Ensemble Kalman filtering

By By P. L. Houtekamer, H. Mitchell. 2005

445 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/795d97e01d4d011d18c7d0e86e4c7ad9d8621a44

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Citations in the corpus

Model Error Representation in an Operational Ensemble Kalman Filter (2009, methodology): “The presence of these localization operators is a major source of imbalance in our EnKF implementation [Houtekamer and Mitchell 2005, their Table 3]. c.”
Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation (2012, background): “In this case, there is less theoretical foundation for choosing a localization and a number of functions have been used (Whitaker et al. 2004; Houtekamer and Mitchell 2005).”
Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments (2010, methodology): “Recent versions of the ensemble Kalman filter (EnKF) approach use ensemble estimates of the error covariances of the 4D atmospheric state (Hunt et al. 2004; Houtekamer and Mitchell, 2005).”
Ensemble smoother with multiple data assimilation (2013, background): “, 2003; Keppenne and Rienecker, 2003), numerical weather prediction (Houtekamer and Mitchell, 2005; Szunyogh et al., 2005), hydrology (Reichle et al.”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “Using such techniques, ensemble-based data assimilation algorithms have proven useful in either global atmospheric data assimilation (Mitchell et al. 2002; Houtekamer and Mitchell 2005; Buehner 2005) or regional mesoscale atmospheric data assimilation (Snyder and Zhang 2003; Xue et al. 2006).”
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction (2013, methodology): “D background-error covariances obtained from 192 EnKF background ensemble members stored every hour over the 6 h assimilation window, as they are also used in the EnKF version that was also implemented operationally on 13 February 2013.”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, methodology): “…the serial algorithm with “traditional” covariance localization (and adaptive observation thinning). d. Accounting for system errors As discussed in Houtekamer and Mitchell (2005), ensemble data assimilation systems are suboptimal because of (i) sampling error in the estimation of…”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “…(Zupanski 2005) and background (Harlim and Hunt 2007), and the time interpolation of the background forecasts to the observations so as to produce timecoherent assimilations of all the observations available within the assimilation window (Hunt et al. 2004; Houtekamer and Mitchell 2005).”
Stochastic parametrization and model uncertainty (2009, methodology): “The parametrized system error scheme (Houtekamer and Mitchell, 2005) simulates the effect of model error sources that degrade the quality of the initial conditions and that are not taken into account by the MSC procedure used to generate the ensemble initial conditions, by adding a random…”
The ensemble Kalman filter for combined state and parameter estimation (2009, unknown): “An implementation of EnKF at the Canadian Meteorological Centre in [19] demonstrates EnKF for operational atmospheric data assimilation and reviews EnKF with focus on localization and sampling errors.”
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 ﬂow-dependent (e.g., localized) additive noise could also prove useful for larger-scale applications.”
The Twentieth Century Reanalysis Project (2009, background): “analyses of the marine observations themselves (Kent and Berry, 2005; Chang, 2005, 2007). Assuming that the largest part of the observation error is the so-called error of representativeness, observation errors were kept fixed over the entire reanalysis period. The results of Kent and Berry (2005) a”
The Multiensemble Approach: The NAEFS Example (2009, methodology): “The initial conditions perturbations are provided by an ensemble Kalman filter (EnKF; Houtekamer and Mitchell 2005) and isotropic perturbations are added in order to arrive at sufficient ensemble spread in the early forecast ranges.”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, methodology): “Ensemble Kalman ﬁlter methods are not as general as particle ﬁlters but practical variants exist that work for very large problems like operational numerical weather prediction (Houtekamer and Mitchell 2005; Whitaker et al. 2008; Szunyogh et al. 2008).”