In this case study, the rapidly growing initial errors in vertical thermal structure (VTS) prediction associated with complex topography areas in the South China Sea were captured by the Regional Ocean Modeling System (ROMS) in combination with the Conditional Nonlinear Optimal Perturbation (CNOP) approach. Two types of CNOP-based rapidly growing initial errors with opposing patterns were concentrated both vertically within the thermocline range and horizontally along the coast. The associated preferential observation area was further determined by computing the vertically integrated total energy of these errors over the entire depth range. The results demonstrated that the additional observations should be prioritized in areas with significant horizontal depth gradients, particularly in steep slope zones, in order to improve the VTS prediction in complex topography areas in the South China Sea. To confirm the effect of observation suggestions, relevant observation system simulation experiments (OSSEs) and observation system experiments (OSEs) will be carried out in the future.

Wind‐driven near‐inertial internal waves (NIWs) play a key role in ocean mixing, yet their intermittent nature, complex generation, and propagation processes pose significant challenges in achieving accurate predictions. In this study, we utilize a deep learning (DL) model to forecast NIW velocities. Trained on 193 days of hourly NIW velocity and wind data, NIWs over the subsequent 60 days are predicted. Compared to the classic slab model, the DL model achieves a 25–59% error reduction within a 48 hr forecast window. Notably, the DL model maintains a correlation exceeding 0.80 within the first 24 hr and can effectively extend NIW predictions to multiple depths below the mixed layer. Furthermore, the pretrained model exhibits strong generalization capability, achieving a correlation coefficient of 0.82 at an independent station and during a different observation period. These findings underscore the potential of DL‐based approaches to advance NIW forecasting beyond conventional methods.

Abstract To reduce the cost of ocean observations and improve prediction accuracy of the Kuroshio region temperature, this study investigates the related targeted observation by using the conditional nonlinear optimal perturbation (CNOP) approach. Results show that the scheme of vertical-integrated energy is more suitable for the identification of sensitive area in the related targeted observation. By conducting a set of observation system simulation experiments (OSSEs), we discovered that the sensitive areas identified by the CNOP exert substantial influence on temperature predictions within the target area. The dynamic diagnosis further indicated that the pressure gradient and Coriolis force in the momentum equations greatly contribute the development of the prediction biases. These findings implied that the implement of CNOP-based targeted observation represents a cost-effective strategy for enhancing temperature predictions in the Kuroshio region.

Ocean Acoustic Tomography (OAT) is an efficient and economical marine acoustic observation technique. Targeted observation is an appealing procedure to reduce the uncertainty of ocean environment prediction through additional observation. This study aimed to assess the validity of OAT as an observation method for targeted observation. OAT based on Niche Genetic Algorithm was employed to extract sound speed and temperature profiles from acoustic transmission time, utilizing data from the 2019 Yellow Sea experiment. The inversion results were compared with measurement data, which are found to be accurate and reliable. To further evaluate OAT as targeted observation method, the vertical bias structure of OAT was added on synchronous measurement data in the sensitive area of targeted observation to simulate OAT observation in sensitive area. This simulated data was then incorporated into a 3D-Var assimilation system to improve the short-term prediction of the target region. Comparing the predictions derived with the measurement data at the verification time, it shows that the simulated OAT observation improved the quality of target region prediction, indicating that OAT can be an effective observation method for targeted observation. An Observing System Simulation Experiment was conducted to assess the impact of OAT characteristics on prediction improvement. The results show that both adding observation nodes and extending the observation duration have positive effects, while extending the observation duration performs better.

原位观测是海洋环境观监测与安全保障的基础手段,对提升海洋环境时空变化规律的认识和数值预报水平起着至关重要的作用.但是实施基于原位观测的海洋环境保障措施存在覆盖范围有限、观测成本高昂,以及非常时期实施困难等问题.随着海洋强国战略的深入推进,未来国家海洋利益空间将不断拓展,海洋环境保障的立体性、复杂性、未知性将不断增强,这对系统化、信息化、智能化的海洋环境保障提出了更高要求.当前有效解决方案之一是通过实施环境适应性保障对有限的原位观测资源布局进行优化设计,最大化海洋环境观测网建设效益.本文系统介绍了美国海军在海洋环境适应性保障建设方面的成果与经验,通过海上试验展现了实施海洋环境适应性保障措施的必要性和优越性,随后梳理了高分辨率海洋水文环境数值模拟技术、海洋水文环境适应性观测敏感区诊断技术、无人移动平台协同组网观测技术及无人移动平台观测资料同化技术等海洋环境适应性保障建设涉及的关键技术及其发展现状,最后总结了我国在海洋环境适应性保障建设方面的研究进展.作为一项复杂的系统工程,海洋环境适应性保障将有助于颠覆传统海洋环境保障模式,牵引我国海洋环境保障装备发展,推动相关研究领域理论和技术进步,对我国海洋环境保障体系建设产生深远影响.

目的 分析幼儿园园长职业倦怠特点、对领导力的影响以及社会支持在二者关系中的影响作用.方法 采用普查方法对山东省青岛市6区所有幼儿园688名园长进行职业倦怠问卷(MBI)、社会支持量表(SSRS)和园长领导力问卷调查;采用描述性分析、相关分析、一般线性回归分析和结构方程模型进行统计分析.结果 园长职业倦怠阳性率20.3％(140/688),其中情感衰竭、成就感低落和去个性化维度阳性率分别为7.3％(50/688)、15.1％(104/688)和 1.2％(8/688).职业倦怠 M(P25～P75)为 1.20(0.53～1.73).其中情感衰竭 M(P25～P75)为 1.40(0.80～2.15),成就感低落 M(P25～P75)为 1.17(0.00～2.50),去个性化 M(P25～P75)为0.25(0.00～1.00).社会支持 M(P25～P75)为48.00(42.00～52.00).园长领导力 M(P25～P75)为4.67(4.44～4.83).职业倦怠和社会支持显著负相关(P＜0.01);职业倦怠总均分、情感衰竭维度与成就感低落维度与园长领导力显著负相关(P＜0.01),去个性化维度同园长领导力相关不显著(P=0.07);社会支持和园长领导力显著正相关(P＜0.05).职业倦怠负向预测园长领导力(β=-0.14,P＜0.01);社会支持在职业倦怠对园长领导力影响中起完全中介作用(S.E.=0.11,P＜0.05),中介效应值为-0.27.结论 幼儿园园长存在一定程度的职业倦怠,以成就感低落为主要特征.可通过给予其充分的社会支持,提高职业倦怠园长的领导力水平,不断提高幼儿园保育教育质量.

针对浅海声速剖面反演问题,采用小生境遗传算法,结合声线搜索的最快本征声线匹配反演,实现浅海负跃层条件下的声速剖面估计.利用经验正交函数对声速剖面的多参数不确定性降维,依据声场计算模型获取的最快特征声线传播时延与观测声传播时长进行匹配,采用小生境遗传优化算法,获取最优经验正交函数估计,实现声速剖面反演.按上述方法反演处理浅海声传播实验数据,结果表明,该方法针能够有效反演浅海声速剖面,并且显著优于传统遗传算法反演结果.

Targeted observation is an appealing procedure for improving model predictions. However, studies on oceanic targeted observations have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field targeted observation that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation approach to improve the 7th day thermal structure prediction in the Yellow Sea. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, an observing strategy is designed and validated by a set of Observing System Simulation Experiments. Then, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that, compared to conventional local data assimilation, conducting targeted observations in the sensitive areas can yield more benefit at the verification time. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontal temperature advection driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

The sensitive area of targeted observation for the short-term prediction of the vertical thermal structure in the summer Yellow Sea is investigated by utilizing the Conditional Nonlinear Optimal Perturbation (CNOP) method and a adjoint-free algorithm with the Regional Ocean Modeling System. We use a vertical integration scheme of temperature to locate the sensitive area, in which reducing the initial errors are expected to yield great improvements in vertical thermal structure prediction of the verification area. We perform a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive area. Our results show that, initially adding random perturbations in the sensitive area have the greatest negative effects on the prediction than in other areas (eg. the verification area, regions east and northeast of the verification area). Moreover, Observing System Simulation Experiments (OSSEs) indicate that, eliminating the initial errors in the sensitive area can lead to a more refined prediction than in other selected areas (including the verification area itself). Our study suggests that implementing targeted observation is a feasible way to improve the short-term prediction of the vertical thermal structure in the summer Yellow Sea.

Targeted observation is an appealing procedure to improve oceanic model predictions by taking additional assimilation of collected measurements. However, studies on targeted observation in the oceanic field have been largely based on modeling efforts, and there is a need for field validating observations. Here, we report the preparatory work of a field campaign, which is designed based on the identified sensitive area by the Conditional Nonlinear Optimal Perturbation (CNOP) approach, to improve the short-range summer thermal structures prediction in the Yellow Sea (YS). We firstly simulated the hindcasting (2016-2018) temperature structures in the summertime, and found that the locations of the sensitive areas are generally consistent in space for each hindcast year. Then, we introduced the technique of multiple-assimilation and the definition of time-varying sensitive area, and designed observing strategies for the YS summer campaign. Observing System Simulation Experiments (OSSEs) were conducted prior to address the plan on field campaign in the Yellow Sea in August 2019. Results show that, reducing the initial errors in the sensitive area can lead to more improvement on thermal structures prediction than that in other area.

Targeted observation is an appealing procedure for improving model predictions through the assimilation of additional collected measurements. However, studies on targeted observations in the oceani...

Focusing on the transfer and evolvement of initial perturbation from temperature of ocean to the underwater acoustic propagation,comparing with the remote sensing data,optimizing Ocean-Acoustic Coupled Model, the reliability of this model is verified. On this basis,global and regional error development experiments are carried out by adding perturbation on the initial temperature of a controlled test. The results show that after 5 days evolution of the initial temperature field,the global perturbation of the propagation loss is saturated, and the perturbation structure is basically consistent with the law of the dynamic ocean.For the target area in Kuroshio region, the initial perturbation in the upstream region is the fastest. This conclusion can provide the basis for the adaptive observation of ocean acoustics.

针对水声传播模型的计算量大,难以满足实时化、精细化水下声传播信息保障需求的难题,基于MPI+OpenMP混合并行编程方法,开展了WKBZ简正波模型混合并行计算方法研究,实现了水下声场2级混合并行计算.该方法通过节点间消息传递、节点内内存共享的方式,有效克服了MP I并行编程模型通信开销大和OpenMP并行编程环境可扩展性差的缺点,较好地解决了水下声传播快速计算的问题.测试结果表明,该方法能够较好地利用SMP集群节点间和节点内多级并行机制,充分发挥消息传递编程模型和共享内存编程模型各自的优势,大幅降低MP I进程间通信带来的时间开销,有效提升程序的可扩展性和并行效率.

传统的海洋水声环境观测通常是局部区域范围内短时间的有限观测,非常时期观测平台在作战海区实施系统观测十分困难,如何利用适当的观测方式获取有效的观测数据来提高观测效率是目前亟需解决的问题.该文介绍了海洋声学敏感区诊断与适应性观测概念,总结了相关研究进展,该项工作有助于改进目前传统的观测系统设计,提高观测网建设效益,根据敏感区分布特点建立移动的补充观测系统开展适应性观测,从而发展我国新型的海洋观测体系,为改善海洋水声环境预报质量提供技术支撑.

敏感区诊断是适应性观测的关键问题,集合变换卡尔曼方法(Ensemble Transform Kalman Filter,ETKF)是目前主要的诊断方法之一.将集合变换卡尔曼方法应用于海洋环境适应性观测,根据ROMS海洋模式数据构建海表温度集合预报,以黑潮流域宫古海峡附近海域为验证区进行敏感区诊断计算,分析不同间隔时间条件下敏感区分布情况,结合模拟系统观测试验验证在敏感区进行适应性观测对预报质量的提升效果.结果表明,在诊断所得敏感区内添加观测能够提升预报质量;随时间间隔增大,敏感区向上游区域平移且预报质量提升效果减小;与在验证区整体添加观测相比,敏感区观测对预报质量提升效果基本相同并且观测成本明显减少.

深海环境复杂多样,船舶噪声信号特征受环境影响较大,不利于目标位置及运动态势判断.文章在深海声道形成条件及其声场传播规律研究基础上,基于波导不变量理论,对典型深海环境不同位置船舶噪声距离-频率干涉条纹特征进行理论分析和仿真验证.理论分析和仿真结果表明,深海声道会聚区及反射会聚区船舶噪声信号存在干涉条纹特征,且对应的波导不变量 β值不同,故两者干涉条纹特征不同.文中提出了利用深海船舶目标噪声的干涉条纹特征,对不同位置深海船舶目标及其运动态势进行判断的方法,仿真结果和试验数据表明,该判断方法可行有效.

为改善海洋与水声环境预报质量,针对常规观测成本高、资料利用率低等问题,将适应性观测方法应用于海洋声学领域.结合海洋-声学耦合模式与集合卡尔曼转换敏感区诊断方法,以东中国海宫古海峡北部区域为验证区,计算并分析不同条件下海洋环境敏感区与声学敏感区分布,通过观测系统模拟试验验证适应性观测对验证区预报的提升效果.结果表明,两种敏感区位置随时间间隔增加均向验证区上游移动,海洋环境敏感区相比于声学敏感区分布更为集中,且平移特征更明显;对海洋环境敏感区和声学敏感区添加适应性观测均能提升海洋与水声环境的预报质量,提升效果随时间间隔增加而减小,在某种类型敏感区的适应性观测对相对应参数的预报质量提升效果优于对其他类型敏感区进行观测的效果.

Focusing on the rapid prediction of acoustic field uncertainty in environment with temporal and spatial sound speed perturbation, evolvement of sound speed structure over time is predicted based on the ocean-acoustic coupled model to obtain the uncertainty distribution of the vertical structure of sound speed. Further, a method combining the arbitrary polynomial chaos expansion with the empirical orthogonal function is proposed to reduce the dimensionality of uncertain parameters and to obtain the uncertainty distribution of the acoustic field. Simulations have shown that the computational complexity can be reduced by 2 orders of magnitude compared to the conventional polynomial chaos expansion while ensures the same precision. Moreover, the computational complexity is not influenced by the complexity of the sound speed profile. The acoustic field and uncertainty predicted in uncertain environment by proposed method also have been tested with the experimental data.

Adaptive observation is an efficacious idea by operating additional observation in sensitive area to improve the quality of model forecast. The ETKF (Ensemble Transform Kalman Filter) method has been proved an effective method for identifying sensitive area and widely applied in atmosphere but barely in ocean field. In this paper, an adaptive observation system based on ETKF is applied in East China Sea. Simulations are operated based on the ROMS model data of particular area. Several ETKF method parameters are selected and optimized. Sensitive areas of separate ocean environment parameters are identified through distinct adaptive observation types aimed for different areas. There are a number of enlightening conclusions gained from the analysis of simulations.

针对深海会聚区目标运动态势判别困难的问题,提出了一种基于深海波导不变性的会聚区目标运动态势分析方法.会对深海波导不变性,利用简正波理论分别分析了反射简正波和反转简正波对深海波导不变量的影响,即反射简正波形成的波导不变量近似为1,反转简正波形成的波导不变量为负值.态势分析方法依据声纳听音信号LOFAR谱图特点,实现目标运动态势判别.仿真数据与海试数据验证结果表明:改进方法可以在预知环境信息较少的情况下,实现深海会聚区目标及运动态势的正确判定.