The Relationship Between Turbulent Shear Layers and Precipitation Structures Within Northeast United States Coastal Winter Storms

The origins and lifecycle of many mesoscale precipitation structures within extratropical cyclones are still unknown. Multi-year climatologies of mulitbands (Ganetis et al. 2018) and smaller precipitation structures (Yeh et al. in prep) highlight the transient nature of these structures, making investigations into their development and lifecycle using conventional weather radars difficult. The Ka-Band Scanning Polarimetric Radar (KASPR) at Stony Brook University is ideally suited to sample these short-lived precipitation structures within coastal winter storms due to its 1-s sampling rate and spatial resolutions of 15 m and 45 m for the vertical profiles and PPI/RHI scans, respectively. During these storms, KASPR often observes enhanced spectrum width layers (SWLs) which are found in regions of enhanced vertical wind shear and turbulent vertical motions. A climatology of these SWLs has shown that the majority of these SWLs are very transient (persist < 30s) and very thin (< 150m thick). This SWL climatology dataset and precipitation object dataset are used to answer the questions: (1) Do we see an increase in precipitation object frequency when we see increased SWL occurrence? (2) Do we see a lagged relationship in time between SWLs and precipitation objects? (3) Do we see larger precipitation objects when higher magnitude SWLs are observed?

KASPR was operating from 2017 - 2023, in part with the NASA Investigation of Microphysics and Precipitation of Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign in 2020 & 2022. To develop the SWL climatology, 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. Characteristics such as height, thickness, duration, and magnitude are computed for all SWLs, as well as across-layer changes in reflectivity and dual polarimetric variables to assess the microphysical changes occurring across the SWLs. The precipitation structure dataset was developed using an object-based approach to automatically identify precipitation objects in reflectivity composites within cool-season extratropical cyclones from the WSR-88D radar network. Precipitation objects that were identified prior to 2017, or those identified when KASPR was not operational, are excluded. Precipitation object characteristics such as mean reflectivity and aspect ratio are computed. To answer our science questions, precipitation objects are filtered to be within a target range from KASPR and time thresholds are set to match the two object datasets in time and space.

Preliminary results suggest that SWLs are more prevalent in cases that are characterized by more precipitation objects. This presentation will compare the SWLs in relation to the distribution of precipitation objects, and select cases will be highlighted. RAP Reanalysis will be used to investigate the thermodynamic environment to understand the role of temperature gradients and stability on the relationship between SWLs and precipitation objects.