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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, 2026. Т. 23. № 3. С. 121-133

Express algorithm for determining cloud liquid water from measurements of the MTVZA-GYa No. 2-2 radiometer

D.S. Sazonov 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 20.04.2026
DOI: 10.21046/2070-7401-2026-23-3-121-133
The paper presents a regression algorithm for quick estimation of cloud liquid water content over the global ocean surface using radiometric measurements from the MTVZA-GYa (Atmospheric Temperature and Humidity Sensing Module) instrument installed on the Meteor-M No. 2-2 satellite. The algorithm is based on the frequency-polarization difference between measured antenna temperatures in the 18.7 and 36.7 GHz channels (at vertical and horizontal polarizations). As demonstrated in the paper, this difference is almost invariant to changes in ocean surface temperature, surface wind speed, and vapor content, while being sensitive to liquid water and precipitation. To estimate the regression coefficients, all available MTVZA-GYa No. 2-2 measurements for 2020 were combined with ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis using the point-to-point method, taking into account temporal dynamics. The accuracy of the cloud liquid water content retrieval was estimated at a linear correlation coefficient of 0.68±0.03 and a standard deviation of 0.063±0.005 kg/m2, which is consistent with the results of similar studies. The quality of cloud liquid water retrieval was also assessed using statistical histograms, which showed good agreement with the results obtained from SSMI (Special Sensor Microwave Imager) measurements. The analysis also revealed retrieval underestimation for high magnitudes of cloud liquid water.
Keywords: remote sensing, cloud liquid water content, MTVZA-GYa, brightness temperature, microwave radiation, modeling, regression relationship
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