[1] [1] Wang G, Zhang J. Generalised variational assimilation of cloud-affected brightness temperature using simulated hyper-spectral atmospheric infrared sounder data[J]. Advances in Space Research, 2014, 54(1): 49-58.

[2] [2] Yang J, Zhang Z, Wei C, et al. Introducing the new generation of Chinese geostationary weather satellites, FengYun 4[J]. Bulletin of the American Meteorological Society, 2017,98(8): 1637-1658.

[6] [6] Eyre J R. A bias correction scheme for simulated TOVS brightness temperatures[R]. ECMWF Research Dept Technical Memorandum, 1992.

[7] [7] Harris B A, Kelly G. A satellite radiance bias correction scheme for data assimilation[J]. Quarterly Journal of the Royal Meteorological Society, 2001, 127: 1453-1468.

[8] [8] Dee D P. Variational bias correction of radiance data in the ECMWF system[C]. Reading： Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, 2004.

[9] [9] Auligne T, McNally A P, Dee D P. Adaptive bias correction for satellite data in a numerical weather prediction system[J]. Quarterly Journal of the Royal Meteorological Society, 2007, 133: 631-642.

[10] [10] Watts P D, McNally A P. Identification and correction of radiative transfer modeling errors for atmospheric sounders: AIRS and AMSU-A[C]. Reading：Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, 2004.

[11] [11] Elana J F, Seung J B, Brianr H, et al. Observation bias correction with an ensemble Kalman filter[J]. Tellus, 2009, 61(2): 210-226.

[13] [13] Han W, Bormann N. Constrained adaptive bias correction for satellite radiances assimilation in the ECMWF 4D-Var[R]. ECMWF Research Dept Technical Memorandum, 2016.

[14] [14] Jason A O, Roland P, Amos S L. Nonlinear Bias Correction for Satellite Data Assimilation Using Taylor Series Polynomials[J]. Monthly Weather Review, 2018, 146: 263-285.

[15] [15] Jin J B, Lin H X, Segers A, et al. Machine learning for observation bias correction with application to dust storm data assimilation[J]. Atmospheric Chemistry and Physics, 2019, 19: 10009-10026.

[16] [16] Yin R Y, Han W, Gao Z Q, et al. The evaluation of FY4A ′s Geostationary Interferometric Infrared Sounder (GIIRS) longwave temperature sounding channels using the GRAPES global 4D-Var[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(728): 1459-1476.

[17] [17] Breiman L.Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

[18] [18] Wang G, Han W, Lu S. Precipitation retrieval by the L1-norm regularization: typhoon Hagibis case[J]. Quarterly Journal of the Royal Meteorological Society, 2021, 147(735): 773-785.

[20] [20] Lee Y J, Daehyeon H, Ahn M H, et al. Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network[J]. Remote Sensing, 2019, 11(15): 1741.

[22] [22] Saunders R, Hocking J, Turner E, et al. An update on the RTTOV fast radiative transfer model (currently at version 12)[J]. Geoscientific Model Development, 2018， 11： 2717-2737.

[23] [23] Min M, Wu C Q, Li C, et al. Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series[J]. Journal of Meteorological Research, 2017, 31(4): 708-719.

[24] [24] Zhang Q, Yu Y, Zhang W M, et al. Cloud Detection from FY-4A ′s Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches[J]. Remote Sensing, 2019,11(24):3035.