Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1947-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1947-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Mar 2025
Development and technical paper |
| 25 Mar 2025
| 25 Mar 2025
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Yi-Ning Shi
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
Jun Yang
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
Lujie Han
Meteorological Observation Center, China Meteorological Administration, Beijing, 100081, China
Zhejiang Lin'an Atmospheric Background National Observation and Research Station, Hangzhou, 311300, China
Jiajia Mao
Meteorological Observation Center, China Meteorological Administration, Beijing, 100081, China
Wanlin Kan
Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing, 210041, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
Fuzhong Weng
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
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Cited articles
Cady-Pereira, K. E., Turner, E., and Saunders, R.: Inter-comparison of line-by-line radiative transfer models MonoRTM and AMSUTRAN for microwave frequencies from the Top-Of-Atmosphere, Tech. Rep. NWPSAF-MO-VS-057, NWP SAF, https://nwp-saf.eumetsat.int/publications/vs_reports/nwpsaf-mo-vs-057.pdf (lsat access: 30 January 2024), 2021. a
Cao, Y., Shi, B., Zhao, X., Yang, T., and Min, J.: Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall, Remote Sens., 15, 4314, https://doi.org/10.3390/rs15174314, 2023. a
Caumont, O., Cimini, D., Löhnert, U., Alados-Arboledas, L., Bleisch, R., Buffa, F., Ferrario, M. E., Haefele, A., Huet, T., Madonna, F., and Pace, G.: Assimilation of humidity and temperature observations retrieved from ground-based microwave radiometers into a convective-scale NWP model, Q. J. Roy. Meteor. Soc., 142, 2692–2704, https://doi.org/10.1002/qj.2860, 2016. a
Chen, H., Han, W., Wang, H., Pan, C., An, D., Gu, S., and Zhang, P.: Why and How Does the Actual Spectral Response Matter for Microwave Radiance Assimilation?, Geophys. Res. Lett., 48, e2020GL092306, https://doi.org/10.1029/2020GL092306, 2021. a
Chen, Y., Han, Y., Van Delst, P., and Weng, F.: On water vapor Jacobian in fast radiative transfer model, J. Geophys. Res.-Atmos., 115, D12303, https://doi.org/10.1029/2009JD013379, 2010. a
Chen, Y., Han, Y., and Weng, F.: Comparison of two transmittance algorithms in the community radiative transfer model: Application to AVHRR, J. Geophys. Res.-Atmos., 117, D06206, https://doi.org/10.1029/2011JD016656, 2012. a
China Meteorological Administration: China Meteorological Data Service Center, https://data.cma.cn/en (last access: 2 June 2024), 2024. a
Cimini, D., Hewison, T., Martin, L., Güldner, J., Gaffard, C., and Marzano, F.: Temperature and humidity profile retrievals from ground-based microwave radiometers during TUC, Meteorol. Z., 15, 45–56, https://doi.org/10.1127/0941-2948/2006/0099, 2006. a
Cimini, D., Rosenkranz, P. W., Tretyakov, M. Y., Koshelev, M. A., and Romano, F.: Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals, Atmos. Chem. Phys., 18, 15231–15259, https://doi.org/10.5194/acp-18-15231-2018, 2018. a
Cimini, D., Hocking, J., De Angelis, F., Cersosimo, A., Di Paola, F., Gallucci, D., Gentile, S., Geraldi, E., Larosa, S., Nilo, S., Romano, F., Ricciardelli, E., Ripepi, E., Viggiano, M., Luini, L., Riva, C., Marzano, F. S., Martinet, P., Song, Y. Y., Ahn, M. H., and Rosenkranz, P. W.: RTTOV-gb v1.0 – updates on sensors, absorption models, uncertainty, and availability, Geosci. Model Dev., 12, 1833–1845, https://doi.org/10.5194/gmd-12-1833-2019, 2019. a, b, c, d, e, f
Clough, S., Shephard, M., Mlawer, E., Delamere, J., Iacono, M., Cady-Pereira, K., Boukabara, S., and Brown, P.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005. a, b, c
De Angelis, F., Cimini, D., Hocking, J., Martinet, P., and Kneifel, S.: RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations, Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, 2016. a, b, c, d, e, f, g, h, i
De Angelis, F., Cimini, D., Löhnert, U., Caumont, O., Haefele, A., Pospichal, B., Martinet, P., Navas-Guzmán, F., Klein-Baltink, H., Dupont, J.-C., and Hocking, J.: Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network, Atmos. Meas. Tech., 10, 3947–3961, https://doi.org/10.5194/amt-10-3947-2017, 2017. a
Errico, R. M.: What Is an Adjoint Model?, B. Am. Meteorol. Soc., 78, 2577–2592, https://doi.org/10.1175/1520-0477(1997)078<2577:WIAAM>2.0.CO;2, 1997. a
Gordon, I., Rothman, L., Hargreaves, R., Hashemi, R., Karlovets, E., Skinner, F., Conway, E., Hill, C., Kochanov, R., Tan, Y., Wcisło, P., Finenko, A., Nelson, K., Bernath, P., Birk, M., Boudon, V., Campargue, A., Chance, K., Coustenis, A., Drouin, B., Flaud, J., Gamache, R., Hodges, J., Jacquemart, D., Mlawer, E., Nikitin, A., Perevalov, V., Rotger, M., Tennyson, J., Toon, G., Tran, H., Tyuterev, V., Adkins, E., Baker, A., Barbe, A., Canè, E., Császár, A., Dudaryonok, A., Egorov, O., Fleisher, A., Fleurbaey, H., Foltynowicz, A., Furtenbacher, T., Harrison, J., Hartmann, J., Horneman, V., Huang, X., Karman, T., Karns, J., Kassi, S., Kleiner, I., Kofman, V., Kwabia–Tchana, F., Lavrentieva, N., Lee, T., Long, D., Lukashevskaya, A., Lyulin, O., Makhnev, V., Matt, W., Massie, S., Melosso, M., Mikhailenko, S., Mondelain, D., Müller, H., Naumenko, O., Perrin, A., Polyansky, O., Raddaoui, E., Raston, P., Reed, Z., Rey, M., Richard, C., Tóbiás, R., Sadiek, I., Schwenke, D., Starikova, E., Sung, K., Tamassia, F., Tashkun, S., Vander Auwera, J., Vasilenko, I., Vigasin, A., Villanueva, G., Vispoel, B., Wagner, G., Yachmenev, A., and Yurchenko, S.: The HITRAN2020 molecular spectroscopic database, J. Quant. Spectrosc. Rad., 277, 107949, https://doi.org/10.1016/j.jqsrt.2021.107949, 2022. a
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.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582c, 2017. a
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. a
Hocking, J., Vidot, J., Brunel, P., Roquet, P., Silveira, B., Turner, E., and Lupu, C.: A new gas absorption optical depth parameterisation for RTTOV version 13, Geosci. Model Dev., 14, 2899–2915, https://doi.org/10.5194/gmd-14-2899-2021, 2021. a, b
Illingworth, A. J., Cimini, D., Haefele, A., Haeffelin, M., Hervo, M., Kotthaus, S., Löhnert, U., Martinet, P., Mattis, I., O’Connor, E. J., and Potthast, R.: How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?, B. Am. Meteorol. Soc., 100, 605–619, https://doi.org/10.1175/BAMS-D-17-0231.1, 2019. a
Karpowicz, B. M., Stegmann, P. G., Johnson, B. T., Christophersen, H. W., Hyer, E. J., Lambert, A., and Simon, E.: pyCRTM: A python interface for the community radiative transfer model, J. Quant. Spectrosc. Ra., 288, 108263, https://doi.org/10.1016/j.jqsrt.2022.108263, 2022. a
Larosa, S., Cimini, D., Gallucci, D., Nilo, S. T., and Romano, F.: PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations, Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, 2024. a
Leuenberger, D., Haefele, A., Omanovic, N., Fengler, M., Martucci, G., Calpini, B., Fuhrer, O., and Rossa, A.: Improving High-Impact Numerical Weather Prediction with Lidar and Drone Observations, B. Am. Meteorol. Soc., 101, E1036–E1051, https://doi.org/10.1175/BAMS-D-19-0119.1, 2020. a
Li, J. and Fu, Q.: Absorption Approximation with Scattering Effect for Infrared Radiation, J. Atmos. Sci., 57, 2905–2914, https://doi.org/10.1175/1520-0469(2000)057<2905:AAWSEF>2.0.CO;2, 2000. a
Lin, H.-C., Sun, J., Weckwerth, T. M., Joseph, E., and Kay, J.: Assimilation of New York State Mesonet Surface and Profiler Data for the 21 June 2021 Convective Event, Mon. Weather Rev., 151, 485–507, https://doi.org/10.1175/MWR-D-22-0136.1, 2023. a
Liou, K.: Radiation and Cloud Processes in the Atmosphere: Theory, Observation and Modeling, Oxford University, ISBN 9780195049107, 1992. a
Martinet, P., Dabas, A., Donier, J.-M., Douffet, T., Garrouste, O., and Guillit, R.: 1D-Var temperature retrievals from microwave radiometer and convective scale model, Tellus A, 67, 27925, https://doi.org/10.3402/tellusa.v67.27925, 2015. a
Martinet, P., Cimini, D., Burnet, F., Ménétrier, B., Michel, Y., and Unger, V.: Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study, Atmos. Meas. Tech., 13, 6593–6611, https://doi.org/10.5194/amt-13-6593-2020, 2020. a
Matricardi, M., Chevallier, F., Kelly, G., and Thépaut, J.-N.: An improved general fast radiative transfer model for the assimilation of radiance observations, Q. J. Roy. Meteor. Soc., 130, 153–173, https://doi.org/10.1256/qj.02.181, 2004. a
McMillin, L. M., Crone, L. J., and Kleespies, T. J.: Atmospheric transmittance of an absorbing gas. 5. Improvements to the OPTRAN approach, Appl. Optics, 34, 8396–8399, https://doi.org/10.1364/AO.34.008396, 1995. a
Mlawer, E. J., Payne, V. H., Moncet, J.-L., Delamere, J. S., Alvarado, M. J., and Tobin, D. C.: Development and recent evaluation of the MT_CKD model of continuum absorption, Philos. T. Roy. Soc. A, 370, 2520–2556, https://doi.org/10.1098/rsta.2011.0295, 2012. a
Moradi, I., Goldberg, M., Brath, M., Ferraro, R., Buehler, S. A., Saunders, R., and Sun, N.: Performance of Radiative Transfer Models in the Microwave Region, J. Geophys. Res.-Atmos., 125, e2019JD031831, https://doi.org/10.1029/2019JD031831, 2020. a, b
Rochon, Y. J., Garand, L., Turner, D. S., and Polavarapu, S.: Jacobian mapping between vertical coordinate systems in data assimilation, Q. J. Roy. Meteor. Soc., 133, 1547–1558, https://doi.org/10.1002/qj.117, 2007. a, b
Rosenkranz, P. W.: Water vapor microwave continuum absorption: A comparison of measurements and models, Radio Sci., 33, 919–928, https://doi.org/10.1029/98RS01182, 1998. a, b
Saunders, R., Matricardi, M., and Brunel, P.: An improved fast radiative transfer model for assimilation of satellite radiance observations, Q. J. Roy. Meteor. Soc., 125, 1407–1425, https://doi.org/10.1002/qj.1999.49712555615, 1999. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Shi, Y.-N., Yang, J., and Weng, F.: Codes and Coefficients for Radiative Transfer for Ground-based Microwave Radiometers (ARMS-gb v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.14032776, 2024. a
Stegmann, P. G., Johnson, B., Moradi, I., Karpowicz, B., and McCarty, W.: A deep learning approach to fast radiative transfer, J. Quant. Spectrosc. Ra., 280, 108088, https://doi.org/10.1016/j.jqsrt.2022.108088, 2022. a
Toon, O. B., McKay, C. P., Ackerman, T. P., and Santhanam, K.: Rapid calculation of radiative heating rates and photodissociation rates in inhomogeneous multiple scattering atmospheres, J. Geophys. Res.-Atmos., 94, 16287–16301, https://doi.org/10.1029/JD094iD13p16287, 1989. a
Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady-Pereira, K. E., and Gaustad, K. L.: Retrieving Liquid Water Path and Precipitable Water Vapor From the Atmospheric Radiation Measurement (ARM) Microwave Radiometers, IEEE T. Geosci. Remote S., 45, 3680–3690, https://doi.org/10.1109/TGRS.2007.903703, 2007. a
Turner, E., Rayer, P., and Saunders, R.: AMSUTRAN: A microwave transmittance code for satellite remote sensing, J. Quant. Spectrosc. Ra., 227, 117–129, https://doi.org/10.1016/j.jqsrt.2019.02.013, 2019. a
Vural, J., Merker, C., Löffler, M., Leuenberger, D., Schraff, C., Stiller, O., Schomburg, A., Knist, C., Haefele, A., and Hervo, M.: Improving the representation of the atmospheric boundary layer by direct assimilation of ground-based microwave radiometer observations, Q. J. Roy. Meteor. Soc., 150, 1012–1028, https://doi.org/10.1002/qj.4634, 2024. a
Wei, J., Shi, Y., Ren, Y., Li, Q., Qiao, Z., Cao, J., Ayantobo, O. O., Yin, J., and Wang, G.: Application of Ground-Based Microwave Radiometer in Retrieving Meteorological Characteristics of Tibet Plateau, Remote Sens., 13, 2527, https://doi.org/10.3390/rs13132527, 2021. a
Wei, Y., Peng, K., Ma, Y., Sun, Y., Zhao, D., Ren, X., Yang, S., Ahmad, M., Pan, X., Wang, Z., and Xin, J.: Validation of ERA5 Boundary Layer Meteorological Variables by Remote-Sensing Measurements in the Southeast China Mountains, Remote Sens., 16, 548, https://doi.org/10.3390/rs16030548, 2024. a
Weng, F. and Liu, Q.: Satellite Data Assimilation in Numerical Weather Prediction Models. Part I: Forward Radiative Transfer and Jacobian Modeling in Cloudy Atmospheres, J. Atmos. Sci., 60, 2633–2646, https://doi.org/10.1175/1520-0469(2003)060<2633:SDAINW>2.0.CO;2, 2003. a
Weng, F., Yu, X., Duan, Y., Yang, J., and Wang, J.: Advanced Radiative transfer Modeling System (ARMS): A New-Generation Satellite Observation Operator Developed for Numerical Weather Prediction and Remote Sensing Applications, Adv. Atmos. Sci., 37, 131–136, https://doi.org/10.1007/s00376-019-9170-2, 2020. a
Wu, J., Guo, J., Yun, Y., Yang, R., Guo, X., Meng, D., Sun, Y., Zhang, Z., Xu, H., and Chen, T.: Can ERA5 reanalysis data characterize the pre-storm environment?, Atmos. Res., 297, 107108, https://doi.org/10.1016/j.atmosres.2023.107108, 2024. a
Yang, J. and Min, Q.: Retrieval of Atmospheric Profiles in the New York State Mesonet Using One-Dimensional Variational Algorithm, J. Geophys. Res.-Atmos., 123, 7563–7575, https://doi.org/10.1029/2018JD028272, 2018. a
Yang, J., Ding, S., Dong, P., Bi, L., and Yi, B.: Advanced Radiative transfer Modeling System developed for satellite data assimilation and remote sensing applications, J. Quant. Spectrosc. Ra., 251, 107043, https://doi.org/10.1016/j.jqsrt.2020.107043, 2020. a
Zhang, F., Wu, K., Li, J., Yang, Q., Zhao, J.-Q., and Li, J.: Analytical Infrared Delta-Four-Stream Adding Method from Invariance Principle, J. Atmos. Sci., 73, 4171–4188, https://doi.org/10.1175/JAS-D-15-0317.1, 2016. a
Zhang, F., Shi, Y.-N., Li, J., Wu, K., and Iwabuchi, H.: Variational Iteration Method for Infrared Radiative Transfer in a Scattering Medium, J. Atmos. Sci., 74, 419–430, https://doi.org/10.1175/JAS-D-16-0172.1, 2017. a
Zhang, F., Wu, K., Li, J., Zhang, H., and Hu, S.: Radiative transfer in the region with solar and infrared spectra overlap, J. Quant. Spectrosc. Ra., 219, 366–378, https://doi.org/10.1016/j.jqsrt.2018.08.025, 2018. a
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds...
