A two-dimensional convolution-based feature recognition algorithm was developed to automatically identify SWLs from KASPR PPI scans, RHI scans, and vertically pointing profiles. Over 50, 130, and 178 hours of cool-season KASPR PPI, RHI, and vertical scans, respectively, were used from the 2017 - 2023 winter seasons. The basic characteristics of SWLs, such as their frequency, altitude, thickness, and duration are explored. Velocity Azimuth Display (VAD) data, generated from the KASPR PPI scans, is used to investigate the relationship between vertical wind shear and SWL development and evolution. In addition to reflectivity gradients, dual polarimetric data from PPI and RHI scans provide additional insight into the microphysical processes occurring in regions of SWLs. Sounding data from the National Weather Service in Upton, NY (OKX) and from Stony Brook, NY, as well as reanalysis data from the Rapid Refresh (RAP) is used to provide context about the environmental conditions.
This presentation will first highlight the climatological characteristics of these SWLs using KASPR data from 2017-2021. The majority of SWLs identified are thin and transient, with more than half of SWLs being <150 m thick and persisting for <30s. Only ~12% of SWLs are >250 m thick and persist >30s. Vertical wind shear was found to be 50% greater in regions where a SWL was present compared to regions where no SWL was present. This presentation will then focus on the more prominent SWLs that have thicknesses > 250 m with durations of tens of minutes, as these are often accompanied by more variations of the precipitation features (gradients, fallstreaks,. etc) which suggests the turbulence within these layers may be modifying the precipitation processes. These SWLs are most often horizontally linear; however, in a few cases they take on a wave-like appearance, are sloped, or have magnitude fluctuations.

