A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics

By R. Bannister. 2008

278 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0e131d983a7aa6f4494a5867e0f893d219bc4f87

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Citations in the corpus

divand-1.0: n -dimensional variational data analysis for ocean observations (2013, methodology): “…define the covariance matrix implicitly by B = VVT whereV is a sequence of operators requiring physical consistency, e.g. using balance relationships and empirical orthogonal functions, and spatial smoothness, e.g. using a projection in spectral space (Dobricic and Pinardi, 2008; Bannister, 2008).”
Inhomogeneous Background Error Modeling for WRF-Var Using the NMC Method (2014, methodology): “The operators Up, Uy , and Uh are described in detail by Barker et al. (2004).”
Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events (2013, background): “mal processes take place by phase conversions between water vapor and cloud condensates (Bannister 2008).”
On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “Assuch,themodellingoftheBECshasreceivedmuch attention(e.g. Bannister,2008a,2008b).”
Data assimilation with correlated observation errors: experiments with a 1-D shallow water model (2013, background): “ensemble, as is more typical in operational forecasting (Bannister, 2008).”
Introduction of the GSI into the NCEP Global Data Assimilation System (2009, methodology): “A thorough review of various methods of modeling and applying background error covariances for variational data assimilation, including a summary of the advantages and disadvantages in the choice of formulation, can be found in Bannister (2008b).”
On the representation error in data assimilation (2018, unknown): “Typical operational systems are pre-conditioned with the square root of the background-error covariance matrix (Bannister, 2008).”
Accounting for correlated error in the assimilation of high‐resolution sounder data (2014, methodology): “However, at most NWP centres, including the Met Office, an incremental cost function is used, in which J is a function of δx = x − xg, where δx is the increment and xg is a known reference state (Bannister, 2008).”