the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Automatic retrieval of wind speed and direction from in situ wave observations of a small buoy
Abstract. This study presents an automatic algorithm for estimating the wind speed and direction from wave observations. The method rests upon the decades old field-validated theory that the energy of a certain part of the wave field is directly proportional to the wind speed. The wind properties were estimated using data from a small wave buoy moored in the sheltered Finnish archipelago and in the exposed Baltic Proper (the Baltic Sea). The estimated wind speed and directions were compared to a nearby weather station (archipelago) and a high-resolution numerical hindcast (Baltic Proper). The algorithm was able to accurately estimate the wind speed (biases 0.3–0.5 m s-1 and root-mean-square-errors 1.5–1.6 m s-1). The wind direction was estimated from the mean wave direction of the shortest waves (1.00–1.28 Hz) and was mostly within 20° of the observed wind direction, with some of the differences clearly explained by land and coastlines tainting the measurements. As an additional measure to previously implemented algorithms, the estimated wind direction was used when determining the wind speed, which is expected to add robustness if strong swell is present. The results of the different places were obtained using slightly different equilibrium level constants, αu, but with otherwise identical algorithm settings, which suggests that the difference is caused by a more fundamental uncertainty of the value of the experimental constant, not the details of the algorithm itself.
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RC1: 'Comment on egusphere-2024-3477', Anonymous Referee #1, 27 Nov 2024
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This manuscript introduces a method to automatically retrieve sea surface wind speed and direction from wave spectra observed by small wave buoys.
From the perspective of completeness, this is a well-written and logically clear Technical Note. However, I fail to see the significant technical contribution of the manuscript:
On this topic, two other papers stand out in my memory. Voermans et al. (2020) provided a detailed theoretical explanation of the principles behind retrieving wind speed and direction from wave data and proposed a semi-analytical model. Jiang et al. (2022), on the other hand, demonstrated that AI could offer a more accurate solution for such a multivariate nonlinear regression problem, building upon the established principles.
In terms of theoretical depth, the discussion and analysis in this manuscript are not as extensive as those in Voermans et al. (2020). Regarding the complexity and accuracy of the method, although the authors have not conducted a direct comparison, I believe it is unlikely to surpass Jiang et al. (2022). (The authors might consider comparing these two algorithms or even comparing their approach with Spotter's operational algorithms.) While Jiang et al. (2022) do not explicitly state theoretical assumptions, the underlying principles are consistent with those of this manuscript.
I am not suggesting that this Technical Note is without value. However, for the reasons stated above, I do not believe it is sufficient for publication in a high-profile journal like Ocean Science. If this were a conference paper for OMAE or ISOPE, or an article submitted to a journal like Frontiers, I think such a piece, showcasing the performance of an existing algorithm on LainePoiss, would be acceptable.
Citation: https://doi.org/10.5194/egusphere-2024-3477-RC1 -
RC2: 'Comment on egusphere-2024-3477', Anonymous Referee #2, 27 Nov 2024
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Authors provide an algorithm for automatic retrieval of wind speed and direction from the LainePoiss wave buoy. This is beneficial for practical applications where in-situ wind observations are not directly available. The simplicity of the algorithm (dependence on a single coefficient alpha) is an advantage. However, it provides some problems in regards to its generality as well if the value of alpha is not known a priori, and that remains largely undiscussed.
While I think the study has added value in general, I think the general message of the work, in my opinion, does not capture the journal aims of a technical note: ‘Technical notes report new developments, significant advances, and novel aspects of experimental and theoretical methods and techniques’. In particular, the algorithm theory/background has considerable overlap with other cited studies, and it becomes somewhat difficult to see where the novelty lies. As per the automation of the algorithm itself, Sofar also has automated something similar.
With that in mind I think the study is not suited for publication in Ocean Sciences.
Aside from the above, I’d like to encourage the authors to keep the study going with more data. I think the success of such an algorithm in practical applications stands or falls on its generality and predictability of alpha. This will require further measurements at distinctly different sites such as energetic open oceans. Currently the study uses data from the Baltics only. While I like the theoretical dependence of alpha on wave age, the increased scatter it produces is a worrisome, which seems to highlight the need for more data to see the robustness of any alpha value chosen. Obviously, I completely understand that ‘more data’ is in most cases not feasible.
Citation: https://doi.org/10.5194/egusphere-2024-3477-RC2 -
EC1: 'Comment on egusphere-2024-3477', Bernadette Sloyan, 03 Dec 2024
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The reviewers have provided two insightful comments. Given that both reviewers find that this technical note does not provide new or further insight with respect to current knowledge I'm not convinced the manuscript is suitable for Ocean Sciences. However, the authors are encouraged to consider the reviewers comments particularly adding additional data comparisons from a wider range of environmental conditions and comparison of various methods.
Citation: https://doi.org/10.5194/egusphere-2024-3477-EC1
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