Flyby (Italy)

With the launch of the Sentinel-5 Precursor (S-5P, lifted-off on 13 October 2017), Sentinel-4 (S-4) and Sentinel-5 (S-5)(from 2021 and 2023 onwards, respectively) operational missions of the ESA/EU Copernicus program, a massive amount of atmospheric composition data with unprecedented quality will become available from geostationary (GEO) and low Earth orbit (LEO) observations. Enhanced observational capabilities are expected to foster deeper insight than ever before on key issues relevant for air quality, stratospheric ozone, solar radiation, and climate. A major potential strength of the Sentinel observations lies in the exploitation of complementary information that originates from simultaneous and independent satellite measurements of the same air mass. The core purpose of the AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project is to investigate this exploitation from a novel approach for merging data acquired in different spectral regions from on board the GEO and LEO platforms. A data processing chain is implemented and tested on synthetic observations. A new data algorithm combines the ultraviolet, visible and thermal infrared ozone products into S-4 and S-5(P) fused profiles. These fused products are then ingested into state-of-the-art data assimilation systems to obtain a unique ozone profile in analyses and forecasts mode. A comparative evaluation and validation of fused products assimilation versus the assimilation of the operational products will seek to demonstrate the improvements achieved by the proposed approach. This contribution provides a first general overview of the project, and discusses both the challenges of developing a technological infrastructure for implementing the AURORA concept, and the potential for applications of AURORA derived products, such as tropospheric ozone and UV surface radiation, in sectors such as air quality monitoring and health.

This paper shows an attempt to build a time variant cost-oriented classifier for two-class classification problems. Such classifier is based on a sliding window, and has been designed as an ensemble of Cost-Oriented Support Vector Machines (CO-SVMs). More precisely, we have integrated the Incremental/Decremental (ID) formulation of SVMs with the Cost-Oriented (CO) formulation, thus obtaining an ensemble of COID-SVMs. At each data arrival, the new pattern is classified by using a dynamic selection of the underlying COID-SVMs in the Receiver Operating Characteristic (ROC) space by means of the ROC convex hull method. Then, once the actual class label of the new pattern is known, the new data and the associated class label are used to perform an incremental learning by each COID-SVM. At the same time, each SVM is updated by performing the decremental learning of the data falling outside the sliding window. This allows to adapt the classification to time varying conditions. The methodology has been applied to the classification of oil spills at sea from remotely sensed optical images.

In this paper we present the Pilot Project PRIMI, designed to provide information on marine oil spills, and its validation campaign, held in august 2009, conducted with the oceanography ship Urania. During the experiment CosmoSkymed has provided an extraordinary contribution supplying almost any day images over the area inspected by the ship and in some cases also images requested with short notice. © 2010 IEEE.