Correcting first‐order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme

A season-long, multiscale, multifrequency radiometric data assimilation experiment is performed to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave (PM) observations at Advanced Microwave Scanning Radiometer-Earth Observing System frequencies and 25 km resolution and synthetic near infrared (NIR) narrowband albedo observations corresponding to Moderate Resolution Imaging Spectroradiometer band 5 (1230–1250 μm) and 1 km resolution are assimilated into a land surface model snow scheme using the ensemble Kalman filter. First-order sources of model uncertainty, including error in precipitation quantity, grain size evolution, precipitation spatial distribution, and vegetation leaf area index are modeled. SWE remote sensing retrieval schemes would be of limited value for these scenarios where snow depth ranged between 1.0 and 2.0 m, grain size varied in space, significant vegetation was present, and the snowpack sometimes contained liquid water. Nevertheless, the true basinwide SWE is recovered, in general, within a root-mean-square error (RMSE) of approximately 2 cm. Synergy is observed between the PM and NIR measurements because of the complementary nature of the multiscale, multifrequency measurements. Results from the assimilation are compared to those from a pure modeling approach and from a remote sensing retrieval approach. The effects of model uncertainty, measurement error, and ensemble size are investigated.

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