Trends of sea surface wind energy over the South China Sea

2019 ◽  
Vol 37 (5) ◽  
pp. 1510-1522
Author(s):  
Bo Jiang ◽  
Yongliang Wei ◽  
Jie Ding ◽  
Rong Zhang ◽  
Yuxin Liu ◽  
...  
Keyword(s):  
South China Sea ◽  
Wind Energy ◽  
South China ◽  
Surface Wind ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea ◽  
Sea Surface Wind

scholarly journals Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 766
Author(s):  
Yi Jiang ◽  
Shuai Han ◽  
Chunxiang Shi ◽  
Tao Gao ◽  
Honghui Zhen ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Surface Wind ◽  
Hainan Island ◽  
The South China Sea ◽  
Wind Data ◽  
The South ◽  
Near Surface ◽  
China Sea

Near-surface wind data are particularly important for Hainan Island and the South China Sea, and there is a wide range of wind data sources. A detailed understanding of the reliability of these datasets can help us to carry out related research. In this study, the hourly near-surface wind data from the High-Resolution China Meteorological Administration (CMA) Land Data Assimilation System (HRCLDAS) and the fifth-generation ECMWF atmospheric reanalysis data (ERA5) were evaluated by comparison with the ground automatic meteorological observation data for Hainan Island and the South China Sea. The results are as follows: (1) the HRCLDAS and ERA5 near-surface wind data trend was basically the same as the observation data trend, but there was a smaller bias, smaller root-mean-square errors, and higher correlation coefficients between the near-surface wind data from HRCLDAS and the observations; (2) the quality of HRCLDAS and ERA5 near-surface wind data was better over the islands of the South China Sea than over Hainan Island land. However, over the coastal areas of Hainan Island and island stations near Sansha, the quality of the HRCLDAS near-surface wind data was better than that of ERA5; (3) the quality of HRCLDAS near-surface wind data was better than that of ERA5 over different types of landforms. The deviation of ERA5 and HRCLDAS wind speed was the largest along the coast, and the quality of the ERA5 wind direction data was poorest over the mountains, whereas that of HRCLDAS was poorest over hilly areas; (4) the accuracy of HRCLDAS at all wind levels was higher than that of ERA5. ERA5 significantly overestimated low-grade winds and underestimated high-grade winds. The accuracy of HRCLDAS wind ratings over the islands of the South China Sea was significantly higher than that over Hainan Island land, especially for the higher wind ratings; and (5) in the typhoon process, the simulation of wind by HRCLDAS was closer to the observations, and its simulation of higher wind speeds was more accurate than the ERA5 simulations.

scholarly journals Purpleback Flying Squid Sthenoteuthis oualaniensis in the South China Sea: Growth, Resources and Association with the Environment

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Chunxu Zhao ◽  
Chunyan Shen ◽  
Andrew Bakun ◽  
Yunrong Yan ◽  
Bin Kang
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Biological Characteristics ◽  
Trophic Levels ◽  
Sea Surface ◽  
Height Anomaly ◽  
The South ◽  
China Sea ◽  
Sthenoteuthis Oualaniensis

The purpleback flying squid (Ommastrephidae: Sthenoteuthis oualaniensis) is an important species at higher trophic levels of the regional marine ecosystem in the South China Sea (SCS), where it is considered to show the potential for fishery development. Accordingly, under increasing climatic and environmental changes, understanding the nature and importance of various factors that determine the spatial and temporal distribution and abundance of S. oualaniensis in the SCS is of great scientific and socio-economic interest. Using generalized additive model (GAM) methods, we analyzed the relationship between available environmental factors and catch per unit effort (CPUE) data of S. oualaniensis. The body size of S. oualaniensis in the SCS was relatively small (<19.4 cm), with a shorter lifespan than individuals in other seas. The biological characteristics indicate that S. oualaniensis in the SCS showed a positive allometric growth, and could be suitably described by the logistic growth equation. In our study, the sea areas with higher CPUE were mainly distributed at 10°–11° N, with a 27–28 °C sea surface temperature (SST) range, a sea surface height anomaly (SSHA) of −0.05–0.05 m, and chlorophyll-a concentration (Chl-a) higher than 0.18 μg/L. The SST was the most important factor in the GAM analysis and the best fitting GAM model explained 67.9% of the variance. Understanding the biological characteristics and habitat status of S. oualaniensis in the SCS will benefit the management of this resource.

Application of deep learning technique to the sea surface height prediction in the South China Sea

2021 ◽  
Vol 40 (7) ◽  
pp. 68-76
Author(s):  
Tao Song ◽  
Ningsheng Han ◽  
Yuhang Zhu ◽  
Zhongwei Li ◽  
Yineng Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China Sea ◽  
South China ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea ◽  
Learning Technique ◽  
Height Prediction

Large conversion rates of NO2 to HNO2 observed in air masses from the South China Sea: Evidence of strong production at sea surface?

2014 ◽  
Vol 41 (21) ◽  
pp. 7710-7715 ◽  
Author(s):  
Qiaozhi Zha ◽  
Likun Xue ◽  
Tao Wang ◽  
Zheng Xu ◽  
Chungpong Yeung ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
Air Masses ◽  
China Sea ◽  
Conversion Rates

scholarly journals Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

2019 ◽  
Vol 11 (8) ◽  
pp. 919 ◽  
Author(s):  
Ziyao Mu ◽  
Weimin Zhang ◽  
Pinqiang Wang ◽  
Huizan Wang ◽  
Xiaofeng Yang
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Observation Data ◽  
The South ◽  
Surface Salinity ◽  
China Sea ◽  
Ocean Salinity

Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation.

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