Correlation of acoustic scattering variables and physical oceanographic parameter time series in the 2001 ASIAEX South China Sea experiment

2002 ◽  
Vol 112 (5) ◽  
pp. 2449-2449
Author(s):  
T. F. Duda ◽  
J. F. Lynch ◽  
A. E. Newhall ◽  
C.‐S. Chiu ◽  
S. R. Ramp ◽  
...  
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Acoustic Scattering ◽  
China Sea ◽  
Oceanographic Parameter

scholarly journals Seasonal distributions and fluxes of <sup>210</sup>Pb and <sup>210</sup>Po in the northern South China Sea

2014 ◽  
Vol 11 (23) ◽  
pp. 6813-6826 ◽  
Author(s):  
C.-L. Wei ◽  
M.-C. Yi ◽  
S.-Y. Lin ◽  
L.-S. Wen ◽  
W.-H. Lee
Keyword(s):  
Time Series ◽  
South China Sea ◽  
Water Column ◽  
South China ◽  
Time Series Data ◽  
Northern South China Sea ◽  
Sediment Traps ◽  
Series Data ◽  
Secular Equilibrium ◽  
China Sea

Abstract. Vertical distributions of dissolved and particulate 210Pb and 210Po in the water column at the SouthEast Asian Time-series Study (SEATS, 18°00´ N and 116°00´ E) station in the northern South China Sea were determined from four cruises between January 2007 and June 2008. A large deficiency of 210Pb, 379 ± 43 × 103 dpm m−2, from the secular equilibrium was found within the 3500 m water column. On the other hand, a smaller deficiency of 210Po, 100 ± 21 × 103 dpm m−2, relative to 210Pb was found in the water column. Time-series data showed insignificant temporal variability of the 210Pb and 210Po profiles. To balance these deficiencies, the removal fluxes for 210Pb and 210Po via particle settling ranging from 45 to 51 dpm m−2d−1 and from 481 to 567 dpm m−2d−1, respectively, are expected at 3500 m. The 210Pb removal flux is comparable with, whereas the 210Po removal flux is much higher than, the flux directly measured by moored sediment traps. The discrepancy between the modeled 210Po flux and the measured flux suggests that sporadic events that enhance 210Po removal via sinking ballast may occur in the water column at the site.

scholarly journals Physical Forcing-Driven Productivity and Sediment Flux to the Deep Basin of Northern South China Sea: A Decadal Time Series Study

2018 ◽  
Vol 10 (4) ◽  
pp. 971 ◽  
Author(s):  
Hon-Kit Lui ◽  
Kuang-Yu Chen ◽  
Chen-Tung Chen ◽  
Bo-Shian Wang ◽  
Hui-Ling Lin ◽  
...  
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Northern South China Sea ◽  
Sediment Flux ◽  
Deep Basin ◽  
Time Series Study ◽  
China Sea ◽  
Physical Forcing ◽  
Series Study

Depth distributions of alkalinity, TCO2 and at SEATS time-series site in the northern South China Sea

2007 ◽  
Vol 54 (14-15) ◽  
pp. 1469-1485 ◽  
Author(s):  
W.C. Chou ◽  
D.D. Sheu ◽  
B.S. Lee ◽  
C.M. Tseng ◽  
C.T.A. Chen ◽  
...  
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Northern South China Sea ◽  
China Sea ◽  
Depth Distributions

scholarly journals GRACE-Derived Time Lag of Mekong Estuarine Freshwater Transport in the Western South China Sea Validated by Isotopic Tracer Age

2021 ◽  
Vol 13 (6) ◽  
pp. 1193
Author(s):  
Zhongtian Ma ◽  
Hok Sum Fok ◽  
Linghao Zhou
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Cross Correlation ◽  
Time Lag ◽  
Time Lags ◽  
Substantial Impact ◽  
China Sea ◽  
Freshwater Transport ◽  
Western South China Sea

Estuarine freshwater transport has a substantial impact on the near-shore ecosystem and coastal ocean environment away from the estuary. This paper introduces two independent methods to track the Mekong freshwater-induced mass transport by calculating the time lag (or equivalently, the phase) between in situ Mekong basin runoff and the equivalent water height (EWH) time series over the western South China Sea from a gravity recovery and climate experiment (GRACE). The first method is the harmonic analysis that determines the phase difference between annual components of the two time series (called the P-method), and the other is the cross-correlation analysis that directly obtains the time lag by shifting the lagged time series forward to attain the highest cross-correlation between the two time series (called the C-method). Using a three-year rolling window, the time lag variations in three versions of GRACE between 2005 and 2012 are computed for demonstrating the consistency of the results. We found that the time lag derived from the P-method is, on average, slightly larger and more variable than that from the C-method. A comparison of our gridded time lag against the age determined via radium isotopes in September, 2007 by Chen et al. (2010) revealed that our gridded time lag results were in good agreement with most isotope-derived ages, with the largest difference less than 6 days. Among the three versions of the GRACE time series, CSR Release 05 performed the best. The lowest standard deviation of time lag was ~1.6 days, calculated by the C-method, whereas the mean difference for all the time lags from the isotope-derived ages is ~1 day by P-method. This study demonstrates the potential of monitoring Mekong estuarine freshwater transport over the western South China Sea by GRACE.

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 Statistical Prediction of the South China Sea Surface Height Anomaly

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Caixia Shao ◽  
Weimin Zhang ◽  
Chunjian Sun ◽  
Xinmin Chai ◽  
Zhimin Wang
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Decadal Variability ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Statistical Prediction ◽  
Height Anomaly ◽  
China Sea ◽  
Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007.

Sign in / Sign up

Export Citation Format

Share Document