Abstract:
Snowfall has widespread impacts on communities through regional ecology, hydrological planning, and socioeconomic effects, such as cost and safety. Additionally, snowfall has important implications to climate through its influence on surface albedo and mass balance of ice sheets. In this presentation, I will feature important findings from snowfall regime-based studies leveraging observations from long-term, ground-based instrument suites in the mid- and high- latitude regions. Realistic estimates of snowfall in weather forecasting and climate models are challenging due to uncertainties in cloud and precipitation parameterization schemes. Additionally, remote-sensing observations require assumptions about hydrometeor microphysical and radiative properties. High temporal resolution, multi-instrument observations of clouds and snowfall can help better constrain processes in models and refine retrieval assumptions, leading to more accurate quantification of snowfall impacts. I will highlight the advantage of utilizing observations to examine key physical and dynamical snowfall processes through the application of regime partitioning. Snowfall regimes are determined using observations of precipitation macrophysical properties, cloud microphysical properties, large-scale environmental conditions, and thermodynamic profile characteristics. Additionally, I will illustrate how these observations can be coupled to satellite and reanalysis data to elucidate larger scale impacts. Key findings demonstrate that regime-dependent characteristics of clouds and precipitation lead to distinct differences in snowfall intensity, particle densities, microphysical properties, and accumulation. Additionally, we will explore how snowfall regimes are tied to significant seasonal, large-scale, and thermodynamic profile conditions such as atmospheric blocking and enhanced integrated water vapor transport.