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A Modular Software Framework for Compression of Structured Climate Data

Cayoglu, Ugur 1,2; Schröter, Jennifer 2; Meyer, Jörg 1; Streit, Achim ORCID iD icon 1; Braesicke, Peter 2
1 Steinbuch Centre for Computing (SCC), Karlsruher Institut für Technologie (KIT)
2 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)


Through the introduction of next-generation models the climate sciences have experienced a breakthrough in high-resolution simulations. In the past, the bottleneck was the numerical complexity of the models, nowadays it is the required storage space for the model output. One way to tackle the data storage challenge is through data compression.

In this article we introduce a modular framework for the compression of structured climate data. Our modular framework supports the creation of individual predictors, which can be customised and adjusted to the data at hand. We provide a framework for creating interfaces and customising components, which are building blocks of individualised compression modules that are optimised for particular applications. Furthermore, the framework provides additional features such as the execution of benchmarks and validity tests for sequential as well as parallel execution of compression algorithms.

Postprint §
DOI: 10.5445/IR/1000088690
Veröffentlicht am 03.01.2022
DOI: 10.1145/3274895.3274897
Zitationen: 2
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
KIT-Zentrum Klima und Umwelt (ZKU)
Steinbuch Centre for Computing (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2018
Sprache Englisch
Identifikator ISBN: 978-1-4503-5889-7
KITopen-ID: 1000088690
HGF-Programm 46.12.01 (POF III, LK 01) Data Life Cycle Labs
Erschienen in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2018, Seattle, WA, November 06-09, 2018
Veranstaltung 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2018), Seattle, WA, USA, 06.11.2018 – 09.11.2018
Verlag Association for Computing Machinery (ACM)
Seiten 556–559
Nachgewiesen in Dimensions
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