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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},
}