Estimation of the error covariance matrix for IASI radiances and its impact on ozone analyses

Abstract

Abstract. In atmospheric chemistry retrievals and data assimilation systems, observation errors associated with satellite radiances are chosen empirically and generally treated as uncorrelated. In this work, we estimate inter-channel error covariances for the Infrared Atmospheric Sounding Interferometer (IASI) and evaluate their impact on ozone assimilation with the chemical transport model MOCAGE (MOdèle de Chime Atmospheric à Grand Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in numerical weather prediction centers. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm−1 to estimate the observation error covariance matrix. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS). The results show significant differences between using the estimated error covariance matrix with respect to the empirical diagonal matrix employed in previous studies. The validation of the analyses against independent data reports a significant improvement especially in the tropical stratosphere. The computational cost has also been reduced when the estimated covariance is employed in the assimilation system.

Impact of IASI thermal infrared measurements on global ozone reanalyses

“…The employed R (Fig. 1) is found to be similar to the error covariance matrices estimated by Aabaribaoune et al (2020), i.e. it presents significant inter-channel error correlations and an error standard deviation matching the typical spectral signature of ozone absorption in the IR.…”

“…The new O 3 reanalysis (hereafter called IASI-r) inherits fundamentally the methodology from IASI-a (Emili et al, 2014;Peiro et al, 2018) but includes the recent developments discussed by Emili et al (2019) and Aabaribaoune et al (2020), concerning the direct assimilation of IASI L1 radiances. Therefore, we detail the novelties / changes introduced for this study and address the reader to the previous studies for a more detailed description on the aspects of the methodology that did not change.…”

“…The single and most significant difference on the assimilation of IASI radiances with respect to Emili et al 2019is that we employed a diagnosed observation error covariance (R) instead of the prescribed and diagonal one used previously. This choice is motivated by the results of Aabaribaoune et al (2020), who found that a diagnosed R with non-zero inter-channel error correlations can reduce stratospheric biases otherwise introduced by IASI assimilation. The diagnostic of R is based on innovation statistics (Desroziers et al, 2005) and we followed the procedure suggested by Aabaribaoune et al (2020)…”

“…we run a first assimilation experiment using the same R as in Emili et al (2019) (diagonal with a standard deviation of 0.7 mW/m2/sr/cm −1 ) ii) we diagnosed R on a average period of one month ii) we used the obtained R to run a second assimilation experiment for a longer period (12 months) and estimated again R. The latter R estimation is the one that is used to compute IASI-r because it provides slightly superior results with respect to the first estimation (not shown, see also the discussion in Aabaribaoune et al (2020)). The employed R (Fig.…”

“…The background error covariance B has been slightly improved with respect to previous studies, which prescribed O 3 error profiles based on a single and fixed tropopause height (Emili et al, 2014;Peiro et al, 2018;Emili et al, 2019;Aabaribaoune et al, 2020). For this study the tropopause height is computed every three hours based on the model temperature profiles (ERA-Interim) and using the 2 K m −1 lapse rate definition of the World Meteorological Organisation (WMO).…”