Towards Improving Winter Weather Hydrometeor-Type Forecasts Using High-Resolution UAS Data

Winter weather, specifically mixed precipitation events, carry heavy societal impacts for the southern portion of the U.S. Powerlines, road conditions, aviation, and ecosystems can all be heavily affected. Due to low predictability of hydrometeor type within mixed precipitation events, a better observational network is needed to provide higher spatial and temporal resolution data to our current short-term models and forecast offices. Most algorithms within models use the vertical temperature profile as the primary metric for determining precipitation type. Research has shown that a 0.5°K change in temperature can alter the precipitation type observed at the surface. Weather balloons are the primary platforms used to obtain vertical temperature profiles but have poor resolution in measuring the boundary layer conditions that are crucial to forecasting precipitation type. Precise measurements of the freezing/meting layer depth should improve model forecasts of all winter weather types and assist in determining precipitation transition zones in the horizontal. Current Unmanned Aircraft Systems (UAS) are improving in quality and are becoming a more reliable platform for obtaining high resolution measurements in the boundary layer. A climatology of winter weather events across Oklahoma has been developed in preparation for UAS sampling of winter storm temperature and humidity vertical profiles, including synoptic conditions and boundary layer characteristics. Since 2000, central and southwestern Oklahoma has seen an increase in winter weather related disasters, compared to climatology from the 1900’s. An increase in winter-related disasters suggest a need for a better understanding of the inner-workings of these storms and their thermodynamic profiles. Analyses of data collected in Oklahoma by UAS in pre-winter storm environments will be included if events occur this winter. Ultimately, this work aims to provide higher resolution data that will improve model forecasts and nowcasting of winter weather events.