More and more spaceborne infrared hyperspectral atmospheric observations are assimilated into data assimilation systems. The key to bias correction (BC) of these instruments depends on selecting predictors. However, it is difficult to find a set of predictors that are highly correlated with the O-B biases in all FY-3E/HIRAS-II channels, due to its multi-channel characteristics. A machine learning model XGBoost (EXtreme Gradient Boosting) BC scheme for FY-3E/HIRAS-II is established in this article. The selected predictors include model skin temperature, model total column water vapor, 1000–300 hPa thickness, 200–50 hPa thickness, scan position, observed brightness temperature (BT) and simulated BT. The method is also compared with the operational static BC and the variational BC, to validate its effect. The two-week data assimilation experiments show that the XGBoost BC is the most effective among the three BC schemes. The mean and standard deviation of O-B in all channels are the smallest after BC, and the effective observations through quality control are the largest, followed by the static BC. The static BC and variational BC are performed based on linear regression, which may lead to a small loss of valid observations in some channels that are weakly correlated with the predictor, whereas machine learning algorithms can search for the nonlinear correlation between biases and predictors. Compared with ERA5, both temperature- and humidity-analysis fields based on XGBoost BC are closest to ERA5 at all levels, and the root mean square errors do not change much over time.
Chen et al. (2026) studied this question.