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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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (08 Mar 2017) by Sagnik Dey
AR by Milan Flach on behalf of the Authors (24 Mar 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (12 Apr 2017) by Sagnik Dey
RR by Reik Donner (17 May 2017)
ED: Publish subject to minor revisions (review by Editor) (08 Jun 2017) by Sagnik Dey
AR by Milan Flach on behalf of the Authors (15 Jun 2017)  Author's response    Manuscript
ED: Publish as is (02 Jul 2017) by Sagnik Dey
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Short summary
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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