Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloTropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe
Anno di pubblicazione2013
FormatoElettronico
Autore/iAntonio Di Noia 1,5, Pasquale Sellitto 2, Fabio Del Frate 1, Marco Cervino 3, Marco Iarlori 4, Vincenzo Rizi 4
Affiliazioni autori1Earth 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
Autori CNR e affiliazioni
  • MARCO CERVINO
Lingua/e
  • inglese
AbstractThe 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.
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RivistaEURASIP Journal on Advances in Signal Processing (Online)
Attiva dal 2007
Editore: Hindawi Publishing Corporation - Cairo
Paese di pubblicazione: Egitto
Lingua: inglese
ISSN: 1687-6180
Titolo chiave: EURASIP Journal on Advances in Signal Processing (Online)
Titolo proprio: EURASIP Journal on Advances in Signal Processing (Online)
Titolo abbreviato: EURASIP J. Adv. Signal Process. (Online)
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DOI10.1186/1687-6180-2013-21
Verificato da refereeSì: Internazionale
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Parole chiaveNeural networks, Remote sensing, Satellite, Ozone
Link (URL, URI)http://asp.eurasipjournals.com/content/2013/1/21/abstract
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Strutture CNR
  • ISAC — Istituto di scienze dell'atmosfera e del clima
Moduli/Attività/Sottoprogetti CNR
  • TA.P05.018.001 : Processi di trasporto turbolento e dispersione in atmosfera per la qualità dell'aria e il clima: teoria e modellistica
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