Articles | Volume 13, issue 5
https://doi.org/10.5194/amt-13-2659-2020
https://doi.org/10.5194/amt-13-2659-2020
Research article
 | 
26 May 2020
Research article |  | 26 May 2020

Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)

Olivier Coopmann, Vincent Guidard, Nadia Fourrié, Béatrice Josse, and Virginie Marécal

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Cited articles

Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model, Mon. Weather Rev., 128, 644–667, 2000. a
Borbas, E. E., Hulley, G., Feltz, M., Knuteson, R., and Hook, S.: The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High Spectral Resolution Application, Remote Sensing, 10, 643, https://doi.org/10.3390/rs10040643, 2018. a
Bormann, N., Bonavita, M., Dragani, R., Eresmaa, R., Matricardi, M., and McNally, A.: Enhancing the impact of IASI observations through an updated observation-error covariance matrix, Q. J. Roy. Meteor. Soc., 142, 1767–1780, 2016. a, b, c, d
Boukachaba, N.: Apport des observations satellitaires hyperspectrales infrarouges IASI au-dessus des continents dans le modèle météorologique à échelle convective AROME, PhD thesis, INP Toulouse, available at: http://www.theses.fr/2017INPT0065 (last access: 18 May 2020), 2017. a
Chevallier, F., Di Michele, S., and McNally, A. P.: Diverse profile datasets from the ECMWF 91-level short-range forecasts, European Centre for Medium-Range Weather Forecasts, 2006. a
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Short summary
The objective of this paper is to make a new selection of IASI channels by taking into account inter-channel observation-error correlations. Our selection further reduces the analysis error by 3 % in temperature, 1.8 % in humidity and 0.9 % in ozone compared to Collard’s selection, when using the same number of channels. A selection of 400 IASI channels is proposed at the end of the paper which is able to further reduce analysis errors.
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