A novel approach to computing super observations for probabilistic wave model validation. (July 2019)

• Record Type: Journal Article
• Authors: Bohlinger, Patrik; Breivik, Øyvind; Economou, Theodoros; Müller, Malte
• Is Part Of: Ocean modelling. Volume 139(2019)
• Publication Date: 2019-07
• Subjects: Wave model -- Validation -- Super observation -- Gaussian process -- Machine learning; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2019.101404
• Languages: English
• ISSNs: 1463-5003

Abstract

Abstract: In the field of wave model validation, the use of super observations is a common strategy to smooth satellite observations and match the simulated spatiotemporal scales. An approach based on averaging along track is widely applied because it is straightforward to implement and adjustable. However, the choice of an appropriate length scale for obtaining the averages can be ambiguous, affecting subsequent analyses. Despite this dilemma, no uncertainty for the validation metric is provided when proceeding with wave model validation. We show that super observations computed from averaging data points applying an inappropriate length scale can lead to a misrepresentation of the wave field which can introduce errors into the wave model validation. Modelling the mean of observations as a Gaussian Process mitigates those errors and reliably identifies outliers by exploiting information hidden in the observational time series. Moreover, the uncertainty accompanying the validation statistic is readily accessible in the Gaussian Process framework. The flexibility of a Gaussian process makes it an attractive candidate for the probabilistic validation of wave models with steadily increasing horizontal resolution. Moreover, this approach can be applied to measurements from other platforms (e.g. buoys) and other variables (e.g. wind). Highlights: We apply a Gaussian Process model with a maximum likelihood based best estimate learned from the available data. Our approach allows for quantitative probabilistic validation of the wave model. Our approach allows further a reliable outlier detection superior to the ordinary approach. The flexibility of our approach makes it an attractive candidate for the validation of ever finer model grids.