基于传输函数的二/三维全时空连续海洋中尺度涡旋流场实时可视化

海洋中尺度涡旋可视化可以将海洋中尺度涡旋的运动规律以图形图像等直观的方式加以展现,是研究海洋中尺度涡旋强有力的工具。然而,现有的可视化方法存在一些不足,如时空连续可视化框架中的流线不能表现全时空连续的运动过程,应用到海洋涡旋提取的特征值受阈值影响严重,交互式传输函数依赖用户经验等。为解决已有的不足,本文首次提出了全时空连续框架和传输函数标准形态模式,将基于区域的涡旋提取技术(?准则)应用到海洋中尺度涡旋可视化中,并提出了全时空连续可视化框架二维及三维的GPU实现方案。实验结果表明,本文的可视化方案达到了实时可交互的级别,对于海洋中尺度涡旋交互式可视化具有重要意义。     

基于YOLO算法的海洋中尺度涡三维结构构建

海洋中尺度涡是一种常见的中尺度海洋现象,研究海洋中尺度涡的分布及运动特性对航运、气候、军事等具有重要作用,海洋中尺度涡的识别是海洋学和计算机科学领域的一个热门研究课题。运用深度学习的方法和框架,对中尺度涡的二维识别和三维结构构建展开研究分析。首先,获取全球海洋再分析数据并进行流线可视化,构建涡旋流线数据集;其次,利用YOLO v5s卷积神经网络对涡旋流线数据集进行训练,并对南海区域中尺度涡进行有效检测。实验结果表明,YOLO v5s训练后得到最优模型经过测试,平均检测精度均值达到了86.10%;最后,根据涡旋检测结果,对检测出的同时刻不同深度的涡旋判断是否属于同一涡旋,确定后进行该涡旋的三维结构构建。     

一种使用卫星高度计数据且基于密度聚类的新型海洋中尺度涡自动探测算法

中尺度涡旋是海洋中典型的中尺度现象，是海洋中能量传递的运输者，中尺度涡识别与提取是物理海洋学研究的重要内容之一，而中尺度涡自动发现算法是最基础的用于寻找与分析中尺度涡的工具。中尺度涡旋探测工作的数据来源主要为卫星高度计数据融合出的SLA数据，该数据可以客观的描述海洋表层高度状态。中尺度涡表示为SLA闭合等值线所包围的局部等值区域，涡旋识别需要从SLA数据中提取出稳定的闭合等值线结构。针对基于SLA数据中的中尺度涡探测的特点，本文提出了一种新的基于聚类方法的中尺度涡自动识别算法，通过对SLA数据集的分割与筛选将中尺度涡区域与背景区域分离，后建立区域内联系并将其映射到SLA地图上来提取中尺度涡结构。本文算法解决了传统探测算法中参数设定的敏感性问题，不需要进行稳定性测试，算法适应性增强。算法中加入了涡旋筛选机制，保证了结果的涡旋结构的稳定性，提高了识别准确率。在此基础上，本文选取了西北太平洋及中国南海地区进行了中尺度涡探测实验，实验结果展示出了本文算法在较传统算法提高算法效率的同时，也保持着较高的算法稳定性，可以在稳定识别各个单涡结构的同时识别稳定的多涡结构。     

基于角动量模型的流场涡旋提取方法

分析海洋流场数据进行流场涡旋提取是流场可视化的重要方法。通常实际存在的涡旋具有不规则性,导致涡旋的提取通常需要复杂的计算。本文分析流场数据中涡旋的特点,提出一种基于角动量最大值模型提取涡旋的方法。建立角动量模型计算获得近似涡旋中心点,然后通过空间聚集分析和流场信息熵的计算来提取区域内涡旋。实验结果表明,与传统的提取方法相比,该方法不需要通过复杂计算即可提取涡旋区域并保证准确度,可以应用于不同空间分辨率的数据,同时解决了流场信息熵计算时鞍点对涡旋影响的问题,对于弱涡旋也有较好的提取效果。     

南海中尺度涡对深层流调控作用的个例研究

南海深层环流作为印太海域热盐环流的重要组成部分,研究其时间变异特征对于深入认识印太海域大洋环流具有重要意义。中尺度涡作为南海极为活跃的动力过程,有观测显示其影响深度可超过2000m。结合海表面高度计资料与潜标观测资料,识别南海区域中尺度涡,并分析了中尺度涡对深层流场以及温度场的影响。针对三个中尺度涡的潜标实测结果表明:在涡旋经过时,海洋深层流动有明显的变异,表现在速度、温度方面,速度增加量能达到5.5cm/s,温度变异可达到0.02℃。涡旋经过时,海洋上层、深层流向呈相反态势,第一斜压模动能显著增强。     

再谈海冰边缘区域中尺度涡旋形成机制——非线性平流

利用三维海洋模式与二维海冰模式耦合,研究海冰边缘区域中尺度涡旋形成最重要的机制之一——非线性平流机制。二维海洋模型模拟结果表明,非线性平流机制在水深比较浅的时候更加重要。不同于把海洋考虑成一个正压流体的二维模型,三维海洋模型中海冰通过海-冰相互作用直接影响海洋表层。我们发现在三维海洋模型实验中,中尺度涡旋和海洋表面抬升都对水深变化敏感。海流速度的垂直结构表面,当海水变浅,各层海流都变得更快。相同风应力作用相同时间之后,表面抬升与海水深度成反比关系。同时我们还发现由于垂直运动,在三维海洋模型实验结果中,海面抬升非常小,只有二维海洋模型实验结果的1%。垂直运动是三维海洋模型和二维海洋模型实验结果不同的根本原因。     

基于Voxler平台的太平洋中尺度涡合成三维结构

为深入研究太平洋中尺度涡的三维结构特征,解决常规一维剖面和二维平面图对温盐空间分布规律分析不足的弊端,文章在三维结构合成分析的基础上首次引入Voxler平台,建立太平洋中尺度涡温盐要素的三维数据模型,实现太平洋中尺度涡三维结构的可视化表达,利用历史观测数据合成的太平洋中尺度涡三维结构,对温盐结构特征进行直观的全空间三维分析。研究结果表明:太平洋反气旋涡温度异常的主体结构深度约200m,呈鹅卵石状,涡旋结构可维持到1 000m;盐度异常的主体结构深度约300m,呈水平扁平水滴状,涡旋结构仅维持约800m;与盐度异常的三维结构相比,温度异常结构较复杂,且涡旋结构更明显,到达深度也更深,即温度受中尺度涡的影响更显著。     

热带西太平洋北赤道逆流区涡旋统计分析

随着海洋技术的发展,中尺度的海洋过程越来越多的被揭示,中尺度涡旋作为重要的中尺度海洋过程,已经被大量的研究。但对于热带西北太平洋海区,中尺度涡旋特征的空间分布、季节变化以及移动规律等方面的研究还有所欠缺。本文使用Chelton提供的涡旋数据集,统计分析了热带西北太平洋海区涡旋特征的空间分布,发现以往研究较少的北赤道逆流(North Equatorial Countercurrent,NECC)区(A海区,120°~180°E,4°~6°N)较临近海域生成涡旋数量更多,涡旋半径、振幅、生命周期及非线性强度更大,移动距离更远,并且A海区涡旋经向移动距离服从伽马分布。涡旋在靠近西边界的区域更易向南移动,而在西边界以东的区域更易向北移动。A海区涡旋的生成数量具有明显的季节变化,主要受到流场剪切强度的影响。同时ENSO会对该区涡旋生成产生影响,其影响机制需要进一步的研究。     

基于深度学习的中尺度涡检测技术及其在声场中的应用

针对海洋中尺度涡的检测与参数提取问题,本文使用中尺度涡SAR图像数据集,提出基于深度学习的EddyYolo目标检测模型进行中尺度涡的涡旋中心和涡旋水平尺度的多目标检测,并且提取涡心位置和涡旋水平半径等参数.实验结果表明:本文提出的EddyYolo模型实现了涡旋中心和涡旋水平尺度的多目标检测,检测准确率达到94%.在此基础上,结合二维高斯涡模型和三维中尺度涡模型,本文提出了基于卫星遥感与声学对中尺度涡的联合建模方法.     

基于HYCOM的南海中尺度涡数值模拟

结合AVISO(Archiving Validation and Interpolation of Satellite Oceanographic Data)高度计资料,利用改进的NERSCHYCOM(Nansen Environmental and Remote Sensing Center-Hybrid Coordinate Ocean Model)大洋环流模式,对南海中尺度涡进行数值模拟研究,主要包括中尺度涡的三维结构、南海EKE(Eddy Kinetic Energy,涡动动能)的垂向变化、黑潮中尺度涡的脱落以及涡旋近岸时的结构变化等。模式再现了2007年2月-3月菲律宾西侧海域的一次暖涡过程,探究了其生命期中各阶段的特征物理量的变化,对其成熟时期的涡旋结构研究表明,中尺度涡的结构呈现不对称性,涡旋两侧的流场空间范围和流场强度均不相同,涡旋的半径和中心位置随深度不断变化,并且由涡旋作用产生的升降流的中心与涡旋自身中心并不重合,二者之间有一定距离。初步探索EKE的垂向分布情况,认为南海年平均EKE在垂向变化上呈现三段式,主要部分分布在300m以浅深度,但同时垂向又能达到海洋深层。分析了一次黑潮中尺度涡脱落的模式模拟个例,推测黑潮中尺度涡脱落原因:黑潮流径西移、外海中尺度涡对黑潮的强迫、地形作用,并且结果表明从黑潮脱落的中尺度涡可以携带大量高温高盐水体进入南海,对南海的温盐性质产生很大的影响。初步探索涡旋近岸时的结构变化,涡旋靠近岸界时,受岸界挤压,流速在一段时间内会增大,继续靠近岸界,由于岸界的摩擦、海底的拖曳,导致能量耗散,流速减小,最终涡旋消亡。     

On the vertical velocity component in the mesoscale Lofoten vortex of the Norwegian Sea

For the first time, the concepts of the theory of helical vortices have been applied to the Lofoten vortex of the Norwegian Sea. The estimates for azimuthal and vertical velocities have been obtained from the Massachusetts Institute of Technology general circulation model (MITgcm) for 1992–2012. The columnar vortex model with helical vorticity lines and distributions has been adapted to Scully and Rayleigh vortices. It has been shown that the vortex parameters can be determined simply from mass balance equations. The parameters of the helical vortex simulating the structure of the Lofoten vortex have been found and the radial distributions of azimuthal and vertical velocity components have been constructed. The resulting data can be interesting for an analysis of the three-dimensional structure of mesoscale vortices in the ocean.     

均匀流中直立圆柱体绕流三维数值模拟

研究直立贯底圆柱体的三维粘性绕流问题。以不可压缩Navier-Stokes方程为控制方程,采用有限体积法和SIMPLE算法,建立了数值模拟方法。考察在不同水平和垂直断面上,圆柱体绕流产生的尾涡和流动速度场的分布特性,成功地数值模拟了直立贯底圆柱体绕流场的三维特性。结果表明,在考虑重力影响的情况下,直立圆柱体周围的流动具有明显的三维特性,而且沿圆柱体轴向不同断面上的尾涡分布是不相同的。     

基于海色融合数据的东海黑潮锋面检测研究

海洋锋是典型的海洋中尺度现象之一。目前卫星遥感主要利用海表温度数据分析海洋锋,但由于西北太平洋海域夏季海表温度的趋同特性,不能进行有效的锋面监测;而不同水团所具有的生物光学特性往往是不同的,且不具有太阳辐射引起的显著性季节变化,因此海色资料也成为检测海洋锋的有效数据源。文中以东海黑潮为例,详细说明了基于叶绿素a浓度融合数据,采用梯度法进行海洋锋面检测的过程,通过比较不同季节不同梯度阈值得到的东海黑潮锋结果,从保持锋面的完整性及对零碎锋区的剔除效应方面,选取了不同季节较优的梯度阈值。总体来说,文中检测出的东海黑潮区域海色锋与海流黑潮强流区较吻合,12月至4月东海黑潮海色锋检测结果不如海温锋,而5-11月东海黑潮海色锋检测结果优于海温锋,特别是台湾以东黑潮区域,不论什么季节海温锋都没有体现,而海色锋始终很明显。利用文中提出的海洋锋检测算法、分析方法及选择的梯度阈值可以有效地检测东海黑潮区域的海洋锋面,结合海色锋和海温锋,可以监测分析东海黑潮强流区的时空变化。     

Three-dimensional structure of an observed cyclonic mesoscale eddy in the Northwest Pacific and its assimilation experiment

Mesoscale eddies play an important role in modulating the ocean circulation. Many previous studies on the threedimensional structure of mesoscale eddies were mainly based on composite analysis, and there are few targeted observations for individual eddies. A cyclonic eddy surveyed during an oceanographic cruise in the Northwest Pacific Ocean is investigated in this study. The three-dimensional structure of this cyclonic eddy is revealed by observations and simulated by the four-dimensional variational data assimilation(4 DVAR) system combined with the Regional Ocean Modeling System. The observation and assimilation results together present the characteristics of the cyclonic eddy. The cold eddy has an obvious dual-core structure of temperature anomaly.One core is at 50–150 m and another is at 300–550 m, which both have the average temperature anomaly of approximately-3.5°C. The salinity anomaly core is between 250 m and 500 m, which is approximately-0.3. The horizontal velocity structure is axis-asymmetric and it is enhanced on the eastern side of the cold eddy. In the assimilation experiment, sea level anomaly, sea surface temperature, and in situ measurements are assimilated into the system, and the results of assimilation are close to the observations. Based on the high-resolution assimilation output results, the study also diagnoses the vertical velocity in the mesoscale eddy, which reaches the maximum of approximately 10 m/d. The larger vertical velocity is found to be distributed in the range of 0.5 to 1 time of the normalized radius of the eddy. The validation of the simulation result shows that the 4 DVAR method is effective to reconstruct the three-dimensional structure of mesoscale eddy and the research is an application to study the mesoscale eddy in the Northwest Pacific by combining observation and assimilation methods.     

Modeling of mesoscale coupled ocean–atmosphere interaction and its feedback to ocean in the western Arabian Sea

Observations of the western Arabian Sea over the last decade have revealed a rich filamentary eddy structure, with large horizontal SST gradients in the ocean, developing in response to the southwest monsoon winds. This summertime oceanic condition triggers an intense mesoscale coupled interaction, whose overall influence on the longer-term properties of this ocean remains uncertain. In this study, a high-resolution regional coupled model is employed to explore this feedback effect on the long-term dynamical and thermodynamical structure of the ocean.The observed relationship between the near-surface winds and mesoscale SSTs generate Ekman pumping velocities at the scale of the cold filaments, whose magnitude is the order of 1 m/day in both the model and observations. This additional Ekman-driven velocity, induced by the wind-eddy interaction, accounts for approximately 10–20% of oceanic vertical velocity of the cold filaments. This implies that Ekman pumping arising from the mesoscale coupled feedback makes a non-trivial contribution to the vertical structure of the upper ocean and the evolution of mesoscale eddies, with obvious implications for marine ecosystem and biogeochemical variability.Furthermore, SST features associated with cold filaments substantially reduce the latent heat loss. The long-term latent heat flux change due to eddies in the model is approximately 10–15 W/m² over the cold filaments, which is consistent with previous estimates based on short-term in situ measurements. Given the shallow mixed layer, this additional surface heat flux warms the cold filament at the rate of 0.3–0.4 °C/month over a season with strong eddy activity, and 0.1–0.2 °C/month over the 12-year mean, rendering overall low-frequency modulation of SST feasible. This long-term mixed layer heating by the surface flux is approximately ±10% of the lateral heat flux by the eddies, yet it can be comparable to the vertical heat flux. Potential dynamic and thermodynamic impacts of this observed air–sea interaction on the monsoons and regional climate are yet to be quantified given the strong correlation between the Somalia upwelling SST and the Indian summer monsoons.     

Modeling of mesoscale coupled ocean–atmosphere interaction and its feedback to ocean in the western Arabian Sea

Observations of the western Arabian Sea over the last decade have revealed a rich filamentary eddy structure, with large horizontal SST gradients in the ocean, developing in response to the southwest monsoon winds. This summertime oceanic condition triggers an intense mesoscale coupled interaction, whose overall influence on the longer-term properties of this ocean remains uncertain. In this study, a high-resolution regional coupled model is employed to explore this feedback effect on the long-term dynamical and thermodynamical structure of the ocean.The observed relationship between the near-surface winds and mesoscale SSTs generate Ekman pumping velocities at the scale of the cold filaments, whose magnitude is the order of 1 m/day in both the model and observations. This additional Ekman-driven velocity, induced by the wind-eddy interaction, accounts for approximately 10–20% of oceanic vertical velocity of the cold filaments. This implies that Ekman pumping arising from the mesoscale coupled feedback makes a non-trivial contribution to the vertical structure of the upper ocean and the evolution of mesoscale eddies, with obvious implications for marine ecosystem and biogeochemical variability.Furthermore, SST features associated with cold filaments substantially reduce the latent heat loss. The long-term latent heat flux change due to eddies in the model is approximately 10–15 W/m² over the cold filaments, which is consistent with previous estimates based on short-term in situ measurements. Given the shallow mixed layer, this additional surface heat flux warms the cold filament at the rate of 0.3–0.4 °C/month over a season with strong eddy activity, and 0.1–0.2 °C/month over the 12-year mean, rendering overall low-frequency modulation of SST feasible. This long-term mixed layer heating by the surface flux is approximately ±10% of the lateral heat flux by the eddies, yet it can be comparable to the vertical heat flux. Potential dynamic and thermodynamic impacts of this observed air–sea interaction on the monsoons and regional climate are yet to be quantified given the strong correlation between the Somalia upwelling SST and the Indian summer monsoons.     

Zonal versus meridional velocity variance in satellite observations and realistic and idealized ocean circulation models

Global, high-quality, satellite-based observation of oceanic currents over the past 13 years has revealed ubiquitous quasi-horizontal eddies in the mesoscale (tens to hundreds of kilometers), confirming the view of a highly turbulent ocean suggested by observational programs in the 1970s. Idealized quasigeostrophic turbulence models suggest mesoscale turbulent flow can vary between isotropic, and highly anisotropic zonal jets. Here we compare the zonal and meridional velocity variance from satellite altimetry. We find that, for an unexplained reason and despite the chaotic nature of turbulence, the surface flow is organized into mesoscale patches where either zonal or meridional velocity variance dominates. The patches persist over 13 years, much longer than the turbulent timescale of a few months. Implications include potentially highly anisotropic redistribution of tracers by the mesoscale flow. Zonally averaged velocity variances reveal a slight preference for meridional over zonal velocity variance. Realistic primitive equation models succeed in reproducing both the patchy structure in local preference for either zonal or meridional velocity variance, and the zonally averaged preference for meridional variance. Idealized models of fully developed, quasigeostrophic turbulence fail in both regards.     

Inversion of the three-dimensional temperature structure of mesoscale eddies in the Northwest Pacific based on deep learning

Mesoscale eddies, which are mainly caused by baroclinic effects in the ocean, are common oceanic phenomena in the Northwest Pacific Ocean and play very important roles in ocean circulation, ocean dynamics and material energy transport. The temperature structure of mesoscale eddies will lead to variations in oceanic baroclinity, which can be reflected in the sea level anomaly (SLA). Deep learning can automatically extract different features of data at multiple levels without human intervention, and find the hidden relations of data. Therefore, combining satellite SLA data with deep learning is a good way to invert the temperature structure inside eddies. This paper proposes a deep learning algorithm, eddy convolution neural network (ECN), which can train the relationship between mesoscale eddy temperature anomalies and sea level anomalies (SLAs), relying on the powerful feature extraction and learning abilities of convolutional neural networks. After obtaining the temperature structure model through ECN, according to climatic temperature data, the temperature structure of mesoscale eddies in the Northwest Pacific is retrieved with a spatial resolution of 0.25° at depths of 0–1 000 m. The overall accuracy of the ECN temperature structure is verified using Argo profiles at the locations of cyclonic and anticyclonic eddies during 2015–2016. Taking 10% error as the acceptable threshold of accuracy, 89.64% and 87.25% of the cyclonic and anticyclonic eddy temperature structures obtained by ECN met the threshold, respectively.