the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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 of the Iberian-Biscay-Ireland region: (i) Galicia, (ii) Tarragona and (iii) Gran Canaria.
Using SAR as 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 and 6º and correlation close to 35 % and 50 %, respectively. Using these ANN forcings in a regional spectral wave model (Copernicus Marine IBI-WAV NRT) lead 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. Furthermore, during extreme events (e.g. storm Arwen at Galicia, November 2021), the Hm0 was corrected close to 0.5m and Tm02 by around 0.4 s.
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- RC1: 'Comment on egusphere-2025-657', Anonymous Referee #1, 04 Jun 2025
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RC2: 'Comment on egusphere-2025-657', Anonymous Referee #2, 04 Jul 2025
This paper describes a method for adjusting operational surface fields, specifically wind speed and current speed, to minimise forecast errors in the coastal zone. Artificial neural networks trained with radar data from land stations (HFR) and satellites (SAR) adjust forcing fields. Overall, the paper reads well and contains all relevant references. However, the vast amount of acronyms limits readability to some extent. The methodology is reasonably well documented, and the verification of surface winds and currents appears sound. I appreciate the attempt to separate errors in the forcings that drive the wave model.
Verification for "short-term wave forecasting" is missing. Perhaps this is out of scope, then the title would need to be edited accordingly. Wave verification is too brief and could benefit from a more detailed analysis, other than a case study. Time series plots presented in Figure 13-16 did not convince me of an improved forecast system. Numerical weather prediction models feature some variability in skill over a complete seasonal cycle. Training and verification for a particular season can introduce seasonal bias in the correction method.
The introduction could benefit from a paragraph on the expected impact on the wave model skill, not just for the wind generation part, but also for the effect of currents on wave predictability.
Specific comments
Referencing in text uses inconsistent punctuation and contains et al. year, et al., year and author, year.
Line 29-30: "Third generation spectral wave models ..., as they address wave generation and propagation". Third-generation models explicitly represent the nonlinear wave-wave interactions. Generation and propagation refer to second-generation models.
Line 31-32: "three factors". What about the representation error? In the coastal zone, many processes are often overlooked, such as wetting and drying, river discharge, variable water depth, changes in beach profile, and seagrass, among others.
Line 34: "(ii) reduce biases and errors in the inputs (Durrant et al 2013) …". Complementary to Durrant et al., Zieger (2025) assessed forecast errors in an operational wave model. (https://doi.org/10.1071/ES25010).
Line 38-39: "Reducing biases and errors in the forcings have certain advantages...it does not require changing the physics." The parameterisations that represent the physics will naturally contain some form of bias that stems from model development. The underlying tuning parameters are bound to the frequency of model fields (daily, 6 hours, 3 hours, hourly) and the underlying statistic (i.e., cumulative, average, instantaneous). As a result, one has to change the physics kind of.
Line 84: "spectacular growth" does not sound well. Perhaps "exponential growth".
Line 90: "concrete". This word does not feel right and does not add clarity to the sentence (omit). There is a slight overuse of the word 'concrete' in the manuscript.
Line 128: Could you please provide more specific details on the ST4 configuration? Could you state the value for BETAMAX used?
Line 186-187: "It has been discarded images" is gibberish. Please rephrase the sentence.
Line 360-361: "So, it can be concluded that the ANN is able to predict winds that are closer to the SAR data than IFS." Would one not verify against an independent dataset (also Figure 5)? The ANN training would be well aware of the characteristics and structure of SAR data.
Line 479: "Benefits from the ANN forcing are more remarkable during extreme events. During storm Arwen, strong Northern winds at the Northern Iberian Peninsula (Figure 12a) …" What is the rationale for showing daily-average marine wind speed to depict extreme events? The most pronounced feature in wind speed difference (Fig. 12b) is the north-west south-east shift around coastlines. In addition, wind speed verification at the GAL station (Fig. 13 and Fig. 14) indicates that surface wind speed is consistently overpredicted. At the same time, the responding wave field exhibits lower magnitudes for the duration of the storms. On the Mediterranean side, GCA station, is not able to capture the double peak on 19th and 20th January. Can you elaborate on this?
Line 671: "The ANN forcings have positive impact on the wave forecast, especially under extreme events." To be honest, the results for significant wave height seem to reduce the variability in the signal. Peaks are less pronounced in Figure 15, and almost no signal in the time series in Figure 16. In general, a diurnal signal appears to be present in the observed wave field, which is lost in the simulation.
Line 674: "Wind ANN tends to decrease the overestimation of ECMWF-IFS wind speed". Is this correct? Time series plots in Figure 13-16 indicate that ANN winds are frequently higher than ECMWF and observations.
Citation: https://doi.org/10.5194/egusphere-2025-657-RC2
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Review of "Enhancing coastal winds and surface ocean currents with deep learning for short-term wave forecasting", manuscript egusphere-2025-657
This manuscript presents a practical approach to improving the performance of numerical wave models by correcting their forcing fields—namely wind and surface currents—using Artificial Neural Networks trained on remote sensing data such as SAR and HFR. The methodology is applied and validated at multiple pilot sites, demonstrating consistent and significant improvements across several key metrics. The corrected forcings lead to better wave height and period predictions, both under normal and extreme conditions. Overall, this work is methodologically sound, relevant to the field of operational ocean forecasting, and contributes meaningful advancements in the integration of remote sensing with data-driven modeling techniques. Therefore, after making some appropriate revisions (mainly formatting issues), I believe this manuscript is suitable for publication. Here are some of my comments about the manuscript.
Major comments:
Detailed comments: