The copyright line for this article was changed on 28 February 2014 after original online publication.
Representativity error for temperature and humidity using the Met Office high-resolution model†
Version of Record online: 19 JUL 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 1189–1197, April 2014 Part B
How to Cite
Waller, J. A., Dance, S. L., Lawless, A. S., Nichols, N. K. and Eyre, J. R. (2014), Representativity error for temperature and humidity using the Met Office high-resolution model. Q.J.R. Meteorol. Soc., 140: 1189–1197. doi: 10.1002/qj.2207
This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.
- Issue online: 12 JUN 2014
- Version of Record online: 19 JUL 2013
- Accepted manuscript online: 20 JUN 2013 12:45PM EST
- Manuscript Accepted: 27 MAY 2013
- Manuscript Revised: 25 APR 2013
- Manuscript Received: 12 SEP 2012
- data assimilation;
- correlated observation error;
- forward model error
The observation-error covariance matrix used in data assimilation contains contributions from instrument errors, representativity errors and errors introduced by the approximated observation operator. Forward model errors arise when the observation operator does not correctly model the observations or when observations can resolve spatial scales that the model cannot. Previous work to estimate the observation-error covariance matrix for particular observing instruments has shown that it contains signifcant correlations. In particular, correlations for humidity data are more significant than those for temperature. However it is not known what proportion of these correlations can be attributed to the representativity errors. In this article we apply an existing method for calculating representativity error, previously applied to an idealised system, to NWP data. We calculate horizontal errors of representativity for temperature and humidity using data from the Met Office high-resolution UK variable resolution model. Our results show that errors of representativity are correlated and more significant for specific humidity than temperature. We also find that representativity error varies with height. This suggests that the assimilation scheme may be improved if these errors are explicitly included in a data assimilation scheme.