A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery

Fan, Chenqing; Wang, Xiaochen; Zhang, Xudong; Gao, Dong

doi:10.1007/s13131-019-1480-2

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…In terms of wind speed retrieval, it is suggested to add parameters of Cvar, skewness, and kurtosis to compensate for the loss of the influence of a swell wave on NRCS. In consideration of the influence of a swell wave on wind speed retrieval, a new empirical model is proposed, taking a unique formulation as follows [34,35]:…”

Section: Influence Of Swell Onmentioning

confidence: 99%

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Wang

Jiao

2021

Journal of Sensors

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The main work of this paper is to explore the influence of swell wave on retrieval of wind speed using ENVISAT ASAR wave mode imagery. The normalized radar cross section (NRCS) scene under different sea states is simulated to investigate the relationship between NRCS variation with swell height, together with swell direction. Moreover, the key parameter of imagery variance (Cvar) is selected to describe the swell wave on SAR imagery. In addition, the imagery parameters of skewness and kurtosis are together analyzed as a function of collocated significant swell wave height and wind speed. Based on the analyzed results, a new method for wind speed retrieval is proposed using ENVISAT ASAR, namely, F(n). Besides the CMOD parameters of NRCS, incidence angle, and relative wind direction, the imagery parameters of Cvar, skewness, and kurtosis are used to compensate for the influence of swell wave on wind speed retrieval in F(n). Finally, the collocated European Centre for Medium-Range Weather Forecast (ECMWF) wind speed dataset and ENVISAT ASAR wave mode imagery are used to verify the retrieval precision and compare with CMOD functions. It is concluded that the F(n) model performs much better than other CMOD functions, with a correlation of 0.89, a bias of 0.08, a RMSE of 1.2 m/s, and an SI of 0.1.

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Section: Influence Of Swell Onmentioning

confidence: 99%

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Wang

Jiao

2021

Journal of Sensors

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“…Machine learning is a data-driven methodology and has been recently applied to wave height prediction [18][19][20]. Based on long-term accurate wave height measurement data obtained through buoys [21], satellites [22] and scatterometers [23], machine learning methods [24,25] predict wave heights in the future by learning the inherent data variability. Deo et al [26] explore a three-layered feed forward network to obtain the output of significant wave heights.…”

Section: Introductionmentioning

confidence: 99%

Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

Zhang

Gao

et al. 2021

JMSE

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This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.

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Retrieval of Significant Wave Height Under Typhoon Conditions from Gaofen-3 SAR Imagery

Wang

Han

Zhong

et al. 2022

J. Ocean Univ. China

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A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery

Cited by 6 publications

References 24 publications

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

Retrieval of Significant Wave Height Under Typhoon Conditions from Gaofen-3 SAR Imagery

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