Snow water equivalent prediction using Bayesian data assimilation methods |
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| Authors: | Marc Leisenring Hamid Moradkhani |
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| Institution: | (1) Department of Civil and Environmental Engineering, Portland State University, Portland, OR 97201, USA;(2) Geosyntec Consultants, Inc., 55 SW Yamhill St., Portland, OR 97201, USA; |
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| Abstract: | Using the U.S. National Weather Service’s SNOW-17 model, this study compares common sequential data assimilation methods,
the ensemble Kalman filter (EnKF), the ensemble square root filter (EnSRF), and four variants of the particle filter (PF),
to predict seasonal snow water equivalent (SWE) within a small watershed near Lake Tahoe, California. In addition to SWE estimation,
the various data assimilation methods are used to estimate five of the most sensitive parameters of SNOW-17 by allowing them
to evolve with the dynamical system. Unlike Kalman filters, particle filters do not require Gaussian assumptions for the posterior
distribution of the state variables. However, the likelihood function used to scale particle weights is often assumed to be
Gaussian. This study evaluates the use of an empirical cumulative distribution function (ECDF) based on the Kaplan–Meier survival
probability method to compute particle weights. These weights are then used in different particle filter resampling schemes.
Detailed analyses are conducted for synthetic and real data assimilation and an assessment of the procedures is made. The
results suggest that the particle filter, especially the empirical likelihood variant, is superior to the ensemble Kalman
filter based methods for predicting model states, as well as model parameters. |
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