Exploration of Synthetic Terrestrial Snow Mass Estimation via Assimilation of AMSR-E Brightness Temperature Spectral Differences Using the Catchment Land Surface Model and Support Vector Machine Regression

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 Advanced Microwave Scanning Radiometer for EOS passive microwave (PMW) brightness temperature spectral differences (ΔT_b) where support vector machine 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 informed approach using different ΔT_b for shallow-to-medium or medium-to-deep snow conditions along with a “data-thinning” strategy is explored. Overall, both strategies improve the model ability to encapsulate more of the evaluation data and mitigate model ensemble collapse. The physically informed DA and 3-days 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 informed DA, and 3-days thinning DA improve SWE the most with ∼30%, 31%, and 24% reduction of RMSE (relative to OL), respectively. Overall, these results highlight the limited utility of PMW ΔT_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 ΔT_b observations in the estimation of SWE.