Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines.

ABSTRACT

To estimate snow mass across North America, brightness temperature observations collected by the Advanced Microwave Scanning Radiometer from 2002 to 2011 were assimilated into the Catchment model using a support vector machine (SVM) as the observation operator and a one-dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground-based measurements and reference snow products. In general, there are no statistically significant skill differences between the domain-averaged, model-only ("open loop", or OL) snow estimates and assimilation estimates. The assessment of improvements (or degradations) in snow estimates is difficult because of limitations in the measurements (or products) used for evaluation. It is found that assimilation estimates agree slightly better in terms of root-mean-square error (RMSE) and Nash-Sutcliffe model efficiency with ground-based snow depth measurements than OL estimates in 82% (56 out of 62) of pixels that are colocated with at least two ground-based stations. Assimilation estimates tend to agree slightly better in terms of mean difference with reference snow products over tun-dra snow, alpine snow, maritime snow, and sparsely-vegetated, snow covered pixels. Changes in snow mass via assimilation translate into improvements (e.g.,by 22% on average in terms of RMSE, relative to OL) in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 snow-dominated basins in Alaska. These results suggest that a SVM can potentially serve as an effective observation operator for snow mass estimation within a radiance assimilation system, but a better observational baseline is required to document a statistically significant improvement.