Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar

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. Plain Language Summary Precipitation, which includes rain, snow, sleet, and hail, recycles water from the atmosphere back to Earth's surface. The dynamic way in which the atmosphere and land interact generates highly variable precipitation rates, particularly in mountain landscapes. To quantify precipitation, we currently use a network of gauges at fixed locations, and to make these measurements more meaningful, we use statistics or models to “fill the blanks.” However, because the “blanks” often incorporate mountain peaks and valleys, it is challenging, if not impossible, to discern the accuracy and precision of our models and understanding of the process. Consequently, we require new ways of measuring mountain precipitation. Unlike rain, snow remains roughly in place following snowfall, thereby preserving the distribution of precipitation. Airborne lidar can now measure these changes in depth after storms at high accuracy and precision. Here we present a methodology that u