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  • Geophysical Research Abstracts Vol. 18, EGU2016-17198  (1)
  • Max Planck Digital Library/German e-Science Conference  (1)
  • PERGAMON-ELSEVIER SCIENCE LTD  (1)
  • 1
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    Geophysical Research Abstracts Vol. 18, EGU2016-17198
    In:  EPIC3EGU General Assembly 2016, Vienna, 2016-04-16-2016-04-22Vienna, Austria, Geophysical Research Abstracts Vol. 18, EGU2016-17198
    Publication Date: 2017-07-07
    Description: The numerical simulation code TsunAWI was developed in the framework of the German-Indonesian Tsunami Early Warning System (GITEWS). The Numerical simulation of prototypic tsunami scenarios plays a decisive role in the a priori risk assessment for coastal regions and in the early warning process itself. TsunAWI is suited for both tasks. It is based on a finite element discretisation, employs unstructured grids with high resolution along the coast, and includes inundation. This contribution presents two fields of applications. In the Indonesian tsunami early warning system, the existing TsunAWI scenario database covers the Sunda subduction zone from Sumatra to the Lesser Sunda Islands with 715 epicenters and 4500 scenarios. In a collaboration with Geoscience Australia, we support the scientific staff at the Indonesian warning center to extend the data base to the remaining tectonic zones in the Indonesian Archipelago. The extentension started for North Sulawesi, West and East Maluku Islands. For the Hydrographic and Oceanographic Service of the Chilean Navy (SHOA), we calculated a small scenario database of 100 scenarios (sources by Universidad de Chile) for a lightweight decision support system prototype (built by DLR). The earthquake and tsunami events on 1 April 2014 and 16 November 2016 showed the practical use of this approach in comparison to hind casts of these events.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 2
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    Max Planck Digital Library/German e-Science Conference
    In:  EPIC3German eScience Conference, Baden-Baden, 2007-05-02-2007-05-04Max Planck Digital Library/German e-Science Conference
    Publication Date: 2019-07-17
    Description: The project „Collaborative Climate Community Data and Processing Grid – C3Grid“ is one of the community projects in D-Grid (sponsored by German government BMBF) and aims at linking distributed data archives in several German institutions. The fundamental architecture is discussed, which includes C3Grid specific components beside the standard middleware. The implemented infrastructure for scientists in climate research provides tools for effective data discovery, data transfer and processing.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
    Location Call Number Limitation Availability
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  • 3
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    PERGAMON-ELSEVIER SCIENCE LTD
    In:  EPIC3Computers & Geosciences, PERGAMON-ELSEVIER SCIENCE LTD, 55, pp. 110-118, ISSN: 0098-3004
    Publication Date: 2019-07-17
    Description: Data assimilation algorithms combine a numerical model with observations in a quantitative way. For an optimal combination either variational minimization algorithms or ensemble-based estimation methods are applied. The computations of a data assimilation application are usually far more costly than a pure model integration. To cope with the large computational costs, a good scalability of the assimilation program is required. The ensemble-based methods have been shown to exhibit a particularly good scalability due to the natural parallelism inherent in the integration of an ensemble of model states. However, also the scalability of the estimation method – commonly based on the Kalman filter – is important. This study discusses implementation strategies for ensemble-based filter algorithms. Particularly efficient is a strong coupling between the model and the assimilation algorithm into a single executable program. The coupling can be performed with minimal changes to the numerical model itself and leads to a model with data assimilation extension. The scalability of the data assimilation system is examined using the example of an implementation of an ocean circulation model with the Parallel Data Assimilation Framework (PDAF) into which synthetic sea surface height data are assimilated.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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