scholarly journals Multi-temporal patterns of upwelling–downwelling dynamics in the South China Sea based on a 47-year time-series of the NOAA-ERD upwelling index

2017 ◽  
Vol 16 ◽  
pp. 225-239 ◽  
Author(s):  
Anthony Banyouko Ndah ◽  
Lalit Dagar ◽  
Kazimierz Becek
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Temporal Patterns ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Multi Temporal ◽  
Upwelling Index

scholarly journals Time Series Analysis of Evaporation Duct Height over South China Sea: A Stochastic Modeling Approach

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1663
Author(s):  
Fei Hong ◽  
Qi Zhang
Keyword(s):  
Time Series ◽  
South China Sea ◽  
Stochastic Modeling ◽  
South China ◽  
Arima Model ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Evaporation Duct ◽  
Evaporation Duct Height

The evaporation duct could significantly affect the work status of maritime microwave communication systems in the South China Sea. Therefore, the exact forecasting of the evaporation duct is vital for the normal operation of the systems. This study presents a stochastic modeling approach to predict the future trends of the evaporation duct over the South China Sea. The autoregressive integrated moving average (ARIMA) model has been used for modeling the monthly evaporation duct height estimated from the Climate Forecast System Reanalysis dataset released by the National Centers for Environment Prediction. The long-term evaporation duct height data were collected for a period of 10 years from 2008 to 2017. The analysis of correlation function reveals the existence of seasonality in the time series. Therefore, a seasonal ARIMA model with the form as ARIMA (0,0,1) × (0,1,2)12 is proposed by fitting the monthly data optimally. The fitted model is further used to forecast the evaporation duct variation for the year 2018 at 95% level of confidence, and high-accuracy results are obtained. Our study demonstrates the feasibility of the proposed stochastic modeling technique to predict the future variations of the evaporation duct over South China Sea.

scholarly journals HIMAWARI-8 GEOSTATIONARY SATELLITE OBSERVATION OF THE INTERNAL SOLITARY WAVES IN THE SOUTH CHINA SEA

2018 ◽  
Vol XLII-3 ◽  
pp. 363-370 ◽  
Author(s):  
Q. Gao ◽  
D. Dong ◽  
X. Yang ◽  
L. Husi ◽  
H. Shang
Keyword(s):  
Time Series ◽  
South China Sea ◽  
Solitary Waves ◽  
South China ◽  
Geostationary Satellite ◽  
The South China Sea ◽  
Internal Solitary Waves ◽  
The South ◽  
China Sea ◽  
New Generation

The new generation geostationary meteorological satellite, Himawari-8 (H-8), was launched in 2015. Its main payload, the Advanced Himawari Imager (AHI), can observe the earth with 10-minute interval and as high as 500–m spatial resolution. This makes the H-8 satellite an ideal data source for marine and atmospheric phenomena monitoring. In this study, the propagation of internal solitary waves (ISWs) in the South China Sea is investigated using AHI imagery time series for the first time. Three ISWs cases were studied at 3:30–8:00 UTC on 30 May, 2016. In all, 28 ISWs were detected and tracked between the time series image pairs. The propagation direction and phase speeds of these ISWs are calculated and analyzed. The observation results show that the properties of ISW propagation not stable and maintains nonlinear during its lifetime. The resultant ISW speeds agree well with the theoretical values estimated from the Taylor-Goldstein equation using Argo dataset. This study has demonstrated that the new generation geostationary satellite can be a useful tool to monitor and investigate the oceanic internal waves.

scholarly journals The SouthEast Asian Time-series Study (SEATS) and the biogeochemistry of the South China Sea—An overview

2007 ◽  
Vol 54 (14-15) ◽  
pp. 1434-1447 ◽  
Author(s):  
George T.F. Wong ◽  
Teh-Lung Ku ◽  
Margaret Mulholland ◽  
Chun-Mao Tseng ◽  
Dong-Ping Wang
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Southeast Asian ◽  
The South China Sea ◽  
The South ◽  
Time Series Study ◽  
China Sea ◽  
Series Study
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