TECA:Petascale pattern recognition for climate science

Prabhat, Byna, Surendra, Vishwanath, Venkatram, Dart, Eli, Wehner, Michael and Collins, William D. ORCID: https://orcid.org/0000-0002-4463-9848 (2015) TECA:Petascale pattern recognition for climate science. In: Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings. Lecture Notes in Computer Science . Springer-Verlag Berlin Heidelberg, MLT, pp. 426-436. ISBN 9783319231167

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Abstract

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © Springer International Publishing Switzerland 2015.
Uncontrolled Keywords:	climate science,data mining,high performance computing,o,pattern detection,petascale,theoretical computer science,general computer science,sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	06 Jul 2026 14:23
Last Modified:	12 Jul 2026 23:00
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103644
DOI:	10.1007/978-3-319-23117-4_37

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