P. Sellitto

Tropospheric Ozone Column Retrieval From ESA-Envisat SCIAMACHY Nadir UV/VIS Radiance Measurements by Means of a Neural Network Algorithm

Spaceborne measurements may significantly support monitoring the concentration of atmospheric constituents affecting air quality, such as ozone. However, retrieving tropospheric ozone concentration information from nadir satellite data is an arduous task, given the weak sensitivity of the earths radiance to ozone variations in the lower part of the atmosphere. We propose a new methodology, based on neural networks (NN), for retrieving the tropospheric ozone column from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) nadir UV/VIS measurements. The design of the NN algorithm is based on an analysis of the information content of measurements in both UV and VIS bands, carried out by a combined radiative transfer model and NN extended pruning procedure. The NN was trained and tested with simulated data and with matching World Ozone and Ultraviolet radiation Data Centre ozonesonde data sets and validated by independent data taken over two test sites. A significant improvement of the retrieval capabilities is observed when VIS wavelengths are included into the input vector. Finally, an example of tropospheric ozone map generated automatically by the methodology at a continental scale is provided and critically discussed.

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

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.

Use of CHRIS PROBA images for land use products

The small hyperspectral imager CHRIS is the principal instrument for the remote sensing on board of the ESA Proba-1 satellite. The platform can tilt itself in the orbit allowing the acquisition of images with different angle over the same area and during the same overpass. In this work we report on the potentialities of such instrument in producing land cover maps. The images that we are using have been acquired over the test site of Frascati and Tor Vergata University campus, located in the surroundings of Rome, Italy, which is characterized by a mixing of several topologies of vegetated areas with residential and commercial buildings. The neural network approach has been used for the decision task. The results of the classification have been evaluated both by the visual interpretation and quantitative assessment. This latter demonstrated an accuracy of more than the 90%.

Volcanic ash retrieval from IR multispectral measurements by means of neural networks: An analysis of the Eyjafjallajokull eruption

The great eruption of the Icelandic Eyjafjallajokull volcano that occurred from the 14th of April to the 23rd of May 2010 injected large and dense ash clouds into the atmosphere, causing major international air traffic disruption worldwide.

Joint Temperature and Nitrogen Dioxide Vertical Profiles from UV/VIS Satellite Data for Air Pollution Monitoring from Space

The design and development of two neural network (NN) algorithms for retrieving atmospheric height-resolved temperature and nitrogen dioxide from UV/VIS satellite radiance data are presented. Sensitivity analyses, NNs optimization and results are discussed, based on radiative transfer simulations. The approach may lead to synergistic monitoring of different atmospheric parameters from the same UV/VIS radiance measurement.

Neural Network Algorithms for Ozone Profile Retrieval from ESA-Envisat SCIAMACHY and NASA-Aura OMI Satellite Data

In this paper we report on the design of Neural Networks algorithms to retrieve height resolved ozone information from Envisat SCIAMACHY and Aura OMI Level 1 data. We defined as input-output pairs the matching of (a) SCIAMACHY UV/VIS reflectances with ozonesondes concentrations, and(b) OMI UV/VIS reflectances with MLS concentrations. Design issues, as input vector dimensionality reduction, vertical resolution problems and topology selection are here analyzed. The inversion results are presented and discussed, with a special emphasis to retrievals at tropospheric height levels.