Assimilation of significant wave height from distributed ocean wave sensors

In-situ ocean wave observations are critical to improve model skill and validate remote sensing wave measurements. Historically, such observations are extremely sparse due to the large costs and complexity of traditional wave buoys and sensors. In this work, we present a recently deployed network of...

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Bibliographic Details
Published in:	Ocean modelling (Oxford) Vol. 159; p. 101738
Main Authors:	Smit, P.B., Houghton, I.A., Jordanova, K., Portwood, T., Shapiro, E., Clark, D., Sosa, M., Janssen, T.T.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-03-2021
Subjects:	Data assimilation Distributed sensor network Ocean waves Distributed sensor network Ocean waves Data assimilation
Online Access:	Get full text
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Summary:	In-situ ocean wave observations are critical to improve model skill and validate remote sensing wave measurements. Historically, such observations are extremely sparse due to the large costs and complexity of traditional wave buoys and sensors. In this work, we present a recently deployed network of free-drifting satellite-connected surface weather buoys that provide long-dwell coverage of surface weather in the northern Pacific Ocean basin. To evaluate the leading-order improvements to model forecast skill using this distributed sensor network, we implement a widely-used data assimilation technique and compare forecast skill to the same model without data assimilation. Even with a basic assimilation strategy as used here, we find remarkable improvements to forecast accuracy from the incorporation of wave buoy observations, with a 27% reduction in root-mean-square error in significant waveheights overall. For an extreme event, where forecast accuracy is particularly relevant, we observe considerable improvements in both arrival time and magnitude of the swell on the order of 6 h and 1 m, respectively. Our results show that distributed ocean networks can meaningfully improve model skill, at extremely low cost. Refinements to the assimilation strategy are straightforward to achieve and will result in immediate further modelling gains. •Increased data from sensor network improves wave model forecast accuracy.•Sequential optimal interpolation is effective for assimilation of wave data.•Magnitude and timing of the swell is improved; larger improvement for larger swell.
ISSN:	1463-5003 1463-5011
DOI:	10.1016/j.ocemod.2020.101738