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Neural networks for CO2 profile retrieval from the data of GOSAT/TANSO-FTS

Abstract

The feasibility of the retrieval of CO2 vertical profiles and its column-averaged concentration by reflected solar radiation measured by the TANSO-FTS sensor onboard the GOSAT satellite is demonstrated. Model spectra in the 0.76-µm O2 band and CO2 bands near 1.6 and 2.06 µm were used to train the neural network for CO2 retrieval. Separate neural networks were developed for each of the four scanning angles; the solar zenith angle was considered as a continuous variable. An accuracy of better than 1 ppm for column-averaged values and better than 4 ppm for the surface CO2 concentration were achieved. A 1: 300 noise level in the spectra was set for all spectral ranges.

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Correspondence to K. G. Gribanov.

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Original Russian Text © K.G. Gribanov, R. Imasu, V.I. Zakharov, 2010, published in Optica Atmosfery i Okeana.

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Gribanov, K.G., Imasu, R. & Zakharov, V.I. Neural networks for CO2 profile retrieval from the data of GOSAT/TANSO-FTS. Atmos Ocean Opt 23, 42–47 (2010). https://doi.org/10.1134/S1024856010010094

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Keywords

  • Hide Layer
  • Solar Zenith Angle
  • Model Spectrum
  • Advance Very High Resolution Radiometer
  • Spectral Channel