Demonstrating GOES-Derived Lake Effect Snow QPE Product and Improvements for the Great Lakes Region

Lake effect snow (LES) is a mesoscale weather phenomenon that occurs when cold, dry air flows over a warm body of water, becoming warm and saturated. As the air continues to cross the lake, the resulting clouds can grow into bands that exhibit vigorous convection and intense snowfall. One of the challenges associated with monitoring LES is the limited availability of nowcasting tools. LES features are often very shallow (cloud top heights typically below 3 km in the northern Great Lakes) . Radar beams quickly overshoot the shallow clouds, creating “blind spots” in the radar mosaic that limit the information available to forecasters. A possible supplement to radar-derived snowfall is quantitative precipitation estimates (QPE) using geostationary satellite data. This study demonstrates the use of an experimental satellite-derived QPE product for shallow LES events and investigates the potential relationships between snowfall intensity and other satellite-derived variables to improve satellite snowfall estimates.

This study utilized Next Generation Weather Radar (NEXRAD), Multi-Radar/Multi-Sensor (MRMS), and Clouds from AVHRR Extended System (CLAVR-x) – a processing system that generates quantitative cloud products for all NOAA operational satellites – data. Four LES events were studied, with three producing moderate to heavy LES to most of Michigan’s Upper Peninsula off of Lake Superior and the other producing intense snowfall to La Porte County, IN as a result of embedded mesovortices. While LES structures for all of these events were effectively monitored by NEXRAD observations near specific radar sites, significant radar observational blind spots were noted. Radar-derived snowfall intensity was also likely underestimated, especially far from radar locations. CLAVR-x cloud water path and cloud effective radius variables, however, effectively identified LES structures for these events and offer a way to augment NEXRAD observations to improve situational awareness. CLAVR-x variables were also compared to radar-derived snowfall rates through nearest neighbor pixel matching. This method revealed evidence of discrete lake effect snow structures and microphysics. Pixel co-location was ultimately used to find potential relationships that could improve satellite-derived QPE and offer a path forward to further the satellite-derived QPE algorithm development.