Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system†

Abstract

This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems.The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties.

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

“…While these results show promise and provide useful guidance, further development is needed to apply these ideas with real observations in operational systems. This work is already underway (Weston, 2011;Pocock et al, 2012;Stewart et al, 2012).…”

“…Nevertheless, attempts have been made to quantify error correlation structure for a few different observation types such as Atmospheric Motion Vectors (Bormann et al, 2003) and satellite radiances (Sherlock et al, 2003;Stewart et al, 2009;Stewart, 2010;Stewart et al, 2012). Using diagnosed correlations such as these in an operational assimilation system is far from straightforward: early attempts by the UK Met Office using IASI and AIRS data have resulted in conditioning problems with the 4D-Var minimisation (Weston, 2011).…”

Diagnosing Horizontal and Inter-Channel Observation Error Correlations for SEVIRI Observations Using Observation-Minus-Background and Observation-Minus-Analysis Statistics

“…Inter-channel correlations have been calculated for observations from satellite instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) using the Desroziers et al diagnostic [12][13][14][15][16]. The literature shows that inter-channel observation errors are correlated and that including these errors in the assimilation leads to improved analysis accuracy, better forecast skill score and the inclusion of more observation information content [6,[16][17][18][19].…”

Data Assimilation with Error-Correlated and Non-Orthogonal Observations: Experiments with the Lorenz-96 Model

“…Yet, this study may provide understandings of the potential analysis performance for the real-world error-correlated observations, and also, may suggest what instrument design would be favorable if we have options. Stewart et al (2013) showed that the diagnosed observationerror correlations for the 130 channels in the Infrared Atmospheric Sounding Interferometer (IASI) had a significant block diagonal structure of the error correlation matrix. This study indicated that estimating only the components which are known to have non-zero values performed better (Fig.…”

“…Desroziers et al (2005) proposed a method to diagnose the observation error correlations. Previous studies applied the Desroziers diagnostics and estimated the observation error correlations such as the inter-channel correlations of the satellite radiances (Garand et al 2007;Stewart et al 2013), vertical correlations of radiosondes (Lönnberg and Hollingsworth 1986), and horizontal correlations of the atmospheric motion vectors, sea-surface winds by satellite scatterometers, and radar data (Keeler and Ellis 2000;Bormann et al 2003). However, the observation error correlations, or off-diagonal components of the observation error covariance matrix, are not considered in the current operational numerical weather prediction (NWP) systems to the best of the authors' knowledge.…”