模块化海洋数据同化系统(MODAS)研究

模块化海洋数据同化系统(Modular Ocean Data Assimilation System,MODAS)是美国海军用来确定全球海洋三维温度和盐度场的主要工具。MODAS通过同化卫星遥感测得的海面温度和海面高度,产生一种动态气候态,能够更接近地预报出海洋的真实状况。文中介绍了MODAS的应用价值和构建原理,对中国海军数字化海洋战场环境的建设具有重要的借鉴作用。     

海洋卫星遥感数据同化在海洋声场研究中的应用

卫星遥感数据同化技术已成为海洋学研究的一个有效手段。本文选取了日本以东黑潮流域作为研究海区,介绍了最优插值同化方法对海洋温度场和盐度场的分析,在此基础上对实验海区声场进行了研究。文中为了说明数据同化的优越性,利用WOA数据与其进行比较,体现了同化数据对真实场描述的可靠性。     

海洋气候数据集生成与分析简介

科技部的"十三五"重点研发计划项目"全球气候数据集生成及气候变化关键过程和要素监测"中资助了"海洋气候数据集生成与分析"课题,针对全球气候变化研究对全球海洋环境数据的需求,该课题拟基于国内外多源卫星遥感数据和现场观测资料,发展与全球气候变化相关的海面能量平衡、海洋动力环境参数与过程、海洋水色和海冰等数据产品生成技术,构建全球海洋气候数据集产品生成系统,生成我国首套长时间序列、高精度、高时空一致性的全球海洋气候数据集,为全球气候变化研究与应对提供海洋数据支撑和服务,提升我国在全球气候变化关键过程和要素方面的监测能力。     

气候模式中海洋数据同化对热带降水偏差的影响

本文采用海洋卫星观测海表温度(SST)和海面高度异常(SLA)数据,对国家海洋局第一海洋研究所地球系统模式FIO-ESM(First Institute of Oceanography Earth System Model version 1.0)中海洋模式分量进行了集合调整卡尔曼滤波(EAKF)同化,对比分析了大气环流、湿度和云量对海洋数据同化的响应,探讨了海洋同化对热带降水模拟偏差的影响。结果表明:海洋数据同化能有效改善海表温度和上层海洋热含量的模拟,30°S~30°N纬度带内年平均SST的绝均差降低60%。同化后大气模式模拟的赤道两侧信风得到明显改善,上升气流在赤道以北热带地区增强而在赤道以南热带地区减弱,热带降水模拟的动力结构更为合理,水汽和云量分布也更切合实际。热带年平均降水的空间分布和强度在同化后均得到改善,赤道以南的纬向年平均降水峰值显著降低,降水偏差明显减小,同化后30°S~30°N纬度带内年平均降水绝均差降低35%。     

海洋数据同化与数据融合技术应用综述

简述了不同数据同化和数据融合方法在海洋环境监测与预测方面的应用、国内外相关业务单位的海洋分析和预报系统的现状,以及海洋数据同化将来的业务化应用的发展趋势。四维变分和集合卡尔曼滤波正在成为国际上海洋环境分析与预报的主要应用方向,海-气耦合数据同化以及海冰数据同化是目前数据同化方法研究的热点。     

基于XML的海洋遥感卫星数据规范化研究

结合XML技术和海洋元数据标准,在分析遥感卫星地面站运行情况的基础上提出了一种基于XML的海洋遥感卫星数据格式,旨在为海洋遥感数据格式的规范化和为海洋遥感卫星数据的交换与集成奠定标准基础.     

海洋水温垂直分布数据同化方法研究

以一维海洋水温模型为例,利用伴随法进行海洋观测数据同化试验,以便为水温的数值预报提供较准确的初始场.文中利用泛函的Gâteaux微分和Hilbert空间上伴随算子的概念讨论了连续的伴随模型的建立,并通过选择适当的差分格式离散伴随模型,使其保持连续时的伴随关系,同时给出了水温初始场最优化过程及相应的同化试验数值结果.     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

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。     

数据同化中的伴随方法及其有关问题的研究

论述了数据同化中的伴随方法与泛函分析中的伴随算子的关系，指出经典的拉格朗日乘子法可以作为伴随方法的理论基础。关于伴随方法应用中只能利用模式的伴随观点，作者认为是有疑问的，并提出了新的观点，所作的数值模拟实验表明，方程的伴随有时明显优于模式的伴随，因而说明方程的伴随也应该引起足够的重视。     

数据同化在海洋数值产品制作及预报中的应用研究

讨论了海洋中数据同化的目的,意义,各种数据同化方法,国内外发展现状及其在海洋数值产品制作及预报中的应用。文中还介绍了数据同化方法中的客观分析法和伴随法的原理,结合海洋中的实际问题进行了数据同化试验,给出了相应的同化试验结果,并讨论了二阶伴随理论。     

最优插值法在海温数据同化中的应用研究

海洋声场环境是决定海军作战行动成败的重要组成要素之一,海洋的垂直温度结构作为影响声速场的最重要因素,为了更精确更为实际的反映其分布特点,对其各种数据进行同化研究是十分必要的。文中针对海温数据来源的特点,提出了一种海温数据同化的最优插值方法,较好地完成了各种观测数据与背景数据的同化,并用各种数据进行了检验,其结果表明,利用最优插值法对海温数据进行同化处理是可行的,且同化效果比较理想。     

基于ECDIS的多数据源支持技术分析

利用多源地理空间数据集成和融合理论,研究了基于ECDIS的多数据源支持技术。介绍了常用的多源海图数据及其使用原则,分析了ECDIS多数据源支持技术的特点及内涵,总结了国内外常用多源海图数据集成方法,从数据可用性角度探讨了提高国产电子海图系统适用性的措施。     

卫星遥感SSH数据在水下温度垂直分布反演中的应用研究

结合MODAS基本原理,应用动态气候态的数据同化概念,阐述应用卫星遥感SSH数据反演水下温度垂直分布的理论和方法,并利用西太平洋海区的ARGO数据进行试验,建立了单点的温度剖面,通过引入SSH数据进行水温反演,演示结果显示了卫星遥感SSH资料同化的有效性,并得出分析结论。     

基于MOM4模式的太平洋区域环流模式的模拟评估与分析

本文初步评估基于MOM4海洋环流模式建立的较高分辨率(0.25(°)×0.25(°))太平洋区域环流模式的模拟能力。研究表明,模式较好的再现了气候态的海表面温度、盐度、流、海表高度、等密面位涡模态、混合层深度等水平分布场;同时也较为准确地模拟了黑潮流轴的流量,温盐流结构等。相对于低分辨率模式,具有高分辨率的模式能有效地控制黑潮的流幅,以及黑潮离岸的位置,并且对模态水的模拟也比较清晰。     

Impact of Ocean Mean Dynamic Topography on Satellite Data Assimilation

The response of an eddy-permitting ocean model to changes imposed by the use of different mean dynamic topographies (MDT) is analyzed in a multivariate assimilation context, allowing the evaluation of this impact, not only on the surface circulation, but also on the interior ocean representation. The assimilation scheme is a reduced-order sequential Kalman filter (SEEK). In a first set of experiments, high resolution sea surface temperature, along-track sea surface height and sea surface salinity from climatology are assimilated into a 1/3° resolution North and Tropical Atlantic version of the HYCOM model. In a second experiment, in situ profile data are assimilated in addition to the surface measurements.

The first set of experiments illustrates that important differences in the representation of the horizontal model circulation pattern are related to differences in the MDT used. The objective of assimilation is to improve the representation of the 3D ocean state. However, the imperfect representation of the mean dynamic topography appears to be an important limiting factor with regard to the degree of realism obtained in the simulated flow.

Vertical temperature and salinity profiles are key observations to drive a general circulation ocean model toward a more realistic state. The second set of experiments shows that assimilating them in addition to sea surface measurements is a far from trivial exercise. A specific difficulty is due to inconsistencies between the dynamic topography diagnosed from in situ observations and that diagnosed from sea surface height. These two fields obtained from different data sources do not contain exactly the same information. In order to overcome this difficulty, a strategy is proposed and validated.     

Impact of Ocean Mean Dynamic Topography on Satellite Data Assimilation

The response of an eddy-permitting ocean model to changes imposed by the use of different mean dynamic topographies (MDT) is analyzed in a multivariate assimilation context, allowing the evaluation of this impact, not only on the surface circulation, but also on the interior ocean representation. The assimilation scheme is a reduced-order sequential Kalman filter (SEEK). In a first set of experiments, high resolution sea surface temperature, along-track sea surface height and sea surface salinity from climatology are assimilated into a 1/3° resolution North and Tropical Atlantic version of the HYCOM model. In a second experiment, in situ profile data are assimilated in addition to the surface measurements.

The first set of experiments illustrates that important differences in the representation of the horizontal model circulation pattern are related to differences in the MDT used. The objective of assimilation is to improve the representation of the 3D ocean state. However, the imperfect representation of the mean dynamic topography appears to be an important limiting factor with regard to the degree of realism obtained in the simulated flow.

Vertical temperature and salinity profiles are key observations to drive a general circulation ocean model toward a more realistic state. The second set of experiments shows that assimilating them in addition to sea surface measurements is a far from trivial exercise. A specific difficulty is due to inconsistencies between the dynamic topography diagnosed from in situ observations and that diagnosed from sea surface height. These two fields obtained from different data sources do not contain exactly the same information. In order to overcome this difficulty, a strategy is proposed and validated.     

资料同化中的伴随方法及其在海洋中的应用

较系统地综述了资料同化中各种方法的分类,并着重说明了伴随方法的基本原理和具体实现步骤。并总结了伴随方法在海洋中的应用现状,并指出由于观测资料特别是遥感资料的日益丰富,伴随方法将有广泛地应用前景。     

Evaluation of the U.S. Navy’s Modular Ocean Data Assimilation System (MODAS) Using South China Sea Monsoon Experiment (SCSMEX) Data

The Navy’s Modular Ocean Data Assimilation System (MODAS) is an oceanographic tool to create high-resolution temperature and salinity on three-dimensional grids, by assimilating a wide range of ocean observations into a starting field. The MODAS products are used to generate the sound speed for ocean acoustic modeling applications. Hydrographic data acquired from the South China Sea Monsoon Experiment (SCSMEX) from April through June 1998 are used to verify the MODAS model. MODAS has the capability to provide reasonably good temperature and salinity nowcast fields. The errors have a Gaussian-type distribution with mean temperature nearly zero and mean salinity of −0.2 ppt. The standard deviations of temperature and salinity errors are 0.98°C and 0.22 ppt, respectively. The skill score of the temperature nowcast is positive, except at depth between 1750 and 2250 m. The skill score of the salinity nowcast is less than that of the temperature nowcast, especially at depth between 300 and 400, where the skill score is negative. Thermocline and halocline identified from the MODAS temperature and salinity fields are weaker than those based on SCSMEX data. The maximum discrepancy between the two is in the thermocline and halocline. The thermocline depth estimated from the MODAS temperature field is 10–40 m shallower than that from the SCSMEX data. The vertical temperature gradient across the thermocline computed from the MODAS field is around 0.14°C/m, weaker than that calculated from the SCSMEX data (0.19°–0.27 °C/m). The thermocline thickness computed from the MODAS field has less temporal variation than that calculated from the SCSMEX data (40–100 m). The halocline depth estimated from the MODAS salinity field is always deeper than that from the SCSMEX data. Its thickness computed from the MODAS field varies slowly around 30 m, which is generally thinner than that calculated from the SCSMEX data (28–46 m).