Atmospheric and Oceanic Optics

, Volume 32, Issue 5, pp 569–577 | Cite as

The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements

  • E. Yu. BiryukovEmail author
  • V. S. KostsovEmail author


Estimates of the error in determining the cloud liquid water path by the multiple linear regression (MLR) technique using different regression relations obtained both by model calculations and by experimental data (for reference, results of the method based on the inversion of the radiative transfer equation were taken) are presented. It is shown that if the MLR method is trained by experimental data and measurements in seven spectral channels of the radiometer, the random component of the liquid water path error in the cloud is 0.015–0.017 kg/m2, which is half that obtained when trained by the results of model calculations. The cloud liquid water path bias does not exceed 0.005 kg/m2. The MLR results allow one to reliably identify periods of clear sky by the criterion of the minimum variance of the water content.


cloud liquid water path troposphere remote sensing microwave radiometer inverse problems linear regression 



This work was supported by the Russian Foundation for Basic Research (project no. 19-05-00372).


The authors declare that they have no conflicts of interest.


  1. 1.
    Ed. Westwater, S. Crewell, C. Matzler, and D. Cimini, “Principles of surface-based microwave and millimeter wave radiometric remote sensing of the troposphere,” Quaderni della Societa Italiana di ElettroMagnetismo 1 (3), 50–90 (2005).Google Scholar
  2. 2.
    C. Maetzler and J. Morland, “Refined physical retrieval of integrated water vapor and cloud liquid for microwave radiometer data,” IEEE Trans. Geosci. Remote Sens 47 (6), 1585–1594 (2009).ADSCrossRefGoogle Scholar
  3. 3.
    E. Meijgaard and S. Crewell, “Comparison of model predicted liquid water path with ground-based measurements during CLIWA-NET,” Atmos. Res 75 (3), 201–226 (2005).CrossRefGoogle Scholar
  4. 4.
    V. S. Kostsov, D. V. Ionov, E. Yu. Biryukov, and N. A. Zaitsev, “Cross-validation of two liquid water path retrieval algorithms applied to ground-based microwave radiation measurements by the RPG-HATPRO instrument,” Int. J. Remote Sens. 39 (5), 1321–1342 (2018).ADSCrossRefGoogle Scholar
  5. 5.
    Radiometer Physics. A Rohde and Schwarz Company. https// (Cited January 25, 2019).Google Scholar
  6. 6.
    T. Rose, S. Crewell, U. Lohnert, and C. Simmer, “A network suitable microwave radiometer for operational monitoring of the cloudy atmosphere,” Atmos. Res 75 (3), 183–200 (2005).CrossRefGoogle Scholar
  7. 7.
    V. S. Kostsov, “Retrieving cloudy atmosphere parameters from RPG-HATPRO radiometer data,” Izv., Atmos. Ocean. Phys. 51 (2), 156–166 (2015).CrossRefGoogle Scholar
  8. 8.
    Thermodynamic Initial Guess Retrieval (TIGR) tigr (Cited January 25, 2019).Google Scholar
  9. 9. (Cited January 25, 2019).Google Scholar
  10. 10.
    E. V. Zabolotskikh, L. M. Mitnik, L. P. Bobylev, and O. M. Iokhannessen, “Development and validation of algorithms for retrieving the near-water wind speed from SSM/I data using neuron networks and physical restrictions,” Issled. Zemli Kosmosa, No. 2, 62–71 (2000).Google Scholar
  11. 11.
    E. V. Zabolotskikh, Yu. M. Timofeev, A. B. Uspenskii, L. M. Mitnik, L. P. Bobylev, O. M. Iokhannessen, and I. V. Chernyi, “Errors of microwave satellite measurements of sea surface wind speed, atmospheric water vapor, and cloud liquid water,” Izv., Atmos. Ocean. Phys. 38 (5), 592–596 (2002).Google Scholar
  12. 12.
    D. Cimini, P. W. Rosenkranz, M. Y. Tretyakov, M. A. Koshelev, and F. Romano, “Uncertainty of atmospheric microwave absorption model: Impact on ground-based radiometer simulations and retrievals,” Atmos. Chem. Phys. 18, 15231–15259 (2018). ADSCrossRefGoogle Scholar
  13. 13.
    R. A. Roebeling, S. Placidi, D. P. Donovan, H. W. J. Russchenberg, and A. J. Feijt, “Validation of liquid cloud property retrievals from SEVIRI using ground-based observations,” Geophys. Rev. Lett. 35, L05814 (2008). ADSCrossRefGoogle Scholar
  14. 14.
    E. N. Kadygrov, A. G. Gorelik, and T. A. Tochilkina, “Study of liquid water in clouds with the "Microradkom” radiometric system,” Atmos. Ocean. Opt. 27 (4), 596–604 (2014).CrossRefGoogle Scholar
  15. 15.
    C. Maetzler, “Ground-based observations of atmospheric radiation at 5, 10, 21, 35, and 94 GHz,” Radio Sci. 27, 403–415 (1992).ADSCrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.St. Petersburg UniversitySt. PetersburgRussia

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