Assimilating Geostationary Satellite Visible Reflectance Data: developing and testing the GSI-EnKF-CRTM-Vis technique

Abstract. Satellite visible reflectance observations in cloud- and precipitation-affected regions contain substantial information on weather systems, while data assimilation (DA) of visible data is still challenging due to the complexity of forward operators and the non-Gaussian distribution of cloud variables. This study developed an interface within the framework of the popular Gridpoint Statistical Interpolation (GSI) system to assimilate synthetic visible reflectance simulated by Community Radiative Transfer Model (CRTM). The interface employed a spatial interpolation to ensure accurate alignment between model grids and satellite data, and also facilitating a bidirectional mapping between the state variable space and the observation space. The key implementations within the newly developed GSI-EnKF-CRTM-Vis DA technique include integrating a new observation type from geostationary visible imager, incorporating the module for simulating visible reflectance in CRTM, and extending cloud-related control variables. We employed an ensemble-based DA framework in which ensemble members were initialized with multiple physical parameterization schemes, thereby better representing the ensemble spread arising from cloud parameterization differences. The performance of the GSI-EnKF-CRTM-Vis, configured with the Ensemble Square Root Filter (ENSRF) algorithm, was evaluated by assimilating the Himawari-8 Advanced Himawari Imager (AHI) 0.64 μm visible reflectance for a heavy rainfall event over East Asia on 21 September 2024 under the framework of Observing System Simulation Experiment (OSSE). The experimental results demonstrated that DA of visible reflectance effectively corrected the overestimated cloud water path (CWP), reducing the mean absolute error by 1.5 % on average with forecast improvements lasting 6 hours. Probability density function analysis confirmed significant correction of thin clouds (with reflectance less than 0.2 and CWP less than 0.1 kg·m⁻²). DA of visible reflectance improved the spatial extent of light precipitation, as is evidenced by the improved Equitable Threat Score (ETS) across thresholds (except the 0.1 mm threshold) and the reduced False Alarm Rate (FAR). For the U- and V-component winds, temperature, water vapor mixing ratio, DA of visible reflectance generated negligible adjustments as visible reflectance data are insensitive to these non-cloud variables. The newly developed GSI-EnKF-CRTM-Vis DA technique facilitates the ensemble-based DA of satellite visible reflectance with ensemble members initialized with multiple physical parameterization schemes.