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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

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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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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Keywords:

  • atmosphere
  • spaceborne lidar
  • carbon dioxide
  • greenhouse gas
  • neural network