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Title: Detecting Extreme Events in Gridded Climate Data

Detecting and tracking extreme events in gridded climatological data is a challenging problem on several fronts: algorithms, scalability, and I/O. Successful detection of these events will give climate scientists an alternate view of the behavior of different climatological variables, leading to enhanced scientific understanding of the impacts of events such as heat and cold waves, and on a larger scale, the El Nin o Southern Oscillation. Recent advances in computing power and research in data sciences enabled us to look at this problem with a different perspective from what was previously possible. In this paper we present our computationally efficient algorithms for anomalous cluster detection on climate change big data. We provide results on detection and tracking of surface temperature and geopotential height anomalies, a trend analysis, and a study of relationships between the variables. We also identify the limitations of our approaches, future directions for research and alternate approaches.
 [1] ;  [1] ;  [1] ;  [2] ;  [2]
  1. North Carolina State University (NCSU), Raleigh
  2. ORNL
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Resource Relation:
Journal Volume: 80; Conference: ICCS 2016. The International Conference on Computational Science, San Diego, CA, USA, 20160606, 20160606
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
climate extremes temperature anomalies reanalysis