Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

2016 ◽  
Vol 25 (11) ◽  
pp. 110502 ◽  
Author(s):  
Jian Zhong ◽  
Gang Dong ◽  
Yimei Sun ◽  
Zhaoyang Zhang ◽  
Yuqin Wu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
South China Sea ◽  
South China ◽  
Time Series Prediction ◽  
Surface Wind ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
The South China Sea ◽  
China Sea

scholarly journals Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 766
Author(s):  
Yi Jiang ◽  
Shuai Han ◽  
Chunxiang Shi ◽  
Tao Gao ◽  
Honghui Zhen ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Surface Wind ◽  
Hainan Island ◽  
The South China Sea ◽  
Wind Data ◽  
The South ◽  
Near Surface ◽  
China Sea

Near-surface wind data are particularly important for Hainan Island and the South China Sea, and there is a wide range of wind data sources. A detailed understanding of the reliability of these datasets can help us to carry out related research. In this study, the hourly near-surface wind data from the High-Resolution China Meteorological Administration (CMA) Land Data Assimilation System (HRCLDAS) and the fifth-generation ECMWF atmospheric reanalysis data (ERA5) were evaluated by comparison with the ground automatic meteorological observation data for Hainan Island and the South China Sea. The results are as follows: (1) the HRCLDAS and ERA5 near-surface wind data trend was basically the same as the observation data trend, but there was a smaller bias, smaller root-mean-square errors, and higher correlation coefficients between the near-surface wind data from HRCLDAS and the observations; (2) the quality of HRCLDAS and ERA5 near-surface wind data was better over the islands of the South China Sea than over Hainan Island land. However, over the coastal areas of Hainan Island and island stations near Sansha, the quality of the HRCLDAS near-surface wind data was better than that of ERA5; (3) the quality of HRCLDAS near-surface wind data was better than that of ERA5 over different types of landforms. The deviation of ERA5 and HRCLDAS wind speed was the largest along the coast, and the quality of the ERA5 wind direction data was poorest over the mountains, whereas that of HRCLDAS was poorest over hilly areas; (4) the accuracy of HRCLDAS at all wind levels was higher than that of ERA5. ERA5 significantly overestimated low-grade winds and underestimated high-grade winds. The accuracy of HRCLDAS wind ratings over the islands of the South China Sea was significantly higher than that over Hainan Island land, especially for the higher wind ratings; and (5) in the typhoon process, the simulation of wind by HRCLDAS was closer to the observations, and its simulation of higher wind speeds was more accurate than the ERA5 simulations.

scholarly journals Seasonal Variability of Waves Within the Indonesian Seas and Its Relation With the Monsoon Wind

2021 ◽  
Vol 26 (3) ◽  
pp. 189-196
Author(s):  
Purwanto Purwanto ◽  
Denny Nugroho Sugianto ◽  
Muhammad Zainuri ◽  
Galuh Permatasari ◽  
Warsito Atmodjo ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Wind Wave ◽  
Surface Wind ◽  
The South China Sea ◽  
The South ◽  
Indonesian Seas ◽  
China Sea ◽  
Arafura Sea ◽  
Monsoon Wind

The previous studies have simulated the variability of the wave within the Indonesian seas which showed that the variability of wave follows the seasonal pattern. However, their analysis only consider the influence of local wind forcings. The bias and error of their simulated wave were also unclear. In the present study, we investigate the variability of wave within the Indonesian seas and its relation with the surface wind speed using the combination of reanalysis and remote sensing data with high accuracies. We split the analysis into swell and wind wave to obtain the influence of local and remote wind forcings. We show that at the inner seas (i.e., the South China Sea, Java Sea, Flores Sea, Banda Sea and Arafura Sea), the variability of significant wave height (SWH) is majorly influenced by the variability of the speed of monsoon wind. The maximum SWH during Northwest monsoon (NWM) season is located at the South China Sea while during Southeast monsoon (SEM) season is at Arafura Sea. This indicates that the wind wave (sea) is dominant at the inner seas. At the open seas (i.e., Pacific Ocean and Indian Ocean) the variability of SWH less corresponds to the the speed of monsoon wind. The remote wind forcings control the wave variability in the open ocean area. This indicates that swell is dominant at the open seas. In general, the magnitude of SWHswell is also more than SWHsea within the Indonesian seas.

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.

Synoptic Flow Patterns and Large-Scale Characteristics Associated with Rapidly Intensifying Tropical Cyclones in the South China Sea

2015 ◽  
Vol 143 (1) ◽  
pp. 64-87 ◽  
Author(s):  
Xiaomin Chen ◽  
Yuqing Wang ◽  
Kun Zhao
Keyword(s):  
Environmental Factors ◽  
South China Sea ◽  
Tropical Cyclones ◽  
South China ◽  
Surface Wind ◽  
Flow Patterns ◽  
The South China Sea ◽  
Intensity Change ◽  
The South ◽  
China Sea

Abstract The typical synoptic flow patterns and environmental factors that favor the rapid intensification (RI) of tropical cyclones (TCs) in the South China Sea (SCS) have been identified based on all TCs formed in the SCS between 1981 and 2011. The quantity RI is defined as the 24-h increase in maximum sustained surface wind speed by 15 m s−1 as in previous studies, which is close to the 95th percentile of 24-h intensity change of all SCS samples excluding those after landfall. There are 4.9% (2.3%) of tropical depressions (tropical storms) that experienced RI. No typhoons satisfied the RI threshold. Six low-level synoptic flow patterns favoring RI have been identified based on 18 RI cases. In the monsoon season very few TCs experience RI due to large vertical wind shear (VWS). Most RI cases occurred in the postmonsoon season when the midlatitude troughs often penetrated into the SCS whereas the southwesterly monsoon flow is still strong in the southern SCS. Compared with those of non-RI cases, the mean initial conditions of RI cases include weak VWS and relatively strong forcing from midlatitude troughs. Several criteria of significant environmental factors for RI are statistically identified based on all TC samples. It is found that 16 non-RI TCs fitted in the RI flow patterns but only two of them satisfy all the criteria, suggesting that a combination of the synoptic flow pattern and the environmental factors can be used to predict RI in the SCS. In addition, two RI cases involving TC–trough interaction are analyzed.

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