Show simple item record

dc.contributor.authorBohlinger, Patrik
dc.contributor.authorBreivik, Øyvind
dc.contributor.authorEconomou, Theodoros
dc.contributor.authorMüller, Malte
dc.PublishedBohlinger P, Breivik Ø, Economou, Müller M. A novel approach to computing super observations for probabilistic wave model validation. Ocean Modelling. 2019;139:101404:1-10eng
dc.description.abstractIn 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).en_US
dc.rightsAttribution CC BYeng
dc.subjectWave modeleng
dc.subjectSuper observationeng
dc.subjectGaussian processeng
dc.subjectMachine learningeng
dc.titleA novel approach to computing super observations for probabilistic wave model validationen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2019 The Authorsen_US
dc.source.journalOcean Modelling

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution CC BY
Except where otherwise noted, this item's license is described as Attribution CC BY