRI Reflectance in the CMA-MESO Model With ARMS as a Forward Operator

Abstract

All-sky assimilation of visible and near-infrared observations has attracted widespread attention. In the CMA-MESO model, this study proposes an optimized reflectance scheme based on the Ross-Li bidirectional reflectance distribution function (BRDF) model. Over land, the Ross-Li the BRDF model is coupled with the ARMS model using a method based on Fourier transform and Gaussian quadrature decomposition. Over ocean, a static reflectance lookup table is employed. For cloudy sky simulation, an effective cloud particle radius parameterization scheme consistent with cloud microphysical assumptions is introduced. For FY-4B AGRI data at visible and near-infrared bands, all-sky observation errors are analyzed for a dynamic update in the 3DVAR data assimilation system. Five assimilation experiments are conducted, including three single-channel experiments assimilating FY-4B AGRI channels 2, 4, and 5 individually, and two combined-channel experiments assimilating channels 2 + 4 and 4 + 5. Among the single-channel cases, channel 4 assimilation shows the greatest improvement in the representation of ice cloud coverage. In contrast, assimilating channel 2 or channel 5 alone led to confusion between liquid and ice cloud increments. The combined-channel assimilation experiments outperformed the single-channel ones, yielding a significant reduction in the standard deviation (STD) of the simulated reflectance. In summary, assimilating FY-4B AGRI observations holds a great potential for improving the accuracy of cloud cover representation.