China-France Oceanography Satellite (CFOSAT) simultaneously observes the typhoon-induced wind and wave fields

Xu Ying; Liu Jianqiang; Xie Lingling; Sun Congrong; Liu Jinpu; Li Junyi

doi:10.1007/s13131-019-1506-3

Volume 38 Issue 11

Turn off MathJax

Article Contents

References

Article Navigation > Acta Oceanologica Sinica > 2019 > 38(11): 158-161

Xu Ying, Liu Jianqiang, Xie Lingling, Sun Congrong, Liu Jinpu, Li Junyi. China-France Oceanography Satellite (CFOSAT) simultaneously observes the typhoon-induced wind and wave fields[J]. Acta Oceanologica Sinica, 2019, 38(11): 158-161. doi: 10.1007/s13131-019-1506-3

Citation:

Xu Ying, Liu Jianqiang, Xie Lingling, Sun Congrong, Liu Jinpu, Li Junyi. China-France Oceanography Satellite (CFOSAT) simultaneously observes the typhoon-induced wind and wave fields[J]. Acta Oceanologica Sinica, 2019, 38(11): 158-161. doi: 10.1007/s13131-019-1506-3

Citation:

Xu Ying, Liu Jianqiang, Xie Lingling, Sun Congrong, Liu Jinpu, Li Junyi. China-France Oceanography Satellite (CFOSAT) simultaneously observes the typhoon-induced wind and wave fields[J]. Acta Oceanologica Sinica, 2019, 38(11): 158-161. doi: 10.1007/s13131-019-1506-3

PDF( 1020 KB)

China-France Oceanography Satellite (CFOSAT) simultaneously observes the typhoon-induced wind and wave fields

doi: 10.1007/s13131-019-1506-3

1.
National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China;Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
2.
Guangdong Key Laboratory of Coastal Ocean Variability and Disaster Prediction, Guangdong Ocean University, Zhanjiang 524088, China

Received Date: 2019-10-18

Abstract

FullText(HTML)

References(9)

References

Li Jing, Zhou Lin, Zheng Chongwei, et al. 2012. Spatial-temporal variation analysis of sea wave field in the Pacific Ocean. Marine Science, 36(6):94-100

Moon I J, Ginis I, Hara T, et al. 2003. Numerical simulation of sea surface directional wave spectra under Hurricane wind forcing. Journal of Physical Oceanography, 33(8):1680-1706, doi: 10.1175/2410.1

Stewart R H. 2008. Introduction to Physical Oceanography. Texas:Texas A & M University, 287

Sun Junchuan, Wei Zexun, Xu Tengfei, et al. 2019. Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas. Acta Oceanologica Sinica, 38(4):154-166, doi: 10.1007/s13131-019-1419-1

Tan Keyi, Zhang Biao, Xie Lingling. 2018. Ocean wave parameter retrieval from satellite SAR with EMD method. Journal of Guangdong Ocean University, 38(4):77-84

Walsh E J, Wright C W, Vandemark D, et al. 2002. Hurricane directional wave spectrum spatial variation at landfall. Journal of Physical Oceanography, 32(6):1667-1684, doi: 10.1175/1520-0485(2002)032<1667:HDWSSV>2.0.CO;2

Xu Yao, He Hailun, Song Jinbao, et al. 2017. Observations and modeling of typhoon waves in the South China Sea. Journal of Physical Oceanography, 47(6):1307-1324, doi: 10.1175/JPO-D-16-0174.1

Xu Qing, Lin Hui, Li Xiaofeng, et al. 2010. Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR. International Journal of Remote Sensing, 31(4):993-1008, doi: 10.1080/01431160902922870

Zhou Liangming, Wang Aifang, Guo Peifang. 2008. Numerical simulation of sea surface directional wave spectra under typhoon wind forcing. Journal of Hydrodynamics, 20(6):776-783, doi: 10.1016/S1001-6058(09)60015-9

Relative Articles

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(38)

1.	Chunxiao Wang, Xin Qi, Yijun Tao, et al. Deep Learning for Typhoon Wave Height and Spectra Simulation. Remote Sensing, 2025, 17(3): 484. doi:10.3390/rs17030484
2.	Hongxuan Wu, Jingkai Li, Zhaohui Chen, et al. Observed surface wave variations in the background current field of the Kuroshio Extension. Ocean Dynamics, 2025, 75(2) doi:10.1007/s10236-024-01655-2
3.	Shaijie Leng, Mengyu Hao, Weizeng Shao, et al. A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning. Remote Sensing, 2024, 16(9): 1644. doi:10.3390/rs16091644
4.	Weizeng Shao, Meng Wei, Ying Xu, et al. Cyclonic Wind Speed Retrieval From SWIM Wave Spectrum Based on Machine Learning. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1. doi:10.1109/LGRS.2024.3403136
5.	Jingwei Xu, Huanping Wu, Xiefei Zhi, et al. Validation of Multisource Altimeter SWH Measurements for Climate Data Analysis in China’s Offshore Waters. Remote Sensing, 2024, 16(12): 2162. doi:10.3390/rs16122162
6.	Chunxiao Wang, Songlin Li, Huaming Yu, et al. Comparison of wave spectrum assimilation and significant wave height assimilation based on Chinese-French oceanography satellite observations. Remote Sensing of Environment, 2024, 305: 114085. doi:10.1016/j.rse.2024.114085
7.	Brandon J. Bethel, Changming Dong, Jin Wang, et al. An analysis of surface waves in the Caribbean Sea based on a high-resolution numerical wave model. Ocean Modelling, 2024, 190: 102377. doi:10.1016/j.ocemod.2024.102377
8.	Xiaoheng Mou, Wenming Lin. An improved wind quality control for the China-France Oceanography Satellite (CFOSAT) scatterometer. Acta Oceanologica Sinica, 2024, 43(5): 100. doi:10.1007/s13131-024-2322-y
9.	K. Tan, L. Xie, P. Bai, et al. Modulation Effects of Mesoscale Eddies on Sea Surface Wave Fields in the South China Sea Derived From a Wave Spectrometer Onboard the China‐France Ocean Satellite. Journal of Geophysical Research: Oceans, 2023, 128(1) doi:10.1029/2021JC018088
10.	Zhengzhong Lai, Mengyu Hao, Weizeng Shao, et al. Wind field reconstruction based on dual-polarized synthetic aperture radar during a tropical cyclone. European Journal of Remote Sensing, 2023, 56(1) doi:10.1080/22797254.2023.2273867
11.	Guojing Xing, Wei Shen, Meng Wei, et al. Wave and Meso-Scale Eddy Climate in the Arctic Ocean. Atmosphere, 2023, 14(6): 911. doi:10.3390/atmos14060911
12.	Xiaoheng Mou, Wenming Lin, Yijun He. Towards a Consistent Wind Data Record for the CFOSAT Scatterometer. Remote Sensing, 2023, 15(8): 2081. doi:10.3390/rs15082081
13.	Ru Yao, Weizeng Shao, Mengyu Hao, et al. The Respondence of Wave on Sea Surface Temperature in the Context of Global Change. Remote Sensing, 2023, 15(7): 1948. doi:10.3390/rs15071948
14.	Bo Li, Junmin Li, Shilin Tang, et al. Evaluation of CFOSAT Wave Height Data with In Situ Observations in the South China Sea. Remote Sensing, 2023, 15(4): 898. doi:10.3390/rs15040898
15.	Jingwei Xu, Huanping Wu, Ying Xu, et al. Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters. Remote Sensing, 2023, 15(4): 1005. doi:10.3390/rs15041005
16.	Mengyu Hao, Weizeng Shao, Shaohua Shi, et al. Validation of Surface Waves Investigation and Monitoring Data against Simulation by Simulating Waves Nearshore and Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image. Remote Sensing, 2023, 15(18): 4402. doi:10.3390/rs15184402
17.	Benlu Zhu, Jinrui Chen, Ying Xu, et al. Validation of the CFOSAT Scatterometer Data With Buoy Observations and Tests of Operational Application to Extreme Weather Forecasts in Taiwan Strait. Earth and Space Science, 2022, 9(3) doi:10.1029/2021EA001865
18.	Yineng Li, Shaotian Li, Shiqiu Peng, et al. An Updated Real-Time Forecasting System for Marine Environments in the North Indian Ocean. Frontiers in Marine Science, 2022, 9 doi:10.3389/fmars.2022.907087
19.	Wei Wu, Yan Du, Yu‐Kun Qian, et al. Large South Equatorial Current Meander in the Southeastern Tropical Indian Ocean Captured by Surface Drifters Deployed in 2019. Geophysical Research Letters, 2022, 49(4) doi:10.1029/2021GL095124
20.	Jin Wang, Brandon J. Bethel, Changming Dong, et al. Numerical Simulation and Observational Data Analysis of Mesoscale Eddy Effects on Surface Waves in the South China Sea. Remote Sensing, 2022, 14(6): 1463. doi:10.3390/rs14061463
21.	Wei Li, Xukang Xie, Wanqiu Li, et al. Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry. Remote Sensing, 2022, 14(19): 4904. doi:10.3390/rs14194904
22.	Sartajvir Singh, Reet Kamal Tiwari, Vishakha Sood, et al. The Legacy of Scatterometers: Review of applications and perspective. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(2): 39. doi:10.1109/MGRS.2022.3145500
23.	Meijie Liu, Ran Yan, Xi Zhang, et al. Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic. Frontiers in Marine Science, 2022, 9 doi:10.3389/fmars.2022.986228
24.	Ru Yao, Weizeng Shao, Xingwei Jiang, et al. Wind speed retrieval from Chinese Gaofen-3 synthetic aperture radar using an analytical approach in the nearshore waters of China’s seas. International Journal of Remote Sensing, 2022, 43(8): 3028. doi:10.1080/01431161.2022.2079019
25.	Weizeng Shao, Tao Jiang, Yu Zhang, et al. Cyclonic Wave Simulations Based on WAVEWATCH-III Using a Sea Surface Drag Coefficient Derived from CFOSAT SWIM Data. Atmosphere, 2021, 12(12): 1610. doi:10.3390/atmos12121610
26.	Yang Gao, François G. Schmitt, Jianyu Hu, et al. Scaling Analysis of the China France Oceanography Satellite Along‐Track Wind and Wave Data. Journal of Geophysical Research: Oceans, 2021, 126(8) doi:10.1029/2020JC017119
27.	Jiuke Wang, Lotfi Aouf, Sergei Badulin. Retrieval of wave period from altimetry: Deep learning accounting for random wave field dynamics. Remote Sensing of Environment, 2021, 265: 112629. doi:10.1016/j.rse.2021.112629
28.	Guozhou Liang, Jungang Yang, Jichao Wang. Accuracy Evaluation of CFOSAT SWIM L2 Products Based on NDBC Buoy and Jason-3 Altimeter Data. Remote Sensing, 2021, 13(5): 887. doi:10.3390/rs13050887
29.	Meijie Liu, Ran Yan, Jie Zhang, et al. Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles. Remote Sensing, 2021, 14(1): 91. doi:10.3390/rs14010091
30.	Xiuzhong Li, Ying Xu, Baochang Liu, et al. Validation and Calibration of Nadir SWH Products From CFOSAT and HY‐2B With Satellites and In Situ Observations. Journal of Geophysical Research: Oceans, 2021, 126(2) doi:10.1029/2020JC016689
31.	J. K. Wang, L. Aouf, A. Dalphinet, et al. The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights From CFOSAT’s SWIM and Scatterometer Using Deep Learning. Geophysical Research Letters, 2021, 48(6) doi:10.1029/2020GL091276
32.	Yanping Shi, Yan Du, Xiaoqing Chu, et al. Asymmetric Wave Distributions of Tropical Cyclones Based on CFOSAT Observations. Journal of Geophysical Research: Oceans, 2021, 126(4) doi:10.1029/2020JC016829
33.	J. K. Wang, L. Aouf, A. Dalphinet, et al. Acquisition of the Significant Wave Height From CFOSAT SWIM Spectra Through a Deep Neural Network and Its Impact on Wave Model Assimilation. Journal of Geophysical Research: Oceans, 2021, 126(6) doi:10.1029/2020JC016885
34.	Sartajvir Singh, Reet Kamal Tiwari, Hemendra Singh Gusain, et al. Potential Applications of SCATSAT-1 Satellite Sensor: A Systematic Review. IEEE Sensors Journal, 2020, 20(21): 12459. doi:10.1109/JSEN.2020.3002720
35.	Huadong GUO, Dong LIANG, Guang LIU. Progress of Earth Observation in China. Chinese Journal of Space Science, 2020, 40(5): 908. doi:10.11728/cjss2020.05.908
36.	Sartajvir Singh, Reet Kamal Tiwari, Vishakha Sood, et al. Current status of the ISRO’s SCATSAT-1 mission, products, utilisation and future enhancements. INTERNATIONAL CONFERENCE ON ADVANCES IN MULTI-DISCIPLINARY SCIENCES AND ENGINEERING RESEARCH: ICAMSER-2021, doi:10.1063/5.0095273
37.	Ran Yan, Xi Zhang, Ying Xu, et al. Sea Ice Types and Sea Water Distinction in the Arctic Using CFOSAT SWIM Data. 2023 Photonics & Electromagnetics Research Symposium (PIERS), doi:10.1109/PIERS59004.2023.10221367
38.	Meijie Liu, Xi Zhang, Ping Chen, et al. Arctic Sea-ice Type Recognition Based on the Surface Wave Investigation and Monitoring Instrument of the China-French Ocean Satellite. 2021 Photonics & Electromagnetics Research Symposium (PIERS), doi:10.1109/PIERS53385.2021.9695052