Optimality of variational data assimilation and its relationship with the Kalman filter and smoother

By Zhijin Li, Ionel M. Navon. 2001

128 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/62c7224ea3fb510f8877f3eaf922756811c2fbc2

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, unknown): “It has been proven that both 4DVAR and a Kalman filter can produce the same results at the end of an assimilation window (Hamill 2002; Li and Navon 2001) under the following conditions: (a) The observation operator and model are linear; (b) the observation and background have Gaussian, unbiased rand”
A parameterized‐background data‐weak approach to variational data assimilation: formulation, analysis, and application to acoustics (2015, background): “‡It thus follows, by association, that the PBDW formulation can be related to filtering approaches [19, 20].”
Determination of optimal nudging coefficients (2003, methodology): “If the model errors are assumed to be Gaussian, Li and Navon (2001) have shown the additive properties of a 4D-Var, i.e. solving the optimisation problem on the whole data assimilation period provides the same results as splitting our data assimilation period in two subspaces 1 . . . n1 and n1 . . .”
An adaptive covariance inflation error correction algorithm for ensemble filters (2007, background): “Model (Hansen, 2002) and observation representativeness (Hamill and Whitaker, 2004) error are shared with variational techniques (Li and Navon, 2001).”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, unknown): “A number of variational DA techniques (e.g., Li and Navon, 2001), which can be viewed as simplifications of the KF (since they do not propagate the state covariance matrix explicitly) have been used operationally, primarily in the numerical weather prediction community (see e.g., Fischer et al.,…”
Can assimilation of crowdsourced data in hydrological modelling improve flood prediction (2017, unknown): “For this reason, in variational data assimilation, the past asynchronous observations are simultaneously used to minimize the cost function that measures the weighted difference between background states and observations over the time interval, and identify the best estimate of the initial state con”