The success of real-time estimation and forecasting applications based on
geophysical models has been possible thanks to the two main existing
frameworks for the determination of the models' initial conditions: Bayesian
data assimilation and variational data assimilation. However, while there
have been efforts to unify these two paradigms, existing attempts struggle to
fully leverage the advantages of both in order to face the challenges posed
by modern high-resolution models – mainly related to model indeterminacy and
steep computational requirements. In this article we introduce a hybrid
algorithm called OPTIMISTS (Optimized PareTo Inverse Modeling through
Integrated STochastic Search) which is targeted at non-linear high-resolution
problems and that brings together ideas from particle filters (PFs),
four-dimensional variational methods (4D-Var), evolutionary Pareto
optimization, and kernel density estimation in a unique way. Streamflow
forecasting experiments were conducted to test which specific configurations
of OPTIMISTS led to higher predictive accuracy. The experiments were
conducted on two watersheds: the Blue River (low resolution) using the VIC
(Variable Infiltration Capacity) model and the Indiantown Run (high
resolution) using the DHSVM (Distributed Hydrology Soil Vegetation Model). By
selecting kernel-based non-parametric sampling, non-sequential evaluation of
candidate particles, and through the multi-objective minimization of
departures from the streamflow observations and from the background states,
OPTIMISTS was shown to efficiently produce probabilistic forecasts with
comparable accuracy to that obtained from using a particle filter. Moreover,
the experiments demonstrated that OPTIMISTS scales well in high-resolution
cases without imposing a significant computational overhead. With the
combined advantages of allowing for fast, non-Gaussian, non-linear,
high-resolution prediction, the algorithm shows the potential to increase the
efficiency of operational prediction systems.</p
