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

Type
Label
  • Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1186/1687-6180-2013-21 (literal)
Alternative label
  • Antonio Di Noia 1,5, Pasquale Sellitto 2, Fabio Del Frate 1, Marco Cervino 3, Marco Iarlori 4, Vincenzo Rizi 4 (2013)
    Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe
    in EURASIP Journal on Advances in Signal Processing (Online)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Antonio Di Noia 1,5, Pasquale Sellitto 2, Fabio Del Frate 1, Marco Cervino 3, Marco Iarlori 4, Vincenzo Rizi 4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://asp.eurasipjournals.com/content/2013/1/21/abstract (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1Earth Observation Laboratory, Department of Civil and Computer Engineering, Tor Vergata University, Via del Politecnico 1, 00133 Rome, Italy 2Laboratoire Inter-universitaire des Systèmes Atmosphériques, UMR7583, CNRS--Universités Paris-Est et Paris Diderot, 61 Avenue du Général de Gaulle, 94010 Créteil, France 3Istituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, via Gobetti 101, 40129, Bologna, Italy 4CETEMPS, Department of Physics, University of L'Aquila, Via Vetoio 1, 67100, Coppito-L'Aquila, Italy 5SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands (literal)
Titolo
  • Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe (literal)
Abstract
  • 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. (literal)
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