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利用西北印度洋船测数据评估基于卫星的海表面温度 总被引：1，自引：1，他引：0
本文描述了一次夏季在西北印度洋进行的调查船水文测量,用船测数据评估卫星海面表温度,并寻找影响海表面温度误差的主要因素。我们考虑了两种卫星数据,第一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据,另外一种是融合了微波,红外线,以及少部分观测数据的融合数据产品——可处理海表温度和海冰分析OSTIA数据。结果表明融合数据的日平均海表面温度的平均误差和均方根误差都比微波遥感小。这一结果证明了融合红外线遥感,微波遥感以及观测数据来提高海表面温度数据质量的必要性。此外,我们分析了海表面温度误差与各项水文参数之间的相关关系,包括风速,大气温度,想对湿度,大气压力,能见度。结果表明风速与TMI海表面温度误差的相关系数最大。而大气温度是影响OSTIA海表面温度误差最重要的因素;与此同时,想对湿度与海表面温度误差的相关系数也很高。 相似文献
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2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1)大气波导先于海雾存在于黄海海面;受高压下沉影响,黄海上空存在逆温层和较强的湿度梯度,表现为较强的贴海表面波导和非贴海表面波导。(2)海雾始于高压西部,并随高压系统逐渐东移减弱,向黄海北部扩展;辐射冷却虽然使雾顶附近逆温增强,但海雾的机械湍流使其顶部湿度梯度减小,雾顶附近对应弱悬空波导或波导消失。(3)高压系统影响使干空气下沉到雾区导致黄海海雾消散;雾顶附近逆温仍存在,同时湿度梯度增大,黄海上空逐渐变为非贴海表面波导。本研究结果表明:高压系统不仅极易为波导的发生提供有利条件,而且有利于海雾的生成,在海雾演变过程中主要是雾顶水汽梯度的变化导致了波导类型及强度的变化。 相似文献
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一次海雾过程大气波导形成机理的数值研究 总被引：1，自引：0，他引：1
依据船舶导航雷达与沿岸气象探空观测数据得知,2005年6月1～3日黄海海域发生了1次大范围波导现象;进一步结合卫星云图与沿岸测站水平能见度观测,发现此次波导伴随1次明显的平流海雾过程。利用WRF模式对此次海雾与波导过程进行了数值模拟,发现:(1)海雾始于黄海中部,绝大部分海雾的雾顶由于弱逆温、湿度梯度较小而不存在波导;(2)雾区随其北部低压的逐渐东移而向东扩展,呈现西部薄、东部厚的结构,雾体顶部由于存在逆温与湿度锐减而形成了波导,混合均匀的雾体则成为波导基础层,薄雾顶部为非贴海表面波导,而厚雾顶部则为悬空波导;(3)雾区受低压西部冷空气的影响向南消退,波导基础层逐渐变薄乃至消失,雾体之上的逆温与湿度锐减层随之下降,非贴海表面波导被强度较弱的贴海波导所替代。分析结果表明:黄海平流海雾与波导有密切的联系,海雾形成及其发展改变了海洋大气边界层的温度与湿度垂直结构,从而导致了波导的发生与演变,大气波导可认为是海雾的"副产品"。 相似文献
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日本信息服务中心自1985年以来就利用卫星测得的海表面温度(SST)来预报捕鱼区的位置.从NOAA极轨卫星上AVHRR获得的数据通过GOES-TAP数据传播系统,并与其它有关的各种数据相结合,形成海表面温度变化的成像.这些成像用来预报捕鱼区,这些预报很快传送到捕鱼队. 相似文献
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利用1909—2008年共100年间的国际综合海洋-大气资料集(International Comprehensive Ocean-Atmosphere Data Set,ICOADS)对北太平洋和东亚海域海雾的发生频率及海雾发生时主要气象要素特征进行了分析。研究发现,北太平洋海雾主要发生在中高纬度海域,从北海道到阿留申群岛以南的海域是海雾发生频率较大的地区,海雾发生频率的最大值在40%以上,而在低纬度海域海雾频率几乎为零。4~8月是北太平洋上发生海雾较为频繁的季节,4~7月中国近海海雾主要发生在黄海、东海和渤海海域。6月份山东半岛以南海域海雾最大频率可达20%,进入8月后,海雾频率突然降低到5%以下。海雾发生时,千岛群岛以东海域风向主要以南风为主,其次为东南风和西南风居多,海上风速在4.4~12.3m·s-1之间。海雾发生时气温通常接近于露点温度,甚至有部分低于露点温度。海雾发生前,千岛群岛以东海域上气海温差多在-1~3℃之间。 相似文献
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利用卫星资料分析黄海海表温度的年际与年代际变化 总被引：1，自引：0，他引：1
海表温度长期变化在一定程度上反映了海域的气候变化信号,卫星遥感资料是获取高时空分辨率水温长期变化的有效手段。基于国家海洋局1982—1999年黄海断面监测器测数据的2 954组水温数据对时空匹配的卫星(NOAA/AVHRR)反演海表温度(SST)进行校验,计算得到卫星反演SST系统偏差为(0.18±1.00)℃。卫星反演的水温空间分布以及长期变化趋势与器测趋势较为一致,可以用来研究海域SST长期变化规律。利用校验后1982-01～2011-08NOAA/AVHRR的SST数据,分析了该时段黄海冬夏季代表月2、8月海表水温的变化规律。结果显示:(1)近30a,黄海冬季水温有2次跃迁:1989—1990年由冷至暖的状态跃迁,2000-2001年出现由暖至冷的状态转变;1990年代冬季水温达最高,相比1880年代,水温升高1.07℃,新世纪水温稍有降低,水温较1990年代下降了0.53℃,温度变化较大区域位于北黄海、山东半岛沿岸,苏北浅滩毗邻海区,该区SST与局地经向风场存在显著正相关,且北极涛动通过影响冬季风间接影响黄海水温变化;(2)夏季海表水温在1994—1995年呈现由冷至暖的状态跃迁,冷、暖期水温相差0.57℃,水温变化较显著的区域为黄东海分界处,其具体变化机制需深入研究。 相似文献
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青岛近海夏季海雾年际变化的低空气象水文条件分析——关于水汽来源的讨论 总被引：1，自引：0，他引：1
本文采用1971-2009年月雾日数资料、1970-2007每日4次能见度观测资料、MICAPS系统提供的每日3h/1次的地面观测资料、JRA-25再分析资料,对青岛近海夏季(6～7月)海雾年际变化的低空气象水文条件进行了合成分析,发现长江口以东的东海海域是影响青岛近海海雾多寡的水汽来源关键区域(122°E～130°E,28°N～32°N),黄海局地海表面蒸发增湿所提供的水汽贡献不大.海表面温度(SST)对海雾的形成在黄、东海起着不同的重要作用.多雾年,东海SST偏高,海面蒸发较大,为低空气流提供了热量和水汽,黄海SST偏低,海面蒸发较小,有利于低空气流的降温增湿,从长江口以东海域向黄海输送的低空暖湿平流是海雾形成的主要物质基础.去掉月平均合成中降水、沙尘等因素对能见度的影响,针对2005-2007年6、7月42个雾日的统计分析,进一步证明了长江口以东海域水汽输送对黄海海雾形成有重要影响;对其中5个雾日的水汽源地追踪,表明在天气时间尺度下,水汽路径是从东海在低空南风的引导下向北到达青岛近海. 相似文献
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青岛地区海雾多发,观测表明海雾对沿海地区影响范围不尽相同,特别是海雾影响内陆的机理尚缺乏研究。本文利用观测资料及数值模式统计了青岛地区4月-7月海雾分布特征,并对不同影响范围海雾典型个例进行对比分析,结果表明:海雾发生日数自沿海向内陆递减。胶州湾沿岸雾日数比黄海沿岸明显减少,胶州湾东北部的雾日数要少于胶州湾西北部。海雾多发生于高空形势稳定,低层偏南流场的天气条件下。大气边界层内逆温层的的范围大致影响着海雾的分布。只影响沿海的海雾,地面为偏南风,风速在3~8 m/s之间,内陆风力减弱不明显。500 m以下大气边界层内风速切变大。湍流作用使海雾向内陆推进过程中倾斜抬升为低云,地面雾区减弱。能够影响内陆地区的海雾,多出现在地面风力较弱的情况之下,大部分在1~3 m/s之间。500 m以下大气边界层内风速切变小,大气边界层内湍流强度不强,使沿海到内陆的逆温层能够始终维持,沿海海雾在弱南风影响下延伸影响内陆地区。 相似文献
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2010年2月一次冬季黄海海雾的成因分析 总被引：1，自引：0，他引：1
利用青岛浮标观测、自动气象站观测、Micaps站点观测、L波段雷达等观测数据,New Generation SST,OI-SST和NCEP提供的FNL和CFSR再分析数据。并利用中尺度模式WRF对这次冬季海雾进行诊断分析。得到以下结论:(1)观测表明,这次海雾首先在黄海北部生成,是由于冷暖空气在黄海海域交汇,增大相对湿度,形成混合雾。在22日12:00时(UTC)之后,暖平流北上,冷平流消失。海雾逐渐转成平流冷却雾。青岛出现的海雾是从黄海发展过来的,并且为平流冷却雾。(2)在黄海,冷暖空气混合增大相对湿度,生成混合雾。与后期的平流冷却雾相比,混合雾的高度明显偏低。(3)海温异常偏低。在2010年2月渤海大面积结冰,海温偏低可能与融冰有关系。(4)模式结果表明,混合雾与冷水域的关系密切。平流冷却雾与冷水域的位置基本一致。混合雾和平流冷却雾都受海温影响较大。混合雾雾区变化很大,因为冷空气在移动过程中变性,不利于混合雾生成。冷海面对平流冷却雾起着很关键的作用。这次冬季海雾与春夏季黄海海雾的不同点在:这次海雾的发生机制不同于典型的春夏季黄海海雾。春夏季典型的黄海海雾主要是平流冷却雾,而这次冬季海雾在生成上首先是混合雾,后来转为平流冷却雾。 相似文献
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Ang Li Xueming Zhu Yunfei Zhang Shihe Ren Miaoyin Zhang Ziqing Zu Hui Wang 《海洋学报(英文版)》2021,40(11):87-103
In order to satisfy the increasing demand for the marine forecasting capacity, the Bohai Sea, the Yellow Sea and the East China Sea Operational Oceanography Forecasting System (BYEOFS) has been upgraded and improved to Version 2.0. Based on the Regional Ocean Modeling System (ROMS), a series of comparative experiments were conducted during the improvement process, including correcting topography, changing sea surface atmospheric forcing mode, adjusting open boundary conditions, and considering atmospheric pressure correction. (1) After the topography correction, the volume transport and meridional velocity maximum of Yellow Sea Warm Current increase obviously and the unreasonable bending of its axis around 36.1°N, 123.5°E disappears. (2) After the change of sea surface forcing mode, an effective negative feedback mechanism is formed between predicted sea surface temperature (SST) by the ocean model and sea surface radiation fluxes fields. The simulation errors of SST decreased significantly, and the annual average of root-mean-square error (RMSE) decreased by about 18％. (3) The change of the eastern lateral boundary condition of baroclinic velocity from mixed Radiation-Nudging to Clamped makes the unreasonable westward current in Tsushima Strait disappear. (4) The adding of mean sea level pressure correction option which forms the mean sea level gradient from the Bohai Sea and the Yellow Sea to the western Pacific in winter and autumn is helpful to increasing the fluctuation of SLA and outflow of the Yellow Sea when the cold high air pressure system controls the Yellow Sea area. 相似文献
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Coupled seasonal variability in the South China Sea 总被引：2，自引：0，他引：2
The present study documents the relationship between seasonal variations in sea surface temperature (SST) and precipitation in the South China Sea (SCS) region. There are strong interactions between the atmosphere and ocean in the seasonal variations of SST and precipitation. During the transition to warm and cold seasons, the SST tendency is primarily contributed by net heat flux dominated by shortwave radiation and latent heat flux with a complementary contribution from ocean advection and upwelling. The contribution of wind-driven oceanic processes depends on the region and is more important in the northern SCS than in the southern SCS. During warm and cold seasons, local SST forcing contributes to regional precipitation by modulating the atmospheric stability and lower-level moisture convergence. The SST difference between the SCS and the western North Pacific influences the convection over the SCS through its modulation of the circulation pattern. 相似文献
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LIU Xiying 《海洋学报(英文版)》2014,33(9):56-67
The Coupling of three model components, WRF/PCE （polar climate extension version of weather research and forecasting model （WRF））, ROMS （regional ocean modeling system）, and CICE （community ice code）, has been implemented, and the regional atmosphere-ocean-sea ice coupled model named WRF/PCE- ROMS-CICE has been validated against ERA-interim reanalysis data sets for 1989. To better understand the reasons that generate model biases, the WRF/PCE-ROMS-CICE results were compared with those of its components, the WRF/PCE and the ROMS-CICE. There are cold biases in surface air temperature （SAT） over the Arctic Ocean, which contribute to the sea ice concentration （SIC） and sea surface temperature （SST） biases in the results of the WRF/PCE-ROMS-CICE. The cold SAT biases also appear in results of the atmo- spheric component with a mild temperature in winter and similar temperature in summer. Compared to results from the WRF/PCE, due to influences of different distributions of the SIC and the SST and inclusion of interactions of air-sea-sea ice in the WRF/PCE-ROMS-CICE, the simulated SAT has new features. These influences also lead to apparent differences at higher levels of the atmosphere, which can be thought as responses to biases in the SST and sea ice extent. There are similar atmospheric responses in feature of distribution to sea ice biases at 700 and 500 hPa, and the strength of responses weakens when the pressure decreases in January. The atmospheric responses in July reach up to 200 hPa. There are surplus sea ice ex- tents in the Greenland Sea, the Barents Sea, the Davis Strait and the Chukchi Sea in winter and in the Beau- fort Sea, the Chukchi Sea, the East Siberian Sea and the Laptev Sea in summer in the ROMS-CICE. These differences in the SIC distribution can all be explained by those in the SST distributions. These features in the simulated SST and SIC from ROMS-CICE also appear in the WRF/PCE-ROMS-CICE. It is shown that the performance of the WRF/PCE-ROMS-CICE is determined to a l 相似文献
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《Ocean Modelling》2011,39(3-4):267-279
Near-surface enhancement of turbulent mixing and vertical mixing coefficient for temperature owing to the effect of surface wave breaking is investigated using a two-dimensional (2-D) ocean circulation model with a tidal boundary condition in an idealized shelf sea. On the basis of the 2-D simulation, the effect of surface wave breaking on surface boundary layer deepening in the Yellow Sea in summer is studied utilizing a 3-D ocean circulation model. A well-mixed temperature surface layer in the Yellow Sea can be successfully reconstructed when the effect of surface wave breaking is considered. The diagnostic analysis of the turbulent kinetic energy equation shows that turbulent mixing is enhanced greatly in the Yellow Sea in summer by surface wave breaking. In addition, the diagnostic analysis of momentum budget and temperature budget also show that surface wave breaking has an evident contribution to the turbulent mixing in the surface boundary layer. We therefore conclude that surface wave breaking is an important factor in determining the depth of the surface boundary layer of temperature in the Yellow Sea in summer. 相似文献
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The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS. 相似文献
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In this study, we evaluate the performance of the recently developed incremental strong constraint 4-dimensional variational (4DVAR) data assimilation applied to the Yellow Sea (YS) using the Regional Ocean Modeling System (ROMS). Two assimilation experiments are compared: assimilating remote-sensed sea surface temperature (SST) and both the SST and in-situ profiles measured by shipboard CTD casts into a regional ocean modeling from January to December of 2011. By comparing the two assimilation experiments against a free-run without data assimilation, we investigate how the assimilation affects the hydrographic structures in the YS. Results indicate that the SST assimilation notably improves the model behavior at the surface when compared to the non-assimilative free-run. The SST assimilation also has an impact on the subsurface water structure in the eastern YS; however, the improvement is seasonally dependent, that is, the correction becomes more effective in winter than in summer. This is due to a strong stratification in summer that prevents the assimilation of SST from affecting the subsurface temperature. A significant improvement to the subsurface temperature is made when the in-situ profiles of temperature and salinity are assimilated, forming a tongue-shaped YS bottom cold water from the YS toward the southwestern seas of Jeju Island. 相似文献
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2013年夏季黄、渤海颗粒有机碳分布及来源分析 总被引：3，自引：3，他引：0
本文根据2013年夏季黄、渤海海域航次获得的颗粒有机碳(particulate organic carbon, POC)、叶绿素a(chlorophyll a, Chl a)和总悬浮颗粒物(total suspended particles, TSP)数据,结合同步获得的水文环境参数,综合探讨该区夏季POC时空分布特征,以及在不同温盐深水团中POC的主要影响因素。结果表明:在整个研究区POC的浓度范围为102.3～1850.0 μg/L,平均值为(383.7±269.6) μg/L,分布呈现出近岸高、远海低、表层低、底层高的特征。苏北外浅滩海域和北黄海东北区域的10 m层和底层为POC高值区,苏北外海域受到陆源输入、沿岸流混合作用和浮游植物光合作用的影响,POC上下混合均匀且浓度高;南黄海中部因受黄海环流的影响,水体中浮游植物生产力水平低,POC浓度较低。在垂直分布上,近岸海域受陆源输入和再悬浮影响POC浓度高,上下混合均匀;在南黄海和北黄海中部受到黄海环流和黄海冷水团的控制,浮游植物生产力水平低,POC浓度低。对不同温盐水团中POC的影响因素分析发现,在高温低盐水团中,POC受浮游植物初级生产和陆源输入的共同影响;在温盐适中区真光层海水中,浮游植物的初级生产是POC的主要来源;底层的冷水团区,POC主要来源为上层海水中颗粒物的沉降和底层再悬浮作用。 相似文献
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对渤海、黄海海域冬、夏两季表层沉积物取样,通过激光粒度仪得出粒度参数,进而分析讨论冬季强的沿岸流的作用、黄海暖流、夏季冷水团的影响以及地形、海底地貌特征、物源特征等对表层沉积物分布造成的影响。结果表明,冬、夏两季渤黄海表层沉积物粒度特征总体上相差不大,但部分海域如渤海中北部、渤海中南部、北黄海西北部近渤海海峡北部海域、山东半岛东北部海域、南黄海中部沉积物粒度特征存在明显季节性差异。表层沉积物粒度特征季节性差异与地形地貌、沿岸流、黄海暖流、黄海冷水团及物源密切相关。本研究对于探讨渤黄海不同季节表层沉积物沉积特征的影响机制、了解渤黄海区海洋动力过程的季节差异有积极意义。 相似文献