Ocean acoustic tomography: estimating the acoustic travel time withphase

Continuous acoustic transmission (133 Hz, 60-ms resolution) between a bottom-mounted source near Oahu, Hawaii, and a bottom-mounted receiver at 4000-km range near the coast of northern California was recorded to learn how to measure precisely the travel time so that basin-scale fluctuations in the Pacific can be detected. Daily incoherent averages of some of the multipaths exhibited stability during this period. The standard deviation of the travel time of the resolved peaks in the daily incoherent averages is about 30 ms. An acoustic method, based on cross-correlation, is derived to estimate the change in the average acoustic phase (travel time) to a precision of about 0.018 cycles (135 μs) every 2 min. Travel-time estimates based on the cross-correlator reduce the aberrations due to internal waves by about 19 dB in comparison with CW transmissions. The new travel-time estimator is applied to the measurements to examine some of the fluctuations of the Pacific     

Weak constraint data assimilation for tides in the Arctic Ocean

Estimation of the open-boundary inputs solving a weak constraint variational formulation for an Arctic tide model is considered as an ill-posed problem in the sense that the solution is very sensitive to the data noise and to grid size. Mathematically, spatial discretization of a cost function to be minimized and penalization of normal flow through the open boundary act as regularization of the problem. An heuristic choosing rule for the regularization parameter is applied to assess a suitable spatial resolution and the weight referred to the open boundary penalty. It is shown that these provide a better fit of the solution to a control data set compared with a finer grid, the value of the energy flux through the open boundary being in agreement with other model estimates. The M₂ solution obtained is much closer to the control data than other modern solutions while the accuracy of the simulated K₁ constituent is within the same error level. The tidal maps for these waves exhibit certain distinctions in comparison with other charts.     

Ocean bottom tiltmeter with acoustic data retrieval system implanted by a submersible

Sensitive Ocean Bottom Implanted Tiltmeters (OBITs) with a sophisticated data retrieval system have been developed in order to observe directly the subduction of oceanic lithosphere. The OBIT is the first long-term geophysical instrument which was designed to be deployed by a manned deep-sea submersible. When the OBIT is put on oceanic lithosphere which is bending and is about to subduct under a deep sea trench, the OBIT records the subduction by observing the tilt of the surface of the lithosphere. The OBIT system has a sensitivity of 10^-8 radian, which is enough to detect the ongoing subduction in months or years. The OBIT may give an answer to the question whether there are fluctuations in the subduction rate.Two OBITs were successfully deployed on a seaward slope of the Kuril Trench by the newly built French deep submersible, Nautile. The OBITs were installed on the northwest shoulder of Erimo seamount, at a depth of 3930 m, in the Kuril Trench. In order to attain stable long-term observations of crustal deformations, the sensing unit was cemented onto bare rock by mortar. We have not yet had an opportunity to recover the data.The life of the instruments is expected to be more than five years. An acoustic data transmission system has been developed for the OBIT data recovery. The stored data can be retrieved at any moment during the observation period, with no need to retrieve the instruments nor to interrupt the observation, by use of the acoustic system. The acoustic system has a high data transmission rate as well as extremely low power consumption. This will be the first long-term crustal deformation measurement on the sea floor.     

An ocean data assimilation system and reanalysis of the World Ocean hydrophysical fields

A new version of the ocean data assimilation system (ODAS) developed at the Hydrometcentre of Russia is presented. The assimilation is performed following the sequential scheme analysis–forecast–analysis. The main components of the ODAS are procedures for operational observation data processing, a variational analysis scheme, and an ocean general circulation model used to estimate the first guess fields involved in the analysis. In situ observations of temperature and salinity in the upper 1400-m ocean layer obtained from various observational platforms are used as input data. In the new ODAS version, the horizontal resolution of the assimilating model and of the output products is increased, the previous 2D-Var analysis scheme is replaced by a more general 3D-Var scheme, and a more flexible incremental analysis updating procedure is introduced to correct the model calculations. A reanalysis of the main World Ocean hydrophysical fields over the 2005–2015 period has been performed using the updated ODAS. The reanalysis results are compared with data from independent sources.     

Response of a coupled ocean-ice-atmosphere model to data assimilation in the tropical zone of the Pacific Ocean

This paper deals with numerical experiments based on the coupled ECHAM-HOPE model. The results of experiments are analyzed. The initial fields for the calculations over time periods from one month to one year are constructed based on the results of the data assimilation of temperature profiles from TOGA-TAO moorings. The perturbations of the initial fields and the propagation of these perturbations with specific computational time intervals are analyzed on the basis of the results of these experiments. It is shown that the strongest impacts of the perturbations are localized in specific regions of the World Ocean corresponding to the energetically active zones of the Earth. The mechanism of the transition of these perturbations is also studied. Different statistical properties of the ensemble of experiments are presented.     

Cycling the Representer Method for 4D-variational data assimilation with the Navy Coastal Ocean Model

The Cycling Representer Method, which is a technique for solving 4D-variational data assimilation problems, has been demonstrated to improve the assimilation accuracy with simpler nonlinear models. In this paper, the Cycling Representer Method will be used to assimilate an array of ADCP velocity observations with the Navy Coastal Ocean Model (NCOM). Experiments are performed in a high-resolution Mississippi Bight domain for the entire month of June, 2004 and demonstrate the usefulness of this assimilation technique in a realistic application.The Representer Method is solved by minimizing a cost function containing the weighted squared errors of velocity measurements, initial conditions, boundary conditions, and model dynamics. NCOM, however, is a highly nonlinear model and in order to converge towards the global minimum of this cost function, NCOM is linearized about a background state using tangent linearization. The stability of this tangent linearized model (TLM) is a very sensitive function of the background state, the level of nonlinearity of the model, open boundary conditions, and the complexity of the bathymetry and flow field. For the Mississippi Bight domain, the TLM is stable for only about a day. Due to this short TLM stability time period, the Representer Method is cycled by splitting the time period of the assimilation problem into short intervals. The interval time period needs to be such that it is short enough for the TLM to be stable, but long enough to minimize the loss of information due to reducing the temporal correlation of the dynamics and data. For each new cycle, a background is created as a nonlinear forecast from the previous cycle’s assimilated solution. This background, along with the data that falls within this new cycle, is then used to calculate a new assimilated solution. The experiments presented in this paper demonstrate the improvement of the assimilated solution as the time window of the cycles is reduced to 1 day. The 1-day cycling, however, was only optimal for the first half of the experiment. This is because there was a strong wind event near the middle of June that significantly reduced the stability of the 1-day cycling and caused substantial errors in the assimilation. Therefore, the 12-h cycling worked best for the second half of the experiment. This paper also demonstrates that the forecast skill is improved as the assimilation system progresses through the cycles.     

最优插值法及其在热带太平洋海表温度数据同化中的应用

利用M IT gcm模式和最优插值法搭建的同化平台对热带太平洋赤道附近的海表温度SST数据进行了数值同化处理。结果表明,同化处理有效兼顾了模式模拟值和观测值,纠正了模式模拟值出现的误差,数值同化结果更接近于观测值。该同化平台能够更好地反映出SST的分布特征,该同化方法可以有效地对海表数据进行数值预报。     

Impact on oceanic dynamics from assimilation of satellite surface height anomaly data into the Hybrid Coordinate Ocean Model Ocean Model (HYCOM) over the Atlantic Ocean

Observed along-track data of sea surface height anomalies (SSHAs) over the Atlantic Ocean from the Jason-1 and Jason-2 satellites were assimilated into the Hybrid-Coordinate Ocean Model (HYCOM) with the Ensemble Optimal Interpolation scheme (EnOI). The impact of assimilation of SSHA with focus on oceanic dynamics was investigated. Time series of analyzed and forecasted values were compared with a model free run with the same forcing but without assimilation. In addition, the results were compared with an independent run, the so-called HYCOM + NCODA analysis from the US Navy. The study shows that the assimilation technique with some modifications allowed substantial improvement in the 24 h ocean prediction by reducing the forecast errors in comparison with the free run. It is also shown that the analyzed sea surface fields contain mesoscale and synoptic variability, which are poorly seen in the free run.     

基于WOA13数据的北大西洋声传播分析

应用WOA13季节平均数据和BELLHOP模型,在季节、声源频率、声源深度和掠射角等因素确定的情况下,分析北大西洋冬季(1-3月)声道轴深度、最小声速值、表层声速值的分布,通过仿真计算研究选用位置点5 m深度声源的声传播规律:反转深度随纬度升高而降低,低纬度海岭东西两侧差别不大,15°N以北为西侧大于东侧。55°N以南海区可形成汇聚区波导,海岭西侧的汇聚区跨度大于海岭东侧,有混合层时还存在一定强度的表面波导,汇聚区处5 m、100 m和250 m接收深度上的传播损失差异较小,增益为7～19 dB,55°N以北海区则为有焦散结构的表面波导。以北大西洋35°N为界,以南以汇聚区波导探测有利,以北以表面波导探测有利。     

4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)

We present the background, development, and preparation of a state-of-the-art 4D variational (4DVAR) data assimilation system in the Regional Ocean Modeling System (ROMS) with an application in the Intra-Americas Sea (IAS). This initial application with a coarse model shows the efficacy of the 4DVAR methodology for use within complex ocean environments, and serves as preparation for deploying an operational, real-time assimilation system onboard the Royal Caribbean Cruise Lines ship Explorer of the Seas. Assimilating satellite sea surface height and temperature observations with in situ data from the ship in 14 day cycles over 2 years from January 2005 through March 2007, reduces the observation-model misfit by over 75%. Using measures of the Loop Current dynamics, we show that the assimilated solution is consistent with observed statistics.     

高度计波高数据同化对印度洋海域海浪模式预报影响研究

为提高海浪模式预报的精度,改善初始场是途径之一。研制了基于最优插值(OI)方法的海浪数据同化并行程序模块,并将其植入第三代海浪模式WAVEWATCH IIITM,建立了印度洋海域海浪同化预报方法,使用卫星高度计波高数据进行了同化预报试验。OI模块的并行设计使得植入同化模块的海浪模式仍能以并行方式运行。文中5°S以北印度洋海域为目标区域,嵌套在WAVE-WATCH IIITM的全球网格中,使得目标区域开边界条件得到较好解决。同化数据使用Jason-2高度计测量有效波高(SWH)沿轨数据。海浪同化预报模式由大气模式WRF(Weather Research andForecasting)输出的1小时一次的海面10 m风场驱动。将同化的模式结果(SWH)、无同化的模式结果(SWH)分别与高度计沿轨数据(SWH)进行比较,表明同化改善模式预报初始场的效果是明显的。以同化初始场出发进行海浪预报试验,结果表明,高度计波高数据同化在一定程度上可改进海浪短期预报的精度。     

4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)

We present the background, development, and preparation of a state-of-the-art 4D variational (4DVAR) data assimilation system in the Regional Ocean Modeling System (ROMS) with an application in the Intra-Americas Sea (IAS). This initial application with a coarse model shows the efficacy of the 4DVAR methodology for use within complex ocean environments, and serves as preparation for deploying an operational, real-time assimilation system onboard the Royal Caribbean Cruise Lines ship Explorer of the Seas. Assimilating satellite sea surface height and temperature observations with in situ data from the ship in 14 day cycles over 2 years from January 2005 through March 2007, reduces the observation-model misfit by over 75%. Using measures of the Loop Current dynamics, we show that the assimilated solution is consistent with observed statistics.     

拉格朗日资料同化的研究进展

拉格朗日资料对了解海洋环流和大气环流的动力学过程起到很重要的作用,同化拉格朗日资料有很大的必要性.拉格朗日观测工具主要提供的是水团或空气微团的位置和速度等信息,因为拉格朗日资料提供的是移动框架的数据而不是欧拉格点上的模式变量,所以随着拉格朗日观测覆盖大部分海洋和全球海洋环流模式的实现,采用先进的同化方法充分利用这些资料来提高分析预报准确性势在必行.在同化方法不断的发展中最优插值、变分同化、卡尔曼滤波这几种方法都有很好的应用.     

Towards a true 4‐dimensional data assimilation algorithm: application of a cycling representer algorithm to a simple transport problem

3‐dimensional variational algorithms are widely used for atmospheric data assimilation at the present time, particularly on the synoptic and global scales. However, mesoscale and convective scale phenomena are considerably more chaotic and intermittent and it is clear that true 4‐dimensional data assimilation algorithms will be required to properly analyze these phenomena. In its most general form, the data assimilation problem can be posed as the minimization of a 4‐dimensional cost function with the forecast model as a weak constraint. This is a much more difficult problem than the widely discussed 4DVAR algorithm where the model is a strong constraint. Bennett and collaborators have considered a method of solution to the weak constraint problem, based on representer theory. However, their method is not suitable for the numerical weather prediction problem, because it does not cycle in time. In this paper, the representer method is modified to permit cycling in time, in a manner which is entirely internally consistent. The method was applied to a simple 1‐dimensional constituent transport problem where the signal was sampled (perfectly and imperfectly) with various sparse observation network configurations. The cycling representer algorithm discussed here successfully extracted the signal from the noisy, sparse observations     

Observational data assimilation in the problem of Black Sea circulation and sensitivity analysis of its solution

The problem of the variational data assimilation of the sea-surface temperature for the model of the Black Sea dynamics has been formulated and numerically studied to reproduce surface heat fluxes. An analysis of sensitivity of the optimal solution to errors in observation data has been conducted. The results of numerical experiments have been presented.     

Some progress on ocean data assimilation in China: Introduction of the special section “Ocean Data Assimilation”

<正>This special section is the scientific legacy of the 13th National Ocean Data Assimilation and Numerical Simulation Conference of China, which was held in Changsha, China during December 3–4, 2020 with more than 160 participants from 35 units in China. It continued a series of National Ocean Data Assimilation conferences which began in 2003,     

Asynchronous data assimilation with the EnKF

This study revisits the problem of assimilation of asynchronous observations, or four-dimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF. The asynchronous EnKF is essentially equivalent to the four-dimensional variational data assimilation (4D-Var). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is feasible for large-scale applications. Unlike 4D-Var, the asynchronous EnKF requires no tangent linear or adjoint model.     

集合资料同化方法的理论框架及其在海洋资料同化的研究展望

在海洋动力系统的数值模拟中,海洋资料同化是一种能够有效融合多源海洋观测资料和数值模式的方法。它不仅可以显著地提高数值模拟的效果,构造海洋再分析资料场,还能有效减少海洋和气候预报时模式初始条件的不确定性。因此,海洋资料同化对于海洋研究和业务化应用具有非常重要的意义。资料同化方法的研究一直是大气、海洋科学的热门课题之一。其中,集合卡尔曼滤波器(EnKF)是一种有效的资料同化方法,自提出以来经过了20多年的发展和改进,已经在海洋资料同化中得到了广泛的研究和应用。近年来,随着动力模式的不断发展和计算能力的提高,粒子滤波器由于不受模型线性和误差高斯分布假设的约束,也逐渐成为了当前资料同化方法研究的热点。本文分析和总结了目前关于集合卡尔曼滤波器和粒子滤波器的一些最新理论研究结果,在贝叶斯滤波理论的框架下讨论了这两类算法的关联和区别,以及各自在资料同化实践中的优势和不足。在此基础上,我们探讨了粒子滤波器应用于海洋模式资料同化的主要困难和目前可行的一些解决方法,展望了集合资料同化方法研究的新趋势,为集合资料同化方法的进一步发展和应用提供理论基础。