Ozone concentration levels in the troposphere have several impacts on both climate and air quality. Monitoring tropospheric ozone concentrations and trends, especially in highly polluted locations, is a relevant topic in recent geosciences research. To observe height-resolved ozone concentrations from satellite platform is an exciting task, owing to the global and continuous nature of the obtained information. The Ozone Monitoring Instrument represents a good chance to contribute at the understanding of ozone related phenomena, also within the troposphere, owing to the relatively small pixel size; it is also a useful means for an effective monitoring of such phenomena, owing to its daily global coverage. Different techniques have been recently proposed to monitor tropospheric ozone content from satellite using the OMI. Mainly two techniques exist within this field: the Tropospheric Ozone Residual (TOR) methodology and the Optimal Estimation (OE). Neural networks (NN) algorithms have demonstrated encouraging capabilities as an alternative tool to retrieve height-resolved ozone concentrations from satellite data. Recently, NN algorithms for both ozone concentration profiles retrieval and tropospheric ozone columns (TOC) retrievals from SCIAMACHY and GOME have been presented. NNs have some advantages over the TOR and the OE techniques. When used for the mentioned applications, NNs are intended to learn the inverse relationship between the satellite reflectance spectra and the TOC from a set of sample data, and a direct model is not necessary. Theyre also expected to be less sensitive to systematic errors on the input spectra with respect to OE. Once trained, a NN is able to operate in real time. Here we present a NN algorithm to retrieve TOCs from OMI Level 1b data, which we called the OMI-TOC NN. First results and a validation exercise will be also discussed. Our NN retrievals are also compared to co-located TOC retrievals obtained with the OE.

Sellitto, P., Bojkov, P., Liu, X., DEL FRATE, F. (2010). Tropospheric ozone column retrieval from the Ozone Monitoring Instrument by means of a neural network algorithm. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? OMI Science Team Meeting, De Bilt, The Netherlands.

Tropospheric ozone column retrieval from the Ozone Monitoring Instrument by means of a neural network algorithm

DEL FRATE, FABIO
2010-01-01

Abstract

Ozone concentration levels in the troposphere have several impacts on both climate and air quality. Monitoring tropospheric ozone concentrations and trends, especially in highly polluted locations, is a relevant topic in recent geosciences research. To observe height-resolved ozone concentrations from satellite platform is an exciting task, owing to the global and continuous nature of the obtained information. The Ozone Monitoring Instrument represents a good chance to contribute at the understanding of ozone related phenomena, also within the troposphere, owing to the relatively small pixel size; it is also a useful means for an effective monitoring of such phenomena, owing to its daily global coverage. Different techniques have been recently proposed to monitor tropospheric ozone content from satellite using the OMI. Mainly two techniques exist within this field: the Tropospheric Ozone Residual (TOR) methodology and the Optimal Estimation (OE). Neural networks (NN) algorithms have demonstrated encouraging capabilities as an alternative tool to retrieve height-resolved ozone concentrations from satellite data. Recently, NN algorithms for both ozone concentration profiles retrieval and tropospheric ozone columns (TOC) retrievals from SCIAMACHY and GOME have been presented. NNs have some advantages over the TOR and the OE techniques. When used for the mentioned applications, NNs are intended to learn the inverse relationship between the satellite reflectance spectra and the TOC from a set of sample data, and a direct model is not necessary. Theyre also expected to be less sensitive to systematic errors on the input spectra with respect to OE. Once trained, a NN is able to operate in real time. Here we present a NN algorithm to retrieve TOCs from OMI Level 1b data, which we called the OMI-TOC NN. First results and a validation exercise will be also discussed. Our NN retrievals are also compared to co-located TOC retrievals obtained with the OE.
OMI Science Team Meeting
De Bilt, The Netherlands
2010
15
KNMI
Rilevanza internazionale
16-giu-2010
2010
Settore ING-INF/02 - CAMPI ELETTROMAGNETICI
English
http://www.knmi.nl/omi/research/project/meetings/ostm15/abstracts-ostm15.php
Intervento a convegno
Sellitto, P., Bojkov, P., Liu, X., DEL FRATE, F. (2010). Tropospheric ozone column retrieval from the Ozone Monitoring Instrument by means of a neural network algorithm. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? OMI Science Team Meeting, De Bilt, The Netherlands.
Sellitto, P; Bojkov, P; Liu, X; DEL FRATE, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/22858
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