Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe

The retrieval of the tropospheric ozone column from satellite data is very important for the characterization of tropospheric chemical and physical properties. However, the task of retrieving tropospheric ozone from space has to face with one fundamental difficulty: the contribution of the tropospheric ozone to the measured radiances is overwhelmed by a much stronger stratospheric signal, which has to be reliably filtered. The Tor Vergata University Earth Observation Laboratory has recently addressed this issue by developing a neural network (NN) algorithm for tropospheric ozone retrieval from NASA-Aura Ozone Monitoring Instrument (OMI) data. The performances of this algorithm were proven comparable to those of more consolidated algorithms, such as Tropospheric Ozone Residual and Optimal Estimation. In this article, the results of a validation of this algorithm with measurements performed at six European ozonesonde sites are shown and critically discussed. The results indicate that systematic errors, related to the tropopause pressure, are present in the current version of the algorithm, and that including the tropopause pressure in the NN input vector can compensate for these errors, enhancing the retrieval accuracy significantly.

• Publication year

  2013

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  2013-02-19

• Fields of study

  Physics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1186/1687-6180-2013-21
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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