Vol. 32, issue 05, article # 6

Filei A. A. Determination of cloud phase using MSU-MR measurements on-board Meteor-M N 2. // Optika Atmosfery i Okeana. 2019. V. 32. No. 05. P. 376–380. DOI: 10.15372/AOO20190506 [in Russian].
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Abstract:

The work presents the algorithm for determining cloud phase using the MSU-MR daily measurements on-board the Russian meteorological satellite Meteor-M N 2. The physical principles of the determination of cloud phase by using the reflectance at wavelengths of 1.6 and 3.7 mm and brightness temperatures at 11 and 12 mm are considered. The results of determining cloud phase with the algorithm presented are compared with the results of the algorithms developed for other satellite radiometers. The accuracy of the comparison is over 80%. The greatest inaccuracies are observed for thin semitransparent clouds because to additional radiation coming from the underlying surface, as well as for mixed clouds due to the specificity of the algorithm presented.

Keywords:

MSU-MR, optical depth, effective radius, cloud phase, cloudiness

References:

  1. Mazin I.P., Khrgian A.Kh. Oblaka i oblachnaya atmosfera: Spravochnik. L.: Gidrometizdat, 1989. 647 p.
  2. Arking A., Childs J.D. Retrieval of cloud cover parameters from multispectral satellite images // J. Clim. Appl. Meteorol. 1985. V. 24. P. 322–333.
  3. Rossow W.B., Schiffer R.A. Advances in understanding clouds from ISCCP // Bull. Am. Meteorol. Soc. 1999. V. 80. P. 2261–2287.
  4. Wolters E.L.A. Roebeling R.A., Feijt A.J. Evaluation of Cloud-Phase Retrieval Methods for SEVIRI on Meteosat-8 Using Ground-Based Lidar and Cloud Radar Data // J. Appl. Meteor. 2008. V. 47(6). P. 1723–1728.
  5. Nakajima T., King M.D. Determination of the optical-thickness and effective particle radius of clouds from reflected solar-radiation measurements. 1. Theory // J. Atmos. Sci. 1990. V. 47(15). P. 1878–1893.
  6. URL: https://refractiveindex.info/ (last access: 13.02.2019).
  7. Mayer B., Kylling A., Emde C., Buras R., Hamann U., Gasteiger J., Richter B. LibRadtran user’s guide. 2017. 155 p. URL: http://www.libradtran.org/doc/libRadtran.pdf (last access: 14.02.2019).
  8. Buras R., Dowling T., Emde C. New secondary-scattering correction in DISORT with increased efficiency for forward scattering // J. Quant. Spectrosc. Radiat. Transfer. 2011. V. 112(12). P. 2028–2034.
  9. Baum B.A., Heymsfield A.J., Yang P., Bedka S.T. Bulk scattering models for the remote sensing of ice clouds. Part I: Microphysical data and models // J. Appl. Meteorol. Clim. 2005. V. 44. P. 1885–1895.
  10.  Baum B.A., Yang P., Heymsfield A.J., Platnick S., King M.D., Hu Y-X., Bedka S.T. Bulk scattering models for the remote sensing of ice clouds. Part II: Narrowband models // J. Appl. Meteorol. Clim. 2005. V. 44. P. 1896–1911.
  11. Hu Y.X., Stamnes K. An accurate parameterization of the radiative properties of water clouds suitable for use in climate models // J. Clim. 1993. V. 6. P. 728–742.
  12. Key J., Intrieri J. Cloud particle phase determination with the AVHRR // J. Appl. Meteorol. 2000. V. 36(10). P. 1797–1805.
  13. Timofeev Yu.M. Global'naya sistema monitoringa parametrov atmosfery i poverkhnosti. SPb.: Sankt-Peterburgskij gos. un-t, 2010. 129 p.
  14. Pavolonis M.J. GOES-R Advanced Baseline Imager (ABI) algorithm theoretical basis document for cloud type and cloud phase. 2010. 86 p. URL: https://www. star.nesdis.noaa.gov / goesr / docs / ATBD / Cloud_Phase.pdf (last access: 14.02.2019).
  15. Pavolonis M.J., Heidinger A.K., Uttal T. Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons // J. Appl. Meteorol. 2005. V. 44(6). P. 804–826.
  16. URL: http://cimss.ssec.wisc.edu/clavrx/google_earth_ main.html (last access: 13.02.2019).