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The Analysis of Capabilities of Neural Networks in CO2 Sounding with Spaceborne IPDA-Lidar with the Use of Different A Priori Data

  • REMOTE SENSING OF ATMOSPHERE, HYDROSPHERE, AND UNDERLYING SURFACE
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Abstract

The possibility of retrieving, using a neural network, the columnar carbon dioxide concentration profile when sounding from a space orbit of 450 km and from a balloon at altitudes of 23 and 10 km are analyzed. The use of a priori data on temperature, pressure, and reflected and scattered signals is considered. The errors of retrieval of the columnar CO2 are 0.15% and 0.5% at altitudes lower than 2 km for lidar with a telescope diameter of 1 m and laser pulse energy of 50 μJ at a resolution of 60 km.

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Authors and Affiliations

  1. V.E. Zuev Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences, 634055, Tomsk, Russia

    G. G. Matvienko, A. Ya. Sukhanov & S. V. Babchenko

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  1. G. G. Matvienko
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  2. A. Ya. Sukhanov
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  3. S. V. Babchenko
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Correspondence to G. G. Matvienko, A. Ya. Sukhanov or S. V. Babchenko.

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Translated by O. Ponomareva

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Matvienko, G.G., Sukhanov, A.Y. & Babchenko, S.V. The Analysis of Capabilities of Neural Networks in CO2 Sounding with Spaceborne IPDA-Lidar with the Use of Different A Priori Data. Atmos Ocean Opt 32, 165–170 (2019). https://doi.org/10.1134/S102485601902009X

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  • DOI: https://doi.org/10.1134/S102485601902009X

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