Treffer: Response‐Based Prediction of Tidal Currents.

This study evaluates the response method for predicting tidal currents. We introduce a coupled response model which explicitly accounts for interactions between velocity components. By leveraging non‐parametric and data‐driven weight estimation, the approach demonstrates superior predictive accuracy compared to classical harmonic analysis (HA), particularly for fast‐moving and non‐linear tidal currents. Using ADCP data from the world's largest deployment of tidal stream turbines, the coupled model achieves superior accuracy with fewer than 30 days of input measurements compared to HA using over 180 days of data. Accuracy improvements extend to both current predictions and the derived harmonic constituents, obtained through a specialized procedure. The response approach shows greater robustness when applied to extremely sparse data. This is reflected by the pseudo‐admittances, which also show the non‐parametric approach advanced can effectively capture unsmooth deviations in the admittance. Analysis of 40 active NOAA current stations highlight when the response approach should and should not be used, yielding average reductions in absolute error of 9.6%. The framework offers new opportunities for studying non‐tidal forcing and sediment transport and has significant implications for tidal energy site development. The proposed method is implemented in the open‐source RTide Python package, providing a practical and accessible tool that reduces the level of expertise required to apply the response method to higher‐order nonlinear processes. Plain Language Summary: This study looks at a new way to predict tidal currents. We develop a method that better captures how different parts of the current interact with each other. Instead of relying on predefining these interactions, it uses patterns learned directly from data. This makes it more accurate than the traditional approach, especially in places where tides move quickly or behave unpredictably. We tested the method using data from the world's largest tidal stream energy site and found that it could deliver better predictions using just 30 days of data—while the older method needed six times as much. Across 40 other NOAA sites, the new method also reduced prediction errors by nearly 10%. This improved approach could help us better understand how tides interact with other forces like wind and sediment, and it could make a real difference in planning and operating tidal energy projects. It is available for anyone to use through the free, open‐source RTide Python package—and importantly, it works without needing expert knowledge to set it up. Key Points: Explicitly coupling the orthogonal tidal current velocities in the response method enables more accurate predictionsResponse method improves estimates of harmonic constituents from short and noisy data‐seriesRTide's automated, data‐driven analysis procedure reduces expertise required to conduct response analyses [ABSTRACT FROM AUTHOR]

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