Anomalous cluster detection in spatiotemporal meteorological fields
Abstract
Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1488872
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Statistical Analysis and Data Mining
- Additional Journal Information:
- Journal Name: Statistical Analysis and Data Mining Journal Volume: 12 Journal Issue: 2; Journal ID: ISSN 1932-1864
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Ramachandra, Bharathkumar, Dutton, Benjamin, and Vatsavai, Ranga Raju. Anomalous cluster detection in spatiotemporal meteorological fields. United States: N. p., 2018.
Web. doi:10.1002/sam.11398.
Ramachandra, Bharathkumar, Dutton, Benjamin, & Vatsavai, Ranga Raju. Anomalous cluster detection in spatiotemporal meteorological fields. United States. https://doi.org/10.1002/sam.11398
Ramachandra, Bharathkumar, Dutton, Benjamin, and Vatsavai, Ranga Raju. Wed .
"Anomalous cluster detection in spatiotemporal meteorological fields". United States. https://doi.org/10.1002/sam.11398.
@article{osti_1488872,
title = {Anomalous cluster detection in spatiotemporal meteorological fields},
author = {Ramachandra, Bharathkumar and Dutton, Benjamin and Vatsavai, Ranga Raju},
abstractNote = {Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.},
doi = {10.1002/sam.11398},
journal = {Statistical Analysis and Data Mining},
number = 2,
volume = 12,
place = {United States},
year = {Wed Dec 26 00:00:00 EST 2018},
month = {Wed Dec 26 00:00:00 EST 2018}
}
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