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  • 1
    Series available for loan
    Series available for loan
    Bremerhaven : Alfred-Wegener-Inst. für Polar- und Meeresforschung
    Associated volumes
    Call number: ZS-090(487) ; ZSP-168-487
    In: Berichte zur Polar- und Meeresforschung
    Type of Medium: Series available for loan
    Pages: VI, 175 S. , graph. Darst.
    ISSN: 1618-3193
    Series Statement: Berichte zur Polar- und Meeresforschung 487
    Classification: D.3.
    Note: Zugl.: Bremen, Univ., Diss., 2004
    Location: Lower compact magazine
    Location: Reading room
    Branch Library: GFZ Library
    Branch Library: AWI Library
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  • 2
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    PANGAEA
    In:  Supplement to: Mu, Longjiang; Losch, Martin; Yang, Qinghua; Ricker, Robert; Losa, Svetlana; Nerger, Lars (2018): Arctic-wide sea ice thickness estimates from combining satellite remote sensing data and a dynamicice-ocean model with data assimilation during the CryoSat-2 period. Journal of Geophysical Research: Oceans, 123(11), 7763-7780, https://doi.org/10.1029/2018JC014316
    Publication Date: 2020-01-17
    Description: An Arctic sea ice thickness record covering from 2010 to 2016 is generated by assimilating satellite thickness from CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS). The model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a local Error Subspace Transform Kalman filter (LESTKF) coded in the Parallel Data Assimilation Framework (PDAF).
    Type: Dataset
    Format: text/tab-separated-values, 35 data points
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  • 3
    Publication Date: 2020-01-17
    Description: The simulated sea ice drift data is a by-product from a sea ice thickness assimilation system that generates the Arctic 'Combined Model and Satellite sea ice Thickness (CMST; doi:10.1594/PANGAEA.891475) ' dataset. The data also provide the ocean current velocity where ice free. To obtain the sea ice drift on the geographic coordinate, a transformation must be done as following: uE = AngleCS * SIuice - AngleSN * SIvice; vN = AngleSN * SIuice + AngleCS * SIvice; where uE and vN are two velocity components on the geographic coordinate; AngleCS and AngleSN can be found in 'grid.cdf'; SIuice and SIvice are sea ice velocity on model mesh.
    Type: Dataset
    Format: text/tab-separated-values, 75 data points
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  • 4
    Publication Date: 2019-07-17
    Description: We present the application of the ensemble Kalman filter to a three-dimensional, convection-driven model of the geodynamo. Our implementation rests on a suitably modified version of the parallel data assimilation framework of Nerger and Hiller (2013). We resort to closed-loop experiments for validation purposes, using a dynamo model of intermediate resolution. Observations for these experiments consist of spectral coefficients describing the surface poloidal magnetic field, with arbitrary truncation. Our synthetic tests demonstrate the efficacy and adaptivity of the method, provided the ensemble comprises O(500) members, in which case the typical spin-up time we find for our system is O(1000)years . In case of a poor resolution of the observations, we find that the knowledge of the full covariance matrix describing the uncertainty affecting the spectral coefficients (as opposed to its sole diagonal) results in a much better estimate of the internal structure of the dynamo.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 5
    Publication Date: 2014-11-11
    Description: Ensemble-based Kalman smoother algorithms extend ensemble Kalman filters to reduce the estimation error of past model states utilizing observational information from the future. Like the filters they extend, current smoothing algorithms are optimal only for linear models. However, the ensemble methods are typically applied with high-dimensional nonlinear models, which also require the application of localization in the data assimilation. In this paper, the influence of the model nonlinearity and of the application of localization on the smoother performance is studied. Numerical experiments show that the observational information can be successfully utilized over smoothing lags several times the error doubling time of the model. Localization limits the smoother lag by spatial decorrelation. However, if the localization is well tuned, the usable lag of the smoother, and hence the usable amount of observational information, is maximized. The localization reduces the estimation errors of the smoother even more than those of the filter. As the smoother reuses the transformation matrix of the filter, it profits stronger from increases of the ensemble size than the filter. With respect to inflation and localization, the experiments also show that the same configuration that yields the smallest estimation errors for the filter without smoothing also results in the smallest errors of the smoothed states. Thus, smoothing only adds the lag as a further tunable parameter.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed , info:eu-repo/semantics/article
    Format: application/pdf
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  • 6
    Publication Date: 2019-07-17
    Description: The different variants of current ensemble square-root Kalman filters assimilate either all observations at once or perform a sequence in which batches of observations or each single observation is assimilated. The sequential observation processing is used in filters algorithms like the ensemble adjustment Kalman filter (EAKF) and the ensemble square-root filter (EnSRF) and can result in computationally efficient algorithms because matrix inversions in the observation space are reduced to the inversion of single numbers. For large scale applications, ensemble filter algorithms require typically the application of localization. The necessary modification of the algorithm leads to an inconsistency of the update equation for the state error covariance matrix as was noted by Whitaker and Hamill (Mon. Wea. Rev. 130 (2002) 1913). Often, this inconsistency does not lead to a significant impact on the assimilation performance. However, using a simple model, we demonstrate with the EnSRF algorithm that the sequential observation processing can significantly deteriorate the assimilation performance under some circumstances. The deterioration can reach a level at which intermediate state realizations in the assimilation sequence over all observation can have a larger root-mean square error than the state estimate without assimilating any observations. This effect can be characterized to appear for small ensembles and a rather strong assimilation impact.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 7
    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 , NonPeerReviewed
    Format: application/pdf
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  • 8
    Publication Date: 2019-07-17
    Description: The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice-ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite-derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
    Format: application/pdf
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  • 9
    Publication Date: 2014-09-29
    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. Thus, NEMO is used to generate 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. To improve the efficiency, 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, the model is extended 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 assimilation program when it is applied with a benchmark configuration of NEMO from the SANGOMA project [http://www.data-assimilation.net].
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 10
    Publication Date: 2014-09-29
    Description: The EU-funded project SANGOMA - Stochastic Assimilation of the Next Generation Ocean Model Applications provides new developments in data assimilation to ensure that future operational systems can make use of state-of-the-art data-assimilation methods and related analysis tools. One task of SANGOMA is to develop a collection of common tools for data assimilation with a uniform interface so that the tools are usable from different data assimilation systems. The tool developments mainly aim at tools that support ensemble-based data assimilation applications like for the generation of perturbations, to perform transformations, to compute diagnostics, as well as further utilities. The tools are implemented in Fortran and as scripts for Matlab or Octave. The tools and their documentation are provided as free open-source programs via the project web site [http://www.data-assimilation.net]. This contribution provides an overview of the tools that are available in the latest release V1 of the SANGOMA tools as well as the plans for the next release.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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