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

Abstract

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.

Estimation of the error covariance matrix for IASI radiances and its impact on the assimilation of ozone in a chemistry transport model

“…Here x a is the analysis state vector, x b is the background state vector, y is the vector of observations, and H is the observation operator that computes model counterpart in the observation space. This method has been used to estimate observation errors and inter-channel error correlations (Stewart et al, 2009;Bormann et al, 2016;Tabeart et al, 2020;Coopmann et al, 2020). It can potentially provide information on imperfectly known observation and background-error statistics with a nearly cost-free computation (Desroziers et al, 2005).…”

“…By assuming Gaussian errors and no correlations between observation and background errors, the error covariance matrix is provided by the statistical average of observation-minusbackground times the observation-minus-analysis residuals. This method has been used in many studies to estimate the observation errors and inter-channel error correlations (Garand et al, 2007;Weston et al, 2014;Bormann et al, 2016;Tabeart et al, 2020;Coopmann et al, 2020).…”

“…The vertical resolution goes from about 100 m in the boundary layer to about 500 m in the free troposphere and to almost 2 km in the upper stratosphere. MOCAGE is forced by meteorological fields from numerical weather prediction mod-els such as the Météo France global model ARPEGE (Action de Recherche Petite Echelle Grande Echelle, (Courtier et al, 1991)), limited-area model AROME (Application de la Recherche à l'Opérationnel à Méso-Echelle), and ECMWF NWP model and assimilation system (Integrated Forecast System, IFS) for air quality predictions and ARPEGE-Climat (Déqué et al, 1994) for climate simulations. In our study, the ECMWF IFS meteorological forecasts fields are used.…”

A channel selection for the assimilation of CrIS and HIRAS instruments at full spectral resolution

“…Since then, with the ever-expending computing capability, it has become possible to use a computationally heavy, albeit more straightforward, approach accounting for the inter-channel correlation and all the parameters at once. Coopmann et al (2020) and Vittorioso et al (2017) have followed such an approach to derive an improved channel selection for IASI and a new one for IASI-NG (New Generation), respectively.…”

Impacts on Initial Condition Modification from Hyperspectral Infrared Sounding Data Assimilation: Comparisons between Full-Spectrum and Channel-Selection Scheme Based on Two-Month Experiments Using CrIS and IASI Observation

“…In this study, B Matrices for January and June are generated respectively, as a series of monthly-separated B Matrix can better describe the sub-seasonal systematic error from NWP model than a unified one. Unlike B Matrix, the DA system inherits observational error covariance matrix (R Matrix) from open-accessed 1D-Var assimilation package released by Numerical Weather Prediction (NWP) Satellite Application Facility (SAF) in European Organization for the Exploitation of Meteorological Satellites (EUMETSAT)[27]-[33]. This decision is made up for the reasons listed below: 1) observational error covariance matrix only comprises instrumental error and fast forward radiative transfer model error, and fast radiative transfer model error is relatively static compared to the error in NWP model; 2) instrumental error is supposed to be steady, as it is only related to instrument's operating and healthy status.…”