Tuesday, 24 January 2017: 4:00 PM
602 (Washington State Convention Center )
It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. We have developed a new method to obtain snow water equivalent (SWE) and snow depth by the spatial interpolation of these quantities from in situ measurements normalized by accumulated snowfall minus snow melt/sublimation. The input datasets for our value-added snow product development include: in situ daily measurements from the Snow Telemetry and Cooperative Observer networks in the United States, and the PRISM daily gridded precipitation and temperature datasets. Using SWE and snow depth themselves (rather than their normalized values) in the spatial interpolation would introduce large errors. The robustness of our method has been demonstrated by withholding 10%, 50%, and even 90% of the in situ data. Our derived snow fraction data also agree very well with an independent dataset from NESDIS.
We have used this dataset to evaluate the SWE products from analysis, reanalysis, and Global Land Data Assimilation Systems (GLDAS). For instance, reanalyses and GLDAS products substantially underestimate SWE in the U.S. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models, and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.
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