Short-Term Variations in Water Temperature of the Antarctic Surface Layer

Hu, Yuan; Shao, Weizeng; Jun, Li; Zhang, Chunyu; Cheng, Lingqiao; Ji, Qiyan

doi:10.3390/jmse10020287

Cited by 7 publications

(7 citation statements)

References 52 publications

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“…The numeric circulation models can be used for current simulation, i.e., the Princeton Ocean Model [39]. Its upgraded version is called the Stony Brook Parallel Ocean Model [40], Finite-Volume Community Ocean Model [41], and HYbrid Coordinate Ocean Model (HYCOM) [42]. Fortunately, the current data from the HYCOM reanalysis system are publicly available to investigators worldwide, making them an accessible and reliable resource.…”

Section: Auxiliary Datamentioning

confidence: 99%

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Leng,

Hao,

Shao

et al. 2024

Remote Sensing

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This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.

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Section: Auxiliary Datamentioning

confidence: 99%

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Leng,

Hao,

Shao

et al. 2024

Remote Sensing

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“…The introduction of the Argo program, coupled with rapid advancements in underwater exploration technology, has created an enabling environment for the advancement of exploration buoys [1]. Currently, there are over 3000 profiling buoys actively operating in the ocean, making significant contributions to marine environmental observation, weather forecasting, and monitoring atmospheric changes, among other critical areas [2,3]. Conventional buoys rely on batteries for power, which cease functioning once their power is depleted.…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of the Teardrop Buoy Driven by Ocean Thermal Energy

Zhao,

Li,

Shi

et al. 2024

JMSE

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With the inception of the Argo program, the global ocean observation network is undergoing continuous advancement, with profiling buoys emerging as pivotal components of this network, thus garnering increased attention in research. In efforts to enhance the efficiency of profiling buoys and curtail energy consumption, a teardrop-shaped buoy design is proposed in this study. Moreover, an optimization methodology leveraging neural networks and genetic algorithms has been devised to attain an optimal profile curve. This curve seeks to minimize drag and drag coefficient while maximizing drainage, thereby improving hydrodynamic performance. Simulation-based validation and analysis are conducted to assess the efficacy of the optimized buoy design. Results indicate that the drag of the teardrop-shaped buoy with a deflector decreased by 9.2% compared to pre-optimized configurations and by 22% compared to buoys lacking deflectors. The hydrodynamic profile devised in this study effectively enhances buoy performance, laying a solid foundation for ocean thermal energy generation and buoyancy regulation control. Additionally, the optimized scheme serves as a valuable blueprint for the design of ocean exploration devices.

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“…To improve the computational efficiency, the Stony Brook Parallel Ocean Model (sbPOM) [39], based on the principle of POM, was proposed using parallel computation techniques. Recently, sbPOM was employed for assessing the sea surface temperature (SST) cooling for binary typhoons [21], and sbPOM was implemented for the analysis of variation in the water temperature at the surface layer in the Southern Ocean [40].…”

Section: Introductionmentioning

confidence: 99%

The Respondence of Wave on Sea Surface Temperature in the Context of Global Change

et al. 2023

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Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over global seas from 1993–2020. The European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Marine Environment Monitoring Service (CMEMS), current and sea level were used as the forcing fields in the WW3 model. The validation of modelling simulations against the measurements from the National Data Buoy Center (NDBC) buoys and Haiyang-2B (HY-2B) altimeter yielded a root mean square error (RMSE) of 0.49 m and 0.63 m, with a correlation (COR) of 0.89 and 0.90, respectively. The terms calculated by WW3-simulated waves, i.e., breaking waves, nonbreaking waves, radiation stress, and Stokes drift, were included in the water temperature simulation by a numerical circulation model named the Stony Brook Parallel Ocean Model (sbPOM). The water temperature was simulated in 2005–2015 using the high-quality Simple Ocean Data Assimilation (SODA) data. The validation of sbPOM-simulated results against the measurements obtained from the Array for Real-time Geostrophic Oceanography (Argo) buoys yielded a RMSE of 1.12 °C and a COR of 0.99. By the seasonal variation, the interrelation of the currents, sea level anomaly, and significant wave heights (SWHs) were strong in the Indian Ocean. In the strong current areas, the distribution of the sea level was consistent with the SWHs. The monthly variation of SWHs, currents, sea surface elevation, and sea level anomalies revealed that the upward trends of SWHs and sea level anomalies were consistent from 1993–2015 over the global ocean. In the Indian Ocean, the SWHs were obviously influenced by the SST and sea surface wind stress. The rise of wind stress intensity and sea level enlarges the growth of waves, and the wave-induced terms strengthen the heat exchange at the air–sea layer. It was assumed that the SST oscillation had a negative response to the SWHs in the global ocean from 2005–2015. This feedback indicates that the growth of waves could slow down the amplitude of water warming.

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Short-Term Variations in Water Temperature of the Antarctic Surface Layer

Cited by 7 publications

References 52 publications

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Design and Optimization of the Teardrop Buoy Driven by Ocean Thermal Energy

The Respondence of Wave on Sea Surface Temperature in the Context of Global Change

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