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The IASI mission has been developed with the objective to provide high-quality radiance spectra and global measurements of atmospheric parameters, for accurate Numerical Weather Predictions (NWP) and improved climate and atmospheric composition (AC) monitoring. This led to the design of an advanced hyperspectral infrared instrument, featuring, at Level1C a large spectral range of the infrared without gaps (645-2760 cm-1), a fairly high spectral resolution (0.5 cm-1) and a low noise. These Level1C radiances have been exploited extensively in the last decade for applications in air quality, climate, tropospheric and stratospheric chemistry. There is in fact an impressive series of atmospheric trace gases that are retrieved from IASI Level1C, including several that display very small spectral signatures or others that appear only around localized pollution sources or in volcanic and fire plumes. Because of the potential loss of information coming with principal component analysis (PCA) –especially for rare signals- the scientific community has, until now, favored the use of the native Level1C over PCA-reconstructed radiances, despite the reduction of noise that the latter allow. In this presentation we will show results obtained during a dedicated study with IASI spectra over a decade, with the objective1. to evaluate how well the IASI Level1C can be reconstructed from the eigenvectors routinely produced at EUMETSAT, and to document the added value of the so-called dynamic hybrid over the static core base eigenvectors for rare or extreme situations. 2. To evaluate to what extent the use of reconstructed radiances impacts the retrieval of trace gases, in background or extreme situations.The results concern ozone (validation of the retrieved profiles against ozone sondes), carbon monoxide, ammonia and sulfur dioxide in volcanic plumes. Based on these results, recommendations for using reconstructed radiances in an operational processing framework are made. This is import, info:eu-repo/semantics/nonPublished

The IASI mission has been developed with the objective to provide high-quality radiance spectra and global measurements of atmospheric parameters, for accurate Numerical Weather Predictions (NWP) and improved climate and atmospheric composition (AC) monitoring. This led to the design of an advanced hyperspectral infrared instrument, featuring, at Level1C a large spectral range of the infrared without gaps (645-2760 cm-1), a fairly high spectral resolution (0.5 cm-1) and a low noise. These Level1C radiances have been exploited extensively in the last decade for applications in air quality, climate, tropospheric and stratospheric chemistry. There is in fact an impressive series of atmospheric trace gases that are retrieved from IASI Level1C, including several that display very small spectral signatures or others that appear only around localized pollution sources or in volcanic and fire plumes. Because of the potential loss of information coming with principal component analysis (PCA) –especially for rare signals- the scientific community has, until now, favored the use of the native Level1C over PCA-reconstructed radiances, despite the reduction of noise that the latter allow. In this presentation we will show results obtained during a dedicated study with IASI spectra over a decade, with the objective 1. to evaluate how well the IASI Level1C can be reconstructed from the eigenvectors routinely produced at EUMETSAT, and to document the added value of the so-called dynamic hybrid over the static core base eigenvectors for rare or extreme situations. 2. To evaluate to what extent the use of reconstructed radiances impacts the retrieval of trace gases, in background or extreme situations.The results concern ozone (validation of the retrieved profiles against ozone sondes), carbon monoxide, ammonia and sulfur dioxide in volcanic plumes. Based on these results, recommendations for using reconstructed radiances in an operational processing framework are made. This is impor, info:eu-repo/semantics/nonPublished

Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth's surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e. TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e. [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [-1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis., SCOPUS: ar.j, info:eu-repo/semantics/published

The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI's large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007-2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm-1 to 0.1 K at 2200 cm-1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5-10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends., SCOPUS: ar.j, info:eu-repo/semantics/published

info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished