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Adv. Sci. Res., 4, 105-114, 2010
https://doi.org/10.5194/asr-4-105-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
15 Nov 2010
A robust method to identify cyclone tracks from gridded data
F. Kreienkamp, A. Spekat, and W. Enke Climate & Environment Consulting Potsdam GmbH, David-Gilly-Str. 1, 14469 Potsdam, Germany
Abstract. A system to derive tracks of barometric minima is presented. It is deliberately using coarse input data in space (order of 2°×2°) and time (6-hourly to daily) as well as information from just one geopotential level. It is argued that the results are, for one robust in the sense of an assumption of the IMILAST Project that the use of as simple as possible metrics should be strived for and for two tailored to the input from reanalyses and GCMs. The methodology presented is a necessary first step towards an automated storm track recognition scheme which will be employed in a second paper to study the future development of atmospheric dynamics in a changing climate. The process towards obtaining storm tracks is two-fold. In its first step cyclone centers are being identified. The performance of this step requires the existence of closed isolines, i.e., a topology in which a grid-point is surrounded by neighbours which all exhibit higher geopotential. The usage of this topology requirement as well as the constraint of coarse data may lead, though, to limitations in identifying centers in geopotential fields with shallow gradients that may occur in the summer months; moreover, some centers may potentially be missed in case of a configuration in which a small scale storm is located at the perimeter of a deep and very large low (a kind of "dent in a crater wall"). The second step of the process strings the identified cyclone centers together in a meaningful way to form tracks. By way of several examples the capability to identify known storm tracks is shown.

Citation: Kreienkamp, F., Spekat, A., and Enke, W.: A robust method to identify cyclone tracks from gridded data, Adv. Sci. Res., 4, 105-114, https://doi.org/10.5194/asr-4-105-2010, 2010.
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