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Keywords:

  • snow;
  • data assimilation;
  • remote sensing

[1] We demonstrate an ensemble-based radiance assimilation methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) radiance observations at 18.7 and 36.5 GHz. A land surface model (LSM) was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations within a data assimilation scheme. Snow depth bias was −53.3 cm prior to the assimilation, and −7.3 cm after the assimilation. Snow depth estimated by a non-assimilation-based retrieval algorithm using the same PM observations had a bias of −18.3 cm. Our results suggest that assimilation of PM radiance observations into LSMs shows promise for snowpack characterization, with the potential for improved results over products from instantaneous (“snapshot”) retrieval algorithms or the assimilation of those retrievals into LSMs.