Evaluation of FY-4A/AGRI visible reflectance using the equivalents derived from the forecasts of CMA-MESO using RTTOV

The Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite provides high spatiotemporal resolution visible reflectance data since March 12th, 2018. Data assimilation experiments under the framework of observing system simulation experiment have shown great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To effectively assimilate the AGRI data, it is important to address the quality the observations. In this study, the FY-4A/AGRI channel 2 (0.55 μm - 0.75 μm) reflectance was evaluated by the equivalents derived from the short-term model forecasts of the China Meteorological Administration Mesoscale Model (CMA-MESO) using the Radiative Transfer for TOVS (RTTOV, v 12.3). It is shown that the observation minus background (O – B) statistics could be used to reveal the abrupt changes related to the measurement calibration processes. In addition, O - B statistics are negatively biased. Potential causes include measurement errors, the unresolved processes, forward-operator errors, etc. The relative mean biases of O-B computed for cloud-free and cloudy pixels were used to correct the systematic differences for cloudy and clear pixels separately. Results indicate that the bias correction method could effectively reduce the biases and standard deviations of O-B. In addition, an ensemble forecast has advantages over a deterministic forecast in correcting the biases in FY-4A/AGRI visible reflectance data. The finding suggests an effective method to monitor the performance of FY-4A/AGRI visible measurements and to correct the biases in the observations.