Articles | Volume 6, issue 4
Atmos. Meas. Tech., 6, 895–915, 2013
https://doi.org/10.5194/amt-6-895-2013
Atmos. Meas. Tech., 6, 895–915, 2013
https://doi.org/10.5194/amt-6-895-2013

Research article 09 Apr 2013

Research article | 09 Apr 2013

Global tropospheric ozone column retrievals from OMI data by means of neural networks

A. Di Noia1,2,*, P. Sellitto3, F. Del Frate1, and J. de Laat2 A. Di Noia et al.
  • 1Earth Observation Laboratory, Department of Civil and Computer Engineering, Tor Vergata University, via del Politecnico 1, 00133 Rome, Italy
  • 2Royal Netherlands Meteorological Institute (KNMI), Wilhelminalaan 10, 3732 GK De Bilt, the Netherlands
  • 3Laboratoire Inter-universitaire des Systèmes Atmosphériques (LISA), UMR7583, Universités Paris-Est et Paris Diderot, CNRS, Rue du Général de Gaulle, 94010 Créteil, France
  • *now at: SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands

Abstract. In this paper, a new neural network (NN) algorithm to retrieve the tropospheric ozone column from Ozone Monitoring Instrument (OMI) Level 1b data is presented. Such an algorithm further develops previous studies in order to improve the following: (i) the geographical coverage of the NN, by extending its training set to ozonesonde data from midlatitudes, tropics and poles; (ii) the definition of the output product, by using tropopause pressure information from reanalysis data; and (iii) the retrieval accuracy, by using ancillary data (NCEP tropopause pressure and temperature profile, monthly mean tropospheric ozone column from a satellite climatology) to better constrain the tropospheric ozone retrievals from OMI radiances. The results indicate that the algorithm is able to retrieve the tropospheric ozone column with a root mean square error (RMSE) of about 5–6 DU in all the latitude bands. The design of the new NN algorithm is extensively discussed, validation results against independent ozone soundings and chemistry/transport model (CTM) simulations are shown, and other characteristics of the algorithm – i.e., its capability to detect non-climatological tropospheric ozone situations and its sensitivity to the tropopause pressure – are discussed.

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