Evaluating Tools for Diagnosis and Nowcasting Precipitation Type and Freezing Rain: Results from the 3–4 February 2022 Winter Storm in the Hudson Valley

Accurate prediction and diagnosis of winter precipitation type (p-type) and freezing rain accumulation is important for mitigating and responding to impacts on aviation, road transportation, power generation and distribution, winter recreation, ecology, and hydrology. Traditionally, airport station observations and manual observations from citizen scientists provide some of the primary datasets used in operational diagnosis of p-type and freezing rain for situational awareness and nowcasting. However, these datasets can have significant limitations in terms of spatial coverage, data quality, and/or latency. Recently developed and emerging tools may augment these traditional observations by providing complementary data and diagnostics that fill gaps and build confidence.

This study evaluates the utility of such tools in the context of a challenging to forecast and impactful winter mixed p-type event that affected the Hudson River Valley of New York State on 3–4 February 2022. Impacts from this event included prolonged power outages resulting from highly localized freezing rain accumulations of up to 0.75 inches (flat ice), which prompted the declaration of a state of emergency in Ulster County. The observations and diagnostic tools evaluated here include profiling radar and radiometer observations, diagnostic products from New York State Mesonet stations, gridded p-type and freezing rain analyses informed by NWP and NEXRAD radar observations (the Spectral Bin Classifier and the Freezing Rain Accumulation National Analysis), and a machine learning based p-type diagnostic. The strengths and weaknesses of each tool, synergies between them, and potential utility in forecasting operations will be discussed.