Sequential Data Assimilation Techniques in Oceanography

By Laurent Bertino, Geir Evensen, Hans Wackernagel. 2003

307 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/7752587579ccc5d1e2c31e083fc2c6fb8eb25f30

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In Atmospheric Science

In Global and Planetary Change

In Oceanography

Citations in the corpus

Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization (2009, methodology): “The iterative EnKF (Wen andChen, 2006;Gu andOliver, 2007) and EnKFwith changes of variables (Bertino et al., 2003; Gu andOliver, 2006) have also been used to account for the non-Gaussian effects.”
Ensemble smoother with multiple data assimilation (2013, methodology): “EnKF has been applied in diverse re-3 search fields including, oceanography (Bertino et al., 2003; Keppenne and4 Rienecker, 2003), numerical weather prediction (Houtekamer and Mitchell,5 2005; Szunyogh et al., 2005), hydrology (Reichle et al., 2002; Chen and Zhang,6 2006; Liu et al., 2008) and…”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, unknown): “[168] L.”
Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment (2009, unknown): “We describe in this section the algorithm of the EnKF with Gaussian anamorphosis suggested by Bertino et al. (2003).”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “Its use has been advocated by Bertino et al. (2003) in the context of geophysical data assimilation (see also the next section).”
Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model (2012, methodology): “This 1D version is the one used by Bertino et al. (2003) to highlight the benefits of the introduction of anamorphosis functions.”
Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter (2003, methodology): “If the forecast probability density function is Gaussian, however, then the analyzed estimate becomes equal to the maximum-likelihood estimate.”
Efficient Ensemble-Based Closed-Loop Production Optimization (2009, methodology): “The EnKF has been found suitable for sequential data assimilation in large-scale nonlinear dynamics (Houtekamer and Mitchell, 2001; Mitchell et al., 2002; Bertino et al., 2003).”
A sequential Monte Carlo approach for marine ecological prediction (2006, methodology): “Recent studies have argued the potential of these methods for data assimilation in oceanography Copyright # 2005 John Wiley & Sons, Ltd. Environmetrics 2006; 17: 435–455 (Bertino et al., 2003).”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, methodology): “For some cases, the EnKF can be transformed to an exact procedure (Bertino et al., 2003; Evensen, 2003).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “This makes the anamorphosis particularly convenient to apply in conjunction with ensemble techniques (Bertino et al., 2003).”
A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model (2010, methodology): “2008), a broad range of Kalman filters (Natvik and Evensen 2001; Eknes and Evensen 2002; Bertino et al. 2003) and Markov Chain Monte Carlo (MCMC) algorithms (Dowd 2006; Dowd 2007; Arhonditsis et al.”