Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations

By P. Houtekamer, H. Mitchell, G. Pellerin, M. Buehner, Martin Charron, Lubos Spacek, Bjarne K. Hansen. 2005

541 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ea14f9276ac5f492af00a34fdf8672cc6d0eb450

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Citations in the corpus

Ensemble Kalman filtering (2005, background): “In particular, in the EnKF, the equation for the gain is modified to permit localization of covariances.”
The Twentieth Century Reanalysis Project (2009, methodology): “, 1998; Houtekamer and Mitchell, 2005; Houtekamer et al., 2005), and ‘deterministic’ algorithms that modify the update equation to reduce potential sampling errors in the stochastic approach, as reviewed, e.g., by Tippett et al. (2003). Both classes are areas of active research (e.”
Ensemble forecasting (2008, background): “It is necessary to represent model error in ensemble-based data assimilation to obtain reliable uncertainty estimates [41].”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, background): “Recent applications to operational numerical weather prediction (NWP) systems have obtained promising results (e.g., Houtekamer and Mitchell 2005; Houtekamer et al. 2005; Bowler et al. 2008; Whitaker et al. 2008; Bonavita et al. 2008, 2010; Miyoshi et al. 2010).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “A version of the EnKF is operational for the atmospheric model at the Canadian Meteorological Centre (CMC) (Houtekamer et al., 2005) Also, the EnKF is used in the ocean system TOPAZ (see Section 5.”
Multimodal ensemble Kalman filtering using Gaussian mixture models (2011, methodology): “The method has been applied in ocean dynamical models [16], in meteorology [17] and, recently, in reservoir simulation models [23].”
Model Error Representation in an Operational Ensemble Kalman Filter (2009, methodology): “In the horizontal the impact of observations is thus smoothly reduced to zero over a distance of 2800 km and similarly the corresponding vertical distance is two units of ln(pressure) [Houtekamer et al. 2005, their Eq.”
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, background): “E-mail: lih@mail.typhoon.gov.cn models (e.g. Houtekamer et al., 2005), and from globalscale (e.g. Whitaker et al., 2008; Szunyogh et al., 2008) to regional-scale models (e.g. Snyder and Zhang, 2003; Zhang et al., 2006; Torn et al., 2006), due to their ease of implementation and the flow-dependent…”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, methodology): “Additive errors (Mitchell et al. 2002; Houtekamer et al. 2005) may avoid some of these problems.”
An economical approach to four-dimensional variational data assimilation (2010, methodology): “As sequential algorithms, asynchronous observations were used at the analysis time ( Houtekamer et al., 2005; Szunoygh et al., 2005).”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, methodology): “Houtekamer et al. (2005) showed diagnostics similar to these for the MSC implementation of the Ensemble Kalman Filter (their Fig.”
Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, methodology): “In a separate study, Annan and Hargreaves (2004) have applied the same concept of parameter estimation to the steady-state climatology of the much more chaotic Lorenz (1963) model. The applicability of the technique used in these two studies for parameter estimation of steady-state climatology is fu”