Monitoring the total atmospheric ozone content using data collected by the Elektro-L Russian geostationary meteorological satellite

The possibility of remotely monitoring the total atmospheric ozone content (TAOC) using data from the multichannel geostationary scanning instrument (MGSI) aboard the Elektro-L no. 1 Russian meteorological satellite is explored. In addition to the MGSI measurements in three channels (8.2–9.2, 9.2–10...

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Bibliographic Details
Published in:Izvestiya. Atmospheric and oceanic physics 2013-12, Vol.49 (9), p.986-992
Main Authors: Kramchaninova, E. K., Uspensky, A. B.
Format: Article
Language:English
Subjects:
Algorithms
Climatology
Data
Earth and Environmental Science
Earth Sciences
Estimates
Forecasting data
Geophysics/Geodesy
Machine learning
Meteorological satellites
Numerical experiments
Ozone
Ozone layer
Ozonosphere
Remote monitoring
Satellite sensing
Satellites
Spaceborne remote sensing
Temperature
Temperature distribution
Terrain
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Summary:The possibility of remotely monitoring the total atmospheric ozone content (TAOC) using data from the multichannel geostationary scanning instrument (MGSI) aboard the Elektro-L no. 1 Russian meteorological satellite is explored. In addition to the MGSI measurements in three channels (8.2–9.2, 9.2–10.2, and 10.2–11.2 μm), data on the vertical temperature distributions in the ozone layer and the temperature and pressure at the underlying terrain level (satellite sensing results or forecast data) are used as additional predictors in the process of TAOC estimation. The TAOC estimates are constructed with the use of a regularized regression algorithm (ridge regression). The learning and check samples are formed using independent TAOC estimates based on the data gathered by the OMI instrument aboard the EOS Aura satellite. Numerical experiments in processing the actual MGSI data gathered over certain periods within the interval from November 2011 to August 2012 reveal the possibility of arranging regular monitoring of the TAOC fields with high spatial and temporal resolutions and an acceptable precision: the absolute value of relative mean deviations and the relative root-mean-square deviations of the estimates based on the MGSI data from the estimates based on the OMI data lie within intervals of 1–2% and 5–7%, respectively, depending on the underlying terrain type.
ISSN:0001-4338
1555-628X
DOI:10.1134/S0001433813090077