基于ARGO数据的日本以南黑潮水温垂直变化研究

以日本以南的黑潮下游段为研究区,使用中国ARGO实时资料,中心提供的2005～2009年的ARGO浮标资料,研究该范围的温度垂直分布规律,并利用同纬度带开阔大洋的ARGO资料进行对比分析.发现在该海域200m深度附近存在—不随季节变化的恒温层,温度常年保持在19.1℃附近,标准偏差0 5℃,且该现象自西向东渐不明显.恒...     

卫星遥感SSH数据在水下温度垂直分布反演中的应用研究

结合MODAS基本原理,应用动态气候态的数据同化概念,阐述应用卫星遥感SSH数据反演水下温度垂直分布的理论和方法,并利用西太平洋海区的ARGO数据进行试验,建立了单点的温度剖面,通过引入SSH数据进行水温反演,演示结果显示了卫星遥感SSH资料同化的有效性,并得出分析结论。     

基于遥感数据的温度剖面预报研究

为了提高温度剖面的预报精度,提出了一种基于遥感数据的温度剖面预报方法.文中使用27个实测温度剖面和遥感SST、SSHA数据实现了对温度剖面的预报,并用该点的ARGO数据进行了检验.实验结果表明,将遥感数据同化到温度剖面的预报中是可行的,并能有效的提高温度剖面的预报精度.     

ARGO剖面浮标电导率传感器漂移问题探讨

ARGO剖面浮标在海上长期工作,其携带的电导率传感器漂移会导致测量数据误差,从而影响观测资料的质量。文章利用误差理论和回归分析、曲线拟合等数理统计方法,检测和处理由ARGO剖面浮标电导率传感器漂移所带来的测量误差,并对太平洋、大西洋和印度洋上已经发现有电导率传感器漂移的ARGO资料进行了校正,取得了比较理想的结果。     

日本海温度跃层分布特征概况

在收集日本海海域ARGO和全球温盐剖面计划(GTSPP)海洋调查资料的基础上,逐站进行计算分析温度跃层,参考相关海洋水文图集,阐明了整个日本海温度跃层的海区和季节特点及其变化规律。日本海地理、气象和水文条件具有明显的多样性,致使温度跃层区域分布差异悬殊、变化复杂。温度跃层对海洋科学、舰艇航行等都有重要的学术意义和实际应用意义。     

ARGO剖面浮标数据质量控制过程剖析

ARGO计划的观测目标是能取得精度分别为0．5℃和0．01PSU的海水温度和盐度资料。然而，由于目前海水盐度是采用海水电导率间接导出的，而测量海水电导率的传感器很容易产生偏差。因此，必须对获得的ARGO数据进行质量控制。文章详细介绍了ARGO剖面浮标资料实时质量控制模式和延时质量控制模式及其采用的质量控制方法等。     

库克群岛海域海洋环境因子对长鳍金枪鱼渔获率的影响

为了提高延绳钓渔业中长鳍金枪鱼(Thunnus alalunga)的渔获率,需了解海洋环境因子对其渔获率的影响。利用2013年9月8日-2013年12月31日库克群岛海域延绳钓渔业调查数据,包括:钓钩作业深度,温度、叶绿素a浓度和海流垂直剖面数据,作业参数,渔获统计数据等,采用逐步回归的方法建立钓钩作业深度预测模型,利用统计和聚类分析的方法分析长鳍金枪鱼渔获率与海洋环境因子的关系。结果表明:在库克群岛附近海域,长鳍金枪鱼渔获率较高的水层、温度、叶绿素a浓度、东西方向、南北方向、垂直方向、水平方向的海流分别为160~200 m、21.0~23.9℃、0.040~0.159μg/L、0~0.1 m/s、0.1~0.2 m/s、0.08~0.10 m/s和0.1~0.2 m/s。在该海域作业时,建议在长鳍金枪鱼渔获率较高的水层、温度、叶绿素a浓度、东西方向、南北方向、垂直方向和水平方向的海流范围内增加钓钩投放数量。     

渤海、黄海、东海海面热量平衡的平均状况——Ⅰ.资料处理和计算方法

渤、黄、东海海面热量平衡状况对该海域海洋水文和中国气候有着重要的影响。我们对该海域热量平衡各分量的年平均和年变化进行了计算,并分别就资料处理和计算方法、年平均状况、年变化特征等几个方面进行了分析。本文作为该项研究的第一部分,讨论了所用的资料和计算公式,提出了在稳定温度层结条件下的交换系数的校正方法。     

渤海、黄海、东海海面热量平衡的平均状况——Ⅰ.资料处理和计算方法

渤、黄、东海海面热量平衡状况对该海域海洋水文和中国气候有着重要的影响。我们对该海域热量平衡各分量的年平均和年变化进行了计算,并分别就资料处理和计算方法、年平均状况、年变化特征等几个方面进行了分析。本文作为该项研究的第一部分,讨论了所用的资料和计算公式,提出了在稳定温度层结条件下的交换系数的校正方法。     

发展剖面探测浮标技术,支持我国参与ARGO计划

本文介绍了国际ARGO计划的背景和科学意义，分析了ARGO计划对我国的正负面影响和我们应采取的对策，分析了ARGO计划使用的自持式剖面循环探测浮标的技术要求和关键技术，介绍了国外已有的几种自持式剖面浮标和国内正在研制的自持式剖面浮标。     

基于最优插值法的Argo数据网格化实验

利用最优插值数据同化方法对Argo资料进行了网格化实验,得到了与WOA05较为一致的结果,验证了最优插值法对Argo资料网格化的有效性,并为加快开发制作适用于海洋模式和数据分析的Argo网格化数据集打下了很好的基础。     

Comparative analysis of the North Atlantic surface circulation reproduced by three different methods

Calculation results are presented for long-term mean annual surface currents in the North Atlantic based on direct drifter measurements and numerical experiments with the ocean general circulation model using both climatic arrays of hydrological data World Ocean Atlas 2009 and Argo profiling data. The calculations show that the technique suggested for model calculations of oceanographic characteristics of the World Ocean with the use of Argo data significantly improves the climatic fields of the temperature and salinity even on a coarse grid. The comparison of the model calculation results with drifter data showed that the temperature and salinity fields found from Argo data with the use of data variational interpolation on a regular grid allow the calculation of realistic currents and can be successfully used as initial conditions in hydrodynamic models of the ocean dynamics.     

Convective structures in the Lofoten Basin based on satellite and Argo data

We discuss the possibility of detecting deep convection in the Lofoten Basin of the Norwegian Sea based on the eddy structures revealed from the satellite data. Satellite altimetry, SAR imagery, and MODIS satellite spectral radiometer sea-surface temperature (SST) data are used in the analysis, along with the data of oceanographic Argo floats. It is shown that the eddies identified from the satellite data correspond to the convective cells in the same region according to the data of the Argo floats. We consider several examples of the summer eddy and one winter eddy and the corresponding structures in the ocean measured by the Argo floats when they were located close to the identified eddies. As this method develops and improves, it can be used for the analysis of the dynamic of oceanic eddies in the region of the Lofoten Basin, and possibly in other regions with active deep convection.     

基于观测的南海越南沿岸次表层涡旋

In this study, subsurface eddies near the Vietnam coast of the South China Sea were observed with in situ observations, including Argo, CTD, XBT and some processed and quality controlled data. Based on temperature profiles from four Argo floats near the coast of Vietnam, a subsurface warm eddy was identified in spring and summer. The multi-year Argo and Global Temperature and Salinity Profile Programme(GTSPP) data were merged on a seasonal basis based on the data interpolating variational analysis(DIVA) method to reconstruct the three-dimensional temperature structure. There is a warm eddy in the central subsurface at 12.5°N, 111°E below300 m depth in spring, which does not exist in autumn and is weak in winter and summer. From CSIRO Atlas of Regional Seas(CARS) and Generalized Digital Environment Model(GDEM) reanalysis data, this subsurface warm eddy is also verified in spring.     

利用 Argo 数据计算吕宋海峡以东海域水文特性参数和流场 *

利用2006年Argo浮标资料分析吕宋海峡以东海域水团季节特性和混合层的月平均变化规律;并分别利用Argo多年季节平均资料与2006年资料,以秋季为例,基于P矢量方法计算该区域流场;同时考虑风生流的影响,将所得结果分别与利用Levitus和高度计资料计算的流场进行比较。结果表明,水团特性季节变化不明显,春冬季表层水团与夏秋季比较表现为低温高盐;次表层、中层和深层季节变化不大;混合层深度明显表现为冬季最深、夏季最浅的季节性变化。利用2002—2009年Argo季节平均资料基于P矢量方法能得到地转流场的基本结构,与Levitus资料的计算结果相比较,除可以反映黑潮,还可以反映一些涡旋结构;利用2006年秋季Argo资料计算流场与高度计资料计算的地转流场比较,其流场结构位置吻合得比较好,但存在流速偏小等不足,这可能与Argo资料较少且分布不均以及插值误差等有关,但其可以获得流场的三维结构,而利用高度计资料计算只能得到表层流场结构。     

全球Argo资料共享与服务平台设计与实现

Argo资料已成为海洋环境和气候变化研究重要的实测资料来源和基础数据支撑。自2007年全球Argo实时海洋观测网建成以来,每年产生的Argo资料稳固增长,数据总量呈现出海量增长趋势,如何实时有效地对Argo数据进行组织管理与信息服务已成为当前Argo资料共享的关键难题。本文针对Argo数据多源异构的时空特性及多元化的信息服务需求,综合运用分布式混合数据库架构,设计了一种适用于全球Argo资料组织管理的弹性扩展云存储模型,建立了基于Matlab的Argo网格化产品高效可视化方法,构建了基于Flex RIA的WebGIS服务框架,并研制了"全球Argo资料共享与服务平台"实例。平台实现了对全球Argo资料的查询浏览、快速下载及可视化分析等功能,并已在中国Argo实时资料中心部署及对外服务。     

Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields

The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.     

基于Web Services的Argo数据应用服务框架与实现

分析了 Argo 数据应用研究现状,结合Web服务技术,提出了 Argo 数据应用服务框架,把 Argo 数据及应用模型均封装成Web服务,从而提高了分布式环境下,数据的互操作性、应用的可移植性及透明性.基于该框架,开发设计了"中国南海 Argo 数据应用服务平台",该系统可以集成分布式环境中,基于异构平台的数据服务和应用服务,可为用户提供透明的、"一站式"的 Argo 数据 Web 应用.     

Linear and non-linear T–S models for the eastern North Atlantic from Argo data: Role of surface salinity observations

Linear and non-linear empirical models for salinity (S) are estimated from the Argo temperature (T) and salinity (delayed) data. This study focuses on the reconstruction of salinity in the upper 1200 m of the eastern North Atlantic Ocean, a region characterized by the presence of many different water masses. While previous studies have found it necessary to split this region by boxes to fit different polynomial models in each box, a unique model valid for the entire region is fitted here. Argo profiles are randomly distributed on two sets: one for fitting the models and one for testing them. Non-linear regressions are built using neural networks with a single hidden layer and the fitting data set is further divided into two subsets: one for adjusting the coefficients (training data) and one for early stopping of the fitting (validation data). Our results indicate that linear regressions perform better than the climatologic T–S relationship, but that non-linear regressions perform better than the linear ones. Non-linear training using a three-data subsets strategy successfully prevents overfitting even when networks with 90 neurons in the hidden layer are being trained. While the presence of local minima may complicate the generalization of non-linear models to new data, network committees (created by training the same network from different random initial weights) are shown to better reproduce the test data. Several predictors are tested, and the results show that geographical, or surface, information does provide significant information. These results highlight the potential applications of future satellite missions measuring sea-surface salinity to reconstruct, when combined with temperature profiles, vertical salinity profiles.