Spacetime prediction of ocean winds
Anders Malmberg
Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology
2005
ISBN 9162864890
LUTFMS10272005

Abstract:

The topic of this thesis is inspired by an experiment in which a vessel,
laying a submarine cable, was provided with forecasts overlaid with satellite
observations of significant wave height. During the

operation, the vessel was close to an adverse weather area and the personnel
on board could confirm that the forecast was not as close to the "ground
truth" as the satellite observation was. One of the outcomes of this experiment
was the suggestion to develop a method providing forecasts merged with satellite
observations. In this thesis such a method is developed for nearsurface
ocean winds.

The thesis consists of four papers (Paper AD). The contribution of Paper
A and B is the development of a statistical framework, in which forecasts
and satellite observations in a bounded area are merged and a measure of
uncertainty is provided. A dimensionreduced Kalman filter is used as an
emulator of the atmospheric dynamics. This is considered in Paper A. The
method of merging Kalman filter forecasts with satellite measurements is
developed in Paper B.

Closely related to Paper A and B is the problem of modelling the covariance
structure of residuals taken as differences between forecasts and satellite
measurements. Two isotropic covariance functions belonging to the Mat\'{e}rn
family are used. However, neither of the functions seem to properly model
the residual field. The contribution of Paper C is an explorative study and
it forms a basis for further research.


Finally, Paper D models the dynamics of a spatiotemporal process based on
an image warping approach. Image warping models the dynamics through the
movement of a set of control points. As well as allowing affine transformations,
the model also allows for nonlinear dynamics. The main contribution of this
paper is the formulation of a penalised likelihood which is used to estimate
the model.


Abstract:
Spacetime Kalman filtering; nearsurface ocean winds;

realtime assimilation; residual wind speed; variogram parameters;

image warping; thinplate splines.


