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Evaluation of FY-4A/AGRI visible reflectance using the equivalents derived from the forecasts of CMA-MESO using RTTOV
Atmospheric Measurement Techniques ( IF 3.8 ) Pub Date : 2024-04-04 , DOI: 10.5194/amt-2024-12 Yongbo Zhou , Yubao Liu , Wei Han , Yuefei Zeng , Haofei Sun , Peilong Yu
Atmospheric Measurement Techniques ( IF 3.8 ) Pub Date : 2024-04-04 , DOI: 10.5194/amt-2024-12 Yongbo Zhou , Yubao Liu , Wei Han , Yuefei Zeng , Haofei Sun , Peilong Yu
Abstract. The Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite provides high spatiotemporal resolution visible reflectance data since 12 March 2018. Data assimilation experiments under the framework of observing system simulation experiments have shown great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To assimilate the AGRI visible reflectance observations, it is important to evaluate the data quality and to correct the biases contained in these data. In this study, the FY-4A/AGRI channel 2 (0.55 μm - 0.75 μm) reflectance data were evaluated by the equivalents derived from the short-term 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. The mean differences of O - B statistics are negatively biased. Potential causes include the NWP model errors, the unresolved aerosol processes, the forward-operator errors, etc. The relative biases of O-B computed for cloud-free and cloudy pixels were used to correct the systematic differences in different conditions. After applying the bias correction method, the biases and standard deviations of O-B departure were reduced. The bias correction based on ensemble forecasts is more robust than deterministic forecasts due to the advantages of the former in dealing with cloud simulation errors. The findings demonstrate that analyzing the O-B departure is effective to monitor the performance of FY-4A/AGRI visible measurements and to correct the associated biases, which facilitates the assimilation of these data in conventional data assimilation applications.
更新日期：2024-04-05
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