ALBERT

Description: The Infrared Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites provides 8461 channels in the infrared spectrum, covering the spectral interval 645–2760 cm−1 at a resolution of 0.5 cm−1. The high volume of data observation resulting from IASI presents many challenges. In current Numerical Weather Prediction (NWP) models, assimilating all channels is not feasible, due to data transmission, data storage and significant computational costs. One of the methods for reducing the data volume is the channel selection. Many NWP centres use a subset of 314 IASI channels including 15 ozone-sensitive channels. However, this channel selection has been carried out assuming uncorrelated observation errors. In addition, these ozone-sensitive channels have been selected only for ozone information. The objective of this study is to carry out a new selection of IASI ozone-sensitive channels from the full spectrum over a spectral range of 1000–1070 cm−1, in a direct radiance assimilation framework. This selection is done with a full observation error covariance matrix to take into account cross-channel error correlations. A sensitivity method based on the channel spectral sensitivity to variables and a statistical approach based on the Degrees of Freedom for Signal (DFS) have been chosen. To be representative of atmospheric variability, 345 profiles from around the world over a one-year period were selected. The new selection, is evaluated in a One-Dimensional Variational (1D-Var) analyses framework. This selection highlights a new set of 15 IASI ozone-sensitive channels. The results are very encouraging since by adding these 15 channels to 122 operational channels, temperature and humidity analyses are improved by 13.8 % and 20.9 % respectively. Obviously, these 15 channels significantly improve ozone analyses. In addition to considering inter-channel observation error correlations, the channel selection method uses a robust background error covariance matrix that takes into account temperature, humidity and ozone errors using a lagged forecast method over a one-year period. The new selection of IASI ozone-sensitive channels will be soon used in the global 4D-Var ARPEGE (Action de Recherche Petite Echelle Grande Echelle) data assimilation system.