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1.
A 1/8° global version of the Navy Coastal Ocean Model (NCOM) is used for simulation of upper-ocean quantities on interannual time scales. The model spans the global ocean from 80°S to a complete Arctic cap, and includes 19 terrain-following σ- and 21 fixed z-levels. The global NCOM assimilates three-dimensional (3D) temperature and salinity fields produced by the Modular Ocean Data Assimilation System (MODAS) which generates synthetic temperature and salinity profiles based on ocean surface observations. Model-data intercomparisons are performed to measure the effectiveness of NCOM in predicting upper-ocean quantities such as sea surface temperature (SST), sea surface salinity (SSS) and mixed layer depth (MLD). Subsurface temperature and salinity are evaluated as well. An extensive set of buoy observations is used for this validation. Where possible, the model validation is performed between year-long time series obtained from the model and time series from the buoys. The statistical analyses include the calculation of dimensionless skill scores (SS), which are positive if statistical skill is shown and equal to one for perfect SST simulations. Model SST comparisons with year-long SST time series from all 83 buoys give a median SS value of 0.82. Model subsurface temperature comparisons with the year-long subsurface temperature time series from 24 buoys showed that the model is able to predict temperatures down to 500 m reasonably well, with positive SS values ranging from 0.18 to 0.97. Intercomparisons of MLD reveal that the model MLD is usually shallower than the buoy MLD by an average of about 15 m. Annual mean SSS and subsurface salinity biases between the model and buoy values are small. A comparison of SST between NCOM and a satellite-based Pathfinder data set demonstrates that the model has a root-mean-square (RMS) SST difference of 0.61 °C over the global ocean. Spatial variations of kinetic energy fields from NCOM show agree with historical observations. Based on these results, it is concluded that the global NCOM presented in this paper is able to predict upper-ocean quantities with reasonable accuracy for both coastal and open ocean locations.  相似文献   

2.
本文主要介绍了南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统的研制概况。预报区域为99°E~135°E，15°S~45°N，包括渤海、黄海、东海和南海及其周边海域。为了给耦合预报模式提供较准确的预报初始场，在预报开始之前，分别进行了海浪模式和海洋模式的前24小时同化后报模拟。海浪模式和海洋模式都采用了集合调整Kalman滤波同化方法，海浪模式同化了Jason-2有效波高数据；海洋模式同化了SST数据、MADT数据和ARGO剖面数据。为了改进海洋温度和盐度的模拟，我们在海洋模式的垂向混合方案中引入波致混合和内波致混合的作用。预报系统的运行主要包括两个阶段，首先海浪模式和海洋模式进行了2014年1月至2015年10月底的同化后报模拟，强迫场源自欧洲气象中心的六小时的再分析数据产品。然后耦合预报系统将同化后报模拟的结果作为初始场进行了14个月的耦合预报。预报产品包括大气产品（气温、风速风向、气压等）、海浪产品（有效波高和波向等）、海流产品（温度、盐度和海流等）。一系列观测资料的检验比较表明该大气-海浪-海洋耦合精细化数值预报系统的预报结果较为可靠，可以为南海及周边海洋资源开发和安全保障提供数据和信息产品服务。  相似文献   

3.
Better forecast of tropical cyclone(TC) can help to reduce risk and enhance management. The TC forecast depends on the scientific understanding of oceanic processes, air-sea interaction and finally, the atmospheric process. The TC Viyaru is taken as an example, which is formed at the end of 11 May 2013 and sustains up to 17 May 2013 during pre-monsoon season. Argo data are used to investigate ocean response processes by comparing pre-and post-conditions of the TC. Eight oceanic parameters including the sea surface temperature(SST), the sea surface salinity(SSS), and the barrier layer thickness(BLT), the 26°C isotherm depth in the ocean(D26), the isothermal layer depth(ILD), the mixed layer depth(MLD), the tropical cyclone heat potential(TCHP) and the effective oceanic layer for cyclogenesis(EOLC) are chosen to evaluate the pre-and post-conditions of the TC along the track of Viyaru. The values of the SST, D26, BLT, TCHP and EOLC in the pre-cyclonic condition are higher than the post-cyclonic condition, while the SSS, ILD and MLD in the post-cyclonic condition are higher than the pre-cyclonic condition of the ocean due to strong cyclonic winds and subsurface upwelling. It is interesting that the strong intensity of the TC reduces less SST and vice versa. The satisfied real time Argo data is not available in the northern Bay of Bengal especially in the coastal region. A weather research and forecasting model is employed to hindcast the track of Viyaru, and the satellite data from the National Center Environmental Prediction are used to assess the hindcast.  相似文献   

4.
Ocean temperature responses to Typhoon Mstsa in the East China Sea   总被引：1，自引：1，他引：0  
The MASNUM wave-tide-circulation coupled model, with 21 layers in the vertical and （1/8） °horizontal resolution, was employed to investigate the oceanic responses to Typhoon Mstsa which traversed the East China Sea （ECS） during the period of 4 - 6 August, 2005. Numerical experiment results are analyzed and compared with observation. The responses of the sea surface temperature （SST）, in a focused area of （27° -29°N, 121° - 124°E）, include heating and cooling stages. The heating is mainly due to warm Kuroshio water transportation and downwelling due to the water accumulation. In the cooling stage, the amplitude of the simulated cold wake （ -3℃ ）, located on the right side of this typhoon track, is compared quite well with that of the satellite observed SST data. The wave-induced mixing（Bv） plays a key role for the SST cooling. Bv still plays a leading role, which accounts for 36%, for the ocean temperature drop in the upper ocean of 0 - 40 m, while the upwelling is responsible for 84% of the cooling for the lower layer of 40 - 70 m. The mixed layer depth （MLD） increased quickly from 28 to 50 m in the typhoon period. However, the simulated MLD without the wave-induced vertical mixing, evolution from 13 to 32 m, was seriously underestimated. The surface wave is too important to be ignored for the ocean responses to a typhoon.  相似文献   

5.
海洋预报是进行海上活动的安全保障,海洋预报系统技术已经成为现代海洋气象业务的技术支撑。海洋观测、数据同化、数值模拟和高性能计算机等技术的进步极大地推动着海洋业务化预报的发展。采用大气数值模式（WRF）、海洋数值模式（CROCO）和海浪数值模式（SWAN）的多模式高分辨率离线耦合方式,添加南京信息工程大学“海洋数值模拟与观测实验室”团队自主研发的一系列海洋模式参数化方案,包括浪致混合参数化方案、亚中尺度参数化方案、海山诱导混合参数化方案以及涡旋诱导的沿等密度面和跨等密度面混合参数化方案,并通过同化技术和最新的人工智能技术与观测资料相结合,构建一种面向中国边缘海的风浪流多参数耦合预报系统,用于海上风电功率的预报和其他海洋灾害预警。实际观测资料的验证表明,该预报系统能较准确地模拟海上风场、海流、海温、波浪、潮汐等海洋气象要素。同时实现了按需实时可视化全景展示。  相似文献   

6.
文章利用果蝇优化广义回归神经网络算法FOAGRNN (fruit fly optimization algorithm, FOA; generalized regression neural network, GRNN)对SODA (simple ocean data assimilation)再分析数据进行训练, 构建海表温度、盐度、海面高度与次表层温盐场之间的投影关系模型, 并在全球范围使用SODA和卫星遥感数据评估了模型的应用性能。首先, 利用独立的2016年SODA海表数据作为模型输入进行理想重构试验, 结果显示全球重构温、盐平均均方根误差(MRMSE)分别为0.36℃和0.08‰, 与世界海洋图集WOA13资料相比减小约50%和60%。然后, 利用卫星观测的海表信息作为模型输入进行实际应用试验, 并与Argo观测剖面进行比较评估。试验结果表明, 重构模型能有效表征海水温、盐特征, 其中重构温、盐MRMSE分别为0.79℃和0.16‰, 相比WOA气候态减小27%和11%。误差的垂向分布显示, 重构温度RMSE从海表向下迅速增大, 至100m达到峰值1.35℃, 而后又迅速回落,至250m处为0.81℃, 跃层往下不断减小; 重构盐度RMSE基本随深度增大而减小, 误差峰值位于25m附近, 约为0.25‰。此外, Argo浮标跟踪分析和区域水团统计结果也表明模型能够较好地刻画海洋三维温盐场的内部结构特征。  相似文献   

7.
The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.  相似文献   

8.
The mean seasonal cycle of mixed layer depth (MLD) in the extratropical oceans has the potential to influence temperature, salinity and mixed layer depth anomalies from one winter to the next. Temperature and salinity anomalies that form at the surface and spread throughout the deep winter mixed layer are sequestered beneath the mixed layer when it shoals in spring, and are then re-entrained into the surface layer in the subsequent fall and winter. Here we document this ‘re-emergence mechanism’ in the North Pacific Ocean using observed SSTs, subsurface temperature fields from a data assimilation system, and coupled atmosphere–ocean model simulations. Observations indicate that the dominant large-scale SST anomaly pattern that forms in the North Pacific during winter recurs in the following winter. The model simulation with mixed layer ocean physics reproduced the winter-to-winter recurrence, while model simulations with observed SSTs specified in the tropical Pacific and a 50 m slab in the North Pacific did not. This difference between the model results indicates that the winter-to-winter SST correlations are the result of the re-emergence mechanism, and not of similar atmospheric forcing of the ocean in consecutive winters. The model experiments also indicate that SST anomalies in the tropical Pacific associated with El Niño are not essential for re-emergence to occur.The recurrence of observed SST and simulated SST and SSS anomalies are found in several regions in the central North Pacific, and are quite strong in the northern (>50°N) part of the basin. The winter-to-winter autocorrelation of SSS anomalies exceed those of SST, since only the latter are strongly damped by surface fluxes. The re-emergence mechanism also has a modest influence on MLD through changes in the vertical stratification in the seasonal thermocline.  相似文献   

9.
In this study, we evaluate the performance of the recently developed incremental strong constraint 4-dimensional variational (4DVAR) data assimilation applied to the Yellow Sea (YS) using the Regional Ocean Modeling System (ROMS). Two assimilation experiments are compared: assimilating remote-sensed sea surface temperature (SST) and both the SST and in-situ profiles measured by shipboard CTD casts into a regional ocean modeling from January to December of 2011. By comparing the two assimilation experiments against a free-run without data assimilation, we investigate how the assimilation affects the hydrographic structures in the YS. Results indicate that the SST assimilation notably improves the model behavior at the surface when compared to the non-assimilative free-run. The SST assimilation also has an impact on the subsurface water structure in the eastern YS; however, the improvement is seasonally dependent, that is, the correction becomes more effective in winter than in summer. This is due to a strong stratification in summer that prevents the assimilation of SST from affecting the subsurface temperature. A significant improvement to the subsurface temperature is made when the in-situ profiles of temperature and salinity are assimilated, forming a tongue-shaped YS bottom cold water from the YS toward the southwestern seas of Jeju Island.  相似文献   

10.
An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, “observations” drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the “identical” twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.  相似文献   

11.
集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。  相似文献   

12.
盐度对变化2014年东北太平洋“暖泡”的作用   总被引：1，自引：0，他引：1  
A significant strong, warm "Blob"(a large circular water body with a positive ocean temperature anomaly)appeared in the Northeast Pacific(NEP) in the boreal winter of 2013–2014, which induced many extreme climate events in the US and Canada. In this study, analyses of the temperature and salinity anomaly variations from the Array for Real-time Geostrophic Oceanography(Argo) data provided insights into the formation of the warm"Blob" over the NEP. The early negative salinity anomaly dominantly contributed to the shallower mixed layer depth(MLD) in the NEP during the period of 2012–2013. Then, the shallower mixed layer trapped more heat in the upper water column and resulted in a warmer sea surface temperature(SST), which enhanced the warm"Blob". The salinity variability contributed to approximately 60% of the shallowing MLD related to the warm"Blob". The salinity anomaly in the warm "Blob" region resulted from a combination of both local and nonlocal effects. The freshened water at the surface played a local role in the MLD anomaly. Interestingly, the MLD anomaly was more dependent on the local subsurface salinity anomaly in the 100–150 m depth range in the NEP.The salinity anomaly in the 50–100 m depth range may be linked to the anomaly in the 100–150 m depth range by vertical advection or mixing. The salinity anomaly in the 100–150 m depth range resulted from the eastward transportation of a subducted water mass that was freshened west of the dateline, which played a nonlocal role.The results suggest that the early salinity anomaly in the NEP related to the warm "Blob" may be a precursor signal of interannual and interdecadal variabilities.  相似文献   

13.
基于中国近海浪-流耦合业务化预报系统（OFS-C）和Lagrangian粒子追踪方法，本文首次建立了辽东湾海蜇增殖放流模型。模型中，海蜇用虚拟的粒子表示，具有昼夜垂直运动（DVM）特性。海蜇运动由海流和次网格参数化引起的随机运动驱动。利用航次调查数据对模型结果进行了验证，模式结果可以很好的模拟放流海蜇分布的主要结构。模型分析了物理过程对辽东湾放流海蜇的输运和分布的影响，结果显示海蜇运动主要受海流和间接的风应力的影响。放流地和捕捞地的连通性分析表明，辽东湾海蜇的主要捕捞地集中于湾顶。另外，连通性矩阵表明，瓦房店放流的部分海蜇不能到达捕捞地，因此，相对于其它放流地来说，瓦房店不是理想的放流地。敏感性实验表明了随机游走的重要性，但是对于游走格式并不敏感。海蜇栖息的深度会影响海蜇最终的分布，若海蜇栖息于海底，则在低层环流的影响下，最终分布于辽东湾中部，部分会流出辽东湾，而不能到达湾顶。  相似文献   

14.
Effect of Langmuir circulation on upper ocean mixing in the South China Sea   总被引：2，自引：0，他引：2  
Effect of Langmuir circulation (LC) on upper ocean mixing is investigated by a two-way wave-current coupled model. Themodel is coupled of the ocean circulationmodel ROMS (regional ocean modeling system) to the surface wave model SWAN (simulating waves nearshore) via the model-coupling toolkit. The LC already certified its importance by many one-dimensional (1D) research andmechanismanalysis work. This work focuses on inducing LC’s effect in a three-dimensional (3-D) model and applying it to real field modeling. In ROMS, theMellor-Yamada turbulence closuremixing scheme is modified by including LC’s effect. The SWAN imports bathymetry, free surface and current information fromthe ROMS while exports significant wave parameters to the ROMS for Stokes wave computing every 6 s. This coupled model is applied to the South China Sea (SCS) during September 2008 cruise. The results show that LC increasing turbulence and deepening mixed layer depth (MLD) at order of O (10 m) in most of the areas, especially in the north part of SCS where most of our measurements operated. The coupled model further includes wave breaking which will bringsmore energy into water. When LC works together with wave breaking,more energy is transferred into deep layer and accelerates the MLD deepening. In the north part of the SCS, their effects aremore obvious. This is consistent with big wind event in the area of the Zhujiang River Delta. The shallow water depth as another reasonmakes themeasy to influence the oceanmixing as well.  相似文献   

15.
A global data set describing the gridded mixed-layer depth (MLD) in 10-day intervals was produced using high-quality Argo float data from 2001 to 2009. The characteristics and advantages provided by the new MLD data set are described here, including a comparison based on two different thresholds and using data sets of different vertical and temporal resolution. The MLD in the data set was estimated on the basis of a shallower depth of the iso-thermal layer (TLD) or iso-pycnal layer (PLD), calculated using the finite difference method. The MLD data are incorporated into 2° × 2° grid in the global ocean, including marginal seas. Also, two threshold values were used to examine differences in the MLD and its seasonal temporal variability. The characteristics and advantages of using the Argo 10-day intervals to determine the MLD were then confirmed by comparing those data with the station buoy daily means and the Argo monthly means. With respect to vertical and temporal resolutions, the Argo 10-day data has two distinct advantages: (1) improved representation of the MLD vertical change due to high vertical resolution, especially during periods of large MLD variability and (2) more detailed representation of the temporal change in MLD than achieved with the Argo monthly mean data, especially from winter to spring in mid and high latitudes. These advantages were maintained in the case of a larger threshold despite the fact that the MLD is rather deep and the detailed variation in its distribution differs depending on the season and location. This study also investigated the relative influence of TLD and PLD to the MLD calculation for each grid. Generally, the MLD is primarily determined based on the PLD at low and mid latitudes (TLD > PLD), whereas the TLD is more important at high latitudes, especially in winter (TLD < PLD). In the case of a larger threshold, the area of the larger PLD influence spreads polewards because of the greater effect of salinity in winter. Although there are some differences in the effect of temperature and salinity in estimations of the MLD, both are indispensable factors for the MLD estimations even at different thresholds.  相似文献   

16.
随着国家战略利益的拓展,国家对全球海洋环境预报保障的需求日益凸显。近年来,国家海洋环境预报中心研发并建立我国首个涵盖全球大洋的"全球海洋数值预报系统",该预报系统由MOM4全球海洋环流模式及三维变分同化系统组成。该系统的建立,实现了全球范围海洋环流预报业务全覆盖,为我国探索深海大洋环境的迫切需求提供有力保障,明显提升了我国海洋环境预报能力,体现了我国海洋数值预报技术的发展和进步。该系统的历史回报试验和业务化试运行结果表明其对全球海洋环境要素具有较好的预报能力,其预测结果已经在实际业务中得到了应用,在"雪龙号"极地遇险脱困、马航MH370失联飞机搜救等重大事件的预报保障任务中发挥了重要作用,为我国实施海洋强国战略,推进实施"21世纪海上丝绸之路"的战略构想,应对海上突发事件、维护国家海洋权益等各个方面提供有力的科技支撑和保障,并成为我国全球海洋预报业务的重要参考依据。  相似文献   

17.
Seasonal evolution of surface mixed layer in the Northern Arabian Sea (NAS) between 17° N–20.5° N and 59° E-69° E was observed by using Argo float daily data for about 9 months, from April 2002 through December 2002. Results showed that during April - May mixed layer shoaled due to light winds, clear sky and intense solar insolation. Sea surface temperature (SST) rose by 2.3 °C and ocean gained an average of 99.8 Wm−2. Mixed layer reached maximum depth of about 71 m during June - September owing to strong winds and cloudy skies. Ocean gained abnormally low ∼18 Wm−2 and SST dropped by 3.4 °C. During the inter monsoon period, October, mixed layer shoaled and maintained a depth of 20 to 30 m. November - December was accompanied by moderate winds, dropping of SST by 1.5 °C and ocean lost an average of 52.5 Wm−2. Mixed layer deepened gradually reaching a maximum of 62 m in December. Analysis of surface fluxes and winds suggested that winds and fluxes are the dominating factors causing deepening of mixed layer during summer and winter monsoon periods respectively. Relatively high correlation between MLD, net heat flux and wind speed revealed that short term variability of MLD coincided well with short term variability of surface forcing.  相似文献   

18.
Satellite-borne sea surface temperature (SST) data were assimilated with the ensemble Kalman filter (EnKF) in a Northwest Pacific Ocean circulation model to examine the effect of data assimilation. The model domain included the northwestern part of the Pacific Ocean and its marginal seas, such as the Yellow Sea and East/Japan Sea. The performance of the data assimilation was evaluated by comparing the simulated ocean state with that observed. Spatially averaged root-mean-squared errors in the SST and sea surface height (SSH) decreased by 0.44 °C and 4 cm, respectively, by the assimilation. The results of the numerical experiments substantiated the effectiveness of the SST assimilation via the EnKF for all marginal seas, as well as the Kuroshio region. The benefit of the data assimilation depended on the characteristics of each marginal sea. The variation of the SST in the East/Japan Sea and the Kuroshio extension (KE) region were improved 34% and those in the Yellow Sea 12.5%. The variation of the SSH was improved approximately 36% in the KE region. This large improvement was achieved in the deep-water regions because assimilation of SST data corrected the separation point of the western boundary currents, such as the Kuroshio and the East Korea Warm Current, and the associated horizontal surface currents. The SST assimilation via the EnKF also improved the subsurface temperature profiles. The effectiveness of SST assimilation was seasonally dependent, with the improvement being relatively larger in winter than in summer, which was related to the seasonal variation of the vertical mixing and stratification in the ocean surface layer.  相似文献   

19.
Assimilation of satellite-derived surface datasets has been explored in the study. Three types of surface data, namely sea level anomaly, sea surface temperature and sea surface salinity, have been used in various data assimilation experiments. The emphasis has been on the extra benefit arising out of the additional sea level assimilation and hence there are two parallel runs, in one of which sea level assimilation has been withheld. The model used is a state-of-the art ocean general circulation model (OGCM) and the assimilation method is the widely used singular evolutive extended Kalman filter (SEEK). Evaluation of the assimilation skill has been carried out by comparing the simulated depth of the 20°C isotherm with the same quantity measured by buoys and Argo floats. Simulated subsurface temperature and salinity profiles have also been compared with the same profiles measured by Argo floats. Finally, surface currents in the assimilation runs have been compared with currents measured by several off-equatorial buoys. Addition of sea level has been found to substantially improve the quality of simulation. An important feature that has been effectively simulated by the addition of sea level in the assimilation scheme is the near-surface temperature inversion (2-3°C) in the northern Bay of Bengal.  相似文献   

20.
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。  相似文献   

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