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http://hdl.handle.net/20.500.11765/13675
Nitrous Oxide Profiling from Infrared Radiances (NOPIR): algorithm description, application to 10 years of IASI observations and quality assessment
Title: | Nitrous Oxide Profiling from Infrared Radiances (NOPIR): algorithm description, application to 10 years of IASI observations and quality assessment |
Authors: | Vandenbussche, Sophie; Langerock, Bavo; Vigouroux, Corinne; Buschmann, Matthias; Deutscher, Nicholas Michael; Feist, Dietrich G.; García Rodríguez, Omaira Elena
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Keywords: | IASI; Nitrous oxide; Greenhouse gas; Validation; Retrieval |
Issue Date: | 2022 |
Publisher: | MDPI |
Citation: | Remote Sensing. 2022, 14(8), p. 1-30 |
Publisher version: | https://doi.org/10.3390/rs14081810 |
Abstract: | Nitrous oxide (N2O) is the third most abundant anthropogenous greenhouse gas (after carbon dioxide and methane), with a long atmospheric lifetime and a continuously increasing concentration due to human activities, making it an important gas to monitor. In this work, we present a new method to retrieve N2O concentration profiles (with up to two degrees of freedom) from each cloud-free satellite observation by the Infrared Atmospheric Sounding Interferometer (IASI), using spectral micro-windows in the N2O n3 band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N2O profiles and integrated partial columns has been produced and validated with collocated ground-based Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) data. The importance of consistency in the ancillary data used for the retrieval for generating consistent time series has been demonstrated. The Nitrous Oxide Profiling from Infrared Radiances (NOPIR) N2O partial columns are of very good quality, with a positive bias of 1.8 to 4% with respect to the ground-based data, which is less than the sum of uncertainties of the compared values. At high latitudes, the comparisons are a bit worse, due to either a known bias in the ground-based data, or to a higher uncertainty in both ground-based and satellite retrievals. |
URI: | http://hdl.handle.net/20.500.11765/13675 |
ISSN: | 2072-4292 |
Appears in Collections: | Artículos científicos 2019-2022 |
Files in This Item:
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![]() | RS_2022_vanden.pdf | 18,49 MB | Adobe PDF | ![]() View/Open |
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