NDLI: Snow water equivalent prediction using Bayesian data assimilation methods

Content Provider	Springer Nature Link
Author	Leisenring, Marc Moradkhani, Hamid
Copyright Year	2010
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.
Starting Page	253
Ending Page	270
Page Count	18
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	25
Issue Number	2
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-12-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Snow modeling Particle filter Ensemble Kalman filter Lake Tahoe SNOW-17 Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Computational Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical Applications in Earth Sciences Probability Theory and Stochastic Processes Math. Application in Environmental Science
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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