The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

This study explores improvements in the estimation of snow water equivalent (SWE) over snow-covered terrain using an ensemble-based data assimilation (DA) framework. The NASA Catchment land surface model is used as the prognostic model in the assimilation of AMSR-E passive microwave (PMW) brightness temperature spectral differences ($\Delta$$T_b$) where support vector machine (SVM) regression is employed as the observation operator. A series of synthetic twin experiments are conducted using different precipitation boundary conditions. The results show, at times, DA degrades modeled SWE estimates (compared to the land surface model without assimilation) over complex terrain. To mitigate this degradation, a physically-constrained approach using different $\Delta$$T_b$ for shallow-to-medium or medium-to-deep snow conditions along with a â\euroœdata-thinningâ\euro strategy are explored. Overall, both strategies improve the model ability to encapsulate more of the evaluation data and mitigate model ensemble collapse. The physically-constrained DA and 3-day thinning DA strategies show marginal improvements of basin-averaged SWE in terms of reduction of bias from $10$ mm (baseline DA) to $-5.2$ mm and $-$2.5 mm, respectively. When the estimated forcings are greater than the truth, the baseline DA, physically-constrained DA, and 3-day thinning DA improve SWE the most with approximately 30\%, 31\%, and 24\% reduction of RMSE (relative to OL), respectively. Overall, these results highlight the limited utility of PMW $\Delta$$T_b$ observations in the estimation of snow in complex terrain, but do demonstrate that a physically-based constraint approach and data thinning strategy can add more utility to the $\Delta$$T_b$ observations in the estimation of SWE.