The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
The Met. Office global three‐dimensional variational data assimilation scheme
Abstract
The Met. Office has developed a variational assimilation for its Unified Model forecast system, which contains a grid‐point mode) that is run operationally in global, mesoscale, and stratospheric configuration. Key characteristics of the design are: a development path from three‐dimensional to four‐dimensional variational assimilation; global and limited‐area configurations; variational analysis of perturbations; and a carefully designed, well conditioned background term. The background term is implemented using a sequence of variable transforms to independent balanced and unbalanced variables, to vertical modes, and to spectral coefficients. The coefficients used are based on statistics from differences of one‐ and two‐day forecasts valid at the same time. The covariance model represents many of the features seen in the covariances of forecast differences. The three‐dimensional variational data assimilation (3D‐Var) system was implemented in the operational global forecast system on 29 March 1999. In parallel trials, the 3D‐Var system gave a 2.7% improvement in a composite skill score (verified against observations and weighted according to the importance of each field).
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Cites
The National Meteorological Center's Spectral Statistical-Interpolation Analysis System
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A strategy for operational implementation of 4D‐Var, using an incremental approach
Courtier, P., Thépaut, J. ‐., & Hollingsworth, A. (1994) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712051912 · Cited by 1,612
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Lorenc, A. C. (1986) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49711247414 · Cited by 1,510
Self-citationData Assimilation Using Incremental Analysis Updates
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Ide, K., Courtier, P., Ghil, M., & Lorenc, A. C. (1997) · Journal of the Meteorological Society of Japan. Ser. II · doi:10.2151/jmsj1965.75.1b_181 · Cited by 821
Self-citationSome numerical experiments with variable-storage quasi-Newton algorithms
Gilbert, J. C., & Lemaréchal, C. (1989) · Mathematical Programming · doi:10.1007/bf01589113 · Cited by 759
Data assimilation in the presence of forecast bias
Dee, D. P., & Da Silva, A. M. (1998) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712454512 · Cited by 438
A reformulation of the background error covariance in the ECMWF global data assimilation system
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The Meteorological Office analysis correction data assimilation scheme
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Atmospheric Modeling, Data Assimilation and Predictability
Kalnay, E. (2002) · doi:10.1017/cbo9780511802270 · Cited by 3,391
The Met Office global four‐dimensional variational data assimilation scheme
Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D., Inverarity, G. W., Lorenc, A. C., & Payne, T. J. (2007) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.32 · Cited by 462
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Metadata
- Authors
- Lorenc, A. C., Ballard, S. P., Bell, R. S., Ingleby, N. B., Andrews, P. L. F., Barker, D. M., Bray, J. R., Clayton, A. M., Dalby, T., Li, D., Payne, T. J., & Saunders, F. W.
- Venue
- Quarterly Journal of the Royal Meteorological Society
- Publisher
- Wiley
- Published
- 2000-10
- Volume
- 126
- Issue
- 570
- Pages
- 2991-3012
- ISSN
- 0035-9009 (ISSN-L)
- 1477-870X
- OpenAlex
- W2138513194
- Cited by
- 478
- References
- 17
- FWCI
- 12.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lorenc, A. C., Ballard, S. P., Bell, R. S., Ingleby, N. B., Andrews, P. L. F., Barker, D. M., Bray, J. R., Clayton, A. M., Dalby, T., Li, D., Payne, T. J., & Saunders, F. W. (2000). The Met. Office global three‐dimensional variational data assimilation scheme. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.49712657002
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