Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations

By Thomas M. Hamill, C. Snyder. 2002

115 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/ddf02baf8745f91c042ed9136ad4500aa49ad7d9

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Citations in the corpus

Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, methodology): “In perfect-model tests, the ensemble filters produced simulations with dramatically lower errors than were achieved with competing methods (e.g., Hamill and Snyder 2000, 2002; Anderson 2001).”
Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting (2007, methodology): “1999), and ensemble-based methods (Bishop et al. 2001; Hamill and Snyder 2002).”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, background): “…of research in numerical weather prediction (NWP) is dedicated to observation- and background-error covariance modelling (Gaspari and Cohn, 1999; Hamill and Snyder, 2002; Lorenc, 2003; Buehner et al., 2005; Frehlich, 2006; Janjić and Cohn, 2006; Bannister, 2008a,b) and to the development of…”
A Method for Identifying the Sensitive Areas in Targeted Observations for Tropical Cyclone Prediction: Conditional Nonlinear Optimal Perturbation (2009, methodology): “Another strategy is ensemble based, for example, the ensemble transform (Bishop and Toth 1999), the ensemble Kalman filter (Hamill and Snyder 2002), and the ensemble transform Kalman filter (Bishop et al.”
Ensemble-Based Sensitivity Analysis (2008, methodology): “Hamill and Snyder (2002) applied a similar technique to estimate the impact of observations on analysis variance, but did not consider forecast impact.”
On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation (2008, background): “A significant amount of research in NWP is focused on modeling the error covariance matrices (Janjic´ and Cohn 2006; Frehlich 2006; Kucuk-karaca and Fisher 2006; Etherton and Bishop 2004; Lorenc 2003; Hamill and Snyder 2002).”