Vol. 34, issue 10, article # 8

Bloshchinskiy V. D., Filei A. A., Kholodov E. I. Retrieval of water vapor content in atmospheric column from Electro-L No. 3 spacecraft data using neural networks. // Optika Atmosfery i Okeana. 2021. V. 34. No. 10. P. 808–811. DOI: 10.15372/AOO20211008 [in Russian].
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The application of a method based on artificial neural networks for assessing the total water vapor content in the atmospheric column from data of the MSU-GS satellite instrument of Electro-L No. 3 geostationary spacecraft is considered. The results of comparing the estimates of the total water vapor content from the MSU-GS data with MODIS satellite instrument data and AERONET measurements showed high agreement. The root mean square error when compared with the MODIS data was 0.311 cm, with the AERONET data, 0.409 cm, and the correlation was 98.2% and 84.7%, respectively. The results indicate the effectiveness of the method for determining the total content of water vapor for solving problems of atmospheric physics.


MSU-GS, Electro-L, gas, water vapor, artificial neural network


  1. Kiehl J.T., Trenberth K.E. Earth's annual global mean energy budget // Bull. Am. Meteorol. Soc. 1997. V. 78, iss. 2. P. 197–208.
  2. Mieruch S., Noël S., Bovensmann H., Burrows J.P. Analysis of global water vapor trends from satellite measurements in the visible spectral range // Atmos. Chem. Phys. 2008. V. 8, iss. 3. P. 491–504.
  3. Wagner T., Beirle S., Grzegorski M., Platt U. Glo­bal trends (1996–2003) of total column precipitable water observed by Global Ozone Monitoring Experiment (GOME on ERS-2) and their relation to near-surface temperature // J. Geophys. Res. 2006. V. 111. P. D12102.
  4. Ren H., Du C., Liu R., Qin Q., Yan G., Li Z., Meng J. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images // J. Geophys. Res.: Atmos. 2015. V. 120, iss. 5. P. 1723–1738.
  5. Jun L., Timothy J.S., Xin J., Graeme M. GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Legacy Atmospheric Moisture Profile, Legacy Atmospheric Temperature Profile, Total Precipitable Water, and Derived Atmospheric Stability Indices, document NOAA/NESDIS, version 3.0, July 2010. [Electronic resource]. URL: https://star.nesdis.noaa.gov/goesr/docs/ATBD/LAP.pdf (last access: 9.08.2021).
  6. Julien Y., Sobrino J.A., Mattar C., Jiménez-Muñoz J.C. Near-real-time estimation of water vapor column from MSG-SEVIRI thermal infrared bands: Implications for land surface temperature retrieval // IEEE Trans. Geosci. Remote Sens. 2015. V. 53, N 8. P. 4231–4237.
  7. Gao B.-C., Kaufman Y.J. Algorithm Technical Background Document, The MODIS Near-IR Water Vapor Algorithm, Product ID: MOD05 – Total Precipitable Water [Electronic resource]. URL: https://modis.gsfc.nasa.gov/data/atbd/atbd_mod03.pdf (last access: 9.08.2021).
  8. Albert P., Bennartz R., Preusker R., Leinweber R., Fischer J. Remote sensing of atmospheric water vapor using the MODerate resolution imaging spectroradiometer // J. Atmos. Ocean. Technol. 2005. V. 22. P. 309–314. // J. Atmos. Ocean. Technol. 2005. V. 22. P. 309–314.
  9. Gao B.-C., Kaufman Y.J. Water vapor retrievals using MODerate resolution Imaging Spectroradiometer (MODIS) near-infrared channel // J. Geophys. Res. 2003. V. 108, iss. D13. P. 4389.
  10. Miao J., Kunzi K., Heygster G., Lachlan-Cope T.A., Turner J. Atmospheric water vapor over Antarctica derived from Special Sensor Microwave/Temperature 2 data // J. Geophys. Res. 2001. V. 106, iss. D10. P. 10187–10203.
  11. Padmanabhan S., Reising S.C., Vivekanandan J., Iturbide-Sanchez F. Retrieval of atmospheric water vapor density with fine spatial resolution using three-dimensional tomographic inversion of microwave brightness temperatures measured by a network of scanning compact radiometers // IEEE Trans. Geosci. Remote Sens. 2009. V. 47, iss. 11. P. 3708–3721.
  12. Kleespies T.J., McMillin L.M. Retrieval of precipitable water from observations in the split window over varying surface temperatures // J. Appl. Meteorol. 1990. V. 29, iss. 9. P. 851–862.
  13. Li Z.-L., Jia L., Su Z., Wan Z., Zhang R. A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area // Int. J. Remote Sens. 2003. V. 24, iss. 24. P. 5095–5117.
  14. Barducci A., Guzzi D., Marcoionni P., Pippi I. Algorithm for the retrieval of columnar water vapor from hyperspectral remotely sensed data // Appl. Opt. 2004. V. 43, iss. 29. P. 5552–5563.
  15. Sobrino J.A., Raissouni N., Simarro J., Nerry F., Petitcolin F. Atmospheric water vapor content over land surfaces derived from the AVHRR data: Application to the Iberian Peninsula // IEEE Trans. Geosci. Remote Sens. 1999. V. 37, iss. 3. P. 1425–1434.
  16. Diouf D., Niang A., Thiria S. Deep learning based multiple regression to predict total column water vapor (TCWV) from physical parameters in west Africa by using Keras library // International journal of Data Mining Knowledge Management Process. 2019. V. 9, N 6. P. 13–21.
  17. Palau J.L., Rovira F., Sales M.J. Satellite observations of the seasonal evolution of total precipitable water vapour over the Mediterranean Sea // Adv. Meteorol. 2017. V. 2017, iss. 4790541.
  18. Ioffe S., Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift // Proc. of the 32nd Internat. Conf. Machine Learn. 2015. V. 37. P. 448–456.
  19. Kingma D.P., Ba J.L. Adam: A method for Stochastic Optimization // ICLR. 2015. 15 p.