Using the Airborne Snow Observatory to Assess Remotely Sensed Snowfall Products in the California Sierra Nevada

Abstract

The Airborne Snow Observatory (ASO) performed two acquisitions over two mountainous basins in California on 29 January and 3 March 2017, encompassing two atmospheric river events that brought heavy snowfall to the area. These surveys produced high‐resolution (50 m) maps of snow depth and snow water equivalent (SWE) that were used to estimate monthly areal snowfall accumulation. Comparison of ASO snow accumulation with point measurements showed that the ASO estimates ranged from −10 to +16% relative bias across three sites, which is likely inflated by the disagreement in areal representation of the quantities from the actual errors in these products. The aggregated SWE accumulations from ASO are then used to evaluate a suite of in situ based and remote sensing precipitation products. During the study period, Parameter‐Elevation Regressions on Independent Slopes Model (PRISM) and Mountain Mapper estimates had relative bias <10% compared with ASO‐based estimates of snow accumulation, but satellite and radar products largely underestimate snowfall accumulation compared to ASO (up to 50%). Despite their underestimation, satellite and radar products show correlation coefficients >0.8 with ASO snow accumulation over the selected grids at the monthly scale. Finally, we leveraged the fine‐scale sampling of the spatially complete ASO products to show that by moving from 100 m to 2 km spatial scales, the perceived bias errors SWE at point locations increased by an order of magnitude, displaying a nonlinear relationship. The study demonstrates that ASO acquisitions in cold months can bring a new and effective approach to spatial evaluation of precipitation products.

Snow depth variability in the Northern Hemisphere mountains observed from space

“…The Alps were hit by several episodes of extreme snowfall in January 2018, caused by a low-pressure area over the western Mediterranean that brought moist air northwards and resulted in the anomalously high snow depths. The Sierra Nevada featured exceptionally deep snow in February 2017, caused by a series of atmospheric river events 43,44 , whereas the snow depth in February 2018 was relatively low. Over both the Alps and Sierra Nevada, similar large-scale patterns in snow depth differences (February 2018 minus 2017) are seen in the Sentinel-1 retrievals (Fig.…”

Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar

“…The distribution represents 60% of total precipitation, equivalent to the top 300 events between 1981 and 2017. The April 2015 storm is demarcated in red and exhibits a more even distribution of precipitation across all elevations relative to the event average and the storms that were mapped by Behrangi et al (), which includes the 8 and 21 February 2017 storms. Finally, the PRISM climatology clearly exhibits strong orographically enhanced precipitation, particularly on the northern edges of the Tuolumne.…”

“…However, ground‐based radar suffers from terrain blocking, beam spreading, the inability to observe precipitation near the surface (Mott et al, ; Scipión et al, ), and ambiguities owing to different scattering properties of water and ice (Austin, ). Furthermore, spaced‐based radiometers and radars lack the spatial resolution for small‐ to medium‐scale watersheds and their snowfall algorithms remain in development (Behrangi et al, ; Skofronick‐Jackson et al, ). To develop future technology and algorithms that might enable space‐based estimates of mountain precipitation at resolutions that are appropriate for small watersheds (National Academies of Sciences, Engineering, & Medicine, ) and improved process representation within weather models, we require validation data sets that can capture precipitation variability across terrain.…”

“…While ASO has bracketed a few other storms during its tenure—for example, flights on 29 January and 3 March 2017 encompassed two atmospheric rivers that Behrangi et al () studied—many of these other events suffer from long revisit periods between flights, multiple storms, or significant melt and sublimation, which all complicate and obscure the precipitation signal. However, the April 2015 storm studied herein was cold and possessed a short revisit period between flights (6 days) that limited snowmelt and other confounding processes.…”

Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California

“…Gathering training data can be costly, so one may want to balance the cost and benefit before pursuing the statistical approach for estimating snow depth for practical purposes. Since we found that removing a large portion of lidar points does not affect the final estimates of total snow volume in areas that are not dense canopy, the detailed modeling at fine resolution may not be necessary for water management, and the coarser-scale lidar and satellite products being proposed for operational hydrology (Behrangi et al, 2018) may provide a sufficient guide.…”