Automated Extraction of Gravity Wave Signatures from the Super Dual Auroral Radar Network (SuperDARN) Database Using Spatio-Temporal Process Discovery Algorithms

Details Automated Extraction of Gravity Wave Signatures from the Super Dual Auroral Radar Network (SuperDARN) Database Using Spatio-Temporal Process Discovery Algorithms Automated Extraction of Gravity Wave Signatures from the Super Dual Auroral Radar Network (SuperDARN) Database Using Spatio-Temporal Process Discovery Algorithms

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufmsm13e..01b&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #SM13E-01

Physics

[1914] Informatics / Data Mining, [2487] Ionosphere / Wave Propagation, [2494] Ionosphere / Instruments And Techniques, [6952] Radio Science / Radar Atmospheric Physics

Scientific paper

A major challenge in space physics research is the automated extraction of recurrent features from multi-dimensional datasets which tend to be irregularly gridded in both space and time. In many cases, the complexity of the datasets impedes their use by scientists who are often times most interested in extracting a simple time-series of higher level data product that can be easily compared with other measurements. As such, the collective archive of space physics measurements is vastly under-utilized at the present time. Application of cutting-edge computer-aided data mining and knowledge discovery techniques has the potential to improve this situation by making space physics datasets much more accessible to the scientific user community and accelerating the rate of research and collaboration. As a first step in this direction, we are applying the principles of feature extraction, sub-clustering and motif mining to the analysis of HF backscatter measurements from the Super Dual Auroral Radar Network (SuperDARN). The SuperDARN database is an ideal test-bed for development of space physics data mining algorithms because: (1) there is a richness of geophysical phenomena manifested in the data; (2) the data is multi-dimensional and exhibits a high degree of spatiotemporal sparseness; and (3) some of the radars have been operating continuously with infrequent outages for more than 25 years. In this presentation we discuss results obtained from the application of new data mining algorithms designed specifically to automate the extraction of gravity wave signatures from the SuperDARN database. In particular, we examine the occurrence statistics of gravity waves as a function of latitude, local time, and geomagnetic conditions.

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