INFRARED, Volume. 45, Issue 4, 31(2024)
Bias Correction of Brightness Temperature in Medium Wave Channel of FY-4A/GIIRS Based on Ensemble Learning
[1] [1] Rabier F, Fourrie N, Chafai D, et al. Channel selection methods for infrared atmospheric sounding interferometer radiances [J]. Quarterly Journal of the Royal Meteorological Society, 2002,128(581) : 1011-1027.
[2] [2] Cardinali C. Forecast sensitivity to observation(FSO) as a diagnostic tool [R]. Reading: Euro-pean Centre for Medium-Range Weather Fore-casts, 2009.
[4] [4] Harris B A, Kelly G. A satellite radiance bias correction scheme for data assimilation[J].Quarterly Journal of the Royal Meteorological Society,2001, 127(574): 1453-1468.
[5] [5] 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.
[6] [6] 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(15): 10009-10026.
[7] [7] Huang P Y, Guo Q, Han C P, et al. An improved method combining ANN and 1D-Var for the retrieval of atmospheric temperature profiles from FY-4A/GIIRS Hyperspectral data [J]. Remote Sensing, 2021, 13(3): 481.
[8] [8] Huang P Y, Guo Q, Han C P, et al. An improved method combining CNN and 1D-Var for the retrieval of atmospheric humidity profiles from FY-4A/GIIRS Hyperspectral data [J]. Remote Sensing, 2021, 13(23): 4737.
[10] [10] Wang G, Chen J, Wang Y. Bias correction of channel brightness temperature of FY-4A hyper spectral GIIRS based on machine learning [J].Meteorological and Environmental Research,2022, 13(1): 26-30.
[12] [12] Yang J, Zhang Z Q, Wei C Y, et al. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4 (FY-4) [J]. Bulletin of the American Meteorological Society,2017, 98(8): 1637-1658.
[13] [13] Qi J F, Liu C Y, Chi J W, et al. An ensemble based machine learning model for estimation of subsurface thermal structure in the South China Sea [J]. Remote Sensing, 2022, 14(13): 3207.
[14] [14] Natras R, Soja B, Schmidt M. Ensemble machine learning of random forest, AdaBoost and XGBoost for vertical total electron content forecasting [J].Remote Sensing, 2022, 14(15): 3547.
[16] [16] Altmann A, Tolosi L, Sander O, et al. Permutation importance: a corrected feature importance measure [J]. Bioinformatics, 2010, 26 (10):1340-1347.
[19] [19] Saunders R, Hocking J, Turner E, et al. An update on the RTTOV fast radiative transfer model(currently at version 12) [J]. Geoscienti fic Model Development, 2018, 11(7): 2717-2732.
[20] [20] 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 4DVar [J].Quarterly Journal of the Royal Meteorological Society, 2020,146(728): 1459-1476.
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WANG Gen, DU Cheng-ming, JIANG Yun, FAN Chuan-yu, PAN Yue, YUAN Song. Bias Correction of Brightness Temperature in Medium Wave Channel of FY-4A/GIIRS Based on Ensemble Learning[J]. INFRARED, 2024, 45(4): 31
Received: Nov. 6, 2023
Accepted: --
Published Online: Sep. 29, 2024
The Author Email: Gen WANG (203wanggen@163.com)