Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC

• Published in: Atmospheric Measurement Techniques
• Main Authors: Kerzenmacher, T., Dils, B., Kumps, N., Blumenstock, T., Clerbaux, C., Coheur, P.-F., Demoulin, P., García, O., George, M., Griffith, D. W. T., Hase, F., Hadji-Lazaro, J., Hurtmans, D., Jones, N., Mahieu, E., Notholt, J., Paton-Walsh, C., Ridder, T., Schneider, M., Servais, C., De Mazière, M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2014
• Subjects: ddc:550; Earth sciences; info:eu-repo/classification/ddc/550; Kiruna; Ny-Ålesund; Ny Ålesund
• Online Access: https://publikationen.bibliothek.kit.edu/1000038959

Carbon monoxide (CO) is retrieved daily and globally from space-borne IASI radiance spectra using the Fast Optimal Retrievals on Layers for IASI (FORLI) software developed at the Université Libre de Bruxelles (ULB). The IASI CO total column product for 2008 from the most recent FORLI retrieval version (20100815) is evaluated using correlative CO profile products retrieved from ground-based solar absorption Fourier transform infrared (FTIR) observations at the following FTIR spectrometer sites from the Network for the Detection of Atmospheric Composition Change (NDACC): Ny-Ålesund, Kiruna, Bremen, Jungfraujoch, Izaña and Wollongong. In order to have good statistics for the comparisons, we included all IASI data from the same day, within a 100 km radius around the ground-based stations. The individual ground-based data were adjusted to the lowest altitude of the co-located IASI CO profiles. To account for the different vertical resolutions and sensitivities of the ground-based and satellite measurements, the averaging kernels associated with the various retrieved products have been used to properly smooth coincident data products. It has been found that the IASI CO total column products compare well on average with the co-located ground-based FTIR total columns at the selected NDACC sites and that there is no significant bias for the mean values at all ...