Enhancing the impact of IASI observations through an updated observation‐error covariance matrix

Abstract

This article investigates the use of an updated observation-error covariance matrix for the Infrared Atmospheric Sounding Interferometer (IASI) in the European Centre for Medium-Range Weather Forecasts (ECMWF) system. The new observation-error covariance matrix is based on observation-space diagnostics and includes interchannel error correlations, but also assigns significantly altered error standard deviations. The update is investigated in detail in assimilation experiments, including an assessment of the role of error inflation and taking interchannel error correlations into account.The updated observation-error covariance leads to a significant improvement in the use of IASI data, especially in the Tropics and the stratosphere and particularly for humidity and ozone. The benefits are especially strong for short-range forecasts, whereas the impact in the medium range is less pronounced.The study highlights the benefits of taking interchannel error correlations into account, which allows the use of an observation-error covariance for IASI that is overall more consistent with departure statistics. At the same time, the study also demonstrates that error inflation can be used to compensate partially, though not fully, for neglected error correlations. Adjustments such as scaling of the originally diagnosed observation-error estimates are also found to be beneficial when the diagnosed interchannel error correlations are taken into account.

The conditioning of least‐squares problems in variational data assimilation

“…Correlated observation error statistics have been diagnosed for certain observation types (e.g., see other works [20][21][22][23][24][25][26][27] ), although there are problems associated with their use. In particular, the methods used to diagnose observation error covariance matrices are imperfect, and the quality of these estimates is unclear.…”

“…There is significant research investigating spatial correlations, 20,23,27,51 but much current work concerns the practical implementation of interchannel correlations for satellite observations. 3,6,9,21,22,26 Although the theory presented in Section 3 applies directly to the case of interchannel correlations, it would be of interest to extend our numerical testing to the interchannel covariance case. In particular, practical experiments have revealed that the minimum eigenvalue of the observation error correlation matrix is important for the conditioning of the Hessian in the case of interchannel correlations, 3,9 which coincides with the theoretical and experimental results presented in this work.…”

“…We can see from (21) that the eigenvectors only depend on N, the dimension of the circulant matrix. Therefore, any N × N circulant matrix will have the same set of eigenvectors.…”

“…Either the operational observation operator is used (e.g., see other works 9,21 ) or the experiments are carried out in a simple linear case where H is taken to be a variant of the identity, as in other works. 5,7,8,54 In this paper, we compare how the condition number of the Hessian is affected by different choices of linear observation operator in order to gain some theoretical insight into the role played by this operator.…”

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

“…Therefore, prognostic error models of the data are a functional requirement, and these models need to undergo validation as well. Wherever possible error covariances should also be provided (Bormann et al, 2016), which include correlations of errors among different aerosol products from a given sensor, correlations of errors in time (especially for re-trievals from geostationary satellites), and correlations of errors in space (e.g., due to the similarity in surface properties or viewing geometries). Additionally, other information is deemed necessary for the correct assimilation of the observations, such as averaging kernels for chemical species.…”