Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an essential instrument for numerical weather prediction (NWP). It measures radiances at the top of the atmosphere using 8461 channels. The huge amount of observations provided by IASI has led the community to develop techniques to reduce observations while conserving as much information as possible. Thus, a selection of the 300 most informative channels was made for NWP based on the concept of information theory. One of the main limitations of this method was to neglect the covariances between the observation errors of the different channels. However, many centres have shown a significant benefit for weather forecasting to use them. Currently, the observation-error covariances are only estimated on the current IASI channel selection, but no studies to make a new selection of IASI channels taking into account the observation-error covariances have yet been carried out. The objective of this paper was therefore to perform a new selection of IASI channels by taking into account the observation-error covariances. The results show that with an equivalent number of channels, accounting for the observation-error covariances, a new selection of IASI channels can reduce the analysis error on average in temperature by 3 %, humidity by 1.8 % and ozone by 0.9 % compared to the current selection. Finally, we go one step further by proposing a robust new selection of 400 IASI channels to further reduce the analysis error for NWP.

In this study, IASI ozone-sensitive channels have been assimilated in 1D-Var data assimilation combined with realistic ozone background coming from a MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle) Chemistry Transport Model (CTM) as a first stage of coupling between Numerical Weather Prediction (NWP) and MOCAGE CTM at Météo-France for global model ARPEGE (Action de Recherche Petite Echelle Grande Echelle). To evaluate the impact of ozone-sensitive channels on analyses, databases of 161 temperatures, humidity, and ozone radiosondes across the globe during a 1-year period have been considered. Ozone forecast from MOCAGE CTM was evaluated with radiosondes and used as input for the Radiative Transfer Model (RTM) RTTOV. Statistics of IASI observations minus simulations show that the use of ozone from MOCAGE CTM allows to better simulate IASI ozone-sensitive channels. The Desroziers method is used to diagnose observation error covariance matrix and estimate realistic ozone observation standard errors taking into account cross-correlations between IASI channels. The background error covariance matrix for ozone is estimated using radiosondes. A control run assimilating 123 IASI operational channels is compared to an experiment which assimilates, in addition, 15 IASI ozone-sensitive channels. Results show potential benefits of IASI ozone-sensitive channels combined with realistic ozone from MOCAGE CTM to improve temperature, humidity and ozone analyses simultaneously. This work is an encouraging first step for the enhancing of the coupling between the global model ARPEGE and MOCAGE CTM.

The Infrared Sounder (IRS) is an infrared Fourier transform spectrometer that will be on board the Meteosat Third Generation series of the future European Organization for the Exploitation of Meteorological Satellites geostationary satellites and will have a unique four‐dimensional look at the atmosphere. After its planned launch in 2024, it will be able to measure the radiance emitted by the Earth at the top of the atmosphere using 1,960 channels in two spectral bands between 680–1,210 cmprefix−1$$ {}^{-1} $$ (long‐wave infrared) and 1,600–2,250 cmprefix−1$$ {}^{-1} $$ (mid‐wave infrared) at a spectral sampling of 0.6 cmprefix−1$$ {}^{-1} $$. It will perform measurements over the full Earth disk with high spatial and temporal resolution of 4 km at nadir and 30 min over Europe. Thus, the huge amount of data from IRS will present challenges, particularly in data transmission, data storage, and assimilation into numerical weather prediction (NWP) models. To reduce the volume of data, various methods are available, including spatial sampling, principal component analysis, and channel selection. The latter technique will be discussed in this paper by proposing general channel selection to provide NWP models. The objective of this selection is to improve essential variables for NWP such as temperature, humidity, skin temperature, and ozone. This work has required the development of a large observation database and takes into account the main developments in assimilation techniques, including the use of full observation‐error covariance matrices or the assimilation of ozone in global models, for example. This study performs a specific analysis of the sensitivity of IRS observations and proposes a first general selection of 300 channels for NWP models. This selection allows us to reduce the analysis error in the troposphere by 48% in temperature, 65% in humidity, and 17% in ozone.