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DTSTART;TZID=Europe/Stockholm:20260701T123000
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UID:submissions.pasc-conference.org_PASC26_sess175_pap107@linklings.com
SUMMARY:An Algorithm for Feature Extraction from Large Scale Meteorologica
 l Data Stores on Irregular Grids
DESCRIPTION:Mathilde Leuridan (ECMWF, University of Cologne); James Hawkes
  and Tiago Quintino (ECMWF); and Martin Schultz (Forschungszentrum Jülich,
  University of Cologne)\n\nIn recent years, the volume of meteorological d
 ata has grown rapidly due to higher model resolutions and more frequent da
 ta output. To handle this increase efficiently, the European Centre for Me
 dium-Range Weather Forecasts has developed new ways to extract data withou
 t accessing full meteorological fields through their feature extraction ca
 pabilities. This includes in particular the Polytope algorithm, which lets
  users extract time series or regional subsets directly from the data back
 ends, greatly reducing data transfer and processing time. However, the cur
 rent algorithm only works on structured iso-latitude grids such as octahed
 ral or HEALPix grids. Many modern meteorological models, by contrast, use 
 unstructured grids where points do not align along latitudes, such as Lamb
 ert conformal or ICON icosahedral grids. In this paper, we extend the Poly
 tope algorithm to support these irregular grids. We first explain why the 
 original approach was limited to structured data before introducing a new 
 extraction method, which uses the well-studied quadtree data structure. As
  a popular data structure in geospatial applications, we focus on describi
 ng how quadtrees can be integrated within the Polytope feature extraction 
 framework to handle complex grid geometries. We then assess the performanc
 e and scalability of this algorithm, which performs on par with state-of-t
 he-art alternatives used in geospatial applications. In particular,  the e
 xtended algorithm outperforms other methods when accessing large or comple
 x regions and therefore offers a compelling alternative to current data ex
 traction techniques for large-scale datasets. Finally, we conclude by disc
 ussing how this algorithm can be integrated into an operational data servi
 ce.\n\nSession Chair: Thorsten Kurth (NVIDIA Inc.)\n\n
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