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| Titel |
Detection, tracking and event localization of jet stream features in 4-D atmospheric data |
| VerfasserIn |
S. Limbach, E. Schömer, H. Wernli |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 5, no. 2 ; Nr. 5, no. 2 (2012-04-16), S.457-470 |
| Datensatznummer |
250002452
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| Publikation (Nr.) |
 copernicus.org/gmd-5-457-2012.pdf |
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| Zusammenfassung |
| We introduce a novel algorithm for the efficient detection and tracking of
features in spatiotemporal atmospheric data, as well as for the precise localization of
the occurring genesis, lysis, merging and splitting events. The algorithm works on
data given on a four-dimensional structured grid. Feature
selection and clustering are based on adjustable local and global criteria, feature
tracking is predominantly based on spatial overlaps of the feature's
full volumes.
The resulting 3-D features and the identified correspondences
between features of consecutive time steps are represented as the nodes and
edges of a directed acyclic graph, the event graph. Merging and splitting
events appear in the event graph as nodes with multiple incoming or outgoing edges,
respectively. The precise localization of the splitting events is based on
a search for all grid points inside the initial 3-D feature that have a similar
distance to two successive 3-D features of the next time step.
The merging event is localized analogously, operating backward in time.
As a first application of our method we present a climatology of
upper-tropospheric jet streams and their events, based on four-dimensional
wind speed data from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses.
We compare our results with a climatology from a previous study, investigate
the statistical distribution of the merging and splitting events,
and illustrate the meteorological significance of the jet splitting events
with a case study. A brief outlook is given on additional potential applications
of the 4-D data segmentation technique. |
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