基于BP神经网络的高频地波雷达海流空间插值

高频地波雷达是海洋环境监测的重要手段,当前已经实现对海流的业务化观测,但是外部因素常引起海流空间探测的不连续性。为解决此问题,尽量保障区域数据的完整性和准确性,本文将BP神经网络技术与空间插值相结合,建立了海流的BP神经网络插值模型,并进行了针对实测数据的缺失插值仿真,通过与反距离权重法和线性插值法插值结果的对比,分析该模型在区域海流大面积缺失、流速整体较大和流速整体较小3个方面的性能。结果表明,BP神经网络插值模型的海流预测效果明显优于其他两种方法,且在流场数据大范围缺失下也取得了良好的效果。     

融合scSE模块的改进Mask R-CNN海洋锋检测方法

海洋锋是重要的中尺度海洋现象,具有数据量小、目标小、弱边缘等特性。针对实际检测任务中弱边缘、小目标海洋锋的检测精度低、错检及漏检率高等问题,融合scSE (spatial and channel Squeeze&Excitation)空间注意力模块构建了一种改进的Mask R-CNN海洋锋检测模型。该方法首先对Mask R-CNN骨干网络结构进行改进,采用scSE模块引导的ResNet-50网络作为特征提取网络,通过加权策略对图像通道和空间位置进行特征突出,提升网络对重要特征的提取能力;其次,针对海洋锋目标边缘定位不准确的问题,引入IoU boundary loss构建新的Mask损失函数,提高边界检测精度。最后,为验证方法的有效性,从训练数据和实验模型上,分别设计多组对比实验。实验结果表明,相比传统Mask R-CNN、YOLOv3神经网络及现有Mask R-CNN改进网络,本文方法对SST梯度影像数据集上的强、弱海洋锋检测效果最好,定位准确率（IoU,Intersection-over-union））及检测精度（mAP,Mean Average Precision）均达0....     

Construction and implementation of RS integrated application information system of China''s coastal zone and offshore

The coastal zone and offshore are clearly of major economic and social importance,in thesame time it causes a series of problems of resources and ecosystem too.The research on and development of integrated application techniques for remote sensing provide not only a microcosmic and dynamic and synchronous technical means to the monitor,but also an integrated technical scheme to harmonically solve the ecological environment problem.The system is designed to focus on the application techniques of multi-sources remote sensing data.By developing remote sensing information extraction module,integrated user platform,and application module objected to the real ocean procedure for China''s coastal zone and offshore,the information system consists of the management of prodigious amount of data,display,analysis,simulation and output will be constructed and implemented.The final objective is to transform the research on ocean remote sensing into application.     

基于ARMA模型的潮位拟合及内插研究

在水深测量过程中,采集的潮位数据往往存在断缺现象,给水深测量内业工作者造成干扰。针对潮位数据断缺问题,对断缺数据简单预处理后,利用ARMA模型对潮位数据序列变化规律进行分析,拟合潮位时间序列数据变化趋势,然后将拟合的值内插到数据断缺处,以建立完整的潮位改正文件,最后对拟合模型进行精度评价分析。实验结果表明,拟合模型精度良好,可以快速获取符合要求的插值,提高工作效率。     

Real time wave forecasting using wind time history and numerical model

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.     

深度学习在海洋信息探测中的应用:现状与展望

深度学习可以通过深度神经网络,使机器理解学习数据,从而提高数据分类效果和预测结果的准确性,因此在海洋信息探测中应用越来越受到重视.作者基于深度学习的基本原理,阐述了海洋上常用的深度学习神经网络模型,并结合海洋信息探测要素,对温度、盐度、风场、有效波高和海冰等进行了海洋环境信息因子的预测分析;同时,对船舶、溢油和涡旋等进...     

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Ni?o was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.     

基于变分模态分解和注意力机制的浪高预测

针对海洋中的海浪高度数据存在非线性和非平稳性的特点,海浪高度的预测就变得相对复杂.基于变分模态分解(VMD),在引入注意力机制(AM)的基础上,对传统长短期记忆(LSTM)神经网络算法进行了改进,提出了一种基于混合模型的海浪高度预测算法.算法通过预处理、预测和重构3个主要步骤,对海浪高度的时间序列进行预测.为了比较和说...     

Representation of topography by porous barriers and objective interpolation of topographic data

We present a porous medium approach to representing topography, and a new algorithm for the objective interpolation of topography, for use in ocean circulation models of fixed resolution. The representation and algorithm makes use of two concepts; impermeable thin walls and porous barriers. Impermeable thin walls allow the representation of knife-edge sub-grid-scale barriers that block lateral flow between model grid cells. Porous barriers permit the sub-grid scale geometry to modulate lateral transport as a function of elevation. We find that the porous representation and the resulting interpolated topography retains key features, such as overflow sill depths, without compromising other dynamically relevant aspects, such as mean ocean depth for a cell. The accurate representation of the ocean depth is illustrated in a simple model of a tsunami that has a cross-basin travel time very much less dependent on horizontal resolution than when using conventional topographic interpolation and representation.     

基于多变量LSTM神经网络模型的PDO指数预测研究

利用1921–2020年的海平面气压、海平面高度、热含量数据以及海冰密集度作为太平洋年代际振荡（Pacific Decadal Oscillation, PDO）指数的预报要素,建立了关于PDO指数时间序列预测的多变量长短期记忆（Long Short Term Memory, LSTM）神经网络模型,对比分析了2011–2020年不同时间序列预测模型的PDO指数预测结果,最后利用多变量LSTM神经网络模型实现了2021–2030年的PDO指数预测。结果显示,多变量LSTM神经网络模型的预测值与观测值经过交叉验证后的平均相关系数和均方根误差分别为0.70和0.62;PDO未来10年将一直处于冷位相,PDO神经网络指数出现两次波动,于2025年出现最小值。相比于其他时间序列预测模型,本文采用的多变量LSTM神经网络模型预测结果误差小、拟合效果好,可以作为一种新型的预测PDO指数的手段。     

Very high-frequency radar mapping of surface currents

An ocean surface current radar (OSCR) in the very high frequency (VHF) mode was deployed in South Florida Ocean Measurement Center (SFOMC) during the summer of 1999. During this period, a 29-d continuous time series of vector surface currents was acquired starting on 9 July 1999 and ending 7 August 1999. Over a 20-min sample interval, the VHF radar mapped coastal ocean currents over a 7.5 km × 8 km domain with a horizontal resolution of 250 m at 700 grid points. A total of 2078 snapshots of the two-dimensional current vectors were acquired during this time series and of these samples, only 69 samples (3.3%) were missing from the time series. During this period, complex surface circulation patterns were observed that included coherent, submesoscale vortices with diameters of 2 to 3 km inshore of the Florida Current. Comparisons to subsurface measurements from moored and ship-board acoustic Doppler current profiles revealed regression slopes of close to unity with biases ranging from 4 to 8 cm s^-1 between surface and subsurface measurements at 3 to 4 m beneath the surface. Correlation coefficients were 0.8 or above with phases of - 10 to - 20° suggestive of an anticyclonic veering of current with depth relative to the surface current. The radar-derived surface current field provided spatial context for an observational network using mooring-, ship- and autonomous underwater vehicle-sensor packages that were deployed at the SFOMC     

Cost and value of multidisciplinary fixed-point ocean observatories

Sustained ocean observations are crucial to understand both natural processes occurring in the ocean and human influence on the marine ecosystems. The information they provide increases our understanding and is therefore beneficial to the society as a whole because it contributes to a more efficient use and protection of the marine environment, upon which human livelihood depends. In addition the oceans, which occupy 73% of the planet surface and host 93% of the biosphere, play a massive role in controlling the climate. Eulerian or fixed-point observatories are an essential component of the global ocean observing system as they provide several unique features that cannot be found in other systems and are therefore complementary to them. In addition they provide a unique opportunity for multidisciplinary and interdisciplinary work, combining physical, chemical and biological observations on several time scales. The fixed-point open ocean observatory network (FixO3) integrates the 23 European open ocean fixed-point observatories in the Atlantic Ocean and in the Mediterranean Sea. The programme also seeks to improve access to key installations and the knowledge they provide for the wider community, from scientists, to businesses, to civil society. This paper summarises the rationale behind open ocean observatories monitoring the essential ocean variables. It also provides an estimate of the costs to operate a typical fixed-point observatory such as those included in the FixO3 network. Finally an assessment of the type of data and services provided by ocean observations and their value to society is also given.     

面向拓扑分析的海洋流场临界点提取算法研究

临界点是海洋流场拓扑结构中的重要构成要素,基于临界点的特征提取对于揭示海洋流场拓扑特征、开展海洋流场拓扑分析具有重要意义。本文基于临界点理论和Sperner引理,综合改进后的双线性插值算法和Sperner完全标号法,对海洋流场数据进行了临界点特征提取。首先,在双线性插值算法中添加滑动窗口处理,筛选临界点的候选网格单元,并采用聚合思想通过降低网格分辨率解决了网格插值中的二义性问题,同时考虑了0值网格存在的9种情形,通过迭代聚合思想滑动筛选候选网格单元,解决了插值网格均为0的情况。其次,提出了基于Sperner完全标号的最小值法临界点提取规则,将速度向量模最小的网格中心作为临界点,解决了实际流场物理场景中非0值的临界点提取。对两次提取结果进行合并、去重等处理,可以得到较为全面的临界点提取与分类结果。最后,通过对多个海域、不同深度流场数据的实验结果分析,证明了综合后的临界点提取方法的有效性及可行性。     

基于谱分析的GPS浮标测量参数提取方法

介绍采用谱分析原理提取海浪信息的方法。根据GPS浮标测量得到的海浪高度数据,先通过滑动平均的方法分离海浪信息和潮流潮位信息,然后对海浪信息采用AR模型法进行功率谱估计,并对海浪信息进行连续小波变换,对AR模型法功率谱估计结果和小波变换结果进行综合研究,提取海浪特征参数———周期。最后通过一个GPS浮标试验对上述方法进行了验证。     

Are trends in SeaWiFS chlorophyll time-series unusual relative to historic variability

For selected locations in the Atlantic and Pacific Ocean, we compared surface ocean chlorophyll time series extracted from SeaWiFS imagery from 1997–2004 with the results of an ocean coupled circulation and biogeochemical model covering the period 1958–2004. During the 1997–2004 time period, linear trends in model and satellite time series were significantly correlated at most of the 44 sites we studied. Eleven sites were selected for further study, and we used the longer time series of the model to assess whether trends observed during the SeaWiFS period at these 11 sites were unusual in relation to those observed over the longer historical period covered by the model. The results show that the trends observed during the SeaWiFS period were not unusual and fell well within the range in magnitude of linear trends observed in other 8-year periods of model output. This result implies that the SeaWiFS satellite ocean color time series is not yet sufficiently long, on its own, to directly observe any long term changes in phytoplankton chlorophyll that may be occurring in the surface waters of the open ocean as a result of increased ocean stratification linked to global climate changed.     

我国海岸带及近海卫星遥感应用信息系统构建和运行的基础研究

我国海岸带和近海的高度开发极大地促进了沿海经济的发展,也引发出一系列的资源和生态环境问题.研究和发展适用于我国海岸带及近海卫星遥感综合应用技术系统,旨在实现宏观、动态、同步监测区域资源开发和保护,以及为生态环境问题提供全新的技术支撑和协调解决整体技术方案,对此着重阐述了面向海岸带及近海卫星遥感应用技术系统的构建.本技术系统主要包括海岸带科学数据平台和信息提取模块、针对海洋过程的应用模块、我国海岸带及近海生态环境集成用户平台等,其目的是推进我国海洋遥感由研究阶段转向应用阶段,并为实现数字海洋提供技术基础.     

A hybrid efficient method to downscale wave climate to coastal areas

Long-term time series of sea state parameters are required in different coastal engineering applications. In order to obtain wave data at shallow water and due to the scarcity of instrumental data, ocean wave reanalysis databases ought to be downscaled to increase the spatial resolution and simulate the wave transformation process. In this paper, a hybrid downscaling methodology to transfer wave climate to coastal areas has been developed combining a numerical wave model (dynamical downscaling) with mathematical tools (statistical downscaling). A maximum dissimilarity selection algorithm (MDA) is applied in order to obtain a representative subset of sea states in deep water areas. The reduced number of selected cases spans the marine climate variability, guaranteeing that all possible sea states are represented and capturing even the extreme events. These sea states are propagated using a state-of-the-art wave propagation model. The time series of the propagated sea state parameters at a particular location are reconstructed using a non-linear interpolation technique based on radial basis functions (RBFs), providing excellent results in a high dimensional space with scattered data as occurs in the cases selected with MDA. The numerical validation of the results confirms the ability of the developed methodology to reconstruct sea state time series in shallow water at a particular location and to estimate different spatial wave climate parameters with a considerable reduction in the computational effort.     

Dynamic spawning patterns in the California market squid (Doryteuthis opalescens) inferred through paralarval observation in the Southern California Bight, 2012–2019

The California market squid is ecologically and economically important to the California Current Ecosystem and coastal communities. However, the population undergoes periodic and large-scale fluctuations on the order of magnitudes, largely as a result of warm ocean temperature and reduced ocean productivity related to cycles in El Niño Southern Oscillation. These fluctuations can lead to cascading trophic effects on the ecosystem, market uncertainty, and substantial revenue losses for fishing communities. In order to investigate the role that spawning behavior may play in mitigating the detrimental environmental effects on the population as well as allowing the species to capitalize during favorable ocean conditions, surveys for market squid paralarvae were conducted three times per hatching season from 2012 to 2019. The specific objectives were to (a) assess whether there was a large-scale synchronous peak in paralarval abundance each season, (b) investigate whether market squid paralarvae were found at certain locations in similar abundances throughout the duration of a hatching season, and (c) to understand the influence of oceanographic parameters, such as zooplankton availability, sea surface temperature, and chl-a on paralarvae abundance during each hatching season. Adaptable spawning strategies that varied over time were observed. Market squid displayed large-scale synchronous spawning during cool and productive oceanographic conditions; protracted spawning was observed during warm and oligotrophic conditions. No overall site fidelity was observed across all effort, but during two seasons where habitat compression was possible, market squid consistently spawned in certain areas. Generalized additive models were used to explore the effects of a small set of oceanographic variables on paralarval density on a seasonal basis. Sea surface temperature and geographic variables were generally the most important variables. These findings indicate that market squid spawning behaviors and habitat are adaptable and dependent on oceanography and population density.     

利用热带太平洋TAO浮标阵列评估全球高分辨率浪-潮-流耦合模式同化结果

In order to evaluate the assimilation results from a global high resolution ocean model, the buoy observations from tropical atmosphere ocean(TAO) during August 2014 to July 2015 are employed. The horizontal resolution of wave-tide-circulation coupled ocean model developed by The First Institute of Oceanography(FIOCOM model) is 0.1°×0.1°, and ensemble adjustment Kalman filter is used to assimilate the sea surface temperature(SST), sea level anomaly(SLA) and Argo temperature/salinity profiles. The simulation results with and without data assimilation are examined. First, the overall statistic errors of model results are analyzed. The scatter diagrams of model simulations versus observations and corresponding error probability density distribution show that the errors of all the observed variables, including the temperature, isotherm depth of 20°C(D20), salinity and two horizontal component of velocity are reduced to some extent with a maximum improvement of 54% after assimilation. Second, time-averaged variables are used to investigate the horizontal and vertical structures of the model results. Owing to the data assimilation, the biases of the time-averaged distribution are reduced more than70% for the temperature and D20 especially in the eastern Pacific. The obvious improvement of D20 which represents the upper mixed layer depth indicates that the structure of the temperature after the data assimilation becomes more close to the reality and the vertical structure of the upper ocean becomes more reasonable. At last,the physical processes of time series are compared with observations. The time evolution processes of all variables after the data assimilation are more consistent with the observations. The temperature bias and RMSE of D20 are reduced by 76% and 56% respectively with the data assimilation. More events during this period are also reproduced after the data assimilation. Under the condition of strong 2014/2016 El Ni?o, the Equatorial Undercurrent(EUC) from the TAO is gradually increased during August to November in 2014, and followed by a decreasing process. Since the improvement of the structure in the upper ocean, these events of the EUC can be clearly found in the assimilation results. In conclusion, the data assimilation in this global high resolution model has successfully reduced the model biases and improved the structures of the upper ocean, and the physical processes in reality can be well produced.