2020
DOI: 10.1029/2019wr026853
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Improving SWE Estimation With Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty

Abstract: Snow depth observations can be leveraged with data assimilation (DA) to improve estimation of snow density and snow water equivalent (SWE). A key consideration for mission and campaign design is how snow depth retrieval characteristics (including observation timing/frequency and sampling error) influence SWE accuracy and uncertainty in a DA framework. To quantify these effects, we implement a particle filter (PF) assimilation technique to assimilate depth and validate this approach against observed snow densit… Show more

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Cited by 27 publications
(40 citation statements)
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“…Previous results from Smyth et al. (2020) demonstrate that model SWE error is not sensitive to the number of assimilated observations, whereas SWE uncertainty is sensitive to both the number/timing of observations and measurement uncertainty. Therefore, the degree to which DA reduces model sensitivity to canopy‐related parameters may vary with the platform used to retrieve snow depth measurements for assimilation.…”
Section: Discussionmentioning
confidence: 93%
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“…Previous results from Smyth et al. (2020) demonstrate that model SWE error is not sensitive to the number of assimilated observations, whereas SWE uncertainty is sensitive to both the number/timing of observations and measurement uncertainty. Therefore, the degree to which DA reduces model sensitivity to canopy‐related parameters may vary with the platform used to retrieve snow depth measurements for assimilation.…”
Section: Discussionmentioning
confidence: 93%
“…We calculated the PF “best estimate” of snowpack depth, density, and SWE as the weighted average of all particle simulations over time—with weights generated by the PF at every assimilation timestep and carried backwards in time through the preceding interval (as in Smyth et al., 2020). We also generated an OL control run for comparison at all sites and years—the equivalent of the simulation one would use in the absence of DA (Figures 2a–2c).…”
Section: Data Methods and Experimentsmentioning
confidence: 99%
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