Estimation of global ocean surface winds blending reanalysis, satellite and buoy datasets

Escobar, Claudia Jiménez; Restrepo, David

doi:10.1016/j.rsase.2023.101012

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…A notable feature is the discernible nonuniformity in the distribution of the data points. The overall distribution roughly follows a Weibull curve, as with most of the wind speed distribution [27]. The peak is observed around 7 m • s −1 and there are far fewer data points beyond a wind speed of 15 m • s −1 .…”

Section: Envisat/asar Wave Modementioning

confidence: 63%

Global Investigation of Wind–Wave Interaction Using Spaceborne SAR Measurements

Li,

2024

JMSE

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Spaceborne synthetic aperture radar (SAR) has been widely acknowledged for its advantages in collecting ocean surface measurements under all weather conditions during day and night. Despite the strongly nonlinear imaging process, SAR measurements of ocean waves provide an invaluable resource for studies into wave dynamics at the global scale. In this study, we take advantage of a newly defined parameter, the mean cross-spectrum (MACS) at a discrete wavenumber along the sensor line-of-sight axis, to further investigate the ocean wave properties. With the range peak wavenumber extracted from the MACS profile, together with the collocated model winds, the inverse wave age (iwa) is estimated. As an indicator of local wind–wave coupling, the global map of the iwa depicts a distinct pattern, with larger iwa values observed in the storm tracks. In addition to the mean, stronger variability in the iwa is also found in the storm tracks, while the iwa remains relatively steady in the trade winds with lower variability. This makes the SAR-derived iwa a significant parameter in reflecting the varying degrees of wind–wave coupling in variable geographical locations across the ocean basins. It will help to promote the practical application of SAR measurements, as well as advancing our understanding of ocean wave dynamics.

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Section: Envisat/asar Wave Modementioning

confidence: 63%

Global Investigation of Wind–Wave Interaction Using Spaceborne SAR Measurements

Li,

2024

JMSE

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“…The accuracy evaluation of OSWS retrievals commonly employs the root mean square error (RMSE) [36,37] as a metric. In addition, mean absolute error (MAE) [37,38] and symmetric mean absolute percentage error (SMAPE) [39,40] were selected as the accuracy metrics. The ERA-5 Reanalysis Wind Speed product provides analyzed 10 m wind speed and wind direction with a resolution of 12.5 km [30], typically considered a reliable reference data source for comparison.…”

Section: Accuracy Evaluationmentioning

confidence: 99%

Super Resolution Mapping of Scatterometer Ocean Surface Wind Speed Using Generative Adversarial Network: Experiments in the Southern China Sea

Wan,

Liu,

Guo

et al. 2024

JMSE

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This paper designed a Generative Adversarial Network (GAN)-based super-resolution framework for scatterometer ocean surface wind speed (OSWS) mapping. An improved GAN, WSGAN, was well-trained to generate high-resolution OSWS (~1/64 km) from low-resolution OSWS (~12.5 km) retrieved from scatterometer observations. The generator of GAN incorporated Synthetic Aperture Radar (SAR) information in the training phase. Therefore, the pre-trained model could reconstruct high-resolution OSWS with historical local spatial and texture information. The training experiments were executed in the South China Sea using the OSWS generated from the Advanced SCATterometer (ASCAT) scatterometer and Sentinel-1 SAR OSWS set. Several GAN-based methods were compared, and WSGAN performed the best in most sea states, enabling more detail mining with fewer checkerboard artifacts at a scale factor of eight. The model reaches an overall root mean square error (RMSE) of 0.81 m/s and an overall mean absolute error (MAE) of 0.68 m/s in the collocation region of ASCAT and Sentinel-1. The model also exhibits excellent generalization capability in another scatterometer with an overall RMSE of 1.11 m/s. This study benefits high-resolution OSWS users when no SAR observation is available.

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Estimation of global ocean surface winds blending reanalysis, satellite and buoy datasets

Cited by 2 publications

References 55 publications

Global Investigation of Wind–Wave Interaction Using Spaceborne SAR Measurements

Global Investigation of Wind–Wave Interaction Using Spaceborne SAR Measurements

Super Resolution Mapping of Scatterometer Ocean Surface Wind Speed Using Generative Adversarial Network: Experiments in the Southern China Sea

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