Toward the characterization of snowpack from space-borne satellite measurements: ...

A multi-frequency multi-scale data assimilation approachSpeaker:
Prof. Steve Margulis,
Dept. of Civil & Environmental Engineering
UCLAAbstract:
The cryosphere represents an important component of the Earth system, with 30% of the overall global land surface covered seasonally by snow (which greatly impacts surface albedo and hence surface energy partitioning) and one-sixth of the global population living in areas where streamflow is dominated by snowmelt runoff (which in some cases makes up more than 75% of the annual water supply). Hence the ability to accurately characterize the snowpack state over large regions has significant implications for weather, climate, and water resources planning. Traditionally, snow water equivalent (SWE) estimation by water agencies has been done using data from snow surveys (performed at select locations in space and periodically during the winter months) in conjunction with regressions based on the historical record. These methods can be inaccurate due to sampling problems and the fact that regression-based schemes are suspect in the context of a changing climate. In the last couple of decades researchers have begun exploring the ability to map snowpack states using space-borne remote sensing measurements. These efforts generally include techniques to either map the presence/absence of snow or retrieve the snow water equivalent. These techniques generally do not provide the desired quantity (SWE) at the necessary resolution and accuracy over large scales. Here we discuss recent work aimed at attempting to assess the feasibility of estimating snowpack characteristics in mountainous terrain by merging remote sensing data spanning the electromagnetic spectrum from the visible to the microwave with process models describing the evolution of the distributed snowpack and its associated radiative transfer. Some future implications of the work include improved lead-time water supply forecasts as well as initial conditions in seasonal climate forecasts.

Location: Kaprielian Hall (KAP) - rielian Hall, 355

Audiences: Everyone Is Invited

Contact: Evangeline Reyes