Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches

By Thomas M. Hamill, J. Whitaker. 2005

189 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2b04320796336d8665b96f0ed37f0e4af2a5c0c2

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

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).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, background): “Some techniques dealing with model errors in ensemblebased data assimilation have been proposed (Hamill and Whitaker 2005; Houtekamer and Mitchell 2005; Meng and Zhang 2007).”
Accounting for Model Errors in Ensemble Data Assimilation (2009, methodology): “To address this problem, most applications account for model errors by simply enlarging the background error variance (multiplicative inflation, Anderson and Anderson 1999), adding additional terms to the background error variance (additive inflation, Mitchell and Houtekamer 2000; Mitchell et al. 20”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, background): “This choice of additive inflation was shown to work well in a study of the effect of model error on an ensemble data assimilation in an idealized general circulation model (Hamill and Whitaker 2005).”
Additive Noise for Storm-Scale Ensemble Data Assimilation (2009, methodology): “We infer from the work of Houtekamer and Mitchell (2005) and Hamill and Whitaker (2005) that using flowdependent (e.”
Ensemble forecasting (2008, methodology): “Idealised experiments with models of different spatial resolutions suggest that the accuracy of ensemble data assimilation techniques is sensitive to the the specification of the spatial correlations of the model error term [55].”
Spatially and temporally varying adaptive covariance inflation for ensemble filters (2009, background): “Covariance inflation has been effective for a variety of ensemble filter algorithms and geophysical applications, but it has several problems (Hamill and Whitaker, 2005).”
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study (2008, methodology): “Two common ways to cope with this problem are the multiplicative covariance inflation method (Anderson 2001) and the additive covariance inflation method (Hamill and Whitaker 2005; Houtekamer et al. 2005).”
Model Error Representation in an Operational Ensemble Kalman Filter (2009, background): “Hamill and Whitaker (2005) explore various ways to determine the covariances for the additive model error.”