Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar
W. Tyler Brandt, K. J. Bormann, Forest Cannon, J. S. Deems, T. H. Painter, Daniel F. Steinhoff, Jeff Dozier
Abstract
Abstract California depends on snow accumulation in the Sierra Nevada for its water supply. Snowfall is measured by a combination of snow pillows, snow courses, and rain gauges. However, the paucity of locations of these measurements, particularly at high elevations, can introduce artifacts into precipitation estimates that are detrimental for hydrologic forecasting. To reduce errors, we need high‐resolution, spatially complete measurements of precipitation. Remotely sensed snow depth and snow water equivalent (SWE), with retrieval time scales that resolve storms, could help mitigate this problem in snow‐dominated watersheds. Since 2013, National Aeronautics and Space Administration's Airborne Snow Observatory (ASO) has measured snow depth in the Tuolumne basin of California's Sierra Nevada to advance streamflow forecasting through improved estimates of SWE. In early April 2015, two flights 6 days apart bracketed a single storm. The work herein documents a new use for ASO and presents a methodology to directly measure the spatial variability of frozen precipitation. In an end‐to‐end analysis, we also compare gauge‐interpolated and dynamically downscaled estimates of precipitation for the given storm with that of the ASO change in SWE. The work shows that the extension of ASO operations to additional storms could benefit our understanding of mountain hydrometeorology by delivering observations that can truly evaluate the spatial distribution of snowfall for both statistical and numerical models.