An automated, multi-parameter pattern recognition algorithm is devised to locate drylines present in numerical weather prediction (NWP) model output. As model resolution and NWP ensemble sizes increase, the amount of information available increases as well. However, the identification of complex meteoro- logical features is still performed primarily by hand. The time involved in these analyses places a limit on the amount of data that can be examined manually. Creating a system in which automated, objective analysis is possible allows the scale of various studies to be greatly expanded by increasing the number of pa- rameter modifications attempted, cases studied, or days examined, to name only a few. The algorithm outlined in this research is designed to detect drylines, a feature common to the US High Plains (Hoch and Markowski, 2005). Drylines are a boundary between two air masses, one characterized by relatively moist air, the other by relatively dry air. This boundary is often an important feature in the initiation of storms, including those that develop into tornadic supercells (e.g., Ziegler and Rasmussen, 1998; McCarthy and Koch, 1982). The identifi- cation of drylines is achieved through the examination of multiple parameters, some of which are, to an extent, shared with similar meteorological features (e.g., wind shift along a cold front) (Bluestein, 1993; Schaefer, 1986), making objective identification non-trivial. The fully automated algorithm outlined in this thesis makes use of image processing and pattern recognition techniques adapted for spatial grids to identify drylines present in a single timestep of NWP model output.
Andrew MacKenzie (2013). An Automated, Multi-parameter Dryline Identification and Tracking Algorithm. Interdisciplinary Master's Thesis, Graduate School, University of Oklahoma.
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Last modified June 12, 2017 12:57 PM