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| Titel |
Lagrangian analysis by clustering. An example in the Nordic Seas. |
| VerfasserIn |
Inga Koszalka, Joseph H. LaCasce |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250035657
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| Zusammenfassung |
| We propose a new method for obtaining average velocities and eddy
diffusivities from Lagrangian data. Rather than grouping the
drifter-derived velocities in uniform geographical bins, as is
commonly done, we group a specified number of nearest-neighbor
velocities. This is done via a clustering algorithm operating on the
instantaneous positions of the drifters. Thus it is the data
distribution itself which determines the positions of the averages
and the areal extent of the clusters. A major advantage is that
because the number of members is essentially the same for all
clusters, the statistical accuracy is more uniform than with
geographical bins.
We illustrate the technique using synthetic data from a stochastic
model, employing a realistic mean flow. The latter is an accurate
representation of the surface currents in the Nordic Seas and is
strongly inhomogeneous in space. We use the clustering algorithm to
extract the mean velocities and diffusivities (both of which are
known from the stochastic model). We also compare the results to
those obtained with fixed geographical bins. Clustering is more
successful at capturing spatial variability of the mean flow and
also improves convergence in the eddy diffusivity estimates. We
discuss both the future prospects and shortcomings of the new
method. |
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