The goal of the present study is to provide more relevant LST-related inputs to the ANN. Specifically, to provide data that better characterizes the LST pattern responsible for generating the wind pattern that can trigger convection. The Canny edge detection algorithm was used to identify regions within each box region that represent a thermal boundary or gradients of LST sufficient for micro-scale/meso-alpha scale wind development. Two sets of results from the Canny technique were generated one for lightning cases and the other for non-lightning cases, based on the reasoning that fundamentally divergent LST patterns would emerge between lightning and null cases. Subsequent quantification of these patterns would provide more relevant LST-related inputs into the ANN, which should result in an improvement in ANN performance. The data set represents the 2003-2006 period.
The implications of this technique are significant. Utility of this ANN model would suggest that the current paradigm of improving thunderstorm prediction by increasing the resolution of NWP models to explicitly predict convection may not be necessary. Instead, a supervised ANN can be developed that inputs mesoscale NWP model output parameters and microscale data that directly contributes to convective initiation. The utility of this method is further enhanced by studies which by inference questions the ability of high-resolution NWP models to predict convection on scales smaller than 5-km. Further, the computing resources necessary for operational NWP forecast on the micro-scale/meso-alpha scale are enormous. Yet, the ANN model described herein does not require NWP output at such high resolutions.