The Met. Office global three‐dimensional variational data assimilation scheme

By AC Lorenc, Spence Ballard, R. Bell, N. Ingleby, P. Andrews, DM Barker, J. Bray, A. Clayton, T. Dalby, D. Li and 2 more. 2000

505 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1bdff713c07430252175a2830af4e0bc46578730

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Citations in the corpus

A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “(23) Cost function (21) is minimized with respect to (δχ var, δγ var), which is an easier and better conditioned problem than minimizing (10) with respect to (δx, δη) (Lorenc et al., 2000; Haben et al., 2011) and useful physical constraints can be built into U (Ingleby, 2001; Bannister, 2008).”
The SPARC Intercomparison of Middle-Atmosphere Climatologies (2004, methodology): “Since November 2000 the Met Office stratospheric analyses have been produced using a new three-dimensional variational data assimilation (3DVAR) system (Lorenc et al. 2000).”
On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, background): “The L03 and B05 framework should be easier to apply in model-space variational schemes where preconditioning is performed with respect to the background term (e.g., Parrish and Derber 1992; Lorenc et al. 2000; Gauthier et al. 1999; L03; Barker et al. 2004).”
Three- and Four-Dimensional Variational Assimilation with a General Circulation Model of the Tropical Pacific Ocean. Part I: Formulation, Internal Diagnostics, and Consistency Checks (2003, methodology): “In meteorology, optimal interpolation (OI) was the standard technique used for many years for producing initial conditions for numerical weather prediction (NWP; Gandin 1965; Rutherford 1972; Lorenc 1981).”
Convection‐permitting models: a step‐change in rainfall forecasting (2016, methodology): “In 1999, a 3-hourly continuously cycling 3D-Var system, based on that used in the global model (Lorenc et al., 2000), was introduced for use with the UK model, which was then a 12 km model.”
divand-1.0: n -dimensional variational data analysis for ocean observations (2013, methodology): “A preconditioning usingV−1 amounts to transforming the background error covariance matrix into the identity matrix (Lorenc et al., 2000; Cullen, 2003; Haben et al., 2011).”
Efficiency of a POD‐based reduced second‐order adjoint model in 4D‐Var data assimilation (2007, background): “Since in practice it is difficult to provide statistical information on the errors, simplifying assumptions need to be made [30].”
Computation of the Streamfunction and Velocity Potential for Limited and Irregular Domains (2006, background): “The anisotropy and inhomogeneity of the self-correlations and large values of the cross correlations make them difficult to represent appropriately in a data assimilation algorithm (Daley 1991, chapter 5; Lorenc et al. 2000).”
Forecast impact experiment with GPS radio occultation measurements (2005, methodology): “In NWP, a wide variety (and large number) of conventional and satellite observations are passed through a ‘‘data assimilation system’’ [e.g., Lorenc, 1986 ] in order to correct forecast errors and determine the ‘‘analysis’’.”