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  • 1
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    Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation (2016)
    American Meteorological Society
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    Publication Date: 2016-01-01
    Description: This work assesses the large-scale applicability of the recently proposed nonlinear ensemble transform filter (NETF) in data assimilation experiments with the NEMO ocean general circulation model. The new filter constitutes a second-order exact approximation to fully nonlinear particle filtering. Thus, it relaxes the Gaussian assumption contained in ensemble Kalman filters. The NETF applies an update step similar to the local ensemble transform Kalman filter (LETKF), which allows for efficient and simple implementation. Here, simulated observations are assimilated into a simplified ocean configuration that exhibits globally high-dimensional dynamics with a chaotic mesoscale flow. The model climatology is used to initialize an ensemble of 120 members. The number of observations in each local filter update is of the same order resulting from the use of a realistic oceanic observation scenario. Here, an importance sampling particle filter (PF) would require at least 106 members. Despite the relatively small ensemble size, the NETF remains stable and converges to the truth. In this setup, the NETF achieves at least the performance of the LETKF. However, it requires a longer spinup period because the algorithm only relies on the particle weights at the analysis time. These findings show that the NETF can successfully deal with a large-scale assimilation problem in which the local observation dimension is of the same order as the ensemble size. Thus, the second-order exact NETF does not suffer from the PF’s curse of dimensionality, even in a deterministic system.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 2
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    On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods (2014)
    American Meteorological Society
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    Publication Date: 2014-05-28
    Description: In data assimilation applications using ensemble Kalman filter methods, localization is necessary to make the method work with high-dimensional geophysical models. For ensemble square root Kalman filters, domain localization (DL) and observation localization (OL) are commonly used. Depending on the localization method, appropriate values have to be chosen for the localization parameters, such as the localization length and the weight function. Although frequently used, the properties of the localization techniques are not fully investigated. Thus, up to now an optimal choice for these parameters is a priori unknown and they are generally found by expensive numerical experiments. In this study, the relationship between the localization length and the ensemble size in DL and OL is studied using twin experiments with the Lorenz-96 model and a two-dimensional shallow-water model. For both models, it is found that the optimal localization length for DL and OL depends linearly on an effective local observation dimension that is given by the sum of the observation weights. In the experiments no influence of the model dynamics on the optimal localization length was observed. The effective observation dimension defines the degrees of freedom that are required for assimilating observations, while the ensemble size defines the available degrees of freedom. Setting the localization radius such that the effective local observation dimension equals the ensemble size yields an adaptive localization radius. Its performance is tested using a global ocean model. The experiments show that the analysis quality using the adaptive localization is similar to the analysis quality of an optimally tuned constant localization radius.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 3
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    State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems (2018)
    Taylor & Francis
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    Publication Date: 2018-01-01
    Print ISSN: 0280-6495
    Electronic ISSN: 1600-0870
    Topics: Geography , Geosciences , Physics
    Published by Taylor & Francis on behalf of Department of Meteorology (MISU), Stockholm University.
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  • 4
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    The smoother extension of the nonlinear ensemble transform filter (2017)
    Taylor & Francis
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    Publication Date: 2017-01-01
    Print ISSN: 0280-6495
    Electronic ISSN: 1600-0870
    Topics: Geography , Geosciences , Physics
    Published by Taylor & Francis on behalf of Department of Meteorology (MISU), Stockholm University.
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  • 5
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    Extending NEMO for ensemble data assimilation on supercomputers with the parallel data assimilation framework PDAF (2014)
    In:  EPIC3EGU General Assembly, April 27 - May 3, 2014, Vienna, Austria (Geophysical Research Abstracts, Vol. 16, EGU2014-2191)
    Details
    Publication Date: 2014-05-08
    Description: The NEMO model is a state-of-the-art ocean circulation model. For data assimilation applications with ensemble Kalman filters like the SEEK filter, e.g. for operational ocean forecasting, NEMO is typically run separately from the assimilation algorithm. This procedure generates a set of restart files on disks that hold the ensemble of model forecasts providing the error covariance matrix information for the ensemble Kalman filter. These files need to be read by a separate assimilation program that computes the analysis step of the filter algorithm and generates new restart files for NEMO. This scheme requires a large amount of disk storage as well as time to read and write restart files and to perform the model restarts. Here, a data assimilation system for NEMO is introduced that is built using the parallel data assimilation framework PDAF (http://pdaf.awi.de). Inserting a few subroutine calls to the source code of NEMO, one extends NEMO to a data assimilation system that consists of a single program. Utilizing the parallelization capacity of today’s supercomputers, the system performs both the ensemble forecasts and the analysis step of the filter algorithm in a single execution of the program. The features of the resulting assimilation system are discussed as well as the parallel performance of the program when it is applied with an idealized double-gyre configuration of NEMO.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 6
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    High-Dimensional Nonlinear Data Assimilation with the Nonlinear Ensemble Transform Filter (NETF) and its Smoother Extension (2016)
    In:  EPIC3Fifth International Symposium on Data Assimilation, Reading, UK, July 18-22, 2016
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    Publication Date: 2016-07-24
    Description: The recently proposed nonlinear ensemble transform filter (NETF) is extended to a fixed lag smoother. The NETF approximates Bayes' equation by applying a square root update based on weights computed from a particle filter. As an ensemble transform filter the NETF shares similarities with the widely used ETKF and can be localized analogously. Further, the smoother extension NETS can by obtained by applying the transform matrix for filtering to the ensembles at previous analysis times. To assess the nonlinear assimilation method in a high-dimensional test case, the effectiveness of the nonlinear filter and the new smoother is assessed by twin experiments with a square box configuration of NEMO ocean model. The results show that the NETF reaches a comparable assimilation performance as the LETKF. The smoothing in the NETS effectively reduces the errors in the state estimates. Different variables show very similar optimal smoothing lags, which allows for a simultaneous tuning of the lag to obtain minimal smoothing errors. In comparison to the LESTKS, the NETS is slightly less effective and the optimal lag in the NETS is shorter. This difference is caused by the different update mechanisms of both filters and can depend on the nonlinearity of the model.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev , info:eu-repo/semantics/conferenceObject
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  • 7
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    Efficient Ensemble-Based Data Assimilation for High-Dimensional Models with the Parallel Data Assimilation Framework PDAF (2019)
    In:  EPIC3GODAE OceanView Symposium 2019 - OceanPredict '19, May 6-10, 2019, Halifax, Canada
    Details
    Publication Date: 2020-02-28
    Description: Discussed is how we can build a data-assimilative model by augmenting a forecast model by data assimilation functionality for efficient ensemble data assimilation. The implementation strategy uses a direct connection between a coupled simulation model and ensemble data assimilation software provided by the open-source Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de), which also provides fully-implemented and parallelized ensemble filters. The combination of a model with PDAF yields a data assimilation program with high flexibility and parallel scalability with only small changes to the model. The direct connection is obtained by first extending the source code of the coupled model so that it is able to run an ensemble of model states. In addition, a filtering step is added using a combination of in-memory access and parallel communication to create an online-coupled ensemble assimilation program. The direct connection avoids the common need to stop and restart a whole forecast model to perform the assimilation of observations in the analysis step of ensemble-based filter methods like ensemble Kalman or particle filters. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler. This strategy can be applied with forced uncoupled models or coupled Earth system models, where it even allows for cross-domain data assimilation. The structure, features and performance of the data assimilation systems is discussed on the example of the ocean circulation models MITgcm and NEMO.
    Repository Name: EPIC Alfred Wegener Institut
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  • 8
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    Building Ensemble-Based Data Assimilation Systems for High-Dimensional Models with the Parallel Data Assimilation Framework PDAF (2016)
    In:  EPIC3Workshop on Data Assimilation in Terrestrial Systems, Bonn, Germany, September 19-21, 2016
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    Publication Date: 2016-10-02
    Description: Data assimilation applications with high-dimensional numerical models show extreme requirements on computational resources. Thus, good scalability of the assimilation system is necessary to make these applications feasible. Sequential data assimilation methods based on ensemble forecasts, like ensemble-based Kalman filters and particle filters, provide such good scalability, because the forecast of each ensemble member can be performed independently. This parallelism has to be combined with the parallelization of both the numerical model and the data assimilation algorithm. While the filter algorithms can be implemented so that they are nearly independent from the model into which they assimilate observations, they need to be coupled to the numerical model. Using separate programs for the model and the data assimilation step coupled by disk files to exchange the model state information between model and ensemble data assimilation methods can be inefficient for high-dimensional models. More efficient is an online coupling strategy in which subroutine calls for the data assimilation are directly inserted into the model source code and augment the numerical model to become a data assimilative model. This strategy avoids model restarts as well as excessive writing of ensemble information into disk files and can hence lead to excellent computational scalability on supercomputers. The required modifications to the model code are very limited, such this strategy allows one to quickly extent a model to a data assimilation system. The online coupling is provided by the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de), which is designed to simplify the implementation of scalable data assimilation systems based on existing numerical models. Further, it includes several optimized parallel filter algorithms. We will discuss the coupling strategy, features, and scalability of data assimilation systems based on PDAF.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 9
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    Localization-induced filter instability and a simple adaptive localization method (2016)
    In:  EPIC3Workshop "Mathematical and Algorithmic Aspects of Data Assimilation in the Geosciences", Mathematical Research Institute Oberwolfach, Oberwolfach, Germany, October 2-8, 2016
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    Publication Date: 2016-10-11
    Repository Name: EPIC Alfred Wegener Institut
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  • 10
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    High-dimensional nonlinear data assimilation with the nonlinear ensmeble transform filter (NETF) and its smoother extension (2017)
    In:  EPIC3Seminar at National Marine Environmental Forecasting Center (NMEFC), Beijing, China, November 9, 2017
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    Publication Date: 2018-01-07
    Repository Name: EPIC Alfred Wegener Institut
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