Articles | Volume 19, issue 20
https://doi.org/10.5194/amt-19-6553-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-6553-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Oct 2026
Research article |
| 19 Oct 2026
| 19 Oct 2026
A prototype algorithm for all-sky atmospheric temperature profile retrieval over ocean based on channel optimization for the FY-4M satellite
Yu Wang
School of Earth and Space Sciences, Joint Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei, 230026, China
China Meteorological Administration Xiong'an Atmospheric Boundary Layer Key Laboratory, Xiong'an New Area, Baoding, 071800, China
Xinbin Ma
School of Earth and Space Sciences, Joint Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei, 230026, China
Banghai Wu
Aviation Industry Corporation of China Leihua Electronic Technology Institute, Wuxi, 214063, China
Qiang Guo
National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing, 100081, China
Jing Xi
CORRESPONDING AUTHOR
School of Earth and Space Sciences, Joint Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei, 230026, China
China Meteorological Administration Xiong'an Atmospheric Boundary Layer Key Laboratory, Xiong'an New Area, Baoding, 071800, China
Rui Li
School of Earth and Space Sciences, Joint Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei, 230026, China
Xin Wang
National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing, 100081, China
Xiaoqing Li
National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing, 100081, China
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Cited articles
Aires, F., Prigent, C., Orlandi, E., Milz, M., Eriksson, P., Crewell, S., Lin, C.-C., and Kangas, V.: Microwave hyperspectral measurements for temperature and humidity atmospheric profiling from satellite: the clear-sky case, J. Geophys. Res.-Atmos., 120, 11334–11351, https://doi.org/10.1002/2015JD023331, 2015.
Aires, F., Prigent, C., Buehler, S. A., Eriksson, P., Milz, M., and Crewell, S.: Towards more realistic hypotheses for the information content analysis of cloudy/precipitating situations – application to a hyperspectral instrument in the microwave, Q. J. Roy. Meteor. Soc., 145, 1–14, https://doi.org/10.1002/qj.3315, 2019.
Berg, W.: GPM GMI_R Common Calibrated Brightness Temperatures Collocated L1C 1.5 h 13 km V05, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA [data set], https://doi.org/10.5067/GPM/GMI/R/1C/05, 2016.
Bi, Y., Yang, J., Wei, C., Dou, F., Xu, W., An, D., Luan, Y., Feng, J., and Zhang, L.: Atmospheric temperature measurements using microwave hyper-spectrum from geostationary satellite: band design, weighting functions and information content, Remote Sens., 16, 289, https://doi.org/10.3390/rs16020289, 2024.
Birman, C., Mahfouf, J. F., Milz, M., Mendrok, J., Buehler, S. A., and Brath, M.: Information content on hydrometeors from millimeter and sub-millimeter wavelengths, Tellus A Dyn. Meteorol. Oceanogr., 69, 1271562, https://doi.org/10.1080/16000870.2016.1271562, 2017.
Bormann, N., Geer, A., and English, S. J.: Evaluation of the microwave ocean surface emissivity model FASTEM-5 in the IFS, Eur. Centre Medium-Range Weather Forecasts, Reading, UK, Tech. Memo. 667, https://doi.org/10.21957/l936s9df, 2012.
Chedin, A., Scott, N. A., Wahiche, C., and Moulinier, P.: The improved initialization inversion method: a high resolution physical method for temperature retrievals from satellites of the TIROS-N series, J. Appl. Meteor. Climatol., 24, 128–143, https://doi.org/10.1175/1520-0450(1985)024<0128:TIIIMA>2.0.CO;2, 1985.
Chen, K., Suo, Z., Han, W., and Cai, B.: OSSEs on the FY-4M geostationary microwave satellite based on CMA-GFS and CMA-MESO, IEEE Trans. Geosci. Remote Sens., 62, 5302819, https://doi.org/10.1109/TGRS.2024.3487859, 2024.
Chen, K., Suo, Z., Han, W., Cai, B., and Chen, Y.: Assessing the potential impact of microwave sounders on typhoon prediction using OSSEs: a comparative study between LEO constellation and GEO satellites, IEEE Geosci. Remote Sens. Lett., 22, 1–5, https://doi.org/10.1109/LGRS.2025.3548908, 2025.
Christensen, J., Carlstrom, A., Ekstrom, H., Emrich, A., Embretsén, J., and De Maagt, P.: GAS: The geostationary atmospheric sounder, in: Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Barcelona, Spain, 23–28 July 2007, 223–226, https://doi.org/10.1109/IGARSS.2007.4422770, 2007.
Copernicus Climate Change Service, Climate Data Store: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023a.
Copernicus Climate Change Service, Climate Data Store: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023b.
Draper, D. W., Newell, D. A., Wentz, F. J., Krimchansky, S., and Skofronick-Jackson, G. M.: The global precipitation measurement (GPM) microwave imager (GMI): instrument overview and early on-orbit performance, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 8, 3452–3462, https://doi.org/10.1109/JSTARS.2015.2403303, 2015.
Evans, K. F., Turk, J., Wong, T., and Stephens, G. L.: A Bayesian approach to microwave precipitation profile retrieval, J. Appl. Meteor. Climatol., 34, 260–279, https://doi.org/10.1175/1520-0450-34.1.260, 1995.
Gasiewski, A. J., Barrett, J. W., Bonanni, P. G., and Staelin, D. H.: Aircraft-based radiometric imaging of tropospheric temperature and precipitation using the 118.75-GHz oxygen resonance, J. Appl. Meteor. Climatol., 29, 620–632, https://doi.org/10.1175/1520-0450(1990)029<0620:ABRIOT>2.0.CO;2, 1990.
Gong, J. and Wu, D. L.: Microphysical properties of frozen particles inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) polarimetric measurements, Atmos. Chem. Phys., 17, 2741–2757, https://doi.org/10.5194/acp-17-2741-2017, 2017.
He, J., Zhang, S., and Wang, Z.: Advanced microwave atmospheric sounder (AMAS) channel specifications and T/V calibration results on FY-3C satellite, IEEE Trans. Geosci. Remote Sens., 53, 481–493, https://doi.org/10.1109/TGRS.2014.2324173, 2015.
He, W., Chen, H., Xuan, Y., Li, J., Duan, M., and Nan, W.: Ground mobile observation system for measuring multisurface microwave emissivity, Atmos. Meas. Tech., 14, 7069–7078, https://doi.org/10.5194/amt-14-7069-2021, 2021.
He, W., Xia, X., Wu, S., Zhang, P., Chen, H., Xia, X., Zhou, Y., and Cai, M.: A rapid assessment of MWRI-RM/FY3G brightness temperature, Atmos. Ocean. Sci. Lett., 17, 100466, https://doi.org/10.1016/j.aosl.2024.100466, 2024.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hu, J., Li, R., Zhang, P., Wang, Y., Wu, S., Letu, H., and Weng, F.: Global retrieval of harmonized microwave land surface emissivity leveraging multi-sensor measurements from GMI, AMSR2 and MWRIs, Remote Sens. Environ., 334, 115169, https://doi.org/10.1016/j.rse.2025.115169, 2026.
Koshelev, M. A., Vilkov, I. N., Makarov, D. S., Tretyakov, M. Yu., Vispoel, B., Gamache, R. R., Cimini, D., Romano, F., and Rosenkranz, P. W.: Water vapor line profile at 183-GHz: Temperature dependence of broadening, shifting, and speed-dependent shape parameters, J. Quant. Spectrosc. Radiat. Transfer, 262, 107472, https://doi.org/10.1016/j.jqsrt.2020.107472, 2021.
Kummerow, C.: GPM GMI (GPROF) Climate-based Radiometer Precipitation Profiling L2A 1.5 h 4 km × 4 km V07, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA [data set], https://doi.org/10.5067/GPM/GMI/GPROFCLIM/2A/07, 2022.
Kummerow, C., Berg, W., Thomas-Stahle, J., and Masunaga, H.: Quantifying global uncertainties in a simple microwave rainfall algorithm, J. Atmos. Ocean. Tech., 23, 23–37, https://doi.org/10.1175/JTECH1827.1, 2006.
Kummerow, C. D., Ringerud, S., Crook, J., Randel, D., and Berg, W.: An observationally generated a priori database for microwave rainfall retrievals, J. Atmos. Ocean. Tech., 28, 113–130, https://doi.org/10.1175/2010JTECHA1468.1, 2011.
Kummerow, C. D., Randel, D. L., Kulie, M., Wang, N.-Y., Ferraro, R., Munchak, S. J., and Petkovic, V.: The evolution of the Goddard profiling algorithm to a fully parametric scheme, J. Atmos. Ocean. Tech., 32, 2265–2280, https://doi.org/10.1175/JTECH-D-15-0039.1, 2015.
Kummerow, C. D., Poczatek, J. C., Almond, S., Berg, W., Jarrett, O., Jones, A., Kantner, M., and Kuo, C. P.: Hyperspectral microwave sensors – Advantages and limitations, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 15, 764–775, https://doi.org/10.1109/JSTARS.2021.3133382, 2022.
Lambrigtsen, B., Tanner, A., Gaier, T., Kangaslahti, P., and Brown, S.: Prototyping a new earth observing sensor – GeoSTAR, in: Proc. IEEE Aerosp. Conf., Big Sky, Montana, USA, 3–10 March 2007, 1–9, https://doi.org/10.1109/AERO.2007.352690, 2007.
Lambrigtsen, B., Gaier, T., Kangaslahti, P., Lim, B., Tanner, A., and Ruf, C.: Enabling the NASA decadal–survey “PATH” mission, in: Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Beijing, China, 10–15 July 2016, 3949–3951, https://doi.org/10.1109/IGARSS.2016.7730026, 2016.
Lawrence, H., Carminati, F., Bell, W., Bormann, N., Newman, S., Atkinson, N., Geer, A., Migliorini, S., Lu, Q., and Chen, K.: An evaluation of FY-3C MWRI and assessment of the long-term quality of FY-3C MWHS-2 at ECMWF and the Met Office, Eur. Centre Medium-Range Weather Forecasts, Reading, UK, Tech. Memo. 798, https://doi.org/10.21957/lhuph6fb3, 2017.
Li, R., Wang, Y., Hu, J., Wang, Y., Min, Q., Bergeron, Y., Valeria, O., Gao, Z., Liu, J., and Fu, Y.: Spatiotemporal variations of satellite microwave emissivity difference vegetation index in China under clear and cloudy skies, Earth Space Sci., 7, e2020EA001145, https://doi.org/10.1029/2020ea001145, 2020.
Li, R., Hu, J., Wu, S., Zhang, P., Letu, H., Wang, Y., Wang, X., Fu, Y., Zhou, R., and Sun, L.: Spatiotemporal variations of microwave land surface emissivity (MLSE) over China derived from four-year recalibrated Fengyun 3B MWRI data, Adv. Atmos. Sci., 39, 1536–1560, https://doi.org/10.1007/s00376-022-1314-0, 2022.
Liu, G.: A fast and accurate model for microwave radiance calculations, J. Meteorol. Soc. Jpn., 76, 335–343, https://doi.org/10.2151/jmsj1965.76.2_335, 1998.
Liu, Q., Weng, F., and English, S. J.: An improved fast microwave water emissivity model, IEEE Trans. Geosci. Remote Sens., 49, 1238–1250, https://doi.org/10.1109/TGRS.2010.2064779, 2011.
Liu, S., Xu, J., Chen, L., Liu, H., Wang, W., He, J., Zhu, S., and Shi, J.: Comprehensive validation of temperature and humidity profile retrievals from FengYun-3E, IEEE Trans. Geosci. Remote Sens., 64, 4102214, https://doi.org/10.1109/TGRS.2026.3658572, 2026.
Mahfouf, J.-F., Birman, C., Aires, F., Prigent, C., Orlandi, E., and Milz, M.: Information content on temperature and water vapour from a hyper-spectral microwave sensor, Q. J. Roy. Meteor. Soc., 141, 3268–3284, https://doi.org/10.1002/qj.2608, 2015.
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.
Mo, T., Goldberg, M. D., Crosby, D. S., and Cheng, Z.: Recalibration of the NOAA microwave sounding unit, J. Geophys. Res.-Atmos., 106, 10145–10150, https://doi.org/10.1029/2001JD900027, 2001.
Rodgers, C. D.: Information content and optimisation of high spectral resolution remote measurements, Adv. Space Res., 21, 361–367, https://doi.org/10.1016/S0273-1177(97)00915-0, 1998.
Rodgers, C. D.: Inverse methods for atmospheric sounding: Theory and practice, World Scientific, Singapore, https://doi.org/10.1142/3171, 2000.
Sahoo, S., Bosch-Lluis, X., Reising, S. C., and Vivekanandan, J.: Radiometric information content for water vapor and temperature profiling in clear skies between 10 and 200 GHz, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 8, 859–871, https://doi.org/10.1109/JSTARS.2014.2364394, 2015.
Tretyakov, M. Y., Parshin, V. V., Shanin, V. N., Myasnikova, S. E., Koshelev, M. A., and Krupnov, A. F.: Real atmosphere laboratory measurements of the 118-GHz oxygen line: Shape, shift, and broadening of the line, J. Mol. Spectrosc., 208, 110–112, https://doi.org/10.1006/jmsp.2001.8363, 2001.
Wang, D., Tong, L., Gong, X., Guan, X., Wang, P., and Gao, B.: Retrieval of atmospheric temperature profiles from hyperspectral microwave radiative data based on the neural network, in: Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Brussels, Belgium, 11–16 July 2021, 7095–7098, https://doi.org/10.1109/IGARSS47720.2021.9554282, 2021.
Weng, F., Zhao, L., Ferraro, R. R., Poe, G., Li, X., and Grody, N. C.: Advanced microwave sounding unit cloud and precipitation algorithms, Radio Sci., 38, 8068, https://doi.org/10.1029/2002RS002679, 2003.
Weng, F., Zou, X., Sun, N., Yang, H., Tian, M., Blackwell, W. J., Wang, X., Lin, L., and Anderson, K.: Calibration of Suomi national polar‐orbiting partnership advanced technology microwave sounder, J. Geophys. Res.-Atmos., 118, 11187–11200, https://doi.org/10.1002/jgrd.50840, 2013.
Wentz, F. J. and Draper, D.: On-orbit absolute calibration of the Global Precipitation Measurement Microwave Imager, J. Atmos. Ocean. Tech., 33, 1393–1412, https://doi.org/10.1175/JTECH-D-15-0212.1, 2016.
Wu, B., Li, X., Wang, Y., Fan, X., Zhang, P., Chen, L., and Fu, Y.: A prototype precipitation retrieval algorithm for passive microwave observations from the FY-3G satellite, J. Remote Sens., 6, 1057, https://doi.org/10.34133/remotesensing.1057, 2026.
Xiao, X. and Weng, F.: A comparison of information content at microwave to millimeter wave bands for atmospheric sounding, Remote Sens., 14, 6124, https://doi.org/10.3390/rs14236124, 2022.
Xie, Z., Li, X., Yao, C., Jiang, L., and Li, X.: Research on geostationary orbit microwave radiometer technology, Aerosp. Shanghai, 35, 20–28, https://doi.org/10.19328/j.cnki.1006-1630.2018.02.003, 2018a.
Xie, Z., Xu, H., An, D., He, J., Zhang, D., and Zhu, Z.: Remote sensing technology of experimental microwave radiometer in geostationary orbit, Aerosp. Shanghai, 35, 49–59, https://doi.org/10.19328/j.cnki.1006-1630.2018.02.006, 2018b.
Xue, Q., Guan, L., and Shi, X.: One-dimensional variational retrieval of temperature and humidity profiles from the FY4A GIIRS, Adv. Atmos. Sci., 39, 471–486, https://doi.org/10.1007/s00376-021-1032-z, 2022.
Yan, X., Liang, C., Jiang, Y., Luo, N., Zang, Z., and Li, Z.: A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer, IEEE Trans. Geosci. Remote Sens., 58, 8427–8437, https://doi.org/10.1109/TGRS.2020.2987896, 2020.
Yang, H., Chen, Z., Sun, S., Li, M., Cai, W., Wu, L., Cai, J., Sun, B., Ma, K., and Ma, X.: Observations reveal intense air‐sea exchanges over submesoscale ocean front, Geophys. Res. Lett., 51, e2023GL106840, https://doi.org/10.1029/2023GL106840, 2024.
Yin, M. and Liu, G.: Developing an a priori database for passive microwave snow water retrievals over ocean, J. Geophys. Res.-Atmos., 122, 12960–12981, https://doi.org/10.1002/2017JD027636, 2017.
Zhang, L., Ma, Y., Lei, L., Wang, Y., Jin, S., and Gong, W.: Improving atmospheric temperature and relative humidity profiles retrieval based on ground-based multichannel microwave radiometer and millimeter-wave cloud radar, Atmosphere, 15, 1064, https://doi.org/10.3390/atmos15091064, 2024.
Zhang, P., Xu, Z., Guan, M., Xie, L., Xian, D., and Liu, C.: Progress of Fengyun meteorological satellites since 2020, Chinese J. Space Sci., 42, 724–732, https://doi.org/10.11728/cjss2022.04.yg14, 2022.
Zhang, P., Gu, S., Chen, L., Shang, J., Lin, M., Zhu, A., Yin, H., Wu, Q., Shou, Y., Sun, F., Xu, H., Yang, G., Wang, H., Li, L., Zhang, H., Chen, S., and Lu, N.: FY-3G satellite instruments and precipitation products: First report of China's Fengyun rainfall mission in-orbit, J. Remote Sens., 3, 0097, https://doi.org/10.34133/remotesensing.0097, 2023.
Zhou, L., Lei, L., Whitaker, J. S., and Tan, Z.-M.: An adaptive channel selection method for assimilating the hyperspectral infrared radiances, Mon. Weather Rev., 152, 793–810, https://doi.org/10.1175/MWR-D-23-0131.1, 2024.
Short summary
China’s Fengyun-4M, planned for launch in late 2026, will be the first geostationary satellite to carry a passive microwave instrument, opening a new era of weather observation. To better use its more than one hundred channels, we developed a retrieval method and an optimized channel setup for all-sky temperature profile retrieval over ocean. The results indicate that this algorithm shows low errors while reducing computing time, which is a preferred choice for future operational use.
China’s Fengyun-4M, planned for launch in late 2026, will be the first geostationary satellite...
