Abstract
Abstract. This paper presents how to account for the lack of sampling variability in model data when they are combined with wave measurements. We addressed the dissimilarities between the types of data by either (i) low-pass filtering the observations or (ii) adding synthetic sampling variability to the model. Measurement–model times series combined with these methods served as the basis for return period estimates of a high wave event in January 2019. During this storm northerly wind speeds in the Baltic Sea rose to 32.5 m s−1 and an unprecedented significant wave height of 8.1 m was recorded in the Bothnian Sea sub-basin. Both methods successfully consolidated the combined time series but produced slightly different results: using low-pass-filtered observations gave lower estimates for the return period than using model data with added sampling variability. Extremes in both types of data followed the same type of theoretical distributions, and our best estimate for the return period was 104 years (95 % confidence 39–323 years). A similar wave event can potentially be more likely in the future climate, and this aspect was discussed qualitatively.
Highlights
We have two fundamental ways to get wave information
We investigated methods to combine wave measurements and wave model results into a coherent time series
This study was motivated by the need to compensate for insufficient measurement data in order to more reliably estimate the return period of a wave event
Summary
Recreate past events, and quantify impacts of future changes in forcing. Their major weakness is that they are not necessarily accurate enough for rare events, such as storms (Cavaleri, 2009). Measurements, again, can provide a certain ground truth that is unmatched even by the best of models. Point measurements cannot confidently represent large areas – neither can they be made in the past or in the future. For certain purposes remote sensing products combine the versatility of (numerical) models and the reliability of (in situ) observations Models and measurements are used in combination; typically a model is validated and calibrated with observations Models and measurements are used in combination; typically a model is validated and calibrated with observations (e.g. Bidlot et al, 2002; Haiden et al, 2018) before being used to extend limited measurements in time or space (e.g. Caires et al, 2006; Soomere et al, 2008; Breivik et al, 2013)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
