Abstract. Meteorological satellite data have been extensively utilized in global numerical weather prediction systems and have a positive impact to improve forecast accuracy. In order to correctly assimilate satellite radiance observations in data assimilation systems, the systematic observation biases must be corrected to conform to a Gaussian normal distribution with a mean of 0. By selecting appropriate air-mass predictors through correlation assessment, a two-step bias correction scheme (namely the scan-angle bias correction and the air-mass bias correction) is established in this paper based on radiance observations of FY-3E/ HIRAS-II from 1 to 31 January 2023. The results indicate that FY-3E/HIRAS-II O−B (observation-simulation) bias exhibits scanning angle bias dependence from nadir to limb field of view. Statistics have found that this scanning angle bias does not depend on latitude band. After scan-angle bias correction using statistical scan-angle correction coefficients, the dependence of the O−B biases on the scan angle can be eliminated. The second step is to perform air-mass correction. Our correction scheme is compared with the air-mass bias correction scheme in NCEP-GSI. Although the scan angle influence is also considered in NECP-GSI scheme, it does not account for the water vapor effect in the atmosphere. Consequently, the correction effect is not good for channels with lower peak height of weighting function, resulting in a slightly residual positive bias after correction. The combination of air-mass predictors (model surface skin temperature, model total column water vapor, thickness of 1000–300 hPa, and thickness of 200–50 hPa) selected through importance assessment in this study effectively eliminates the air-mass biases. The systematic biases between observed brightness temperature and background simulated brightness temperature from background atmospheric field for all HIRAS-II channels significantly decrease after bias correction, and the bias distribution essentially follows a Gaussian normal distribution with a mean of 0. The FY-3E/HIRAS-II data assimilation experiments show that the selected air-mass predictors (EXP-2 scheme) is the most effective among the four experiments. The mean O−B and O−A in all channels are the smallest after bias correction. Compared with the independent ERA5 objective analysis fields, the EXP-2 scheme has a significant improvement for the temperature analysis at upper air and near surface. The water vapor profiles of the EXP-2 scheme are the closest to ERA5 at almost all height levels.
Chen et al. (Tue,) studied this question.