Towards Multisensor Snow Assimilation: A Simultaneous Radiometric and Gravimetric Framework
Dr. Barton Forman, University of Maryland/ Civil and Environmental Engineering
Snow is a critical resource and serves as the dominant freshwater supply for 1+ billion people worldwide. Recent events in California, for example, highlight the importance of snow and its impact on extreme drought. Accurate measurements of snow are vital for predicting (and mitigating) the effects of extreme drought. However, global estimates of snow mass (a.k.a. snow water equivalent [SWE]) contain significant uncertainty and are often unavailable in regions of the globe where SWE is greatest. Further, satellite-based remote sensing products of SWE are severely limited when the snow pack contains liquid water, internal ice layers, surface ice crusts, or is overlain by forest canopy. Recent advances in data assimilation offer the potential to improve our estimates of global SWE. In particular, the merger of passive microwave remote sensing (e.g., AMSR-E) with satellite-based gravimetric retrievals (e.g., GRACE) offers unique opportunities to bridge remote sensing scales in space and time, "see" deeper into the snow pack, and add vertical resolution to the gravimetric retrievals that currently does not exist. A discussion of current and emerging data assimilation techniques as applied to snow is presented with an emphasis on regional- and continental-scale SWE estimation.