Abstract
Moisture boundaries, or drylines, are common over the southern U.S. high plains and are one of the most important airmass boundaries for convective initiation over this region. In favorable environments, drylines can initiate storms that produce strong and violent tornadoes, large hail, lightning, and heavy rainfall. Despite their importance, there are few studies documenting climatological dryline location and frequency, or performing systematic dryline forecast evaluation, which likely stems from difficulties in objectively identifying drylines over large datasets. Previous studies have employed tedious manual identification procedures. This study aims to streamline dryline identification by developing an automated, multiparameter algorithm, which applies image-processing and pattern recognition techniques to various meteorological fields and their gradients to identify drylines. The algorithm is applied to five years of high-resolution 24-h forecasts from Weather Research and Forecasting (WRF) Model simulations valid April–June 2007–11. Manually identified dryline positions, which were available from a previous study using the same dataset, are used as truth to evaluate the algorithm performance. Generally, the algorithm performed very well. High probability of detection (POD) scores indicated that the majority of drylines were identified by the method. However, a relatively high false alarm ratio (FAR) was also found, indicating that a large number of nondryline features were also identified. Preliminary use of random forests (a machine learning technique) significantly decreased the FAR, while minimally impacting the POD. The algorithm lays the groundwork for applications including model evaluation and operational forecasting, and should enable efficient analysis of drylines from very large datasets.
Scaling associative classification for very large datasets
