Statistical analyses of sea state conditions in South China Sea

The statistical characterization of sea conditions in the South China Sea(SCS) was investigated by analyzing a 30-year(1976–2005) numerically simulated daily wave height and wind speed data. The monthly variation of these parameters shows that wave height and wind speed have minimum values of 0.54 m and 4.15 ms~(-1), respectively in May and peak values of 2.04 m and 8.12 ms~(-1), respectively in December. Statistical analysis of the daily wave height and wind speed and the subsequent characterization of the annual, seasonal and monthly mean sea state based on these parameters were also done. Results showed that, in general, the slight sea state prevails in the SCS and has nearly the highest occurrence in all seasons and months. The moderate sea condition prevails in the winter months of December and January while the smooth(wavelets) sea state prevails in May. Furthermore, spatial variation of sea states showed that calm and smooth sea conditions have high occurrences(25%–80%) in the southern SCS. The slight sea condition shows the largest occurrence(25%–55%) over most parts of the SCS. High occurrences(8%–17%) of the rough and very rough seas distribute over some regions in the central SCS. Sea states from high to phenomenal conditions show rare occurrence(12%) in the northern SCS. The calm(glassy) sea condition shows no occurrence in the SCS.     

Comparison of remote sensing data with in-situ wind observation during the development of the South China Sea monsoon

Wind measurements derived from QuikSCAT data were compared with those measured by anemometer on Yongxing Island in the South China Sea (SCS) for the period from April 2008 to November 2009. The comparison confirms that QuikSCAT estimates of wind speed and direction are generally accurate, except for the extremes of high wind speeds (>13.8m/s) and very low wind speeds (<1.5m/s) where direction is poorly predicted. In-situ observations show that the summer monsoon in the northern SCS starts between May 6 and June 1. From March 13, 2010 to August 31, 2010, comparisons of sea surface temperature (SST) and rainfall from AMSR-E with data from a buoy located at Xisha Islands, as well as wind measurements derived from ASCAT and observations from an automatic weather station show that QuikSCAT, ASCAT and AMSR-E data are good enough for research. It is feasible to optimize the usage of remote-sensing data if validated with in-situ measurements. Remarkable changes were observed in wind, barometric pressure, humidity, outgoing longwave radiation (OLR), air temperature, rainfall and SST during the monsoon onset. The eastward shift of western Pacific subtropical high and the southward movement of continental cold front preceded the monsoon onset in SCS. The starting dates of SCS summer monsoon indicated that the southwest monsoon starts in the Indochinese Peninsula and forms an eastward zonal belt, and then the belt bifurcates in the SCS, with one part moving northeastward into the tropical western North Pacific, and another southward into western Kalimantan. This largely determined the pattern of the SCS summer monsoon. Wavelet analysis of zonal wind and OLR at Xisha showed that intra-seasonal variability played an important role in the summer. This work improves the accuracy of the amplitude of intra-seasonal and synoptic variation obtained from remote-sensed data.     

Wind wave characteristics and engineering environment of the South China Sea

Wave simulation was conducted for the period 1976 to 2005 in the South China Sea (SCS) using the wave model, WAVEWATCH-III. Wave characteristics and engineering environment were studied in the region. The wind input data are from the objective reanalysis wind datasets, which assimilate meteorological data from several sources. Comparisons of significant wave heights between simulation and TOPEX/Poseidon altimeter and buoy data show a good agreement in general. By statistical analysis, the wave characteristics, such as significant wave heights, dominant wave directions, and their seasonal variations, were discussed. The largest significant wave heights are found in winter and the smallest in spring. The annual mean dominant wave direction is northeast (NE) along the southwest (SW)-NE axis, east northeast in the northwest (NW) part of SCS, and north northeast in the southeast (SE) part of SCS. The joint distributions of wave heights and wave periods (directions) were studied. The results show a single peak pattern for joint significant wave heights and periods, and a double peak pattern for joint significant wave heights and mean directions. Furthermore, the main wave extreme parameters and directional extreme values, particularly for the 100-year return period, were also investigated. The main extreme values of significant wave heights are larger in the northern part of SCS than in the southern part, with the maximum value occurring to the southeast of Hainan Island. The direction of large directional extreme H _s values is focus in E in the northern and middle sea areas of SCS, while the direction of those is focus in N in the southeast sea areas of SCS.     

Numerical simulation of wave field in the South China Sea using WAVEWATCH III

Wave fi elds of the South China Sea(SCS) from 1976 to 2005 were simulated using WAVEWATCH III by inputting high-resolution reanalysis wind fi eld datasets assimilated from several meteorological data sources. Comparisons of wave heights between WAVEWATCH III and TOPEX/Poseidon altimeter and buoy data show a good agreement. Our results show seasonal variation of wave direction as follows: 1. During the summer monsoon(April–September), waves from south occur from April through September in the southern SCS region, which prevail taking about 40% of the time; 2. During the winter monsoon(December–March), waves from northeast prevail throughout the SCS for 56% of the period; 3. The dominant wave direction in SCS is NE. The seasonal variation of wave height H s in SCS shows that in spring, H s ≥1 m in the central SCS region and is less than 1 m in other areas. In summer, H s is higher than in spring. During September–November, infl uenced by tropical cyclones, H s is mostly higher than 1 m. East of Hainan Island, H s 2 m. In winter, H s reaches its maximum value infl uenced by the north-east monsoon, and heights over 2 m are found over a large part of SCS. Finally, we calculated the extreme wave parameters in SCS and found that the extreme wind speed and wave height for the 100-year return period for SCS peaked at 45 m/s and 19 m, respectively, SE of Hainan Island and decreased from north to south.     

Long-Term Characterization of Sea Conditions in the East China Sea Using Significant Wave Height and Wind Speed

In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), which was simulated and computed using the WAVEWATCH Ⅲ(WW3) model. The monthly variations of these parameters showed that the significant wave height and wind speed have minimum values of 0.73 m and 5.15 ms~(-1) and 1.73 m and 8.24 ms~(-1) in the month of May and December, respectively. The annual, seasonal, and monthly mean sea state characterizations showed that the slight sea generally prevailed in the ECS and had nearly the highest occurrence in all seasons and months. Additionally, the moderate sea prevailed in the winter months of December and January, while the smooth(wavelets) sea prevailed in May. Furthermore, the spatial variation of sea states showed that the calm and smooth sea had the largest occurrences in the northern ECS. The slight sea occurred mostly(above 30%) in parts of the ECS and the surrounding locations, while higher occurrences of the rough and very rough seas were distributed in waters between the southwest ECS and the northeast South China Sea(SCS). The occurrences of the phenomenal sea conditions are insignificant and are distributed in the northwest Pacific and its upper region, which includes the Southern Kyushu-Palau Ridge and Ryukyu Trench.     

Analysis on monthly-averaged distribution of sea surface wind and wave over the seas southeast of Asia using ERS-2 scatterometer data

The wind system over the seas southeast of Asia (SSEA) plays an important role in China's climate variation. In this paper, ERS scatterometer winds covering the period from January 2000 to December 2000 and the area of 2-41 °N, 105- 130°E were analyzed with a distance-weighting interpolation method and the monthly mean distribution of the sea surface wind speed were given. The seasonal characteristics of winds in the SSEA were analyzed. Based on WAVEWATCH Ⅲ model, distribution of significant wave height was calculated.     

The formation of wind curl in the marine atmosphere boundary layer over the East China Sea Kuroshio in spring

Various data are used to investigate the characteristics of the surface wind field and rainfall on the East China Sea Kuroshio (ESK) in March and April, 2011. In March, the wind speed maximum shows over the ESK front (ESKF) in the 10 meter wind field, which agrees with the thermal wind effect. A wind curl center is generated on the warm flank of the ESKF. The winds are much weaker in April, so is the wind curl. A rainband exists over the ESKF in both the months. The Weather Research and Forecasting (WRF) model is used for further researches. The winds on the top of the marine atmosphere boundary layer (MABL) indicate that in March, a positive wind curl is generated in the whole MABL over the warm flank of the ESKF. The thermal wind effect forced by the strong SST gradient overlying the background wind leads to strong surface northeasterly winds on the ESKF, and a positive shearing vorticity is created over the warm flank of the ESKF to generate wind curl. In the smoothed sea surface temperature experiment, the presence of the ESKF is responsible for the strong northeast winds in the ESKF, and essential for the distribution of the rainfall centers in March, which confirms the mechanism above. The same simulation is made for April, 2011, and the responses from the MABL become weak. The low background wind speed weakens the effect of the thermal wind, thus no strong Ekman pumping is helpful for precipitation. There is no big difference in rainfall between the control run and the smooth SST run. Decomposition of the wind vector shows that local wind acceleration induced by the thermal wind effect along with the variations in wind direction is responsible for the pronounced wind curl/divergence over the ESKF.     

基于空间插值的风场模拟方法比较分析

计算流体动力学方法是目前风场空间格局模拟的主要方法之一,该方法由于受到软硬件局限,多应用于小尺度的风场模拟及分析。该方法精确程度极大地依赖于3D建模的精细程度和迭代计算模型的准确程度,与现实风场的发育过程存在明显差异。而随着物联网技术的发展,我们可通过大量的现场传感器进行风场数据的实时采集,为风场动态实时化模拟提供精确的参数。为了确定风场动态实时化模拟的最佳方法,本文以中国科学院城市环境研究所园区内32个风场传感器的月平均风速数据为研究案例,综合分析了反距离权重插值、全局多项式插值、局域多项式插值、径向基函数插值、最近邻域法插值、普通克里格插值6种空间插值方法,并采用交叉验证的方法对插值结果进行比较。结果表明,反距离权重插值在模拟的误差范围、模拟的准确度、反映极值的能力上优于其他5种方法,为1:500尺度的风场空间格局模拟提供了参考。     

Typhoon wind hazard model and estimation on return period of typhoon wind speed

Typhoons are one of the most serious natural disasters that occur annually on China's southeast coast.A technique for analyzing the typhoon wind hazard was developed based on the empirical track model,and used to generate 1 000-year virtual typhoons for Northwest Pacific basin.The influences of typhoon decay model,track model,and the extreme value distribution on the predicted extreme wind speed were investigated.We found that different typhoon decay models have least influence on the predicted extreme wind speed.Over most of the southeast coast of China,the predicted wind speed by the nonsimplified empirical track model is larger than that from the simplified tracking model.The extreme wind speed predicted by different extreme value distribution is quite different.Four super typhoons Meranti(2016),Hato(2017),Mangkhut(2018),and Lekima(2019) were selected and the return periods of typhoon wind speeds along the China southeast coast were estimated in order to assess the typhoon wind hazard.     

Interannual variability of latent and sensible heat fluxes in the South China Sea

The South China Sea (SCS) is significantly influenced by El Niño and the Southern Oscillation (ENSO) through ENSO-driven atmospheric and oceanic changes. We analyzed measurements made from 1960 to 2004 to investigate the interannual variability of the latent and sensible heat fluxes over the SCS. Both the interannual variations of latent and sensible heat fluxes are closely related to ENSO events. The low-pass mean heat flux anomalies vary in a coherent manner with the low-pass mean Southern Oscillation Index (SOI). Time lags between the heat flux anomalies and the SST anomalies were also studied. We found that latent heat flux anomalies have a minimum value around January of the year following El Niño events. During and after the mature phase of El Niño, a change of atmospheric circulation alters the local SCS near-surface humidity and the monsoon winds. During the mature phase of El Niño, the wind speed decreases over the entire sea, and the air-sea specific humidity difference anomalies decreases in the northern SCS and increases in the southern SCS. Thus, a combined effect of wind speed anomalies and air-sea specific humidity difference anomalies results in the latent heat flux anomalies attaining minimum levels around January of the year following an El Niño year.     

Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea

We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.     

Projections of wind changes for 21st century in China by three regional climate models

This paper examines the capability of three regional climate models(RCMs),i.e.,RegCM3(the International Centre for Theoretical Physics Regional Climate Model),PRECIS(Providing Regional Climates for Impacts Studies)and CMM5(the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA,NCAR Mesoscale Model)to simulate the near-surface-layer winds(10 m above surface)all over China in the late 20th century.Results suggest that like global climate models(GCMs),these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country.However,RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed.In view of their merits,these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century.The results show that 1)summer mean wind speed for 2020-2029 will be lower compared to those in 1990-1999 in most area of China; 2)annual and winter mean wind speed for 2081-2100 will be lower than those of 1971-1990 in the whole China; and 3)the changes of summer mean wind speed for 2081-2100 are uncertain.As a result,although climate models are absolutely necessary for projecting climate change to come,there are great uncertainties in projections,especially for wind speed,and these issues need to be further explored.     

SPATIAL PATTERN OF THE AIR-SEA INTERACTION NEAR THE SOUTH CHINA SEA DURING WINTER

mODUCnONTheSOuthChinaSea(SCS)isasend-enclosedoceanbasinlocatednearthewesternPeripheryofthePacificOcean.SpreadingfIDmtheeqUatorto20"Nands~ngzonallaboutl5'inlooptUde,theSCSlocatesbetweenthesouthChinacoastandtheInaritha6continent,andissurroundedbyInanislandcountries.Duringwinter,S0UthwwhmedngcoldSUrges,mwhfiedbytheSST,affectthepressure,tempethe,andwindfieldsneartheInaritimecontinent,andsomeInayeveninIluencetheS0uthernHdrispheremonsoon(Davids0netal.,1983).msuniqUegeOpophyoftheSCS…     

中国风能资源空间分布的估算

风能是一种清洁的可再生能源,是太阳能的一种转化形式,但风能开发利用的成本比太阳能开发利用的成本要低,它是可再生能源中最具开发前景的一种能源。科学、准确地估算我国风能潜力及其空间分布是国家对风能资源开发中一项极其重要的基础性工作。本文利用全国395个气象站10年、每日4次的气象观测数据,计算了每个气象站所在地区常年有效风能密度和有效风时数,在此基础上,通过空间内插,形成全国范围的风能密度和有效风时数分布数据。结果显示,在全国范围内:有效风能密度大于150W/m²、100～150W/m²、50～100W/m²、小于50W/m²的区域面积占全国国土总面积的百分比分别为2.51%、16.45%、53.39%和27.65%;有效风时数大于5 000h、4 000～5 000h、2 000～4 000h、小于2 000h的区域面积占国土总面积的百分比分别为5.28%、22.19%、53.54%和18.98%。用风能密度和有效风时数两个指标分别表达风能资源潜力虽然存在局部差异,但在总体态势上基本一致,二者之间的相关系数,...     

The research on typhoon wave spectrum in northwestern South China Sea

Based upon the one-year wind wave measurement data, collected from the South China Sea (SCS) at coordinates 20° 36.298′N, 110°45.433′E. by Acoustic Wave And Current (AWAC), we analyzed the wave characteristics and concluded that the most common wave direction was E and the second most common direction was ENE, the mean and the maximum values of significant height was 1.2 m and 4.36 m, respectively. The mean period was 4.0 s. We also evaluated the wave spectrums under conditions existing in three typhoons: Rumbi, Jeti and Utor. We found that unimodal spectrums occurred more often than others, and the maximum spectrum peak was 30.7911 m² s. The minimum peak frequency was 0.0625 Hz, and the mean peak frequency was 0.126 Hz. The wave period is important for the design of marine structures, especially the position of peak frequency had a great influence on the stress calculation. Spectral analysis showed that the values of peak frequency distributed between 0.063 Hz and 0.217 Hz, with the mean value 0.114 Hz. We fit the normalized spectrum with 6 theoretical spectral models, out of which, the Wen spectrum, JONSWAP spectrum and Wallops spectrum were proved to give the best fit. What distinguished the Wen Spectrum from the rest was that it does not rely on the measured spectrum for parameter estimation. Hence, we recommend that the Wen spectrum should be widely used in marine construction.     

基于高斯曲线拟合-FFT方法的南海风场反演

海面风场是海洋学的基本参量,获取海面风场对了解海洋的物理过程以及海洋与大气之间的相互作用至关重要。宽阔的海域面积及复杂的海面状况通常使南海海面上的风场信息很难被及时获取。ENVISAT ASAR是一种全天候全天时监测海面的微波雷达传感器,可实时获取海面风场数据。本文基于已有ASAR数据对南海海面风场进行反演实验,首先将结合高斯曲线拟合的FFT风向反演方法应用于南海风向反演,并参考Cross-Calibrated Multi-Platform （CCMP）风场数据去除180o方向模糊获得海面风向。然后,将高斯曲线拟合-FFT风向与传统的峰值-FFT风向进行对比,最后将准确率较高的高斯曲线拟合-FFT风向分别输入CMOD4模型和CMOD5模型获得海面风速大小。实验结果与CCMP参考数据的比较结果表明,在风条纹不明显的情况下,利用结合高斯曲线的FFT风向反演方法和CMOD4模型风速反演方法可有效地进行南海海面风场反演。该成果对利用SAR数据实时获取南海大面积海面风场信息,尤其是观测点缺乏海域的风场信息,具有重要的指导意义。     

An Alternative Index for the Onset of the Summer Monsoon over the South China Sea

1　Introduction　TheindicesfortheAsianmonsoonhavebeenstud iedinmanyworks .Recently ,thechoiceofpropermonsoonindiceshasreceivedexceptionalattentionandraisedcontroversy (WebsterandYang ,1 992 ;Goswa mietal.,1 999;Goswami,2 0 0 0 ;Wang ,2 0 0 0 ) .Us ingzona…     

Annual and interannual variability of scatterometer ocean surface wind over the South China Sea

To investigate the annual and interannual variability of ocean surface wind over the South China Sea (SCS), the vector empirical orthogonal function (VEOF) method and the Hilbert-Huang transform (HHT) method were employed to analyze a set of combined satellite scatterometer wind data during the period from December 1992 to October 2009. The merged wind data were generated from European Remote Sensing Satellite (ERS)-1/2 Scatterometer, NASA Scatterometer (NSCAT) and NASA’s Quick Scatterometer (QuikSCAT) wind products. The first VEOF mode corresponds to a winter-summer mode which accounts for 87.3% of the total variance and represents the East Asian monsoon features. The second mode of VEOF corresponds to a spring-autumn oscillation which accounts for 8.3% of the total variance. To analyze the interannual variability, the annual signal was removed from the wind data set and the VEOFs of the residuals were calculated. The temporal mode of the first interannual VEOF is correlated with the Southern Oscillation Index (SOI) with a four-month lag. The second temporal interannual VEOF mode is correlated with the SOI with no time lag. The time series of the two interannual VEOFs were decomposed using the HHT method and the results also show a correlation between the interannual variability and El Niño-Southern Oscillation (ENSO) events.     

The Upper Ocean Thermal Structure and the Genesis Locations of Tropical Cyclones in the South China Sea

The relationship between the upper ocean thermal structure and the genesis locations of tropical cyclones （TCs） in the South China Sea （SCS） is investigated by using the Joint Typhoon Warning Center （JTWC） best-track archives and high resolution （1/4 degree） temperature analyses of the world＇s oceans in this paper In the monthly mean genesis positions of TCs from 1945 to 2005 in the SCS, the mean sea surface temperature （SST） was 28.8℃ and the mean depth of 26℃ water was 53.1 m. From the monthly distribution maps of genesis positions of TCs, SST and the depth of 26℃ water in the SCS, we discovered that there existed regions with SST exceeding 26℃ and 26℃ water depth exceeding 50m where no tropical cyclones formed from 1945 to 2005 in the SCS, which suggests that there were other factors unfavorable for TC formation in these regions.     

THE REGIONAL AIR-SEA COUPLED OSCILLATION IN THE SOUTH CHINA SEA

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