• Citer

Global distributions of major OVOCs derived from IASI measurements

par Franco, Bruno ;Clarisse, Lieven ;Hadji-Lazaro, Juliette;Clerbaux, Cathy ;Coheur, Pierre
Référence ATMOS 2024 (01-05 July 2024: Bologna, Italy)
Publication Publié, 2024-07-01

Poster de conférence

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

Oxygenated volatile organic compounds (OVOCs) have a significant impact on atmospheric oxidative capacity, affecting methane's lifetime. However, uncertainties persist in current estimates of their atmospheric budget due to limited global and consistent measurements, hindering the precise determination of the distribution and magnitude of their primary and/or secondary sources. Nadir-viewing infrared sensors aboard meteorological satellites can help reduce these uncertainties. Yet, retrieving atmospheric OVOCs from spaceborne infrared radiance spectra remains challenging due to their weak and sometimes broadband spectral absorptions.We have applied a general framework for fast retrieval of OVOC column abundance from the IASI (Infrared Atmospheric Sounding Interferometer) observations. Initially developed for the retrieval of ammonia, the ANNI (Artificial Neural Network for IASI) method relies on a hyperspectral range index (HRI) for the quantification of the gas spectral signature and on an artificial neural network to convert the HRI into gas total column. This sensitive and robust method enables to retrieve total columns of five major atmospheric OVOCs - methanol (CH3OH), formic acid (HCOOH), PAN (CH3C(O)O2NO2), acetone (CH3C(O)CH3), and acetic acid (CH3COOH) – globally and twice daily. Additionally, its computational efficiency allows processing of the decade-long IASI observational dataset (since 2007).The IASI-derived columns for these OVOCs provide an opportunity for in-depth exploration of their distribution, sources, transport, and seasonal as well as inter-annual variability. We also highlight the improvements introduced in the version 4 of the ANNI framework, which now ensures greater consistency across the IASI time series, incorporates a more detailed uncertainty assessment, and provides averaging kernels to address IASI’s vertical sensitivity when compared with model data or independent measurements.