Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation

Abstract

The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun (FY)-4A geostationary satellite has provided high-spatiotemporal-resolution visible reflectance data since 12 March 2018. Data assimilation experiments under the framework of observing system simulation experiments have shown the great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To assimilate the AGRI visible reflectance in real-world cases, it is important to evaluate the quality and to quantify the observation errors in these data. In this study, the FY-4A AGRI channel 2 (0.55–0.75 µm) reflectance data (O) were compared with the equivalents (B) derived from the short-term forecasts of the China Meteorological Administration Mesoscale (CMA-MESO) model using the Radiative Transfer for the Television Infrared Observation Satellite Operational Vertical Sounder (RTTOV, v12.3). It is shown that the O–B biases could be used to reveal the abrupt change related to the measurement calibration processes. In general, the O–B departure was positively biased in most cases. Potential causes include the deficiencies of the NWP model, the forward-operator errors, and the unresolved aerosol processes. The relative biases of O–B computed from cloud-free and cloudy pixels were used to correct the systematic biases for the corresponding scenarios over land and sea surfaces separately. In general, the method effectively reduced the O–B biases. Moreover, the bias-correction method based on an ensemble forecast is more robust than a deterministic forecast due to the advantages of the former in dealing with uncertainties in cloud simulations. The findings demonstrate that analyzing the O–B biases has a potential to monitor the performance of the FY-4A AGRI visible instrument and to correct the systematic biases in the observations, which will facilitate the assimilation of these data in conventional data assimilation applications.