By simultaneously assessing the importance of several parameters known to be related to severe potential in thunderstorms, the amount of cognitive processing required by the human is reduced, and decisions can be made faster, yet more informed. After considering which part of the warning operations problem I would like to tackle, I have chosen to concentrate my efforts on supercell thunderstorms, which account for a small percentage of thunderstorms, and yet a disproportionate amount of severe weather damage. I have confidence that, ignoring time constraints, even the most most junior forecasters in the field can recognize an isolated, mature supercell thunderstorm, and can interrogate this storm with some effectiveness. However, in high pressure warning situations, with time constraints, and several potentially severe thunderstorms in the radar domain, even some of the more veteran forecasters can be forced to sacrifice thoroughness for speed, and even miss some dangerous storms. In order to reduce processing complexity, facilitate organization, and handle data and output in an intuitive fashion, SIAA is constructed as a cell-based algorithm.
Storm cells are meteorologically significant because they represent discrete updrafts.
Cells also define a discrete object of interest, significant for software engineering. If a storm possesses these attributes to a high degree, then it may be classified as a supercell. Also, store the location of this meso. I use the marginal value because it is more representative of simple Weak Echo Regions, indicative of a strong updraft. Fit approximating B-Splines to the storms boundary to remove a large amount of the noise.