Using the Airborne Snow Observatory to Assess Remotely Sensed Snowfall Products in the California Sierra Nevada
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
CitationBehrangi, A., Bormann, K. J., & Painter, T. H. ( 2018). Using the Airborne Snow Observatory to assess remotely sensed snowfall products in the California Sierra Nevada. Water Resources Research, 54, 7331– 7346. https://doi.org/10.1029/2018WR023108
JournalWATER RESOURCES RESEARCH
Rights©2018. American Geophysical Union. All Rights Reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThe 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.
Note6 month embargo; published online: 10 September 2018
VersionFinal published version
SponsorsNational Aeronautics and Space Administration; NASA Energy and Water Cycle Study [NNH13ZDA001N-NEWS]; NASA Terrestrial Hydrology Programs; NASA Western Water Applications Office