An Automated, Multi-parameter Dry line Detection Algorithm

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Tuesday, 4 February 2014: 4:15 PM
Room C204 (The Georgia World Congress Center )
Andrew J. MacKenzie, Univ. of Oklahoma, Norman, OK; and V. Lakshmanan, A. McGovern, R. A. Brown, and A. J. Clark

With increases in the temporal and spatial resolution of numerical weather prediction models and expanding ensemble sizes comes an increase in available data. The quantity of data can hinder operational forecasting and scientific investigation as researchers attempt to keep pace. In particular, studies requiring manual identification of features may be forced to be reduced in scope as more and more data is available for analysis of a single case. However, by creating a system by which automated, objective analysis is possible, the scale of these studies may be greatly expanded. This may occur through increasing the number of parameter modifications attempted, the number of cases studied, or days examined.

The technique produced by this project automatically detects dry lines, a feature common to the US High Plains. Dry lines are a boundary between two air masses, one characterized by relatively warm and moist air, the other by relatively hot and dry air. This boundary is often an important feature in the initiation of storms, including those that develop into tornadic supercells. The algorithm identifies and tracks these moisture boundaries by examining multiple parameters while comparing potential dry line regions against similar meteorological features. Additionally, it attempts to account for the diurnal and seasonal variability of the dry line. Results and potential expansions of the technique are presented.