TECA: Petascale pattern recognition for climate science
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
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.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- DOE Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1407276
- Resource Relation:
- Conference: International Conference on Computer Analysis of Images and Patterns (CAIP 2015), Valletta (Malta), 2-4 Sep 2015; Related Information: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Country of Publication:
- United States
- Language:
- English
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