TY - GEN
T1 - Discovering dynamic dipoles in climate data
AU - Kawale, Jaya
AU - Steinbach, Michael S
AU - Kumar, Vipin
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of opposite polarity appearing at two different locations at the same time. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Niño climate phenomenon is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a novel approach for dipole discovery that outperforms existing state of the art algorithms. Our approach is based on a climate anomaly network that is constructed using the correlation of time series of climate variables at all the locations on the Earth. One novel aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding dipoles. Another key insight provided by our work is the importance of modeling the time dependent patterns of the dipoles in order to better capture the impact of important climate phenomena on land. The results presented in this paper show that these innovations allow our approach to produce better results than previous approaches in terms of matching existing climate indices with high correlation and capturing the impact of climate indices on land.
AB - Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of opposite polarity appearing at two different locations at the same time. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Niño climate phenomenon is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a novel approach for dipole discovery that outperforms existing state of the art algorithms. Our approach is based on a climate anomaly network that is constructed using the correlation of time series of climate variables at all the locations on the Earth. One novel aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding dipoles. Another key insight provided by our work is the importance of modeling the time dependent patterns of the dipoles in order to better capture the impact of important climate phenomena on land. The results presented in this paper show that these innovations allow our approach to produce better results than previous approaches in terms of matching existing climate indices with high correlation and capturing the impact of climate indices on land.
UR - http://www.scopus.com/inward/record.url?scp=84866008206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866008206&partnerID=8YFLogxK
U2 - 10.1137/1.9781611972818.10
DO - 10.1137/1.9781611972818.10
M3 - Conference contribution
AN - SCOPUS:84866008206
SN - 9780898719925
T3 - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
SP - 107
EP - 118
BT - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
PB - Society for Industrial and Applied Mathematics Publications
T2 - 11th SIAM International Conference on Data Mining, SDM 2011
Y2 - 28 April 2011 through 30 April 2011
ER -