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Adaptive Lossy Compression of Complex Environmental Indices Using Seasonal Auto-Regressive Integrated Moving Average Models

Cayoglu, Ugur; Braesicke, Peter; Kerzenmacher, Tobias ORCID iD icon; Meyer, Jörg; Streit, Achim ORCID iD icon

Abstract:

Significant increases in computational resources have enabled the development of more complex and spatially better resolved weather and climate models. As a result the amount of output generated by data assimilation systems and by weather and climate simulations is rapidly increasing e.g. due to higher spatial resolution, more realisations and higher frequency data. However, while compute performance has increased significantly because of better scaling program code and increasing number of cores the storage capacity is only increasing slowly. One way to tackle the data storage problem is data compression. Here, we build the groundwork for an environmental data compressor by improving compression for established weather and climate indices like El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Quasi-Biennial Oscillation (QBO). We investigate options for compressing these indices by using a statistical method based on the Auto Regressive Integrated Moving Average (ARIMA) model. The introduced adaptive approach shows that it is possible to improve accuracy of lossily compressed data by applying an adaptive compression method which preserves selected data with higher precision. ... mehr


Postprint §
DOI: 10.5445/IR/1000076761
Veröffentlicht am 03.01.2022
Originalveröffentlichung
DOI: 10.1109/eScience.2017.45
Scopus
Zitationen: 2
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 10.2017
Sprache Englisch
Identifikator ISBN: 978-1-5386-2686-3
KITopen-ID: 1000076761
HGF-Programm 46.12.01 (POF III, LK 01) Data Life Cycle Labs
Erschienen in IEEE 13th International Conference on e-Science (e-Science), Auckland, NZ, October, 24-27, 2017
Veranstaltung 13th International Conference on e-Science (2017), Auckland, Neuseeland, 24.10.2017 – 27.10.2017
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 315-324
Bemerkung zur Veröffentlichung Best Paper Award
Nachgewiesen in Scopus
Dimensions
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