Assimilation of ocean colour data into a biochemical model of the North Atlantic: Part 1. Data assimilation experiments

By L. Natvik, Geir Evensen. 2003

143 citations recorded by the source corpus.

Subfields: Atmospheric Science, Oceanography

View Paper: https://www.semanticscholar.org/paper/989031693e57556841440d4fc074250e33382a85

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Citations in the corpus

A comparison of error subspace Kalman filters (2005, methodology): “For the EnKF as a Monte Carlo method, it has been found that ensembles of the order of 100 members are required (Evensen and van Leeuwen, 1996; Natvik and Evensen, 2003).”
Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter (2003, background): “This is due to the insulating effect of the ice cover, and because of salt fluxes between ice and ocean when sea ice freezes or melts.”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “In parallel, the assimilation of sea ice concentrations in a coupled ice–ocean model was demonstrated (Lisæter, Rosanova, & Evensen, 2003) as well as the assimilation of remotely sensed ocean color in a coupled physical– ecosystem model (Natvik & Evensen, 2003), both with the EnKF.”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, methodology): “Studies of coupled biophysical models using an EnKF have been successful with ensemble sizes of 100 to 200 (Allen et al., 2003; Natvik and Evensen, 2003), while SIR based applications have used ensemble sizes of 1000 (Losa et al., 2003).”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, background): “Natvik and Evensen (2003a,b) presented the ﬁrst realistic 3-”
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): “It is made up of 100 members obtained by running the model from 10 July 1997 with perturbations of the atmospheric fields in the physical model only (as done in Natvik and Evensen, 2003a).”
A decade of satellite ocean color observations. (2009, methodology): “Natvik & Evensen (2003a, 2003b) used the ensemble Kalman filter with a basin-scale three-dimensional coupled ecosystem-dynamical model of the North Atlantic spring bloom (April and May, 1998).”
A sequential Monte Carlo approach for marine ecological prediction (2006, methodology): “In marine ecology, for example, experiments have been carried out with the ensemble Kalman filter (Allen et al., 2003; Natvik and Evensen, 2003) and sequential importance resampling (Losa et al., 2003).”
A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model (2010, methodology): “7 Australian Meteorological and Oceanographic Journal 59 (2010) 7-16”