Significant wave height, H_s, is a measure of the variability of the ocean surface and is defined as four times the standard deviation of the vertical displacement of the sea surface. Estimates of H_s can be considered as two-dimensional random fields that develop over time. We propose a method for constructing models for estimates of H_s based on fitting random field models. The proposed model is parametric and the spatial parameters are estimated applying a new methodology based on total variation of the H_s estimates from satellites. Reanalysis data is used to estimate the sea-state motion which is modelled as a hidden Markov chain in a state space framework by means of an AR(1) process or in the presence of the dispersion relation. Parametric covariance models with and without dynamics are fitted to reanalysis and satellite data and compared to the empirical covariance functions. The derived models have been validated against satellite and buoy data.

Keywords: Space-time models; Significant wave height; Dynamics; State-space models

Biography: Has a PhD in Mathematical Statistics from Lund University and is mainly working on statistical and probabilistics aspects of random fields. The main application being models for the ocean energy as measured by significant wave height.