[1] TURNER D D, LÖHNERT U. Ground-based temperature, and humidity profiling: Combining active and passive remote sensors[J]. Atmospheric Measurement Techniques, 2021, 14(4): 3033-3048, https://doi.org/10.5194/amt-14-3033-2021
[2] ZHANG L, YIN X, WANG Z, et al. Preliminary analysis of the potential and limitations of MICAP for the retrieval of sea surface salinity[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11 (9): 2979-2990, https://doi.org/10.1109/JSTARS.2018.2849408
[3] EBELL K, LÖHNERT U, PÄSCHKE E, et al. A 1 ‐ D variational retrieval of temperature, humidity, and liquid cloud properties: Performance under idealized and real conditions[J]. Journal Geophysical Research: Atmospheres, 2017, 122(3): 1746-1766, https://doi.org/10.1002/2016JD025945
[4] ZHANG R, KUMMEROW C, RANDEL D, et al. Tropical cyclone rain retrievals from FY-3B MWRI brightness temperatures using the goddard profiling algorithm (GPROF)[J]. Journal of Atmospheric and Oceanic Technology, 2019, 36(5): 849-864, http://doi.org/10.1175/JTECH-D-18-0167.1
[5] LI N, HE J, ZHANG S, et al. Precipitation retrieval using 118.75 GHz and 183.31 GHz channels from MWHTS on FY-3C satellite[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11 (11): 4373-4389, http://doi.org/10.1109/JSTARS.2018.2873255
[6] EBELL K, ORLANDI E, HÜNERBEIN A, et al. Combining ground-based with satellite-based measurements in the atmospheric state retrieval: assessment of the information content[J]. Journal Geophysical Research: Atmospheres, 2013, 118(13): 6940-6956, https://doi.org/10.1002/jgrd.50548
[7] POLYAKOV A, TIMOFEYEV Y M, VIROLAINEN Y. Comparison of different techniques in atmospheric temperature-humidity sensing from space[J]. International Journal of Remote Sensing, 2014, 35(15): 5899-5912, https://doi.org/10.1080/01431161.2014.945004
[8] TAN H, MAO J, CHEN H, et al. A study of a retrieval method for temperature and humidity profiles from microwave radiometer observations based on principle component analysis and stepwise regression[J]. Journal of Atmospheric and Oceanic Technology, 2011, 28(3): 378-389, https://doi.org/10.1175/2010JTECHA1479.1
[9] GOHIL B S, GAIROLA R M, MATHUR A K, et al. Algorithms for retrieving geophysical parameters from the MADRAS and SAPHIR sensors of the Megha-Tropiques satellite: Indian scenario[J]. Quarterly Journal of the Royal Meteorological Society, 2013, 139(673): 954-963, https://doi.org/10.1002/qj.2041
[10] CHAKRABORTY R, MAITRA A. Retrieval of atmospheric properties with radiometric measurements using neural network[J]. Atmospheric Research, 2016, 181: 124-132, https://doi.org/10.1016/j.atmosres.2016.05.011
[11] HE Q, WANG Z, LI J. Application of the deep neural network in retrieving the atmospheric temperature and humidity profiles from the microwave humidity and temperature sounder onboard the Feng-Yun-3 satellite[J]. Sensors, 2021, 21(14): 4673, https://doi.org/10.3390/s21144673
[12] HE J, ZHANG S, WANG Z. The retrievals and analysis of clear-sky water vapor density in the Arctic regions from MWHS measurements on FY-3A satellite[J]. Radio Science, 2012, 47(2): RS2009, https://doi.org/10.1029/2010RS004612
[13] LIU Q, WENG F. One-dimensional variational retrieval algorithm of temperature, water vapor, and cloud water profiles from advanced microwave sounding unit (AMSU)[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(5): 1087-1095, https://doi.org/10.1109/TGRS.2004.843211
[14] AIRES F, PRIGENT C, ORLANDI E, et al. Microwave hyper-spectral measurements for temperature and humidity atmospheric profiling from satellite: the clearsky case[J]. Journal Geophysical Research: Atmospheres, 2015, 120(21): 11334-11351, https://doi.org/10.1002/2015JD023331
[15] BOUKABARA S A, GARRETT K, CHEN W, et al. MiRS: An all-weather 1DVAR satellite data assimilation and retrieval system[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3249-3272, https://doi.org/10.1109/TGRS.2011.2158438
[16] HE Q R, WANG Z Z, HE J Y, et al. A comparison of the retrieval of atmospheric temperature profiles using observations of the 60 GHz and 118.75 GHz absorption lines[J]. Journal of Tropical Meteorology, 2018, 24(2): 151-162, https://doi.org/10.16555/j.1006-8775.2018.02.004
[17] BLACKWELL W J, CHEN F W. Neural Networks in Atmospheric Remote Sensing[M]. Norwood: Artech House Press, 2009: 19-32.
[18] HE Q, WANG Z, LI J. Fusion retrieval of sea surface barometric pressure from the microwave humidity and temperature sounder and microwave temperature Sounder-Ⅱ onboard the Fengyun-3 satellite[J]. Remote Sensing, 2022, 14(2): 276, https://doi.org/10.3390/rs14020276
[19] ROSENKRANZ P W. Retrieval of temperature and moisture profiles from AMSU-A and AMSU-B measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2429-2435, https://doi.org/10.1109/36.964979
[20] ISHIMOTO H, OKAMOTO K, OKAMOTO H, et al. Onedimensional variational (1D-Var) retrieval of middle to upper tropospheric humidity using AIRS radiance data [J]. Journal Geophysical Research: Atmospheres, 2014, 119(12): 7633-7645, https://doi.org/10.1002/2014JD021706
[21] RODGERS C D. Inverse Methods for Atmospheric Sounding: Theory and Practice[M]. Hackensack & London: World Scientific Press, 2000: 67-75.
[22] SUSSKIND J, ROSENFIELD J, REUTER D. An accurate radiative transfer model for use in the direct physical inversion of HIRS2 and MSU temperature sounding data [J]. Journal Geophysical Research: Oceans, 1983, 88 (C13): 8550-8568, https://doi.org/10.1029/JC088iC13p08550
[23] 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(7): 2717-2737, https://doi.org/10.5194/gmd-11-2717-2018
[24] LIU Q, BOUKABARA S. Community radiative transfer model (CRTM) applications in supporting the Suomi National Polar-orbiting Partnership (SNPP) mission validation and verification[J]. Remote Sensing of Environment, 2014, 140: 744-754, https://doi.org/10.1016/j.rse.2013.10.011
[25] BUEHLER S A, ERIKSSON P, KUHN T, et al. ARTS, the atmospheric radiative transfer simulator[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2005, 91(1): 65-93, https://doi.org/10.1016/j.jqsrt.2004.05.051
[26] ERIKSSON P, BUEHLER S A, DAVIS C P, et al. ARTS, the atmospheric radiative transfer simulator, version 2[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2011, 112(10): 1551-1558, https://doi.org/10.1016/j.jqsrt.2011.03.001
[27] ELACHI C, ZYL J V. Introduction to the Physics and Techniques of Remote Sensing[M]. Hoboken: John Wiley & Sons Inc Press, 2006: 385-412.
[28] YAN X, LIANG C, JIANG Y, et al. A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8427-8437, https://doi.org/10.1109/TGRS.2020.2987896
[29] GUO Y, LU N M, QI C L, et al. Calibration and validation of microwave humidity and temperature sounder onboard FY-3C satellite[J]. Chinese Journal of Geophysics (in Chinese), 2015, 58(1): 20-31, https://doi/10.6038/cjg20150103 doi: 10.6038/cjg20150103
[30] WANG Z, LI J, HE J, et al. Performance analysis of microwave humidity and temperature sounder onboard the FY-3D satellite from prelaunch multiangle calibration data in thermal / vacuum test[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(3): 1664-1683, https://doi/10.1109/TGRS.2018.2868324 doi: 10.1109/TGRS.2018.2868324
[31] HE Q R, WANG Z Z, HE J Y, et al. Effects of a cloud filtering method for Fengyun-3C microwave humidity and temperature sounder measurements over ocean on retrievals of temperature and humidity[J]. Journal of Tropical Meteorology, 2018, 24(1): 29-41, https://doi/10.16555/j.1006-8775.2018.01.003 doi: 10.16555/j.1006-8775.2018.01.003
[32] CARMINATI F, MIGLIORINI S. All-sky data assimilation of MWTS-2 and MWHS-2 in the Met Office global NWP system[J]. Advances Atmospheric Sciences, 2021, 38(10): 1682-1694, https://doi.org/10.1007/s00376-021-1071-5
[33] DEE D P, UPPALA S M, SIMMONS A J, et al. The ERAInterim reanalysis: configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656): 553-597, https://doi.org/10.1002/qj.828
[34] ULABY F T, MOORE R K, FUNG A K. Microwave Remote Sensing: Active and Passive[M]. Norwood: Artech House Press, 1981: 121-145.
[35] ZHOU Y, GRASSTOTTI C. Development of a machine learning-based radiometric bias correction for NOAA's microwave integrated retrieval system (MiRS)[J]. Remote Sensing, 2020, 12(19): 3160, https://doi.org/10.3390/rs12193160
[36] LI Q, WEI M, WANG Z, et al. Improving the retrieval of cloudy atmospheric profiles from brightness temperatures observed with a ground-based microwave radiometer[J]. Atmosphere, 2021, 12(5): 648, https://doi.org/10.3390/atmos12050648
[37] TAN J, NOURELDEEN N, MAO K, et al. Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China[J]. Sensors, 2019, 19(13): 2987, https://doi.org/10.3390/s19132987
[38] RODGERS C D. Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation[J]. Reviews of Geophysics and Space Physics, 1976, 14(4): 609-624, https://doi.org/10.1029/RG014i004p00609
[39] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521: 436-444, https://doi.org/10.1038/nature14539
[40] LEE Y, HAN D, 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, https://doi.org/10.3390/rs11151741
[41] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958 http://www.cs.utoronto.ca/~hinton/absps/JMLRdropout.pdf
[42] SAHOO S, BOSCH-LLUIS X, REISING S C, et al. Optimization of background information and layer thickness for improved accuracy of water-vapor profile retrieval from ground-based microwave radiometer measurements at k-band[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(9): 4284-4295, https://doi.org/10.1109/JSTARS.2014.2370073
[43] DEE D P. Bias and data assimilation[J]. Quarterly Journal of the Royal Meteorological Society, 2005, 131(613): 3323-3343, https://doi.org/10.1256/qj.05.137
[44] 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(624): 631-642, https://doi.org/10.1002/qj.56
[45] GAYFULIN D, TSYRULNIKOV M, USPENSKY A, et al. Assessment and adaptive correction of observations in atmospheric sounding channels of the satellite microwave radiometer MTVZA-GY[J]. Pure and Applied Geophysics, 2018, 175(10): 3653-3670, https://doi.org/10.1007/s00024-018-1917-7
[46] KAZUMORI M. Satellite radiance assimilation in the JMA operational mesoscale 4DVAR system[J]. Monthly Weather Review, 2014, 142(3): 1361-1381, https://doi.org/10.1175/MWR-D-13-00135.1
[47] ZHU Y, DERBER J, COLLARD A, et al. Enhanced radiance bias correction in the national centers for environmental prediction' s gridpoint statistical interpolation data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2014, 140 (682): 1479-1492, https://doi.org/10.1002/qj.2233
[48] DEE D P, UPPALA S. Variational bias correction of satellite radiance data in the ERA-Interim reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2009, 135(644): 1830-1841, https://doi.org/10.1002/qj.493
[49] HE Q, WANG Z, HE J. Bias correction for retrieval of atmospheric parameters from the Microwave Humidity and Temperature Sounder onboard the Fengyun-3C satellite[J]. Atmosphere, 2016, 7(12): 156, https://doi.org/10.3390/atmos7120156
[50] LÖHNERT U, CREWELL S, SIMMER C. An integrated approach toward retrieving physically consistent profiles of temperature, humidity, and cloud liquid water[J]. Journal of Applied Meteorology, 2004, 43(9): 1295-1307, https://doi.org/10.1175/1520-0450(2004)043<1295:AIATRP>2.0.CO;2 doi: 10.1175/1520-0450(2004)043<1295:AIATRP>2.0.CO;2