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@INPROCEEDINGS{Leuridan:1052688,
      author       = {Leuridan, Mathilde and Bradley, Christopher and Hawkes,
                      James and Quintino, Tiago and Schultz, Martin},
      title        = {{P}erformance {A}nalysis of an {E}fficient {A}lgorithm for
                      {F}eature {E}xtraction from {L}arge {S}cale {M}eteorological
                      {D}ata {S}tores},
      address      = {New York, NY, USA},
      publisher    = {ACM},
      reportid     = {FZJ-2026-01056},
      pages        = {9 p.},
      year         = {2025},
      comment      = {Proceedings of the Platform for Advanced Scientific
                      Computing Conference},
      booktitle     = {Proceedings of the Platform for
                       Advanced Scientific Computing
                       Conference},
      abstract     = {In recent years, Numerical Weather Prediction (NWP) has
                      undergone a major shift with the rapid move towards
                      kilometer-scale global weather forecasts and the emergence
                      of AI-based forecasting models. Together, these trends will
                      contribute to a significant increase in the daily data
                      volume generated by NWP models. Ensuring efficient and
                      timely access to this growing data requires innovative data
                      extraction techniques. As an alternative to traditional data
                      extraction algorithms, the European Centre for Medium-Range
                      Weather Forecasts (ECMWF) has introduced the Polytope
                      feature extraction algorithm. This algorithm is designed to
                      reduce data transfer between systems to a bare minimum by
                      allowing the extraction of non-orthogonal shapes of data. In
                      this paper, we evaluate Polytope's suitability as a
                      replacement for current extraction mechanisms in operational
                      weather forecasting. We first adapt the Polytope algorithm
                      to operate on ECMWF's FDB (Fields DataBase) meteorological
                      data stores, before evaluating this integrated system's
                      performance and scalability on real-time operational data.
                      Our analysis shows that the low overhead of running the
                      Polytope algorithm, which is in the order of a few seconds
                      at most, is far outweighed by the benefits of significantly
                      reducing the size of the extracted data by up to several
                      orders of magnitude compared to traditional bounding box
                      methods. Our ensuing discussion focuses on quantifying the
                      strengths and limitations of each individual part of the
                      system to identify potential bottlenecks and areas for
                      future improvement.},
      month         = {Jun},
      date          = {2025-06-16},
      organization  = {PASC '25: Platform for Advanced
                       Scientific Computing Conference, FHNW
                       University of Applied Sciences and Arts
                       Northwestern Switzerland Brugg-Windisch
                       (Switzerland), 16 Jun 2025 - 18 Jun
                       2025},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1145/3732775.3733573},
      url          = {https://juser.fz-juelich.de/record/1052688},
}