Enhancing coastal winds and surface ocean currents with deep learning for short-term wave forecasting

Abstract

Accurate short-term wave forecasts are crucial for numerous maritime activities. Wind and surface currents, the primary forcings for spectral wave models, directly influence forecast accuracy. While remote sensing technologies like Satellite Synthetic Aperture Radar (SAR) and High Frequency Radar (HFR) provide high-resolution spatio-temporal data, their integration into operational ocean forecasting remains challenging. This contribution proposes a methodology for improving these operational forcings by correcting them with Artificial Neural Networks (ANNs). These ANNs leverage remote sensing data as targets, learning complex spatial patterns from the existing forcing fields used as predictors. The methodology has been tested at three pilot sites in the Iberian–Biscay–Ireland region: (i) Galicia, (ii) Tarragona and (iii) Gran Canaria. Using SAR as a reference, the ANN corrected winds present Root Mean Square Deviation (RMSD) reductions close to 35 % respect to ECMWF-IFS, and improvements close to 3 % for the scatter-index. Surface currents are also improved with ANNs, reaching speed and directional biases close to 2 cm s−1 and 6° and correlation close to 35 % and 50 %, respectively. Using these ANN forcings in a regional spectral wave model (Copernicus Marine IBI-WAV NRT) leads to improvements in the Wave Height (Hm0) bias and RMSD around 10 % and 5 % at the NE Atlantic. Mean wave period (Tm02) also improves, with reductions of 17 % and 5 % in bias and RMSD. Preliminary moderate improvements were also present in extreme events (e.g. storm Arwen at Galicia, November 2021), as the Hm0 was corrected close to 0.5 m and Tm02 by around 0.4 s. However, properly quantifying this impact requires further assessment.

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