An Algorithm to Estimate Traditional Snowfall Measurements from Ultrasonic Snow Depth Sensors at U.S. Observing Sites

The measurement of snowfall is a climatologically important variable measured at U.S. observing sites dating back to the late 1800’s. Snowfall is inherently difficult to measure due to wind redistribution, compaction and melting that can occur prior to a snowfall measurement taking place. With automation of many U.S. observing sites in the 1990’s, snowfall measurements were abandoned due to lack of technology to continue the measurements at many locations. This study aims to test the feasibility of restoring traditional snowfall measurements at automated stations using changes in snow depth from ultrasonic sensors. The ultrasonic sensors were installed in a triplicate configuration along with a Geonor precipitation gage and a wetness sensor for verification. Results show fairly good agreement between manual measurements and automated snowfall estimates. Differences are mainly due to low density snow that is not measured by the ultrasonic sensors and also does not register measureable precipitation in the Geonor gage. Wind driven events also cause problems for the algorithm, but overall these events were mainly less than 2.5 cm (1 inch) as manually measured but not estimated by the algorithm at all.