Dependence of Estimating Whitecap Coverage on Currents and Swells

Liu, Min; Yang, Bailin; Jia, Nan; Zou, Zhongshui

doi:10.1007/s11802-021-4521-x

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…This is different from the effects of the deviation of wind and current directions. Winds following currents may decrease W [32]. These main factors, contributing to the spread of W, may also be the main source of Since sea-salt aerosol production is linearly related to the W, the deviation of W calculated with the NN-W from W calculated with A16 will directly affect the sea-salt aerosol estimation.…”

Section: Understanding Of W Parameterizationmentioning

confidence: 99%

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Whitecap Fraction Parameterization and Understanding with Deep Neural Network

Zhou

Shi

2022

Remote Sensing

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Accurate calculation of the whitecap fraction is of great importance for the estimation of air-sea momentum flux, heat flux and sea-salt aerosol flux in Earth system models. Past whitecap fraction parameterizations were mostly power functions of wind speed, lacking consideration of other factors, while the single wind speed dependence makes it difficult to explain the variability of the whitecap fraction. In this work, we constructed a novel multivariate whitecap fraction parameterization using a deep neural network, which is diagnosed and interpreted. Compared with a recent developed parameterization by Albert and coworkers, the new parameterization can reduce the computational error of the whitecap fraction by about 15%, and it can better characterize the variability of the whitecap fraction, which provides a reference for the uncertainty study of sea-salt aerosol estimation. Through a permutation test, we ranked the importance of different input variables and revealed the indispensable role of variables such as significant wave height, sea surface temperature, etc., in the whitecap fraction parameterization.

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Section: Understanding Of W Parameterizationmentioning

confidence: 99%

Section: Understanding Of W Parameterizationmentioning

confidence: 99%

Whitecap Fraction Parameterization and Understanding with Deep Neural Network

Zhou

Shi

2022

Remote Sensing

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Evaluation of Sea Surface Temperature-Dependent Whitecap Coverage Parameterizations Using In Situ Data

Liu¹,

Yang

2022

Ocean Sci. J.

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Using Optical Flow Trajectories to Detect Whitecaps in Light-Polluted Videos

Meng

et al. 2022

Remote Sensing

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Whitecap formation is an important factor in the exchange of momentum, heat, and gas on the ocean surface. The long-term measurement of whitecaps is necessary to deepen our understanding of the mechanisms of ocean surface motion. However, traditional detection methods are highly sensitive to illumination. Under various illumination conditions, significant light pollution may be introduced into images. The poor performance caused by using images degraded with light pollution is not conducive to automated long-term whitecap measurement. In this study, we propose a two-step method for the detection of whitecaps under various illumination conditions. An abnormal detection method based on previous whitecap detection methods for the accurate detection of whitecaps in light-polluted areas is proposed as the first step. Using the detection results, we propose a post-processing method based on optical flow trajectories at two sampling rates to separate actual whitecap components in images containing false positives. Experiments show that the method proposed in this study can more accurately detect whitecaps in images with light pollution when compared to existing methods.

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Dependence of Estimating Whitecap Coverage on Currents and Swells

Cited by 3 publications

References 44 publications

Whitecap Fraction Parameterization and Understanding with Deep Neural Network

Whitecap Fraction Parameterization and Understanding with Deep Neural Network

Evaluation of Sea Surface Temperature-Dependent Whitecap Coverage Parameterizations Using In Situ Data

Using Optical Flow Trajectories to Detect Whitecaps in Light-Polluted Videos

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