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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 4, pp. 9-23

The use of Community Radiative Transfer Model for analysis of MTVZA-GYa microwave radiometer measurements

A.A. Filei 1 , Yu.A. Shamilova 1 
1 Far Eastern Center of SRC "Planeta", Khabarovsk, Russia
Accepted: 01.07.2024
DOI: 10.21046/2070-7401-2024-21-4-9-23
This paper presents the functionality of the fast Community Radiative Transfer Model (CRTM) for the analysis and validation of MTVZA-GYa (Imaging/Sounding Microwave Radiometer) measurements on board the Meteor-M satellite series. The main aspects of calculating and adding weighting coefficients to CRTM to quickly calculate atmospheric transmittance coefficients in MTVZA-GYa channels are presented. The computational performance and accuracy of calculations of MTVZA-GYa measurements were assessed using the example of comparison with the fast radiation transfer model RTTOV (Radiative Transfer for TOVS). According to the results of comparison of simulated values of brightness temperatures obtained using CRTM and RTTOV, the average error in the MTVZA-GYa channels does not exceed 2K. The functionality of CRTM allows not only to simulate measurements in the MTVZA-GYa channels, but also to calculate weighting functions and Jacobians. Thus, CRTM is an excellent tool for developing methods for solving inverse problems of microwave radiation transfer in the atmosphere in order to obtain various types of information products. In addition, the experience gained in calculating the weighting coefficients will make it possible in the future to implement functionality for modeling measurements in the channels of any Russian satellite instruments.
Keywords: MTVZA-GYa, CRTM, RTTOV, Meteor-M, modeling, fast radiative transfer model
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References:

  1. Uspensky A. B., Rublev A. N., Rusin E. V., Pyatkin V. P., Fast radiative transfer model for “Meteor-M” satellite-based hyperspectral IR sounders, Issledovanie Zemli iz kosmosa, 2013, Vol. 6, pp. 16–24 (in Russia), DOI: 10.7868/S0205961413060109.
  2. Chernyavsky G. M., Mitnik L. M., Kuleshov V. P. et al., Brightness temperature modeling and first results derived from the MTVZA-GY radiometer of the Meteor-M No. 2-2 satellite, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 3, pp. 51–65(in Russia), DOI: 10.21046/2070-7401-2020-17-3-51-65.
  3. Boukabara S.-A., Garrett K., Chenet W. et al., MiRS: An all-weather 1DVAR satellite data assimilation and retrieval system, IEEE Trans. Geoscience and Remote Sensing, 2011, Vol. 49(9), pp. 3249–3272, DOI: 10.1109/TGRS.2011.2158438.
  4. Chen Y., Han Y., van Delst P., Weng F., On water vapor Jacobian in fast radiative transfer model, J. Geophysical Research, 2010, Vol. 115, Issue D12, Article D12303, DOI: 10.1029/2009JD013379.
  5. Chen Y., Han Y., Weng F., Comparison of two transmittance algorithms in the community radiative transfer model: Application to AVHRR, J. Geophysical Research Atmospheres, 2012, Vol. 117, Issue D6, Article D06206, DOI: 10.1029/2011jd016656.
  6. Cherny I. V., Chernyavsky G. M., Mitnik L. M. et al., Advanced Microwave Imager/Sounder MTVZA-GY-MP for new Russian meteorological satellite, Proc. IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), 2017, pp. 1220–1223, DOI: 10.1109/IGARSS.2017.8127178.
  7. Chevallier F., Michele S. D., McNally A. P., Diverse profile datasets from the ECMWF 91-level short-range forecast, NWP SAF Rep. NWPSAF-EC-TR-010, 2006, 16 p.
  8. Clough S. A., Shephard M. W., Mlawer E. J. et al., Atmospheric radiative transfer modeling: A summary of the AER codes, J. Quantitative Spectroscopy Radiative Transfer, 2005, Vol. 91(2), pp. 233–244, DOI: 10.1016/j.jqsrt.2004.05.058.
  9. Hocking J., Saunders R., Geer A., Vidot J., RTTOV v13, Users Guide, NWPSAF-MO-UD-046, EUMETSAT, 2022, 169 p., https://raw.githubusercontent.com/wiki/JCSDA/crtm/files/CRTM_User_Guide.pdf.
  10. Johnson B. T., Stegmann P., Dang C., van Delst P., Community Radiative Transfer Model v2.4.0, User Guide, Joint Center for Satellite Data Assimilation, 2020, 208 p., DOI: 10.5281/zenodo.7415561.
  11. Johnson B. T., Dang C., Stegmann P. et al., The Community Radiative Transfer Model (CRTM): Community-focused collaborative model development accelerating research to operations, Bull. American Meteorological Society, 2023, Vol. 104, pp. 1817–1830, DOI: 10.1175/BAMS-D-22-0015.1.
  12. Kazumori M., English S. J., Use of the ocean surface wind direction signal in microwave radiance assimilation, Quarterly J. Royal Meteorological Society, 2015, Vol. 141(689), pp. 1354–1375, DOI: 10.1002/qj.2445.
  13. Liang X., Ignatov A., Kihai Yu., Implementation of the Community Radiative Transfer Model (CRTM) in Advanced Clear-Sky Processor for Oceans (ACSPO) and validation against nighttime AVHRR radiances, J. Geophysical Research, 2009, Vol. 114, Issue D6, Article D06112, DOI: 10.1029/2008JD010960.
  14. Liebe H. J., MPM — An atmospheric millimeter-wave propagation model, Intern. J. Infrared and Millimeter Waves, 1989, Vol. 10(6), pp. 631–650, DOI: 10.1007/BF01009565.
  15. Liebe H., Rosenkranz P., Hufford G., Atmospheric 60-GHz oxygen spectrum: New laboratory measurements and line parameters, J. Quantitative Spectroscopy and Radiative Transfer, 1992, Vol. 48(5–6), pp. 629–643, DOI: 10.1016/0022-4073(92)90127-p.
  16. Moradi I., Goldberg M., Brath M. et al., Performance of radiative transfer models in the microwave region, J. Geophysical Research: Atmospheres, 2020, Vol. 125(6), Article e2019JD031831, DOI: 10.1029/2019JD031831.
  17. Rienecker M., Suarez M., Gelaro R. et al., MERRA: NASA’s Modern-Era Retrospective analysis for research and applications, J. Climate, 2011, Vol. 24(14), pp. 3624–3648, DOI: 10.1175/JCLI-D-11-00015.1.
  18. Rosenkranz P. W., Water vapor microwave continuum absorption: A comparison of measurements and models, Radio Science, 1998, Vol. 33(4), pp. 919–928, DOI: 10.1029/98RS01182.
  19. Rosenkranz P. W., Rapid radiative transfer model for AMSU/HSB channels, IEEE Trans. Geoscience Remote Sensing, 2003, Vol. 41, No. 2, pp. 362–368, DOI: 10.1109/TGRS.2002.808323.
  20. Sanò P., Casella D., Camplani A. et al., A machine learning snowfall retrieval algorithm for ATMS, Remote Sensing, 2022, Vol. 14(6), Article 1467, DOI: 10.3390/rs14061467.
  21. Saunders R. M., Matricardi M., Brunel P., An improved fast radiative transfer model for assimilation of satellite radiance observation, Quarterly J. Royal Meteorological Society, 1999, Vol. 125, pp. 1407–1425, DOI: 10.1256/smsqj.55614.
  22. Saunders R., Hocking J., Turner E. et al., An update on the RTTOV fast radiative transfer model (currently at version 12), Geoscientific Model Development, 2018, Vol. 11, pp. 2717–2737, DOI: 10.5194/gmd-11-2717-2018.
  23. Stegmann P., Transmittance Coefficient Generation, Joint Center for Satellite Data Assimilation, 2020, 17 p., https://wiki.ucar.edu/display/CRTM/Transmittance+Coefficient+Generation.
  24. Thodsan T., Wu F., Torsri K. et al., Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu, Forecasts, Atmosphere, 2022, Vol. 13(6), Article 956, DOI: 10.3390/atmos13060956.
  25. Turner D.D., Cadeddu M.P., Loehnert U. et al., Modifications to the water vapor continuum in the microwave suggested by ground-based 150-GHz observations, IEEE Trans. Geoscience and Remote Sensing, 2009, Vol. 47(10), pp. 3326–3337, DOI: 10.1109/tgrs.2009.2022262.
  26. Strow L. L., Hannon S. E., Souza-Machado S. D. et al., An overview of the AIRS radiative transfer model, IEEE Trans. Geoscience Remote Sensing, 2003, Vol. 41(2), pp. 303–313, DOI: 10.1109/TGRS.2002.808244.
  27. Weng F., Han Y., Delst P., Liu Q., Kleespies T., Yan B., Le Marshal J., JCSDA community radiative transfer model (CRTM) —Version 1, NOAA Technical Report, 2006, 122 p.
  28. You Y., Meng H., Dong J. et al., Snowfall detection algorithm for ATMS over ocean, sea ice, and coast, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2022, Vol. 15, pp. 1411–1420, DOI: 10.1109/JSTARS.2022.3140768.
  29. Zou X., Xiaoyong Z., Weng Z. F., Characterization of bias of advanced Himawari Imager infrared observations from NWP background simulations using CRTM and RTTOV, J. Atmospheric and Oceanic Technology, 2016, Vol. 33(12), pp. 2553–2567, DOI: 10.1175/JTECH-D-16-0105.1.