Evaluating the contribution of satellite measurements to the re-construction of three-dimensional ocean temperature fields in combination with Argo profiles

Assimilation systems absorb both satellite measurements and Argo observations. This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis, allowing for better configuration of assimilation parameters. To achieve this, two comparative reconstruction schemes were designed under the optimal interpolation framework. Using a static scheme, an in situ-only field of ocean temperature was derived by correcting climatology with only Argo ...     

Effects of interannual salinity variability on the dynamic height in the western equatorial Pacific as diagnosed by Argo

In this paper, interannual variations of the ocean dynamic height over the tropical Pacific are diagnosed using three-dimensional temperature and salinity fields from Argo profiles, with a focus on the...     

一种利用SMOS卫星观测的海表面盐度反演海水盐度廓线的方法

This paper proposes a new method to retrieve salinity profiles from the sea surface salinity(SSS) observed by the Soil Moisture and Ocean Salinity(SMOS) satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS(SMOS_SSS) with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error(RMSE) of the linear and nonlinear model are 0.08–0.16 and 0.08–0.14 for the upper 400 m, which are 0.01–0.07 and 0.01–0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.     

基于Argo资料的北太平洋绝对地转流计算

针对海洋实测流速资料极其匮乏的事实, 利用2004 年1 月~2009 年12 月月平均的Argo 温盐格点资料, 结合改进的P-vector 方法重构北太平洋绝对地转流流场。与卫星高度计和实测流速的比较以及相关性分析表明, 重构的绝对地转流是可信的, 可以用来研究不同大尺度环流特征及其动力结构。可以为研究海洋动力过程和气候变化提供一套有用的流场数据。     

基于GIS的南海海温时空过程分析研究

海温是海洋环境影响因子之一,对于海洋生态环境、海水养殖业等尤为重要。本文以Argo数据提取的南海海洋温度场数据为例,结合GIS(GeographicInformationSystem)技术,研究南海海温点过程、面过程可视化表达的方法。利用GIS作为可视化框架提供南海海温场的可视化显示,包括放大、缩小和拖动等基本功能,绘制多种形式的数值图像并进行空间分析,包括海温数据曲线绘制、海温值查询、空间插值、等值线等。基于Argo数据可视化表达的结果,从南海海温年变化、随纬度的变化、垂向变化、季节分布特征四方面对南海的海温时空特征进行了分析,结果表明:(1)南海海表温度高温的持续时间比较长,升温过程比降温过程相对要短一些;(2)随着纬度的降低,温度整体升高,温度的年变化幅度越来越小。夏秋两季随着纬度的变化,温度变化不大,春冬两季的温度变化较大;(3)在0~30 m温度变化很小,在深度为30m处温度开始逐渐下降,到达500m以下,海温一年四季都比较接近;(4)冬季海温总体最低,夏季海温总体最高。冬季和秋季,在南海西南方向等值线呈现西北—东南向,等值线比较密集。海温的时空变化研究可以对海洋温跃层、海洋温度锋,海水不同层次的结构的研究提供一定参考。     

基于机器学习的海洋浮标寿命及轨迹预测

Argo计划(Array or Real-time Geostrophic Oceanography)为海洋和大气研究提供了宝贵的资料,在短期天气预报和长期气候预测中起到了重要作用.为保证Argo观测阵列的正常运转,需要时刻关注浮标的运行状态,以保证研究区域内维持一定数量和密度的浮标.然而Argo浮标投放费用高昂,投放...     

基于Argo浮标的南极海冰范围变化分析

南极海冰的生消冻融与全球气候变化息息相关,多年以来受到国际社会的高度关注。目前已有诸多关于南极海冰范围变化的研究,但大部分是基于卫星遥感影像来展开分析。Argo观测网遍布全球各大洋,为海冰范围研究提供了一种新的思路,即根据浮标GPS点的南部边界推算南极海冰边界,由浮标的年、月累积数据得到南极海冰范围的年际、月际变化规律。对于这种研究思路提出了三种实现方法:(1)绘制专题图,可以清晰直观地看到南极海冰在2—9月间的生消变化情况;(2)利用南极附近浮标GPS点数量占全球比例的变化情况来分析海冰变化规律,在月际变化趋势上与影像数据一致,在年际变化上稍显不足;(3)使用点密度分析方法估计海冰边界,建立基于浮标GPS点密度的海冰-海水分界模型,可得到南极海冰范围变化规律的定量分析结果。浮标数据与影像数据互为补充,可为全球气候变化研究提供更多参考。     

西太平洋暖池海域上层海洋热盐含量初步研究

基于2001年1月～2014年7月期间的Argo温盐剖面资料,利用循环平稳经验正交函数（CSEOF）分解、最大熵谱分析和相关分析等方法,研究了西太平洋暖池海域上层海洋热盐含量的空间分布、季节和年际变化特征,并探讨了其影响机制。结果表明,暖池海域近表层与次表层热含量逐年变化呈反位相变化模态,同样盐含量变化趋势也不尽相同。无论热含量还是盐含量,都存在着明显的季节和年际变化。CSEOF分析表明,暖池海域热含量第一模态空间场具有显著的东—西反相位年际振荡,盐含量第一模态则呈正-负-正的三极子模态,但时间序列显示,热含量在2007年以后经过3次位相调整,而盐含量2007年以后只经过一次位相调整,且这种年际变化都与ENSO事件有关,且热含量相比于盐含量受ENSO影响更大。El Niño期间,暖池海域西部热含量减少, 东部增加,La Niña期间则相反;研究海域南北部盐含量在El Niño期间增加,中部（暖池高温中心）减少,La Niña期间则相反;进一步分析表明,热含量变化主要受到局地风场以及纬向流的影响,而盐含量变化则受淡水通量和纬向流的影响。     

声速剖面反演预测方法

利用2006—2017年我国南海部分区域(112°～114°E,10°～12°N)的Argo观测数据,对该海区声速剖面进行了仿真分析和研究。在此基础上,利用遗传算法(GA)优化的径向基函数(RBF)神经网络建立反演预测模型(GA-RBF),结合海区表面实测温度和历史数据,研究了该区域2016—2017年的声速剖面实时预测情况,并获得该海区6月和12月的声速剖面平均均方根误差值为0.845 m/s和0.815 m/s;而采用平均声速剖面方法获得该海区6月和12月的声速均方根误差分别是2.393 m/s和2.176 m/s。仿真结果表明:基于GA-RBF网络模型并利用海区表面实测温度的反演预测结果更趋近实测声速剖面,该模型可用于海区垂直声速剖面的实时预测。     

Seasonal variability of sonic layer depth in the Central Arabian Sea

The seasonal variability of sonic layer depth (SLD) in the central Arabian Sea (CAS) (0 to 25°N and 62-66°E) was studied using the temperature and salinity (T/S) profiles from Argo floats for the years 2002–2006. The atmospheric forcing responsible for the observed changes was explored using the meteorological data from NCEP/NCAR and Quickscat winds. SLD was obtained from sound velocity profiles computed from T/S data. Net heat flux and wind forcing regulated SLD in the CAS. Up-welling and down-welling (Ekman dynamics) associated with the Findlater Jet controlled SLD during the summer monsoon. While in winter monsoon, cooling and convective mixing regulated SLD in the study region. Weak winds, high insolation and positive net heat flux lead to the formation of thin, warm and stratified sonic layer during pre and post summer monsoon periods, respectively.