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

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 chemistry transport model MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in many numerical weather prediction centres. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm−1 to estimate the observation-error covariance matrix. This spectral range includes ozone-sensitive and atmospheric window channels. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS) and by the Ozone Monitoring Instrument (OMI). 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 matrix is employed in the assimilation system, by reducing the number of iterations needed for the minimizer to converge.

The relationship between two methods for estimating uncertainties in data assimilation

“…The first can be satisfied by introducing a symmetric version of DBCP, $$$$ \hat{\mathbf{R}}\equiv \frac{1}{2}\left({\hat{\mathbf{R}}}_u+{\hat{\mathbf{R}}}_u^{\mathrm{T}}\right). $$$$ This is not unnatural, and a practical use of DBCP that attempts to estimate correlations typically employs symmetrization (e.g., Gauthier et al ., 2018; Waller et al ., 2019; Aabaribaoune et al ., 2021; Cheng and Qiu, 2021). The second requirement of positive semidefiniteness must be observed carefully when constructing covariances from finite samples.…”

Impact of Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared measurements on global ozone reanalyses

“…This choice is motivated by the results of El Aabaribaoune et al ( 2021), 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 El Aabaribaoune et al ( 2021): (i) we first ran an assimilation experiment using the same R as in Emili et al (2019) (diagonal with a standard deviation of 0.7 mW m −2 sr −1 cm), (ii) we diagnosed R on an average period of 1 month, (iii) we used the obtained R to run a second assimilation experiment for a longer period, (12 months) and we again estimated 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 El Aabaribaoune et al, 2021). The employed R (Fig.…”

“…Finally El Aabaribaoune et al ( 2021) improved the L1 assimilation scheme by employing a more realistic observation error covariance for the L1 radiances, which reduced residual stratospheric biases. However, both the analyses of Emili et al (2019) andEl Aabaribaoune et al (2021) were limited to a single summer month in 2010 and could not draw conclusions on the capacity of IR assimilation to reproduce the seasonal O 3 variability or trends in the extra-tropics. Note that the assimilation of IASI radiances sensitive to O 3 was also investigated in the framework of numerical weather prediction (NWP) by Dragani and Mcnally (2013) and, more recently, Coopmann et al (2018).…”

Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model

“…Note also that simplifications are often introduced to represent a flow dependency of the background term. For example, in several studies using MOCAGE, the 3DVar background error standard deviations are specified as a percentage of the firstguess field (El Amraoui et al, 2020;El Aabaribaoune et al, 2021;Peiro et al, 2018) -which is very different from the forecast-error variance in an ensemble Kalman filter (EnKF) that results from the ensemble estimation and the dynamics of the uncertainty along the previous analysis and forecast cycles.…”