Wind speed and direction estimation from wave spectra using deep learning

Jiang, Haoyu

doi:10.5194/amt-15-1-2022

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Thomson et al (2013) and Voermans et al (2020) demonstrated the validity of ocean surface wind estimation based on the relationship with ocean wave spectra in the high-frequency range measured by ocean wave buoys. Jiang (2022) established a deep neural network for the estimation of sea surface wind from wave spectra, although the relationship between high-frequency ocean wave spectra and wind was not explicitly used. A small GPS wave buoy, Spotter (Houghton et al, 2021), incorporates a wind estimation function based on the methods of Thomson et al (2013) and Voermans et al (2020).…”

Section: Shimura Et Almentioning

confidence: 99%

Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean

et al. 2022

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Ocean surface wind and wave information is important in a wide variety of areas, such as coastal disaster reduction, offshore structure design, and atmosphere‐ocean flux estimation. This study proposed a new method for ocean surface wind estimation from surface wave spectrum information measured by small global positioning system buoys. The concept of this method relies on the assumption that the high‐frequency part of the ocean‐wave spectrum is proportional to u∗f−4 ${u}_{\ast }{f}^{-4}$ where u∗ ${u}_{\ast }$ is the friction velocity and f $f$ is the frequency. The determination algorithm for the coefficient of f−4 ${f}^{-4}$ was optimized in this study. The wind direction was determined by the wave cross‐spectrum, assuming that the wind direction aligns the propagation direction of the high‐frequency part of the wave. The proposed wind estimation method was applied to bay and open ocean observations, and the performance of the proposed wind estimation method was similar between the bay and the open ocean. The proposed method improves the wind estimation especially in coastal areas and at high wind speeds in the open ocean compared with the previous method. The performance of the method of the previous study differs between the bay and open ocean due to their spectral shape differences. High‐quality wind and wave information can be obtained using the proposed method. If the mass deployment of small drifting buoys covered the global ocean, the information based on the proposed method could be considerably powerful, and could compensate for the weakness of satellite‐based wind and wave estimations.

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Section: Shimura Et Almentioning

confidence: 99%

Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean

et al. 2022

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“…They are well suited for the problem that there are quantitative relationship inputs and outputs but the relationship is complex in its explicit form. Due to its powerful ability in nonlinear regression, DL has been widely used in many aspects of oceanography (e.g., Ham et al 2019, Jiang 2022, including modelling ocean waves (e.g., James et al 2018, Song andJiang 2023). Regarding DL-based downscaling, James et al (2018) showed that DL is able to statistically downscale the bulk wave parameters very efficiently in a semi-closed basin, which can serve as a surrogate to NWMs.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Coastal Directional Wave Spectra Using Deep Learning

Gao,

Jiang

2024

Preprint

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The modelling of coastal Directional Wave Spectra (DWSs) often requires downscaling techniques integrating DWSs from open ocean boundaries. Dynamic downscaling methods reliant on numerical wave models are often computationally expensive. In coastal areas, wave dynamics are strongly influenced by the topography, implying that once the DWSs at the open ocean boundary are known, the DWSs at various locations along the coast are almost determined. This property can be utilized for statistical downscaling of coastal DWSs. This study presents a deep learning approach that can compute coastal DWSs from open ocean DWSs. The performance of the proposed downscaling model was evaluated using both numerical wave model data and buoy data in the Southern California Bight. The results show that the deep learning approach can effectively and efficiently downscale coastal DWSs without relying on any predefined spectral shapes, thereby holding promise for coastal wave climate studies.

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Statistical downscaling of coastal directional wave spectra using deep learning

Gao,

Jiang

2024

Coastal Engineering

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Wind speed and direction estimation from wave spectra using deep learning

Cited by 8 publications

References 14 publications

Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean

Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean

Statistical Downscaling of Coastal Directional Wave Spectra Using Deep Learning

Statistical downscaling of coastal directional wave spectra using deep learning

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