TY - GEN
T1 - A novel and scalable spatio-temporal technique for ocean eddy monitoring
AU - Faghmous, James H
AU - Chamber, Yashu
AU - Boriah, Shyam
AU - Liess, Stefan
AU - Kumar, Vipin
AU - Vikebø, Frode
AU - Dos Sontos Mesquita, Michel
PY - 2012
Y1 - 2012
N2 - Swirls of ocean currents known as ocean eddies are a crucial component of the ocean's dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patterns is a central climate challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we frame the challenge of monitoring ocean eddies as an unsupervised learning problem. We present a novel change detection algorithm that automatically identifies and monitors eddies in sea surface height data based on heuristics derived from basic eddy properties. Our method is accurate, efficient, and scalable. To demonstrate its performance we analyze eddy activity in the Nordic Sea (60-80° N and 20° W - 20° E), an area that has received limited attention and has proven to be difficult to analyze using other methods.
AB - Swirls of ocean currents known as ocean eddies are a crucial component of the ocean's dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patterns is a central climate challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we frame the challenge of monitoring ocean eddies as an unsupervised learning problem. We present a novel change detection algorithm that automatically identifies and monitors eddies in sea surface height data based on heuristics derived from basic eddy properties. Our method is accurate, efficient, and scalable. To demonstrate its performance we analyze eddy activity in the Nordic Sea (60-80° N and 20° W - 20° E), an area that has received limited attention and has proven to be difficult to analyze using other methods.
UR - http://www.scopus.com/inward/record.url?scp=84868272091&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868272091&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84868272091
SN - 9781577355687
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 281
EP - 287
BT - AAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
T2 - 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
Y2 - 22 July 2012 through 26 July 2012
ER -