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Snow water equivalent prediction using Bayesian data assimilation methods

Authors
  • Leisenring, Marc1, 2
  • Moradkhani, Hamid1
  • 1 Portland State University, Department of Civil and Environmental Engineering, Portland, OR, 97201, USA , Portland (United States)
  • 2 Geosyntec Consultants, Inc., 55 SW Yamhill St., Portland, OR, 97201, USA , Portland (United States)
Type
Published Article
Journal
Stochastic Environmental Research and Risk Assessment
Publisher
Springer-Verlag
Publication Date
Dec 08, 2010
Volume
25
Issue
2
Pages
253–270
Identifiers
DOI: 10.1007/s00477-010-0445-5
Source
Springer Nature
Keywords
License
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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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