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Stride search: A general algorithm for storm detection in high-resolution climate data

Journal Article · · Geoscientific Model Development (Online)
 [1];  [1];  [1];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of California Los Angeles, Los Angeles, CA (United States)
+ Show Author Affiliations

This study 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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1333537
Report Number(s):
SAND-2015-4839J; 594917
Journal Information:
Geoscientific Model Development (Online), Vol. 9, Issue 4; ISSN 1991-9603
Publisher:
European Geosciences UnionCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer
  • Montella, Raffaele; Di Luccio, Diana; Ciaramella, Angelo
  • Internet and Distributed Computing Systems: 12th International Conference, IDCS 2019, Naples, Italy, October 10–12, 2019, Proceedings, p. 444-456 https://doi.org/10.1007/978-3-030-34914-1_42
book November 2019

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54 ENVIRONMENTAL SCIENCES
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