[The copyright line in this article was changed on 14 May 2014 after original online publication.]
Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system†
Article first published online: 21 AUG 2013
© 2013 The Authors and Crown copyright. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Quarterly Journal of the Royal Meteorological Society
Volume 140, Issue 681, pages 1236–1244, April 2014 Part B
How to Cite
Stewart, L. M., Dance, S. L., Nichols, N. K., Eyre, J. R. and Cameron, J. (2014), Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system. Q.J.R. Meteorol. Soc., 140: 1236–1244. doi: 10.1002/qj.2211
This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.
- Issue published online: 12 JUN 2014
- Article first published online: 21 AUG 2013
- Accepted manuscript online: 21 JUN 2013 02:18PM EST
- Manuscript Accepted: 7 JUN 2013
- Manuscript Revised: 5 JUN 2013
- Manuscript Received: 13 SEP 2012
- satellite observations;
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.