Journal article
Feature tracking in high–resolution regional climate data
- Abstract:
- In this paper, a suite of algorithms are presented which facilitate the identification and tracking of storm–indicative features, such as mean sea-level pressure minima, in high resolution regional climate data. The methods employ a hierarchical triangular mesh, which is tailored to the regional climate data by only subdividing triangles, from an initial icosahedron, within the domain of the data. The regional data is then regridded to this triangular mesh at each level of the grid, producing a compact representation of the data at numerous resolutions. Storm indicative features are detected by first subtracting the background field, represented by a low resolution version of the data, which occurs at a lower level in the mesh. Anomalies from this background field are detected, as feature objects, at a mesh level which corresponds to the spatial scale of the feature being detected and then refined to the highest mesh level. These feature objects are expanded to an outer contour and overlapping objects are merged. The centre points of these objects are tracked across timesteps by applying an optimisation scheme which uses of five hierarchical rules. Objects are added to tracks based on the highest rule in the scheme they pass and, if two objects pass the same rule, the cost of adding the object to the track. An object exchange scheme ensures that adding an object to a track is locally optimal. An additional track optimisation phase is performed which exchanges segments between tracks and merges tracks to obtain a globally optimal track set. To validate the suite of algorithms they are applied to the ERA-Interim reanalysis dataset and compared to other storm–indicative feature tracking algorithms.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 4.8MB, Terms of use)
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- Publisher copy:
- 10.1016/j.cageo.2016.04.015
Authors
- Publisher:
- Elsevier
- Journal:
- Computers and Geosciences More from this journal
- Volume:
- 93
- Pages:
- 36–44
- Publication date:
- 2016-01-01
- Acceptance date:
- 2016-04-28
- DOI:
- EISSN:
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1873-7803
- ISSN:
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0098-3004
- Keywords:
- Pubs id:
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pubs:618480
- UUID:
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uuid:f0687fce-ee57-472c-93e2-35935f2d763c
- Local pid:
-
pubs:618480
- Source identifiers:
-
618480
- Deposit date:
-
2016-04-29
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier Ltd
- Copyright date:
- 2016
- Notes:
- Copyright © 2016 Elsevier Ltd. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.cageo.2016.04.015
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