Lake-Effect Snow Quantitative Precipitation Estimation Nowcasting through Blended GOES, NEXRAD, and PIP Observations

Abstract:

The Laurentian Great Lakes region of North America is a Canadian-American expanse, home to approximately 60 million people. The Great Lakes have a huge impact on the surrounding regional weather and climate. Cold air outbreaks are a common wintertime occurrence in this area and often lead to lake-effect snow (LeS) events, which have significant impacts on the leeward shores of the Great Lakes. LeS events produce severe winter weather conditions such as heavy snow, blowing snow, reduced visibilities, and hazardous road conditions, which often warrant advisories and warnings from the National Weather Service (NWS). LeS forecasting is challenging due to high variability of the intensity and locations of the snow bands, further exacerbated by Next Generation Weather Radar (NEXRAD) observational limitations due to the shallow precipitation profile. Here we demonstrate a proof-of-concept LeS nowcasting product that has been developed in coordination with colleagues at the Marquette (MQT) NWS office in Michigan. Currently, we leverage the GOES-16 Algorithm Working Group (AWG) cloud properties and derive an empirical parameterization between GOES-16 derived cloud liquid water path, NEXRAD-derived reflectivity (Z) to snow accumulation (S), and in-situ observations of liquid water equivalent snow rate for the upper Great Lakes (MQT). We use observations and snow products from the NASA-developed Precipitation Imaging Package (PIP) to assess current regional NWS Z to S assumptions and examine snow to liquid ratios and their relation with environmental conditions. The PIP snow products allow for improved ground-based observations of storm accumulations that are incorporated with retrievals of GOES AWG cloud properties to enhance satellite-based estimates of snow rate. By combining these observations and products, we obtain GOES-16 derived estimates of snow rate (in liquid water equivalent) and snow depth based on coincident snow to liquid ratios. The resulting GOES-16 LeS nowcasting product is available near real time and the NWS MQT office will be assessing and implementing this product during the 2020-2021 winter season.