A reduced-order strategy for 4D-Var data assimilation
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TLDR
In this paper, a reduced-order approach for four-dimensional variational data assimilation, based on a prior EO F analysis of a model trajectory, is presented, which implies a natural model-based definition of a mul tivariate background error covariance matrix, and an important decrease of the computational burden o f the method, due to the drastic reduction of the control space.About:
This article is published in Journal of Marine Systems.The article was published on 2005-08-01 and is currently open access. It has received 60 citations till now.read more
Citations
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Data assimilation in the geosciences: An overview of methods, issues, and perspectives
TL;DR: Data assimilation (DA) as mentioned in this paper is a state estimation theory in geosciences, which is commonly referred to as data assimilation in meteorology and weather prediction, and it has been applied in many other areas of climate, atmosphere, ocean, and environment modeling.
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A reduced‐order approach to four‐dimensional variational data assimilation using proper orthogonal decomposition
TL;DR: In this article, a 4DVAR approach based on proper orthogonal decomposition (POD) is proposed to reduce the dimension of control space and reduce the size of dynamical model, both in dramatic ways.
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An oceanographic three-dimensional variational data assimilation scheme
Srdjan Dobricic,Nadia Pinardi +1 more
TL;DR: In this paper, the authors describe the development and evaluation of an oceanographic 3D-VAR data assimilation scheme based on a novel specification of the background error covariances.
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An iterative ensemble Kalman smoother
TL;DR: The iterative ensemble Kalman filter (IEnKF) was proposed in this article to improve the performance of Ensemble Kalman filtering with strongly nonlinear geophysical models.
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A Dual-Weighted Approach to Order Reduction in 4DVAR Data Assimilation
TL;DR: In this article, a general framework of the proper orthogonal decomposition (POD) method is considered and a cost-effective approach is proposed to incorporate DAS information into the order-reduction procedure.
References
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A strategy for operational implementation of 4D-Var, using an incremental approach
TL;DR: In this paper, an approximation to 4D-Var, namely the incremental approach, is considered and is shown to produce the same result at the end of the assimilation window as an extended Kalman filter in which no approximations are made in the assimilating model but in which instead a simplitied evolution of the forecast error is introduced.
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Ensemble Forecasting at NCEP and the Breeding Method
Zoltan Toth,Eugenia Kalnay +1 more
TL;DR: In this paper, it is shown that the analysis cycle is like a breeding cycle: it acts as a nonlinear perturbation model upon the evolution of the real atmosphere, and the perturbations (i.e., the analysis error), carried forward in the first-guess forecasts, is scaled down at regular intervals by the use of observations.
OPA 8.1 Ocean General Circulation Model reference manual
TL;DR: OPA as discussed by the authors is a primitive equation model of both the regional and global ocean circulation, which is intended to be a flexible tool for studying ocean and its interactions with the others components of the earth climate system (atmosphere, sea-ice, biogeochemical tracers,...) over a wide range of space and time scale.
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Unified Notation for Data Assimilation : Operational, Sequential and Variational (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice)
TL;DR: In this article, a self-consistent notation for atmospheric and oceanic data assimilation is proposed that bridges sequential and variational methods, on the one hand, and operational usage.
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The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics
TL;DR: In this paper, a comprehensive set of physical parametrizations has been linearized for use in the European Centre for Medium-Range Weather Forecasts (ECMWF's) incremental four-dimensional variational (4D-Var) system described in Part I.