Abstract
Abstract. Particle filters are
becoming increasingly popular for state and parameter estimation in
hydrology. One of their crucial parts is the resampling after the
assimilation step. We introduce a resampling method that uses the full
weighted covariance information calculated from the ensemble to generate new
particles and effectively avoid filter degeneracy. The ensemble covariance
contains information between observed and unobserved dimensions and is used
to fill the gaps between them. The covariance resampling approximately
conserves the first two statistical moments and partly maintains the
structure of the estimated distribution in the retained ensemble. The
effectiveness of this method is demonstrated with a synthetic case – an
unsaturated soil consisting of two homogeneous layers – by assimilating
time-domain reflectometry-like (TDR-like) measurements. Using this approach we can estimate state and
parameters for a rough initial guess with 100 particles. The estimated states
and parameters are tested with a forecast after the assimilation, which is
found to be in good agreement with the synthetic truth.
Funder
Deutsche Forschungsgemeinschaft
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
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