Fig 15 - available via license: Creative Commons Attribution 4.0 International
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Global fields of the tropopause sensitivity factor computed for the OMITROPO3-NN algorithm on 17 (top panel) and 26 August (bottom panel), 2006.
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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 algorithm further develops previous studies in order to improve: (i) the geographical coverage of the NN, by extending its training set to ozonesonde data from midlatitudes, tropics and...
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Citations
... In recent years neural networks have been adopted for a wide range of applications from atmospheric sciences to electromagnetic modeling. The developed applications include, e.g., forward and inverse radiative transfer problems (Krasnopolsky, 2008), the prediction of atmospheric parameters (Grivas and Chaloulakou, 2006), the inversion and post processing of remotely sensed data (Mas and Flores, 2008;Del Frate and Schiavon, 1998), ozone retrievals (Di Noia et al., 2012;Sellitto et al., 2011Sellitto et al., , 2012, cloud classification (Christodoulou et al., 2003), land cover classification (Aitkenhead and Aalders, 2008), and feature extraction (Del Frate et al., 2005). Below, we describe the design for the cloud detection algorithm applied to OMI cloud fraction determination. ...
The discrimination of cloudy from cloud-free pixels is required in
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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.
