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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 M.
Feist, Dietrich G.
García, Omaira
Hannigan, James W.
Hase, Frank
Kivi, Rigel
Kumps, Nicolas
Makarova, Maria
Millet, Dylan B.
Morino, Isamu
Nagahama, Tomoo
Notholt, Justus
Ohyama, Hirofumi
Ortega, Ivan
Petri, Christof
Rettinger, Markus
Source :
Remote Sensing; Apr2022, Vol. 14 Issue 8, pN.PAG-N.PAG, 30p
Publication Year :
2022

Abstract

Nitrous oxide (N<subscript>2</subscript>O) 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 N<subscript>2</subscript>O 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 N<subscript>2</subscript>O ν <subscript>3</subscript> band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N<subscript>2</subscript>O 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) N<subscript>2</subscript>O 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
8
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
156596928
Full Text :
https://doi.org/10.3390/rs14081810