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  • 1
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    Quasi-linear approach : exact solution for nonlinear Green function (1994)
    EDP Sciences
    Details
    Publication Date: 1994-05-01
    Print ISSN: 1155-4339
    Electronic ISSN: 1764-7177
    Topics: Physics
    Published by EDP Sciences
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  • 2
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    TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic (2012)
    Copernicus
    Details
    Publication Date: 2012-08-15
    Description: We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.
    Print ISSN: 1812-0784
    Electronic ISSN: 1812-0792
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
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    Application of a hybrid EnKF-OI to ocean forecasting (2009)
    Copernicus
    Details
    Publication Date: 2009-04-15
    Description: Data assimilation methods often use an ensemble to represent the background error covariance. Two approaches are commonly used; a simple one with a static ensemble, or a more advanced one with a dynamic ensemble. The latter is often non-practical due to its high computational requirements. Some recent studies suggested using a hybrid covariance, which is a linear combination of the covariances represented by a static and a dynamic ensemble. Here, the use of the hybrid covariance is first extensively tested with a quasi-geostrophic model and with different analysis schemes, namely the Ensemble Kalman Filter (EnKF) and the Ensemble Square Root Filter (ESRF). The hybrid covariance ESRF (ESRF-OI) is more accurate and more stable than the hybrid covariance EnKF (EnKF-OI), but the overall conclusions are similar regardless of the analysis scheme used. The benefits of using the hybrid covariance are large compared to both the static and the dynamic methods with a small dynamic ensemble. The benefits over the dynamic methods become negligible, but remain, for large dynamic ensembles. The optimal value of the hybrid blending coefficient appears to decrease exponentially with the size of the dynamic ensemble. Finally, we consider a realistic application with the assimilation of altimetry data in a hybrid coordinate ocean model (HYCOM) for the Gulf of Mexico, during the shedding of Eddy Yankee (2006). A 10-member EnKF-OI is compared to a 10-member EnKF and a static method called the Ensemble Optimal Interpolation (EnOI). While 10 members seem insufficient for running the EnKF, the 10-member EnKF-OI reduces the forecast error compared to the EnOI, and improves the positions of the fronts.
    Print ISSN: 1812-0806
    Electronic ISSN: 1812-0822
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
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    Application of a hybrid EnKF-OI to ocean forecasting (2009)
    Copernicus
    Details
    Publication Date: 2009-10-01
    Description: Data assimilation methods often use an ensemble to represent the background error covariance. Two approaches are commonly used; a simple one with a static ensemble, or a more advanced one with a dynamic ensemble. The latter is often non-practical due to its high computational requirements. Some recent studies suggested using a hybrid covariance, which is a linear combination of the covariances represented by a static and a dynamic ensemble. Here, the use of the hybrid covariance is first extensively tested with a quasi-geostrophic model and with different analysis schemes, namely the Ensemble Kalman Filter (EnKF) and the Ensemble Square Root Filter (ESRF). The hybrid covariance ESRF (ESRF-OI) is more accurate and more stable than the hybrid covariance EnKF (EnKF-OI), but the overall conclusions are similar regardless of the analysis scheme used. The benefits of using the hybrid covariance are large compared to both the static and the dynamic methods with a small dynamic ensemble. The benefits over the dynamic methods become negligible, but remain, for large dynamic ensembles. The optimal value of the hybrid blending coefficient appears to decrease exponentially with the size of the dynamic ensemble. Finally, we consider a realistic application with the assimilation of altimetry data in a hybrid coordinate ocean model (HYCOM) for the Gulf of Mexico, during the shedding of Eddy Yankee (2006). A 10-member EnKF-OI is compared to a 10-member EnKF and a static method called the Ensemble Optimal Interpolation (EnOI). While 10 members seem insufficient for running the EnKF, the 10-member EnKF-OI reduces the forecast error compared to the EnOI, and improves the positions of the fronts.
    Print ISSN: 1812-0784
    Electronic ISSN: 1812-0792
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 5
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    Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems (2012)
    Copernicus
    Details
    Publication Date: 2012-06-25
    Description: The finite-size ensemble Kalman filter (EnKF-N) is an ensemble Kalman filter (EnKF) which, in perfect model condition, does not require inflation because it partially accounts for the ensemble sampling errors. For the Lorenz '63 and '95 toy-models, it was so far shown to perform as well or better than the EnKF with an optimally tuned inflation. The iterative ensemble Kalman filter (IEnKF) is an EnKF which was shown to perform much better than the EnKF in strongly nonlinear conditions, such as with the Lorenz '63 and '95 models, at the cost of iteratively updating the trajectories of the ensemble members. This article aims at further exploring the two filters and at combining both into an EnKF that does not require inflation in perfect model condition, and which is as efficient as the IEnKF in very nonlinear conditions. In this study, EnKF-N is first introduced and a new implementation is developed. It decomposes EnKF-N into a cheap two-step algorithm that amounts to computing an optimal inflation factor. This offers a justification of the use of the inflation technique in the traditional EnKF and why it can often be efficient. Secondly, the IEnKF is introduced following a new implementation based on the Levenberg-Marquardt optimisation algorithm. Then, the two approaches are combined to obtain the finite-size iterative ensemble Kalman filter (IEnKF-N). Several numerical experiments are performed on IEnKF-N with the Lorenz '95 model. These experiments demonstrate its numerical efficiency as well as its performance that offer, at least, the best of both filters. We have also selected a demanding case based on the Lorenz '63 model that points to ways to improve the finite-size ensemble Kalman filters. Eventually, IEnKF-N could be seen as the first brick of an efficient ensemble Kalman smoother for strongly nonlinear systems.
    Print ISSN: 1023-5809
    Electronic ISSN: 1607-7946
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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  • 6
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    Dynamics of large-amplitude geostrophic flows over bottom topography (1997)
    Copernicus
    Details
    Publication Date: 1997-03-31
    Description: We examine the interaction of near-surface and near- bottom flows over bottom topography. A set of asymptotic equations for geostrophic currents in a three-layer fluid is derived. The depths of the active (top/bottom) layers are assumed small, the slope of the bottom is weak, the interfacial displacement is comparable to the depths of the thinner layers. Using the equations derived, we examine the stability of parallel flows and circular eddies. It is demonstrated that eddies with non-zero near-surface component are always unstable; eddies localized in the near-bottom layer may be stable subject to additional restrictions imposed on their horizontal profiles and bottom topography.
    Print ISSN: 1023-5809
    Electronic ISSN: 1607-7946
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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  • 7
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    Unknown
    TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic (2012)
    Copernicus
    Details
    Publication Date: 2012-04-10
    Description: We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation and the sea ice. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.
    Print ISSN: 1812-0806
    Electronic ISSN: 1812-0822
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 8
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    Joint state and parameter estimation with an iterative ensemble Kalman smoother (2013)
    Copernicus
    Details
    Publication Date: 2013-10-23
    Description: Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS is a candidate tool for joint state and parameter estimation that may inherit the benefits from both the ensemble filtering and variational approaches. In this study, an augmented state IEnKS is tested on its estimation of the forcing parameter of the Lorenz-95 model. Since joint state and parameter estimation is especially useful in applications where the forcings are uncertain but nevertheless determining, typically in atmospheric chemistry, the augmented state IEnKS is tested on a new low-order model that takes its meteorological part from the Lorenz-95 model, and its chemical part from the advection diffusion of a tracer. In these experiments, the IEnKS is compared to the ensemble Kalman filter, the ensemble Kalman smoother, and a 4D-Var, which are considered the methods of choice to solve these joint estimation problems. In this low-order model context, the IEnKS is shown to significantly outperform the other methods regardless of the length of the data assimilation window, and for present time analysis as well as retrospective analysis. Besides which, the performance of the IEnKS is even more striking on parameter estimation; getting close to the same performance with 4D-Var is likely to require both a long data assimilation window and a complex modeling of the background statistics.
    Print ISSN: 1023-5809
    Electronic ISSN: 1607-7946
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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  • 9
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    On the Linear Approximation of Velocity and Density Profiles in the Problem of Baroclinic Instability (1999)
    American Meteorological Society
    Details
    Publication Date: 1999-06-01
    Print ISSN: 0022-3670
    Electronic ISSN: 1520-0485
    Topics: Geosciences , Physics
    Published by American Meteorological Society
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  • 10
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    An iterative ensemble Kalman smoother (2013)
    Wiley
    Details
    Publication Date: 2013-10-29
    Print ISSN: 0035-9009
    Electronic ISSN: 1477-870X
    Topics: Geography , Physics
    Published by Wiley
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