Improving SWE for Free: Data Assimilation of Snow Depth from Lidar and Other Techniques Improves Estimation of Snow Density and SWE
Abstract
Lidar snow depth measurements and modeled snow density can be combined to calculate snow water equivalent (SWE). In this approach, SWE uncertainty is dominated by snow density uncertainty, which depends on meteorological data quality and process representation (e.g., compaction) in models. We test whether assimilating snow depth with the particle filter can improve modeled snow density, thus improving SWE estimated from lidar or other depth observations. First, we use ground-based snow depth observations from Mammoth Mountain (California) as a proxy for lidar depth, along with density and SWE measurements for validation. Relative to open loop simulations, the particle filter reduced overall density and SWE RMSE by 17% and 21% when using high-quality, point-location forcing. We then extend our analysis to nine SNOTEL locations, again using resampled snow depth measurements as proxies for lidar depth, but forcing the model with coarse-resolution NLDAS-2 meteorology. Average assimilation gains were even greater (22% and 28% reduction in density and SWE RMSE). We also examine the effect of different observation intervals, as might be available through various remote sensing techniques and platforms. Ensembles created with precipitation and radiation perturbations led to the greatest improvements in density and SWE. Because modeled depth and density were both generally lower than observations, assimilation favored particles with higher precipitation and thus more overburden compaction. This moved depth and density (and therefore SWE) closer to observations. In contrast, ensemble generation by varying compaction parameters degraded performance. Thus, assimilation of snow depth can improve snow density and SWE derived at the basin-scale from lidar or other techniques. However, supplementary in situ observations are valuable to identify primary error sources in simulated snow depth and density.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.C41B..05S
- Keywords:
-
- 0736 Snow;
- CRYOSPHEREDE: 0740 Snowmelt;
- CRYOSPHEREDE: 0798 Modeling;
- CRYOSPHERE