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Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9\u201310 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses.<\/jats:p>","DOI":"10.3390\/rs15071809","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T01:33:00Z","timestamp":1680053580000},"page":"1809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7730-7391","authenticated-orcid":false,"given":"Xuewei","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Joint International Research Laboratory of Climate and Environment Change (ILCEC)\/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Dongmei","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Joint International Research Laboratory of Climate and Environment Change (ILCEC)\/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8539-6504","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"},{"name":"Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210041, China"}]},{"given":"Feifei","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Joint International Research Laboratory of Climate and Environment Change (ILCEC)\/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1002\/2017JD027096","article-title":"Assimilation of Himawari-8 All-Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction","volume":"123","author":"Honda","year":"2018","journal-title":"J. Geophys. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1175\/MWR-D-10-05040.1","article-title":"Improved Coastal Precipitation Forecasts with Direct Assimilation of GOES-11\/12 Imager Radiances","volume":"139","author":"Zou","year":"2011","journal-title":"Mon. Wea. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1002\/2016MS000674","article-title":"Assimilation of MWHS radiance data from the FY-3B satellite with the WRF Hybrid-3DVAR system for the forecasting of binary typhoons","volume":"8","author":"Xu","year":"2016","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1175\/MWR-D-18-0359.1","article-title":"Satellite Bias Correction in the Regional Model ALADIN\/CZ: Comparison of Different VarBC Approaches","volume":"147","author":"Mile","year":"2019","journal-title":"Mon. Wea. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1002\/qj.2819","article-title":"Observation bias correction schemes in data assimilation systems: A theoretical study of some of their properties","volume":"142","author":"Eyre","year":"2016","journal-title":"Quart. J. Roy. Meteor. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s00376-020-0219-z","article-title":"Assimilating all-sky infrared radiances from Himawari-8 using the 3DVar method for the prediction of a severe storm over north China","volume":"38","author":"Xu","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_7","first-page":"34","article-title":"A bias correction scheme for simulated TOVS brightness temperatures","volume":"186","author":"Eyre","year":"1992","journal-title":"ECMWF Tech. Memo."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1002\/qj.49712757418","article-title":"A satellite radiance-bias correction scheme for data assimilation","volume":"127","author":"Harris","year":"2001","journal-title":"Quart. J. Roy. Meteor. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s00376-014-4239-4","article-title":"Assimilating AMSU-A Radiance Data with the WRF Hybrid En3DVAR System for Track Predictions of Typhoon Megi (2010)","volume":"32","author":"Shen","year":"2015","journal-title":"Adv. Atmos. Sci."},{"key":"ref_10","unstructured":"Watts, P.D., and McNally, A.P. (2004). Identification and Correction of Radiative Transfer Modeling Errors for Atmospheric Sounders: AIRS and AMSU-A, ECMWF."},{"key":"ref_11","unstructured":"Dee, D.P. (July, January 28). Variational bias correction of radiance data in the ECMWF system. Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Berkshire, UK."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1002\/qj.493","article-title":"Variational bias correction of satellite radiance data in the ERA-Interim reanalysis","volume":"135","author":"Dee","year":"2009","journal-title":"Quart. J. Roy. Meteor. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhang, X., Li, H., Wu, H., Shen, F., Shu, A., Wang, Y., and Zhuang, X. (2021). Evaluation of the Simulation of Typhoon Lekima (2019) Based on Different Physical Parameterization Schemes and FY-3D Satellite\u2019s MWHS-2 Data Assimilation. Remote Sens., 13.","DOI":"10.3390\/rs13224556"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1002\/qj.2233","article-title":"Enhanced radiance bias correction in the National Centers for Environmental Prediction\u2019s Gridpoint Statistical Interpolation data assimilation system","volume":"140","author":"Zhu","year":"2014","journal-title":"Quart. J. Roy. Meteor. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1002\/qj.56","article-title":"Adaptive bias correction for satellite data in a numerical weather prediction system","volume":"133","author":"McNally","year":"2007","journal-title":"Quart. J. Roy. Meteor. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1111\/j.1600-0870.2008.00378.x","article-title":"Observation bias correction with an ensemble Kalman filter","volume":"61","author":"Fertig","year":"2009","journal-title":"Tellus A"},{"key":"ref_17","unstructured":"Han, W., and Bormann, N. (2016, January 7\u201322). Constrained adaptive bias correction for satellite radiances assimilation in the ECMWF 4D-Var. Proceedings of the EGU General Assembly 2016, Vienna, Austria."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1002\/qj.3463","article-title":"Comparison of assimilating all-sky and clear-sky infrared radiances from Himawari-8 in a mesoscale system","volume":"145","author":"Okamoto","year":"2019","journal-title":"Quart. J. Roy. Meteor. Soc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4481","DOI":"10.1175\/MWR-D-19-0133.1","article-title":"Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System","volume":"147","author":"Otkin","year":"2019","journal-title":"Mon. Weather Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1175\/MWR-D-16-0357.1","article-title":"Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015)","volume":"146","author":"Honda","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3241","DOI":"10.1175\/MWR-D-17-0367.1","article-title":"Assimilation of All-Sky Infrared Radiances from Himawari-8 and Impacts of Moisture and Hydrometer Initialization on Convection-Permitting Tropical Cyclone Prediction","volume":"146","author":"Minamide","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1175\/MWR-D-17-0171.1","article-title":"Nonlinear Bias Correction for Satellite Data Assimilation Using Taylor Series Polynomials","volume":"146","author":"Otkin","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/LGRS.2009.2023605","article-title":"Machine Learning and Bias Correction of MODIS Aerosol Optical Depth","volume":"6","author":"Lary","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10009","DOI":"10.5194\/acp-19-10009-2019","article-title":"Machine learning for observation bias correction with application to dust storm data assimilation","volume":"19","author":"Jin","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_25","first-page":"39","article-title":"Bias Correction of Brightness Temperatures in Medium-Wave Channel of FY-4A Infrared Hyperspectral GIIRS","volume":"42","author":"Wang","year":"2021","journal-title":"INFRARED."},{"key":"ref_26","first-page":"5302511","article-title":"A Remapping Technique of FY-3D MWRI Based on a Convolutional Neural Network for the Reduction of Representativeness Error","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107043","DOI":"10.1016\/j.jqsrt.2020.107043","article-title":"Advanced radiative transfer modeling system developed for satellite data assimilation and remote sensing applications","volume":"251","author":"Yang","year":"2020","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1007\/s00376-022-1380-3","article-title":"Assimilation of the FY-4A AGRI clear-sky radiance data in a regional numerical model and its impact on the forecast of the \u201c21\u00b77\u201d Henan extremely persistent heavy rainfall","volume":"40","author":"Xu","year":"2023","journal-title":"Adv. Atmos. Sci."},{"key":"ref_29","first-page":"911","article-title":"Characterization of bias in FY-4A advanced geostationary radiation imager observations from ERA5 background simulations using RTTOV","volume":"77","author":"Qu","year":"2019","journal-title":"Acta Meteorol. Sin."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, J., Shu, J., and Guo, W. (2020). Biases Characteristics Assessment of the Advanced Geosynchronous Radiation Imager (AGRI) Measurement on Board Fengyun\u20134A Geostationary Satellite. Remote Sens., 12.","DOI":"10.3390\/rs12182871"},{"key":"ref_31","first-page":"679","article-title":"Analysis of FY-4A AGRI radiance data bias characteristics and a correction experiment","volume":"44","author":"Geng","year":"2020","journal-title":"Chin. J. Atmos. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tang, F., Zhuge, X., Zeng, M., Li, X., Dong, P., and Han, Y. (2021). Applications of the Advanced Radiative Transfer Modeling System (ARMS) to Characterize the Performance of Fengyun\u20134A\/AGRI. Remote Sens., 13.","DOI":"10.3390\/rs13163120"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1175\/JTECH-D-16-0105.1","article-title":"Characterization of Bias of Advanced Himawari Imager Infrared Observations from NWP Background Simulations Using CRTM and RTTOV","volume":"33","author":"Zou","year":"2016","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_34","first-page":"275","article-title":"Bias Characteristics and Bias Correction of GIIRS Sounder onboard FY-4A Satellite for Data","volume":"46","author":"Liu","year":"2022","journal-title":"Chin. J. Atmos. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1175\/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2","article-title":"A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results","volume":"132","author":"Barker","year":"2004","journal-title":"Mon. Weather Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.atmosres.2017.06.007","article-title":"Bias characterization of CrIS radiances at 399 selected channels with respect to NWP model simulations","volume":"196","author":"Li","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_37","first-page":"113","article-title":"A regional ATOVS radiance-bias correction scheme for radiance assimilation","volume":"65","author":"Liu","year":"2007","journal-title":"Acta Meteorol. Sin."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.5194\/gmd-9-3919-2016","article-title":"A method for retrieving clouds with satellite infrared radiances using the particle filter","volume":"9","author":"Xu","year":"2016","journal-title":"Geosci. Model Dev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TGRS.2012.2229283","article-title":"Monitoring satellite radiance biases using NWP models","volume":"51","author":"Saunders","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.5194\/amt-12-4903-2019","article-title":"All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud","volume":"12","author":"Geer","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3323","DOI":"10.1256\/qj.05.137","article-title":"Bias and data assimilation","volume":"131","author":"Dee","year":"2005","journal-title":"Quart. J. Roy. Meteor. Soc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:05:22Z","timestamp":1760123122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071809"],"URL":"https:\/\/doi.org\/10.3390\/rs15071809","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}