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DTSTAMP:20250822T115804Z
LOCATION:Room 5.0A52
DTSTART;TZID=Europe/Stockholm:20250618T113000
DTEND;TZID=Europe/Stockholm:20250618T120000
UID:submissions.pasc-conference.org_PASC25_sess170_pap110@linklings.com
SUMMARY:Performance Analysis of an Efficient Algorithm for Feature Extract
 ion from Large Scale Meteorological Data Stores
DESCRIPTION:Mathilde Leuridan (ECMWF, University of Cologne); Christopher 
 Bradley, James Hawkes, and Tiago Quintino (ECMWF); and Martin Schultz (For
 schungszentrum Jülich, University of Cologne)\n\nIn recent years, Numerica
 l 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 sign
 ificant increase in the daily data volume generated by NWP models. Ensurin
 g efficient and timely access to this growing data requires innovative dat
 a 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 a
 llowing the extraction of non-orthogonal shapes of data.\nIn this paper, w
 e 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 scalab
 ility on real-time operational data. Our analysis shows that the low overh
 ead of running the Polytope algorithm, which is in the order of a few seco
 nds at most, is far outweighed by the benefits of significantly reducing t
 he size of the extracted data by up to several orders of magnitude compare
 d to traditional bounding box methods. Our ensuing discussion focuses on q
 uantifying the strengths and limitations of each individual part of the sy
 stem to identify potential bottlenecks and areas for future improvement.\n
 \nDomain: Climate, Weather, and Earth Sciences\n\nSession Chair: Jan Meisn
 er (Heinrich Heine University Düsseldorf)\n\n
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