Articles | Volume 8, issue 3
Earth Syst. Dynam., 8, 677–696, 2017
https://doi.org/10.5194/esd-8-677-2017
Earth Syst. Dynam., 8, 677–696, 2017
https://doi.org/10.5194/esd-8-677-2017

Research article 08 Aug 2017

Research article | 08 Aug 2017

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Milan Flach et al.

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Revised manuscript accepted for ESD
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Cited articles

Aggarwal, C. C.: Outlier Ensembles, ACM SIGKDD Explorations Newsletter, 14, 49–58, 2012.
Bae, K.-H., Karolyi, G. A., and Stulz, R. M.: A New Approach to Measuring Financial Contagion, Rev. Financ. Stud., 16, 717–763, 2003.
Baldocchi, D., Falge, E., and Wilson, K.: A spectral analysis of biosphere–atmosphere trace gas flux densities and meteorological variables across hour to multi-year time scales, Agr. Forest Meteorol., 107, 1–27, 2001.
Bathiany, S., Notz, D., Mauritsen, T., Raedel, G., and Brovkin, V.: On the Potential for Abrupt Arctic Winter Sea Ice Loss , J. Climate, 29, 2703–2719, 2016.
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Anomalies and extremes are often detected using univariate peak-over-threshold approaches in the geoscience community. The Earth system is highly multivariate. We compare eight multivariate anomaly detection algorithms and combinations of data preprocessing. We identify three anomaly detection algorithms that outperform univariate extreme event detection approaches. The workflows have the potential to reveal novelties in data. Remarks on their application to real Earth observations are provided.
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