HR: 1340h
AN: C23A-0944    [Abstracts]
TI: Estimating snowfall patterns using timeseries of remote sensing images within a Bayesian framework
AU: * Durand, M
EM: durand@seas.ucla.edu
AF: Department of Civil and Environmental Engineering University of California, Los Angeles, 5731 Boelter Hall Box 951593, Los Angeles, CA 90095-1593, United States
AU: Molotch, N P
EM: molotch@seas.ucla.edu
AF: Department of Civil and Environmental Engineering University of California, Los Angeles, 5731 Boelter Hall Box 951593, Los Angeles, CA 90095-1593, United States
AU: Margulis, S A
EM: margulis@seas.ucla.edu
AF: Department of Civil and Environmental Engineering University of California, Los Angeles, 5731 Boelter Hall Box 951593, Los Angeles, CA 90095-1593, United States
AB: Snow water equivalent (SWE) reconstruction methods have been used previously to characterize seasonal SWE accumulation using mass and energy balance models. Recognizing that the spatial signature of the seasonal SWE accumulation is an integration of a series of snowfall events, we have formulated a Bayesian SWE reconstruction which utilizes the ensemble Kalman smoother (EnKS) to combine timeseries of remote sensing estimates of snow covered area (SCA) with a land surface model (LSM) to estimate snowfall distribution. An ensemble-based snow depletion curve (SDC) is used to relate SCA and SWE. We perform a series of synthetic tests to assess how much information concerning snowfall accumulation patterns can be extracted from a timeseries of SCA measurements during the ablation season. The test is performed using vegetation and meteorologic data at the 625 km² Colorado Rabbit Ears pass area studied during the NASA Cold Lands Processes Experiment. We perform experiments to examine sensitivity to a range of physiographic variables (e.g. vegetation cover, magnitude of SWE accumulation, and fraction of total accumulation falling during the ablation season). Sensitivity to over- and underestimation of melt flux, measurement error, and error in the sub-grid precipitation coefficient of variation used to define the LSM SDC are also investigated. Predictions are made about the accuracy of the EnKS posterior SWE estimates (and, thus, the potential usefulness of the Bayesian reconstruction) under a variety of physiographic and uncertainty scenarios.
DE: 0736 Snow (1827, 1863)
DE: 0758 Remote sensing
DE: 1854 Precipitation (3354)
DE: 1863 Snow and ice (0736, 0738, 0776, 1827)
DE: 3315 Data assimilation
SC: Cryosphere [C]
MN: 2007 Fall Meeting