A prototype passive microwave retrieval algorithm for tundra snow density

Abstract

Snow density data are important for a variety of applications, yet, to our knowledge, there are few methods for estimating spatiotemporal varying snow density in the Arctic environment. This research proposes a passive microwave retrieval algorithm to estimate tundra snow density. A two-layer electromagnetic snowpack model, representing depth hoar underlaying a wind slab layer, was used to estimate microwave emissions for use in an inverse model to estimate snow density. The proposed algorithm is predicated on solving the inverse model at boundary conditions for the simulated layers to estimate snow density within a plausible range. An experiment was conducted to assess the algorithm’s ability to reproduce snow density estimates from snow courses at four sites in the Canadian high Arctic. The electromagnetic snowpack model was calibrated to end-of-season conditions at each study site and a novel temporal parameterization was used to expand algorithm retrievals over full winter seasons. Algorithm estimates have the potential, under ideal conditions, to provide snow density information comparable to that collected through in situ sampling. In its current configuration, algorithm performance was best later in the season, with mean absolute percentage error approaching 10 % towards the end-of-season indicating snow density estimation uncertainty was similar to the in situ samples. With some modifications, and more extensive forcing data, this algorithm could be applied across the pan-Arctic to provide snow density information at scales that are not currently available.