The Influence of Snow Depth Observation Timing and Uncertainty on Data Assimilation Improvements to SWE

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 – impact SWE accuracy and uncertainty. To quantify these impacts, we implement a particle filter DA technique and validate against observed snow density and SWE at 49 snow telemetry sites across 9 years. Sampling from in situ snow depth records, we test a range of measurement timing scenarios and two error scenarios representative of remote sensing capabilities. Assimilation reduces SWE bias by over 50%, with little additional benefit to SWE accuracy when assimilating more than one depth observation near peak accumulation, regardless of measurement error. However, more frequent depth observations improve melt-out date timing, reduce SWE uncertainty, and reduce bias in wetter years.