Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation

2015 ◽  
Vol 33 (5) ◽  
pp. 1309-1319 ◽  
Author(s):  
Lei Cao ◽  
Yijun Hou ◽  
Peng Qi
Keyword(s):  
Data Assimilation ◽  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
The South China Sea ◽  
Optimal Interpolation ◽  
Significant Wave ◽  
Ensemble Optimal Interpolation ◽  
Height Data

scholarly journals Long-Term Variability of Extreme Significant Wave Height in the South China Sea

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Adekunle Osinowo ◽  
Xiaopei Lin ◽  
Dongliang Zhao ◽  
Zhifeng Wang
Keyword(s):  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
The South China Sea ◽  
Wave Hindcast ◽  
Significant Wave ◽  
China Sea ◽  
Increasing Trend

This paper describes long-term spatiotemporal trends in extreme significant wave height (SWH) in the South China Sea (SCS) based on 30-year wave hindcast. High-resolution reanalysis wind field data sets are employed to drive a spectral wave model WAVEWATCH III™ (WW3). The wave hindcast information is validated using altimeter wave information (Topex/Poseidon). The model performance is satisfactory. Subsequently, the trends in yearly/seasonal/monthly mean extreme SWH are analyzed. Results showed that trends greater than 0.05 m yr−1are distributed over a large part of the central SCS. During winter, strong positive trends (0.07–0.08 m yr−1) are found in the extreme northeast SCS. Significant trends greater than 0.01 m yr−1are distributed over most parts of the central SCS in spring. In summer, significant increasing trends (0.01–0.05 m yr−1) are distributed over most regions below latitude 16°N. During autumn, strong positive trends between 0.02 and 0.08 m yr−1are found in small regions above latitude 12°N. Increasing positive trends are found to be generally significant in the central SCS in December, February, March, and July. Furthermore, temporal trend analysis showed that the extreme SWH exhibits a significant increasing trend of 0.011 m yr−1. The extreme SWH exhibits the strongest increasing trend of 0.03 m yr−1in winter and showed a decreasing trend of −0.0098 m yr−1in autumn.

scholarly journals Extreme significant wave height of tropical cyclone waves in the South China Sea

2019 ◽  
Vol 19 (10) ◽  
pp. 2067-2077 ◽  
Author(s):  
Zhuxiao Shao ◽  
Bingchen Liang ◽  
Huajun Li ◽  
Ping Li ◽  
Dongyoung Lee
Keyword(s):  
Tropical Cyclone ◽  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
Assessment Method ◽  
The South China Sea ◽  
Significant Wave ◽  
Wave Heights ◽  
Significant Wave Heights

Abstract. Extreme significant wave heights are assessed in the South China Sea (SCS), as assessments of wave heights are crucial for coastal and offshore engineering. Two significant factors include the initial database and assessment method. The initial database is a basis for assessment, and the assessment method is used to extrapolate appropriate return-significant wave heights during a given period. In this study, a 40-year (1975–2014) hindcast of tropical cyclone waves is used to analyse the extreme significant wave height, employing the peak over threshold (POT) method with the generalized Pareto distribution (GPD) model. The peak exceedances over a sufficiently large value (i.e. threshold) are fitted; thus, the return-significant wave heights are highly dependent on the threshold. To determine a suitable threshold, the sensitivity of return-significant wave heights and the characteristics of tropical cyclone waves are studied. The sample distribution presents a separation that distinguishes the high sample from the low sample, and this separation is within the stable threshold range. Because the variation in return-significant wave heights in this range is generally small and the separation is objectively determined by the track and intensity of the tropical cyclone, the separation is selected as a suitable threshold for extracting the extreme sample in the tropical cyclone wave. The asymptotic tail approximation and estimation uncertainty show that the selection is reasonable.

scholarly journals Long-Term Prediction of Significant Wave Height Based on SARIMA Model in the South China Sea and Adjacent Waters

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 88082-88092 ◽  
Author(s):  
Shaobo Yang ◽  
Zhenquan Zhang ◽  
Linlin Fan ◽  
Tianliang Xia ◽  
Shanhua Duan ◽  
...  
Keyword(s):  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
The South China Sea ◽  
Sarima Model ◽  
Significant Wave ◽  
China Sea ◽  
Long Term Prediction

scholarly journals An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 609-627 ◽  
Author(s):  
J. Xie ◽  
F. Counillon ◽  
J. Zhu ◽  
L. Bertino
Keyword(s):  
Data Assimilation ◽  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Optimal Interpolation ◽  
Altimeter Data ◽  
The South ◽  
Validation Data ◽  
China Sea ◽  
Along Track

Abstract. The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation.

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