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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 4
Geosci. Model Dev., 9, 1383–1398, 2016
https://doi.org/10.5194/gmd-9-1383-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 1383–1398, 2016
https://doi.org/10.5194/gmd-9-1383-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 13 Apr 2016

Methods for assessment of models | 13 Apr 2016

Stride Search: a general algorithm for storm detection in high-resolution climate data

Peter A. Bosler1, Erika L. Roesler2, Mark A. Taylor1, and Miranda R. Mundt3 Peter A. Bosler et al.
  • 1Sandia National Laboratories, Center for Computing Research, P.O. Box 5800, Albuquerque NM, 87185-1321, USA
  • 2Sandia National Laboratories, Geophysics and Atmospheric Sciences, P.O. Box 5800, Albuquerque NM, 87185-0750, USA
  • 3University of California Los Angeles, Department of Mathematics, P.O. Box 951555, Los Angeles CA, 90095-1555, USA

Abstract. This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclone detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.

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This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. A new algorithm, "Stride Search," is introduced which is capable of searching for storms at all latitudes including the poles and has better performance than the commonly used grid point search.
This article discusses the problem of identifying extreme climate events such as intense storms...
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