Paper Info
Title
Detecting Extreme Events in Gridded Climate Data.
Abstract
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 Nio 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.
Year
DOI
Venue
2016
10.1016/j.procs.2016.05.537
ICCS
Keywords
Field
DocType
spatio-temporal, co-location, anomaly detection, trend analysis
Meteorology,Data mining,Anomaly detection,Trend analysis,Climate change,Extreme events,Computer science,Cold wave,Geopotential height,Big data,Scalability
Conference
Volume
Issue
ISSN
80
C
1877-0509
Citations 
PageRank 
References 
0
0.34
3
Authors
5
Name
Order
Citations
PageRank
Bharathkumar Ramachandra164.12
Krishna Karthik Gadiraju202.70
Ranga Raju Vatsavai343049.30
Dale P. Kaiser400.34
Thomas P. Karnowski530020.51