  We present a method to estimate two-dimensional, time-invariant oceanic flow
fields based on data from both ensemble forecasts and online measurements. Our
method produces a realistic estimate in a computationally efficient manner
suitable for use in marine robotics for path planning and related applications.
We use kernel methods and singular value decomposition to find a compact model
of the ensemble data that is represented as a linear combination of basis flow
fields and that preserves the spatial correlations present in the data. Online
measurements of ocean current, taken for example by marine robots, can then be
incorporated using recursive Bayesian estimation. We provide computational
analysis, performance comparisons with related methods, and demonstration with
real-world ensemble data to show the computational efficiency and validity of
our method. Possible applications in addition to path planning include active
perception for model improvement through deliberate choice of measurement
locations.
