Assimilation of distributed ocean wave sensors

TL;DR

This paper demonstrates that deploying a network of satellite-connected drifting buoys and applying data assimilation significantly enhances ocean wave forecast accuracy, especially for extreme events, at low cost.

Contribution

It introduces a novel, low-cost distributed sensor network and evaluates its impact on ocean wave model forecast skill using data assimilation techniques.

Findings

27% reduction in root-mean-square error for wave height

6-hour improvement in swell arrival time

1-meter accuracy improvement in swell magnitude

Abstract

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 hours 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.