渤海、黄海、东海冬季海流场温度场数值模拟和同化技术

利用NASA高分辨率的卫星遥感资料SST,采用Nudging同化来模拟渤海、黄海、东海的三维温度场,减小用热通量作上边界条件所带来的误差.结果表明,模拟的海流场能较好地反映渤海、黄海、东海的环流特征.数据同化后的温度场优于未经同化的温度场.3个选择站点的同化值与实测值的均方根误差分别为1.307,0.526,0.744,用热通量资料模拟的水温与实测值的均方根误差分别为2.160,0.979,1.330.尽管只同化了海表温度,但数据同化对三维温度场结构都有影响.     

西北太平洋多源微波辐射计海表温度数据交叉比对分析

海表温度产品是研究全球海洋大气系统的重要数据源,在海洋相关领域的研究和应用方面具有重要价值。以西北太平洋海域为研究区域,本文对2013年和2014年3个微波辐射计海表温度产品(AMSR-2,TMI和WindSat)的产品特性和Argo浮标进行了真实性检验,并对3个传感器数据进行了交叉比对分析,具体涉及海表温度分布、温度梯度分布、观测点分布、匹配点分布、平均偏差分布、均方根误差分布、统计分析结果的逐月演变和海表温度误差棒分析。结果表明,3个微波辐射计在空间尺度上都能比较一致地反映西北太平洋海域的海表温度变化趋势。但遥感数据与浮标数据却存在季节性变化和昼夜差异,其中冬季微波数据与浮标数据的平均偏差和均方根误差较小,降轨数据与浮标数据的结果更接近。AMSR-2的海表温度数据质量比TMI和WindSat的海表温度数据更接近Argo数据。相比于WindSat和TMI,AMSR-2和TMI的海表温度数据质量更为接近,但是由于受到近岸陆地信号干扰,AMSR-2和TMI离岸100 km以内海域的数据应当慎用。     

基于ROMS模式的南海SST与SSH四维变分同化研究

卫星遥感观测获得了大量高分辨率的海面实时信息，包括海面温度（SST）和海面高度（SSH）等，同化进入数值模式可有效提升模拟精度。本文基于ROMS模式与四维变分同化方法（4DVAR），使用AVHRR SST和AVISO SSH数据，开展了南海区域同化实验。为检验同化的效果，分别利用HYCOM再分析资料和Argo温盐实测数据分析了同化结果的海面高度、流场及温盐剖面的精度。对比结果表明，SST和SSH的同化能够改善ROMS的模拟结果:同化后海面高度场能够更为准确地捕捉海洋的中尺度特征，与HYCOM海面高度再分析资料相比，平均绝对偏差和均方根误差分别为0.054 m和0.066 m；与HYCOM 10 m层流场相比，东向与北向流速平均绝对偏差分别为0.12 m/s和0.11 m/s，相比未同化均提升约0.01 m/s；温盐同化结果与Argo温盐实测具有较高的一致性，温度和盐度平均绝对偏差为0.45℃、0.077，均方根误差为0.91℃、0.11，单个的温盐廓线对比说明，同化结果与HYCOM再分析资料精度相当。     

卫星遥感业务系统海表温度误差控制方法

提高卫星遥感海表温度的反演精度是各种反演模型追求的目标,也是遥感系统业务化应用的关键.据相关文献报道,在晴空无云的条件下遥感海表温度的精度达到了0.5℃,但考虑到影响海表温度反演精度的多种因素,在遥感业务系统真正实现SST精度在1℃以内是非常困难的.在北太平洋渔场速报制作系统中,对遥感海表温度与船测温度误差统计显示均方根误差达到5.71℃,匹配点误差分布显示存在大量较大的负误差值,最大的为-17.2℃,遥感温度图也反映出存在片状温度低值区,这些区域很可能被错误地当作冷涡或冷锋区,严重干扰渔情分析,这些异常的温度误差很难通过海表温度反演模式和云检测技术来消除.采用一种标准海表温度参考图用于温度误差控制技术,可有效地检测温度反演异常值,将均方根值从5.71℃降低到1.75℃,如果采用2℃阈值控制计算均方根值,则海表温度精度达到0.785℃.该方法基本消除了遥感海表温度的低值现象,明显提高了遥感海表温度的精度,并已成功地应用于北太平洋渔区的海况速报产品制作中.     

西北太平洋海表温度融合产品交叉比对分析

海表温度产品是研究全球海洋大气系统的重要数据源,在海洋相关领域的研究和应用方面具有重要价值。以西北太平洋海域为研究区域,本文对2007-2014年的3个海表温度融合数据（AVHRR OISST,MISST和OSTIA）的产品特性与Argo浮标进行了真实性检验,并对融合产品进行了交叉比对分析。结果表明,3个融合产品在空间尺度上均能反映西北太平洋海域的海表温度变化趋势。融合数据与Argo浮标的平均偏差在±0.1℃之间,均方根误差小于0.9℃。融合数据与浮标数据存在明显的季节性变化,其中冬季融合数据与浮标数据的平均偏差和均方根误差较小。在高纬海域,融合产品和浮标存在正偏差。与另两个融合产品相比,OSTIA的数据质量与Argo浮标最为接近。3个融合产品在近岸和高纬海域差异较大,三者对海冰的标识和处理方式不同对融合结果也有影响。在2012年6月之前MISST和OSTIA的海表温度数据质量更为接近,但在此之后MISST存在系统误差。红外数据、微波数据和实测数据作为输入数据,是制作高时空分辨率高精度海表温度融合产品必不可少的要素。     

最优插值法在东海赤潮高发区赤潮生消过程数值模拟中的应用

将最优插值法与赤潮数值模型相结合,针对时空分辨率不统一的卫星遥感数据及海洋常规调查数据,进行了东海赤潮高发区海表温度场及海表营养盐浓度场的数据同化研究,并在此基础上进行了该海区春季赤潮过程的数值模拟研究。数据同化结果显示,最优插值数据同化方法不仅在一定程度上修正了海表温度场模拟结果的误差,尤其体现在对赤潮藻的生长影响较大的温度区段18—20℃等值线的南移,而且较为明显地优化了研究海域海表营养盐浓度场,使模拟结果更加接近观测值。对比赤潮过程数值模拟结果发现,在未加同化情况下,海区赤潮藻并未形成大规模赤潮,并且消散较快;而在加入同化后,研究海域交替出现了硅藻及甲藻的大范围赤潮,赤潮生消过程与现场调查数据分析结果较为一致。耦合了最优插值数据同化方法的赤潮数值模型,由于具有改进海洋环境背景场的优势,因此可以在赤潮预报预警工作中得到较好应用。     

基于随机森林方法反演墨西哥湾海表盐度

盐度是表征物理和生物地球化学过程的重要参数之一,光学遥感可满足较高分辨率的监测需要并避免射频干扰问题,为沿海水域的海表盐度研究提供可行的途径。本文基于MODIS-Aqua的412 nm、443 nm、488 nm、555 nm和667 nm波段的遥感反射率(Rrs412、Rrs443、Rrs488、Rrs555、Rrs667)、海表温度以及实测的海表盐度数据构建随机森林模型,基于模型结果分析墨西哥湾海表盐度时空异质性及海表盐度与影响因子(海表温度和遥感反射率)之间的相关关系。研究结果表明:(1)随机森林模型能较准确地反演墨西哥湾海表盐度,其均方根误差为0.335,决定系数为0.931;(2)湾区海表盐度空间分布呈近岸?河口低、离岸高,环状向内增值的态势,其变化受河流流量、风力以及环流的影响;(3)海表温度与海表盐度存在较强的相关性,海表温度对海表盐度的反演影响显著;(4)海表温度、遥感反射率与海表盐度的相关性呈现空间异质性。     

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。     

基于ConvGRU和自注意力的海表温度偏差订正研究

海表温度（Sea Surface Temperature, SST） 是一个重要的海洋物理量, 其准确预报对于水产养殖和预测海洋环境信息等海洋相关领域的研究至关重要。数值预报是目前预报海表温度的一种常用方法, 但数值预报模型所产生的预报结果往往与实际观测数据有偏差, 因此有必要对数值预报产品的偏差进行订正。本文提出了一种结合ConvGRU 神经网络与自注意力（Self-Attention, SA） 的新型时空混合海表温度订正模型（ConvGRU-SA）, 对南海海表温度预报数据进行偏差订正, 该模型适用于利用卫星遥感数据对海表温度数值预报产品进行订正。经与ConvLSTM、ConvGRU 等网络模型对比, 证明了ConvGRU-SA 模型的优越性, 设置不同的超参 数进行实验, 提高模型订正准确率。订正后该区域的海表温度预报均方根误差从0.52 ℃降低至0.32 ℃, 准确率提高了38.4%, 优于现有模型。     

基于卫星遥感海温数据的南海SST预报误差订正

尝试利用卫星遥感高分辨率海表温度资料GHRSST (Group for High Resolution Sea Surface Temperature) 与海表温度(sea surface temperature, SST)数值预报产品之间的误差, 建立一种南海SST模式预报订正方法。首先, 利用南海的Argo浮标上层海温数据对GHRSST 海温数据进行验证, 结果表明两者之间均方根误差约为0.3℃, 相关系数为0.98, GHRSST 海温数据可用于南海业务化数值预报SST的订正。预报订正后的SST与Argo浮标海温数据相比, 24h、48h和72h的均方根误差均由0.8℃左右下降到0.5℃以内。与GHRSST 海温数据相比, 南海北部海域(110°E—121°E, 13°N—23°N)订正后的24h、48h和72h的SST预报空间误差均显著减小, 在冷空气影响南海期间或中尺度涡存在的过程中, SST预报订正效果也较为显著。因此, 该方法可考虑在南海业务化SST数值预报系统中应用。     

集合同化方法在黄海海域海表面温度同化中的应用

The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.     

OSTIA数据在中国近海业务化环流模型中的同化应用

The prediction of sea surface temperature(SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea(BYECS). One is based on a surface net heat flux correction, named as Qcorrection(QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation(En OI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis(OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error(RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91°C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively.Although both two methods are effective in assimilating the SST, the En OI shows more advantages than the QC,and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.     

基于神经网络的全球三维温盐场重构技术研究

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。     

用蒙特卡洛方法研究海洋盐度遥感误差

海洋的盐度观测对于气候和海洋科学的研究有重要的意义,盐度的卫星遥感观测需要估计各种因素带来的误差影响。本文基于海面微波辐射理论和海水相对电容率等模型,采用蒙特卡洛模拟方法研究了在盐度遥感中温度误差、仪器误差以及风速误差对于后续的盐度反演的影响。通过计算温度误差产生的盐度误差,并与敏感性方法的对比发现,在低温低盐时温度误差对盐度反演误差的影响较大,2种方法的偏差较大;而在高温高盐时温度误差对盐度反演误差的影响较小,2种方法的偏差较小。辐射计仪器噪声对盐度误差的影响普遍在0.1psu以上,在低温低盐时可达0.5psu以上。风速误差对盐度反演误差的影响在水平极化状态下随入射角增大,在温度低于20℃时普遍超过1psu;在垂直极化状态下随入射角先减小后增大,在温度低于20℃以及较小的入射角下误差也会超过1psu。对误差的综合分析发现,采用垂直极化状态在高温时这2种误差的影响较小。研究发现,当入射角是45.6°和垂直极化状态下,对于3种典型海面状态(35℃和35psu,20℃和35psu,5℃和30psu),反演的盐度反演误差可达到0.162,0.153和0.444psu,达到了卫星单次扫描对盐度反演的误差要求。     

SMAP卫星海表盐度产品的检验与校正

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.     

Generation of high resolution sea surface temperature using multi-satellite data for operational oceanography

In the present article, we introduce a high resolution sea surface temperature(SST) product generated daily by Korea Institute of Ocean Science and Technology(KIOST). The SST product is comprised of four sets of data including eight-hour and daily average SST data of 1 km resolution, and is based on the four infrared(IR) satellite SST data acquired by advanced very high resolution radiometer(AVHRR), Moderate Resolution Imaging Spectroradiometer(MODIS), Multifunctional Transport Satellites-2(MTSAT-2) Imager and Meteorological Imager(MI), two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2(AMSR2), and Wind SAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation(OI) algorithm. The root-mean-square-errors(RMSEs) of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from January to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71°C and the bias value was –0.08°C. The largest RMSE and bias were 0.86 and –0.26°C respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Japan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60°C than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature(GHRSST) product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System(KOOS) as an input parameter for data assimilation.     

中国海及邻近海域卫星观测资料同化试验

利用1个基于POMgcs海洋模式和多重网格三维变分同化方法建立的中国海及邻近海域海面高与三维温盐流数值预报模型,通过一系列数值试验,研究了同化卫星测高和卫星遥感海面温度观测资料对该模型预报能力的影响。试验结果表明,同化卫星测高资料可明显改善海面高度与三维温度和盐度的分析预报效果,使1 200 m以上的温度预报误差减小0.16℃,并能有效提高对海洋中尺度现象的预报能力;同化卫星遥感海面温度对100 m以上的温度和盐度的预报效果有所改善,可使海面温度的预报误差减小10%。     

卫星高度计资料在三维海温和盐度数值预报中的应用

随着卫星遥感观测技术的发展,越来越多的卫星观测资料被应用于数值模式的同化研究中.基于国家海洋环境预报中心西北太平洋三维湿盐流预报系统,利用法国CLS中心的沿轨高度计资料的海表面高度异常的融合数据,结合基于三维变分的OVALS(ocean variational analysis system)同化系统,在垂向将海面高度...