Can we estimate precipitation rate during snowfall using a scanning terrestrial lidar

ABSRACT: Accurate snowfall measurements in windy areas have proven difficult. To examine a new approach, we have installed an automatic scanning terrestrial LiDAR at Mammoth Mountain, CA. With this LiDAR, we have demonstrated effective snow depth mapping over a small study area of several hundred m2. The LiDAR also produces dense point clouds by detecting falling and blowing hydrometeors during storms. Cumulative raw counts of airborne detections from the LiDAR show excellent agreement (R2> 0.90) with automated and manual snow water equivalent measurements, suggesting that LiDAR observations have the potential to directly estimate precipitation rate. Thus, we suggest LiDAR scanners offer advantages over precipitation radars, which could lead to more accurate precipitation rate estimates. For instance, uncertainties in mass-diameter/mass-fallspeed relationships used in precipitation radar, combined with low reflectivity of snow in the microwave spectrum, produce errors of up to 3X in snowfall rates measured by radar. Since snow has more backscatter in the near-infrared wavelengths used by LiDAR compared to the wavelengths used by radar, and the LiDAR detects individual hydrometeors, our approach has more potential for directly estimating precipitation rate. A key uncertainty is hydrometeor mass. At our study site, we have also installed a Multi Angle Snowflake Camera (MASC) to measure size, fallspeed, and mass of individual hydrometeors. By combining simultaneous MASC and LiDAR measurements, we estimate precipitation concentration and rate