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Journal articles on the topic 'Modeling. South China Sea'

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1

Ren, Zhiyuan, and Hua Liu. "Modeling Tsunami in South China Sea with Boussinesq Equations." Procedia Engineering 116 (2015): 888–96. http://dx.doi.org/10.1016/j.proeng.2015.08.378.

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Green, J. A. Mattias, and Tomos W. David. "Non-assimilated tidal modeling of the South China Sea." Deep Sea Research Part I: Oceanographic Research Papers 78 (August 2013): 42–48. http://dx.doi.org/10.1016/j.dsr.2013.04.006.

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3

He, Lijuan, Liangping Xiong, Jiyang Wang, Jihai Yang, and Weiliang Dong. "Tectono-thermal modeling of the Yinggehai Basin, South China Sea." Science in China Series D: Earth Sciences 44, no. 1 (2001): 7–13. http://dx.doi.org/10.1007/bf02906880.

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4

Simmons, Harper, Ming-Huei Chang, Ya-Ting Chang, et al. "Modeling and Prediction of Internal Waves in the South China Sea." Oceanography 24, no. 4 (2011): 88–99. http://dx.doi.org/10.5670/oceanog.2011.97.

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5

Wang, Jingyan, and Fenghua Li. "Characterization and modeling of typhoon-generated noise in South China Sea." Journal of the Acoustical Society of America 144, no. 3 (2018): 1809. http://dx.doi.org/10.1121/1.5067986.

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6

Xu, Yao, Hailun He, Jinbao Song, Yijun Hou, and Funing Li. "Observations and Modeling of Typhoon Waves in the South China Sea." Journal of Physical Oceanography 47, no. 6 (2017): 1307–24. http://dx.doi.org/10.1175/jpo-d-16-0174.1.

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AbstractBuoy-based observations of surface waves during three typhoons in the South China Sea were used to obtain the wave characteristics. With the local wind speeds kept below 35 m s−1, the surface waves over an area with a radius 5 times that of the area in which the maximum sustained wind was found were mainly dominated by wind-wave components, and the wave energy distribution was consistent with fetch-limited waves. Swells dominated the surface waves at the front of and outside the central typhoon region. Next, the dynamics of the typhoon waves were studied numerically using a state-of-th
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7

Gong, Xiang, Jie Shi, and Huiwang Gao. "Modeling seasonal variations of subsurface chlorophyll maximum in South China Sea." Journal of Ocean University of China 13, no. 4 (2014): 561–71. http://dx.doi.org/10.1007/s11802-014-2060-4.

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8

Gao, Jingsong, Huijie Xue, Fei Chai, and Maochong Shi. "Modeling the circulation in the Gulf of Tonkin, South China Sea." Ocean Dynamics 63, no. 8 (2013): 979–93. http://dx.doi.org/10.1007/s10236-013-0636-y.

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9

Shao, Zhuxiao, Bingchen Liang, Huajun Li, Guoxiang Wu, and Zhaohui Wu. "Blended wind fields for wave modeling of tropical cyclones in the South China Sea and East China Sea." Applied Ocean Research 71 (February 2018): 20–33. http://dx.doi.org/10.1016/j.apor.2017.11.012.

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10

Lu, Zhongming, Jianping Gan, Minhan Dai, Xiaozheng Zhao, and Chiwing Rex Hui. "Nutrient transport and dynamics in the South China Sea: A modeling study." Progress in Oceanography 183 (April 2020): 102308. http://dx.doi.org/10.1016/j.pocean.2020.102308.

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11

He, Lijuan, Liangping Xiong, and Jiyang Wang. "Heat flow and thermal modeling of the Yinggehai Basin, South China Sea." Tectonophysics 351, no. 3 (2002): 245–53. http://dx.doi.org/10.1016/s0040-1951(02)00160-9.

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12

Lu, Zhongming, Jianping Gan, and Minhan Dai. "Modeling seasonal and diurnal pCO2 variations in the northern South China Sea." Journal of Marine Systems 92, no. 1 (2012): 30–41. http://dx.doi.org/10.1016/j.jmarsys.2011.10.003.

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13

Hsu, S. A. "Monthly Dispersion Characteristics over the South China Sea for Air Quality Modeling." Pure and Applied Geophysics 160, no. 1-2 (2003): 349–55. http://dx.doi.org/10.1007/s00024-003-8783-6.

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14

Loy, Kak Choon, Prakash Chandra Sinha, Juneng Liew, Fredolin Tangang, and Mohd Lokman Husain. "Modeling storm surges associated with super typhoon durian in South China Sea." Natural Hazards 70, no. 1 (2010): 23–37. http://dx.doi.org/10.1007/s11069-010-9674-7.

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15

Hong, Yuanyuan, Moriaki Yasuhara, Hokuto Iwatani, and Briony Mamo. "Baseline for ostracod-based northwestern Pacific and Indo-Pacific shallow-marine paleoenvironmental reconstructions: ecological modeling of species distributions." Biogeosciences 16, no. 2 (2019): 585–604. http://dx.doi.org/10.5194/bg-16-585-2019.

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Abstract. Fossil ostracods have been widely used for Quaternary paleoenvironmental reconstructions, especially in marginal marine environments (e.g., for water depth, temperature, salinity, oxygen levels, pollution). But our knowledge of indicator species autoecology, the base of paleoenvironmental reconstructions, remains limited and commonly lacks robust statistical support and comprehensive comparison with environmental data. We analyzed marginal marine ostracod taxa at 52 sites in Hong Kong for which comprehensive environmental data are available. We applied linear regression models to rev
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16

Luo, Zhong Hui, Qi Jun Xiao, and Jun Lan Wu. "Research on the Multi-Parameter Modeling of Submarine Sediment Prediction." Applied Mechanics and Materials 462-463 (November 2013): 13–16. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.13.

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It is presented in this paper the technology of principal component analysis and analyze existing empirical equation and its calculation errors. A principle component model is then built up using the measurement data of sediments from the continental slope and shelf in southern South China Sea. It is also studied in theory how to exclude correlated parameters from a number of parameters that can affect the sound velocity and how to optimize a few parameters that are obviously related to sound velocity without cross-correlation. Using the optimal physical parameters, such as porosity n, median
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17

Yaremchuk, Max, Julian McCreary, Zuojun Yu, and Ryo Furue. "The South China Sea Throughflow Retrieved from Climatological Data*." Journal of Physical Oceanography 39, no. 3 (2009): 753–67. http://dx.doi.org/10.1175/2008jpo3955.1.

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Abstract The salinity distribution in the South China Sea (SCS) has a pronounced subsurface maximum from 150–220 m throughout the year. This feature can only be maintained by the existence of a mean flow through the SCS, consisting of a net inflow of salty North Pacific tropical water through the Luzon Strait and outflow through the Mindoro, Karimata, and Taiwan Straits. Using an inverse modeling approach, the authors show that the magnitude and space–time variations of the SCS thermohaline structure, particularly for the salinity maximum, allow a quantitative estimate of the SCS throughflow a
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18

Li, Lixiao, Yiqing Xiao, Ahsan Kareem, Lili Song, and Peng Qin. "Modeling typhoon wind power spectra near sea surface based on measurements in the South China sea." Journal of Wind Engineering and Industrial Aerodynamics 104-106 (May 2012): 565–76. http://dx.doi.org/10.1016/j.jweia.2012.04.005.

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19

Zhang, Qing-hua, Hai-mei Fan, and Yuan-yuan Qu. "Kuroshio Intrusion into the South China Sea." Journal of Hydrodynamics 18, no. 6 (2006): 702–13. http://dx.doi.org/10.1016/s1001-6058(07)60010-9.

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20

Du, Chuanjun, Jianping Gan, Chiwing Rex Hui, et al. "Dynamics of dissolved inorganic carbon in the South China Sea: A modeling study." Progress in Oceanography 186 (July 2020): 102367. http://dx.doi.org/10.1016/j.pocean.2020.102367.

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21

Chen, Bingzhang, Hongbin Liu, Wupeng Xiao, Lei Wang, and Bangqin Huang. "A machine-learning approach to modeling picophytoplankton abundances in the South China Sea." Progress in Oceanography 189 (November 2020): 102456. http://dx.doi.org/10.1016/j.pocean.2020.102456.

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22

Sun, Zhen, Zhihong Zhong, Myra Keep, et al. "3D analogue modeling of the South China Sea: A discussion on breakup pattern." Journal of Asian Earth Sciences 34, no. 4 (2009): 544–56. http://dx.doi.org/10.1016/j.jseaes.2008.09.002.

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23

Yeh, Yi-Ching, and Shu-Kun Hsu. "Crustal Structures of the Northernmost South China Sea: Seismic Reflection and Gravity Modeling." Marine Geophysical Researches 25, no. 1-2 (2004): 45–61. http://dx.doi.org/10.1007/s11001-005-0732-6.

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24

Nelko, Varjola, Abhishek Saha, and Vivien P. Chua. "On the tidally driven circulation in the South China Sea: modeling and analysis." Ocean Dynamics 64, no. 3 (2014): 413–28. http://dx.doi.org/10.1007/s10236-013-0687-0.

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25

Lu, Wenchao, Gang Li, Bin Zhao, Baojin Zhang, Yuan Gu, and Yan Li. "Seismic Velocity Modeling in the Northwest Pearl River Mouth Basin, South China Sea." IOP Conference Series: Earth and Environmental Science 671, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1755-1315/671/1/012035.

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26

Ji, Xuanliang, Guimei Liu, Shan Gao, Hui Wang, and Miaoyin Zhang. "Comparison of air-sea CO2 flux and biological productivity in the South China Sea, East China Sea, and Yellow Sea: a three-dimensional physical-biogeochemical modeling study." Acta Oceanologica Sinica 36, no. 12 (2017): 1–10. http://dx.doi.org/10.1007/s13131-017-1098-8.

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27

Li, Chuanhui, and Xuewei Liu. "Seismic wave attenuation in hydrate-bearing sediments based on the patchy saturation model in the Shenhu area, South China Sea." Interpretation 5, no. 3 (2017): SM25—SM32. http://dx.doi.org/10.1190/int-2016-0139.1.

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In the Shenhu area, South China Sea, a study on P-wave attenuation in the hydrate-bearing sediments has been developed based on the Biot-Squirt (BISQ) porous medium model. However, the BISQ model has been proven to be inappropriate for the attenuation characteristics at seismic frequencies. We have adopted the patchy-saturation model, which is being increasingly considered to be more suitable for attenuation analysis at seismic frequencies in porous media, to study P-wave attenuation in the hydrate-bearing sediments in the Shenhu area, South China Sea. The theoretical modeling indicates that t
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28

Liu, Aiqun, Peiyuan Zhu, Guangchao Pan, Caiwei Fan, Bing Liu, and Yunpeng Wu. "Three-Dimensional Pressure Modeling of South China Sea in High Temperature High Pressure Field." Open Journal of Marine Science 07, no. 02 (2017): 271–80. http://dx.doi.org/10.4236/ojms.2017.72019.

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29

Zhang, Hong, S. A. Sannasiraj, and Eng Soon Chan. "Wind Wave Effects on Hydrodynamic Modeling of Ocean Circulation in the South China Sea." Open Civil Engineering Journal 3, no. 1 (2009): 48–61. http://dx.doi.org/10.2174/1874149500903010048.

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30

Bachir, Sani A. M. "Maturity Modeling in Bai Yun Depression and Pan Yu Low Uplift, South China Sea." Journal of Applied Sciences 6, no. 10 (2006): 2158–67. http://dx.doi.org/10.3923/jas.2006.2158.2167.

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31

Liu, Jin, Zhaohui Peng, Zhenglin Li, Wenyu Luo, and Xishan Yang. "Measurement and modeling of sound propagation over continental slope in the South China Sea." Journal of the Acoustical Society of America 147, no. 3 (2020): EL209—EL214. http://dx.doi.org/10.1121/10.0000801.

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32

Jiang, Rui, and You-Shao Wang. "Modeling the ecosystem response to summer coastal upwelling in the northern South China Sea." Oceanologia 60, no. 1 (2018): 32–51. http://dx.doi.org/10.1016/j.oceano.2017.05.004.

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33

Twigt, Daniel J., Erik D. De Goede, Ernst J. O. Schrama, and Herman Gerritsen. "Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing." Ocean Dynamics 57, no. 4-5 (2007): 467–84. http://dx.doi.org/10.1007/s10236-007-0123-4.

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34

Sun, Zhen, Di Zhou, Zhihong Zhong, et al. "Research on the dynamics of the South China Sea opening: Evidence from analogue modeling." Science in China Series D: Earth Sciences 49, no. 10 (2006): 1053–69. http://dx.doi.org/10.1007/s11430-006-1053-6.

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35

Chen, Haibo, Wei An, Yunxiang You, Fanghui Lei, Yupeng Zhao, and Jianwei Li. "Modeling underwater transport of oil spilled from deepwater area in the South China Sea." Chinese Journal of Oceanology and Limnology 34, no. 1 (2015): 245–63. http://dx.doi.org/10.1007/s00343-015-4230-7.

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36

Cai, Zhongya, and Jianping Gan. "Formation and Dynamics of a Long-Lived Eddy Train in the South China Sea: A Modeling Study." Journal of Physical Oceanography 47, no. 11 (2017): 2793–810. http://dx.doi.org/10.1175/jpo-d-17-0002.1.

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AbstractA process-oriented numerical modeling study was conducted to investigate the formation and underlying forcing of an anticyclonic eddy train observed in the northern South China Sea. Observations showed that long-lived anticyclonic eddies formed an eddy train along an eastward separated jet across the northern South China Sea in summer. The eddy train plays a critical role in regulating ocean circulation in the region. Forced by the southwesterly monsoon and prevailing dipole wind stress curl in the summer, the northward coastal jet separates from the west boundary of the South China Se
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37

Vlasenko, V., N. Stashchuk, C. Guo, and X. Chen. "Multimodal structure of baroclinic tides in the South China Sea." Nonlinear Processes in Geophysics 17, no. 5 (2010): 529–43. http://dx.doi.org/10.5194/npg-17-529-2010.

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Abstract. The modelling of baroclinic tides generated in the northern South China Sea is studied using a fully-nonlinear non-hydrostatic numerical model. The focus of the modelling efforts was on the vertical structure of internal waves in the vicinity of the Luzon Strait. The barotropic tidal flow interacting with a two-ridge bottom topography in the area of the Luzon Strait produces a complex baroclinic tidal signal. A multimodal baroclinic bore with counter-phase displacement of isopycnals generated over the ridges and propagating westward disintegrates into a series of large-amplitude soli
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38

Chang, Rui, Rong Zhu, Merete Badger, Charlotte Hasager, Xuhuang Xing, and Yirong Jiang. "Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea." Remote Sensing 7, no. 1 (2015): 467–87. http://dx.doi.org/10.3390/rs70100467.

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39

Zhao, Huade, Minhan Dai, Jianping Gan, et al. "River-dominated pCO2 dynamics in the northern South China Sea during summer: A modeling study." Progress in Oceanography 190 (January 2021): 102457. http://dx.doi.org/10.1016/j.pocean.2020.102457.

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40

Wu, Zhiyuan, Jie Chen, Changbo Jiang, and Bin Deng. "Simulation of extreme waves using coupled atmosphere-wave modeling system over the South China Sea." Ocean Engineering 221 (February 2021): 108531. http://dx.doi.org/10.1016/j.oceaneng.2020.108531.

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41

Zhen-Quan, LU, HE Jia-Xiong, JIN Chun-Shuang, et al. "Modeling Effects of Gas Sources on Gas Hydrate Formation in the Northern South China Sea." Chinese Journal of Geophysics 56, no. 1 (2013): 8–14. http://dx.doi.org/10.1002/cjg2.20001.

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42

ZHUANG, Jian, Hai-Bin SONG, and Jia-Biao LI. "Thermal Modeling of the Tectonic Evolution of the Southwest Subbasin in the South China Sea." Chinese Journal of Geophysics 48, no. 6 (2005): 1423–32. http://dx.doi.org/10.1002/cjg2.791.

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43

Cai, Zhongya, and Jianping Gan. "Coupled External‐Internal Dynamics of Layered Circulation in the South China Sea: A Modeling Study." Journal of Geophysical Research: Oceans 124, no. 7 (2019): 5039–53. http://dx.doi.org/10.1029/2019jc014962.

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44

Swapna, P., Jianping Gan, Alexis Lau, and Jimmy Fung. "On the warm/cold regime shift in the South China Sea: Observation and modeling study." Deep Sea Research Part I: Oceanographic Research Papers 56, no. 7 (2009): 1039–56. http://dx.doi.org/10.1016/j.dsr.2009.03.008.

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45

Yang, Fengli, Zuyi Zhou, Na Zhang, Ning Liu, and Bin Ni. "Stress field modeling of northwestern South China Sea since 5.3 Ma and its tectonic significance." Acta Oceanologica Sinica 32, no. 12 (2013): 31–39. http://dx.doi.org/10.1007/s13131-013-0385-2.

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46

Zhou, JianBo, Minhui Zhang, ShengChun Piao, et al. "Low frequency ambient noise modeling and comparison with field measurements in the South China Sea." Applied Acoustics 148 (May 2019): 34–39. http://dx.doi.org/10.1016/j.apacoust.2018.11.013.

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47

Yang, Lei, Dongxiao Wang, Jian Huang, et al. "Toward a Mesoscale Hydrological and Marine Meteorological Observation Network in the South China Sea." Bulletin of the American Meteorological Society 96, no. 7 (2015): 1117–35. http://dx.doi.org/10.1175/bams-d-14-00159.1.

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Abstract Air–sea interaction in the South China Sea (SCS) has direct impacts on the weather and climate of its surrounding areas at various spatiotemporal scales. In situ observation plays a vital role in exploring the dynamic characteristics of the regional circulation and air–sea interaction. Remote sensing and regional modeling are expected to provide high-resolution data for studies of air–sea coupling; however, careful validation and calibration using in situ observations is necessary to ensure the quality of these data. Through a decade of effort, a marine observation network in the SCS
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48

Li, Pan, and Liang Hu. "Three-Dimensional Geological Modeling and its Application in Gas Hydrate." Applied Mechanics and Materials 631-632 (September 2014): 784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.784.

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We selected the Shenhu region in South China Sea as our research area, and use 3D modeling methods to develop visualization technologies oriented to strata modeling of irregular gas hydrate, hybrid rendering of ore bodies and boreholes, and contour extraction. Through real-time display of seismic attribute data volume, 3D geometry building, and integrated modeling of geologic strata and seismic attributes based on these technologies, we accomplished overlay analysis of seismic attributes, and spatial distribution description and detailed geologic feature depiction of gas hydrate bodies in the
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49

Shieh, Owen H., Michael Fiorino, Matthew E. Kucas, and Bin Wang. "Extreme Rapid Intensification of Typhoon Vicente (2012) in the South China Sea." Weather and Forecasting 28, no. 6 (2013): 1578–87. http://dx.doi.org/10.1175/waf-d-13-00076.1.

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Abstract One of the primary challenges for both tropical cyclone (TC) research and forecasting is the problem of intensity change. Accurately forecasting TC rapid intensification (RI) is particularly important to interests along coastlines and shipping routes, which are vulnerable to storm surge and heavy seas induced by intense tropical cyclones. One particular RI event in the western North Pacific Ocean with important scientific implications is the explosive deepening of Typhoon Vicente (2012). Vicente underwent extreme RI in the northern South China Sea just prior to landfall west of Hong K
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50

Zhang, Wei, Zong Dai, Bin Gong, Yahui Wang, Xiaolin Zhang, and Xiao Chen. "Application of an Accurate and Efficient Modeling Approach to a Multiscale Fractured Reservoir in South China Sea." Geofluids 2021 (September 23, 2021): 1–8. http://dx.doi.org/10.1155/2021/9933155.

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Carbonate reservoirs in the South China Sea mostly contain natural fractures with various length scales and different intensities, which causes great challenges in efficient reservoir modeling and flow simulation. Existing efforts based on dual-porosity and dual-permeability models could not reflect the characteristics of production data in certain wells. To accurately and efficiently characterize multiscale fractures, a hybrid fracture characterization method is proposed. Firstly, fractures are divided into two types according to the geometrical size and interpretation approach. Then, small-s
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