In this presentation, we leverage three-dimensional thermodynamical fields produced by global models to define frozen precipitation classes (sleet, freezing rain, and snow) by implementing an ensemble precipitation typing approach using the techniques typically applied to numerical weather prediction output. We validate this approach with ERA5 and station observations from the Automated Surface Observing System (ASOS) and NOAA’s Integrated Surface Database (ISD) across the northeastern U.S. (NEUS) during 1980-2019. Whilst the mean annual climatology of the different precipitation types is reasonable in ERA5, there is varied accuracy at the station and event level. Self-organizing maps (SOMs) are then applied as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMPs) associated with freezing rain events over the NEUS. Trained on ERA5 dynamic and thermodynamic fields, we define atmospheric setups critical for a model to capture in order to accurately simulate frozen extremes. We then apply the reanalysis-derived SOM as a reference in order to evaluate the skill of CMIP6 historical experiments in simulating the synoptic environments that can support freezing rain events over the NEUS.

