AGU Oceans 2012 Poster : A Multi-Scale 3D Variational Data Assimilation Scheme (MS-3DVAR) in the Kurshio Extension (original) (raw)
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A revised scheme to compute horizontal covariances in an oceanographic 3D-VAR assimilation system
We propose an improvement of an oceanographic three dimensional variational assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter (RF) with the third order of accuracy (3rd-RF), instead of a RF with first order of accuracy (1st-RF), to approximate horizontal Gaussian covariances. An advantage of the proposed scheme is that the CPU's time can be substantially reduced with benefits on the large scale applications. Experiments estimating the impact of 3rd-RF are performed by assimilating oceanographic data in two realistic oceanographic applications. The results evince benefits in terms of assimilation process computational time, accuracy of the Gaussian correlation modeling, and show that the 3rd-RF is a suitable tool for operational data assimilation.
Continental Shelf Research, 2014
The implementation of a multi-scale three dimensional variational (MS3DVAR) data assimilation scheme for use with the Navy Coastal Ocean Model (NCOM) in the Kuroshio Extension western boundary current region is presented here. This work leverages on Li et al. , who initially developed this method. MS3DVAR data assimilation allows for the effective assimilation of both spatially coarse and dense collections of observations. Traditional 3DVAR produces an inherent filtering of dynamical features smaller than the decorrelation length. The MS3DVAR allows for a scale selective background error covariance capable of handling a wider range of ocean scales. Here the MS3DVAR is examined in an energetic coastal regime using simulated and real observations. The results show that the MS3DVAR reduces analysis errors when compared to a traditional 3DVAR scheme. Forecast errors appear to be similar for both systems and are most likely due to the coarse resolution of the surface forceing being applied.
Ocean spectral data assimilation without background error covariance matrix
Ocean Dynamics, 2016
Predetermination of background error covariance matrix B is challenging in existing ocean data assimilation schemes such as the optimal interpolation (OI). An optimal spectral decomposition (OSD) has been developed to overcome such difficulty without using the B matrix. The basis functions are eigenvectors of the horizontal Laplacian operator, pre-calculated on the base of ocean topography, and independent on any observational data and background fields. Minimization of analysis error variance is achieved by optimal selection of the spectral coefficients. Optimal mode truncation is dependent on the observational data and observational error variance and determined using the steep-descending method. Analytical 2D fields of large and small mesoscale eddies with white Gaussian noises inside a domain with four rigid and curved boundaries are used to demonstrate the capability of the OSD method. The overall error reduction using the OSD is evident in comparison to the OI scheme. Synoptic monthly gridded world ocean temperature, salinity, and absolute geostrophic velocity datasets produced with the OSD method and quality controlled by the NOAA National Centers for Environmental Information (NCEI) are also presented. Keywords Ocean data assimilation. Optimal spectral decomposition (OSD). Basisfunctions. Lagrangian operator. Background error covariance matrix. Observational error covariance matrix. Optimal interpolation. World ocean synoptic monthly gridded data This article is part of the Topical Collection on the 47th International
A reduced-order strategy for 4D-Var data assimilation
Journal of Marine Systems, 2005
This paper presents a reduced-order approach for four-dimensional variational data assimilation, based on a prior EOF analysis of a model trajectory. This method implies two main advantages: a natural model-based definition of a multivariate background error covariance matrix B r , and an important decrease of the computational burden of the method, due to the drastic reduction of the dimension of the control space. An illustration of the feasibility and the effectiveness of this method is given in the academic framework of twin experiments for a model of the equatorial Pacific ocean. It is shown that the multivariate aspect of B r brings additional information which substantially improves the identification procedure. Moreover the computational cost can be decreased by one order of magnitude with regard to the full-space 4D-Var method.
The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: Formulation
Quarterly Journal of the Royal Meteorological Society, 1998
Structure functions for the 3D-Var assimilation scheme of the European Centre for Medium-Range Weather Forecasts are evaluated from statistics of the differences between two forecasts valid at the same time. Results compare satisfactorily with those reported in the existing literature. Non-separability of the correlation functions is a pervasive feature. Accounting for non-separability in 3D-Var is necessary to reproduce geostrophic characteristics of the statistics, such as the increase of length-scale with height for the horizontal correlation of the mass variable, sharper vertical correlations for wind than for mass and shorter horizontal length-scales for temperature than for mass. In our non-separable 3D-Var, the vertical correlations vary with total wave-number and the horizontal correlation functions vary with vertical level.
An oceanographic three-dimensional variational data assimilation scheme
Ocean Modelling, 2008
This study describes the development and evaluation of an oceanographic three-dimensional variational (3D-VAR) data assimilation scheme based on a novel specification of the background error covariances. The new 3D-VAR scheme allows for regional variability of the background error covariance matrix, complex coastal boundary conditions and variable bottom topography. The error covariance matrix is formed by the successive application of linear operators that can consider vertical EOFs, horizontal covariance functions that consider coastlines, sea level corrections that vary from shallow to deep regions and divergence dumping of velocity corrections near the coasts. The scheme is applied to the Mediterranean Sea and the quality of analysis is assessed by comparing background estimates with observations in the period
Quarterly Journal of the Royal Meteorological Society, 2005
In this study new estimates for the observation-and background-error covariances in a three-dimensional variational analysis system at the Canadian Meteorological Centre are evaluated. In the current system, the observation errors are assumed uncorrelated with the variances estimated following a more or less ad hoc approach for each instrument type. The background-error covariances are computed using the so-called NMC method. The new estimate for the observation error variances is obtained using a practical approach developed at Météo-France that computes the maximum likelihood estimate of the variances. Two new estimates for the stationary background-error covariances are evaluated. The first simply involves tuning the variances in the operational background-error covariances. For the second, the NMC method is replaced by a Monte Carlo approach applied to the existing analysis system.