Spatially and temporally varying adaptive covariance inflation for ensemble filters

By Jeffrey L. Anderson. 2009

395 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5bddd15ce6f9d826c0541988a42e08c927574f25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, unknown): “This choice of g is consistent with previous studies using the EAKF with adaptive inflation for the L96model (e.g., Anderson 2007, 2009).”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, methodology): “A spatially varying adaptive inﬂation (Anderson 2009b) and localization with a 0.2 rad Gaspari–Cohn function were used.”
Additive Noise for Storm-Scale Ensemble Data Assimilation (2009, methodology): “Experimentation and/or adaptive methods that use recent innovation statistics (e.g., Anderson 2009) are required to determine the inﬂation factor g that provides the most favorable results for a particular application.”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, methodology): “Tellus A: Dynamic Meteorology and Oceanography, 59 (2), 210–224.872 Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble873 filters.”
Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation (2012, methodology): “The spatially and temporally varying adaptive inflation algorithm of Anderson (2009a) is used with the inflation standard deviation fixed at 0.6 and the inflation value damped 10% of the distance to 1 before each assimilation step.”
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009, methodology): “…sophisticated treatment of model error, such as the additive noise as in Dowell and Wicker (2009), the use of multiple physical parameterizations as in Fujita et al. (2007), or adaptive inflation (Anderson 2007, 2009), will be an important next step in convective-scale ensemble data assimilation.”
Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study* (2015, methodology): “The simple coupled model The simple model is the stochastically forced, onedimensional, linear, coupled model of Barsugli and Battisti (1998) and Bretherton and Battisti (2000): dTa dt 52aTa1 bTo 1F(t) , m dTo dt 5 cTa2 dTo , where Ta and To are the atmosphere and ocean temperature anomalies,…”
“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation (2011, background): “The inflation for the surface carbon fluxes is estimated to be small, less than 2%, as could be expected for a variable that is not observed [ Anderson, 2009 ].”