A real-time assimilation algorithm applied to near-surface ocean winds
Anders Malmberg, Jan Holst and Ulla Holst
Centre for Mathematical Sciences
Lund Institute of Technology,
Marine operations depend on the ability to forecast suddenly appearing storms
and failures often cause great damage. As a part of a sea state alarm study,
meteorological forecasts overlaid with satellite measurements sent to ships
have been found to be a useful tool. In this paper we present a real-time
assimilation algorithm that extends this tool using statistical methods.
The algorithm is applied to near-surface ocean zonal wind speeds.
The meteorological model is emulated using a Kalman filter technique. Together
with a spatio-temporal state-space model the filter allows us to obtain forecasts
which are overlaid with satellite measurements using a kriging method. Examples
of overlays together with their statistical uncertainties are presented and
Spatio-temporal process; Kalman filter; Real-time assimilation; Near-surface