Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE. Issue 2 (15th February 2019)

• Record Type: Journal Article
• Authors: Smyth, Eric J.; Raleigh, Mark S.; Small, Eric E.
• Is Part Of: Water resources research. Volume 55:Issue 2(2019)
• Page Start: 1296
• Page End: 1311
• Publication Date: 2019-02-15
• Subjects: SWE -- lidar -- particle filter -- snow depth -- snow density -- data assimilation; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR023400
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Snow depth observations 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 observations with the particle filter can improve modeled snow density, thus improving SWE estimated from intermittent depth observations. We model snowpack at Mammoth Mountain (California) over water years 2013–2016, assuming monthly snow depth data (e.g., sampling intervals relevant to lidar or manual surveys) for assimilation, and validate against observed SWE and density. The particle filter reduced density and SWE root‐mean‐square error by 27% and 28% relative to open loop simulations when using high‐quality, point location forcing. Assimilation gains were greater (35% and 51% reduction in density and SWE root‐mean‐square error) when using coarse‐resolution North American Land Data Assimilation System phase 2 meteorology. Ensembles created with both meteorological and compaction perturbations led to the greatest model improvements. 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 (therefore SWE) closer to observations. In contrast, ensemble generation that varied only compaction parameters degraded performance. These results were supported by synthetic experiments with prescribed error sources. Thus, assimilation of snow depth data from lidar or other techniques can likely improve snow density and SWE derived at the basin scale. However, supplementary in situ observations are valuable to identify primary error sources in simulated snow depth and density. Key Points: Particle filter assimilation of monthly snow depth observations, possible with many techniques, improved modeled snow density and SWE The particle filter yielded greater gains when using nonlocal (NLDAS) meteorological forcing than with high‐quality station data Greatest improvements occurred when ensemble generation was designed to address sources of open loop model errors