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J. Brankart, P. Brasseur (1996)
Optimal Analysis of In Situ Data in the Western Mediterranean Using Statistics and Cross-ValidationJournal of Atmospheric and Oceanic Technology, 13
P. Houtekamer, H. Mitchell (1998)
Data Assimilation Using an Ensemble Kalman Filter TechniqueMonthly Weather Review, 126
Barnier (2006)
Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy permitting resolution.Ocean Dyn., 56
Bateman (1954)
Tables of Integral Transforms.
J. Brankart, C. Testut, P. Brasseur, J. Verron (2003)
Implementation of a multivariate data assimilation scheme for isopycnic coordinate ocean models: Application to a 1993–1996 hindcast of the North Atlantic Ocean circulationJournal of Geophysical Research, 108
Landau (1951)
Statistical Physics: Course of Theoretical Physics.
G. Kimeldorf, G. Wahba (1970)
A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by SplinesAnnals of Mathematical Statistics, 41
Abramowitz (1970)
Handbook of Mathematical Functions.
Kalnay (2003)
Atmospheric Modeling, Data Assimilation and Predictability.
(2006)
Submitted to Monthly Weather Review
Tippett (2003)
Ensemble square root filters.Mon. Wea. Rev., 131
B. Barnier, Y. Penhoat, L. Fu, R. Morrow, J. Verron, P. Woodworth (2006)
Editorial (From the issue entitled "Special Issue in honour and in memory of Christian Le Provost - Modelling, observing, and forecasting sea level, ocean tides and ocean circulation: reviews and recent progress")
I. Fukumori (2002)
A Partitioned Kalman Filter and SmootherMonthly Weather Review, 130
Morse (1953)
Methods of Theoretical Physics.
G. Evensen, P. Leeuwen (1996)
Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with
I. Silveira, L. Miranda, W. Brown (1994)
On the origins of the North Brazil CurrentJournal of Geophysical Research, 99
P. McIntosh (1990)
Oceanographic data interpolation: Objective analysis and splinesJournal of Geophysical Research, 95
Reif (1965)
Fundamentals of Statistical and Thermal Physics.
D. Pham, J. Verron, M. Roubaud (1998)
A singular evolutive extended Kalman filter for data assimilation in oceanographyJournal of Marine Systems, 16
C. Testut, P. Brasseur, J. Brankart, J. Verron (2003)
Assimilation of sea-surface temperature and altimetric observations during 1992–1993 into an eddy permitting primitive equation model of the North Atlantic OceanJournal of Marine Systems, 40
Zhiquan Liu, F. Rabier (2002)
The interaction between model resolution, observation resolution and observation density in data assimilation: A one‐dimensional studyQuarterly Journal of the Royal Meteorological Society, 128
David Fratantoni, W. Johns, T. Townsend (1995)
Rings of the North Brazil Current: Their structure and behavior inferred from observations and a numerical simulationJournal of Geophysical Research, 100
Rabier (2006)
Importance of data: A meteorological perspective.
Zhiquan Liu, F. Rabier (2003)
The potential of high‐density observations for numerical weather prediction: A study with simulated observationsQuarterly Journal of the Royal Meteorological Society, 129
Cohn (1997)
An introduction to estimation theory.J. Meteor. Soc. Japan, 75
T. Penduff, J. Sommer, B. Barnier, A. Treguier, J. Molines, G. Madec (2007)
Influence of numerical schemes on current-topography interactions in 1/4° global ocean simulationsOcean Science, 3
Jeffrey Anderson (2003)
A Local Least Squares Framework for Ensemble FilteringMonthly Weather Review, 131
Ott (2004)
A local ensemble Kalman filter for atmospheric data assimilation.Tellus, 56A
In the Kalman filter standard algorithm, the computational complexity of the observational update is proportional to the cube of the number y of observations (leading behavior for large y ). In realistic atmospheric or oceanic applications, involving an increasing quantity of available observations, this often leads to a prohibitive cost and to the necessity of simplifying the problem by aggregating or dropping observations. If the filter error covariance matrices are in square root form, as in square root or ensemble Kalman filters, the standard algorithm can be transformed to be linear in y , providing that the observation error covariance matrix is diagonal. This is a significant drawback of this transformed algorithm and often leads to an assumption of uncorrelated observation errors for the sake of numerical efficiency. In this paper, it is shown that the linearity of the transformed algorithm in y can be preserved for other forms of the observation error covariance matrix. In particular, quite general correlation structures (with analytic asymptotic expressions) can be simulated simply by augmenting the observation vector with differences of the original observations, such as their discrete gradients. Errors in ocean altimetric observations are spatially correlated, as for instance orbit or atmospheric errors along the satellite track. Adequately parameterizing these correlations can directly improve the quality of observational updates and the accuracy of the associated error estimates. In this paper, the example of the North Brazil Current circulation is used to demonstrate the importance of this effect, which is especially significant in that region of moderate ratio between signal amplitude and observation noise, and to show that the efficient parameterization that is proposed for the observation error correlations is appropriate to take it into account. Adding explicit gradient observations also receives a physical justification. This parameterization is thus proved to be useful to ocean data assimilation systems that are based on square root or ensemble Kalman filters, as soon as the number of observations becomes penalizing, and if a sophisticated parameterization of the observation error correlations is required.
Monthly Weather Review – American Meteorological Society
Published: Jun 20, 2008
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