变分伴随数据同化在海表面温度预报中的应用研究

将变分伴随数据同化技术应用于海表面温度(SST)数值预报.采用中国近海海表面温度短期数值预报模式,将船舶测报海表面温度同化到该模型中,对SST初始场进行优化.文中给出了中国近海SST数值预报同化模型5d试报结果与观测值的比较,整个区域的均绝差由同化前的2.71℃降至0.87℃,即变分伴随数据同化对改进SST数值预报的效果是比较明显的,表明它可成为SST数值预报初始化的新方法.     

最优插值方法在西北太平洋海温同化中的应用研究

本文开发出一套与三维温盐流数值模式POM相匹配的最优插值数值同化模块,具有将船舶报资料和Argo海温数据加入数值模拟的能力.利用该同化系统,本文对2001～2002年进行了数值模拟试验,通过模拟结果和未加入同化的模拟结果的对比表明,该同化系统能有效地改进三维海温模拟结果,使之更为接近于观测值.     

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。     

ARGO观测资料的2种同化方案

使用POM(Princeton Ocean Model)海洋环流模式模拟西北太平洋海域的温盐场,之后运行中国科学院大气物理研究所同化系统(Ocean Variational Analysis System,OVALS),使用Argo观测网格化产品进行2006年整个计算域三维变分同化。无论从平面分布还是断面分布来看,除海表面温度(SST)外,温度同化的效果都比较好,尤其是800 m水深以浅,均方差值甚至能减小1.0℃以上。而且随着同化积分时间的增加,同化后误差减小越来越显著,3、4月份的同化效果明显高于1、2月份。与OFES(OGCM for the Earth Simulator)海洋模式结果相比,同化在一定程度上改进了模拟结果。在POM(Princeton Ocean Model)数值模拟基础上,利用3个西太平洋代表性浮标的观测资料,运用松弛逼近法同化2006年6月断面数据。当松弛系数取0.5时,模拟时间大约半天,模拟值就能快速逼近或完全等于观测值。虽然只对观测剖面进行了数据同化,但该同化可以产生更大范围的效果,而且这种效果可以持续更长时间。     

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%。     

卫星遥感海表温度资料和高度计资料的变分同化

利用国家气候中心正在发展的第二代全球海洋资料同化系统(BCC_GODAS2.0),针对多变量同化的协调性问题,发展了一种基于三维变分框架(3DVAR)下的高度计和海表温度(SST)相互约束的同化方法。该方法使海面高度和SST资料在同一个动力约束关系下进行同化。在一般方法中,海面高度和SST观测项是代价函数中2个独立的观测项,海面高度项引入动力高度计算公式,海表温度项用统计关系进行垂向投影。在代价函数的实际求解的计算过程中,虽然其总体积分效应受海面高度观测的约束,但整个水柱中各层温盐分析变量的调整是无序的。针对这个问题,文章提出一种新的同化方案。该方案将SST的观测项并入海面高度观测项中,海面高度的一部分,确切说是上层海洋部分,由SST决定,因此至少在SST的统计关系能影响到深度的上层海洋,在代价函数的求解过程中,温盐的调整是受较强的统计关系约束的,而这种统计关系的有效性已经在很多SST的同化试验中被其他学者广泛应用并证明。利用该方法,对1993—1997年的AVHRR卫星遥感海表温度资料进行变分同化试验,用TAO、OISST和SODA数据集进行检验证明,通过对卫星遥感资料的同化能够有效改进对海洋温度和盐度的估...     

同化海温观测数据研究波浪破碎对海洋上层结构的影响

首先利用考虑波浪破碎效应的Mellor-Yamada 2.5阶湍流闭合方案,探讨了海表温度(SST)对波能因子α和Charnock数β的敏感性问题。然后采用变分数据同化途径,基于Papa海洋天气站(OWS Papa Station)的上层温度观测数据,对该参数化方案中的波能因子α和Charnock数β两个参数进行了最优估计。最优估计的结果表明,当α约为167、β约为4.1×105时,价值函数达到最小值。利用上述参数的最优估计进行海温的数值模拟,可以较好地反映出海表温度的日变化和月变化过程,模拟的上混合层的温度和深度也与观测较为一致。最后利用以上参数的最优估计结果对湍动能方程进行诊断计算,研究了波浪破碎对海洋上层湍能量收支的影响。     

基于Cressman客观分析的南海北部海区数据同化实验

利用POM模式和空间插值法构造的同化方案对南海北部的航次观测资料进行了数据同化模拟.结果表明,数据同化有效兼顾了模式模拟和航次观测,在一定程度上纠正了模式模拟值相对于真实数据的误差;同化结果与模拟结果相比更接近观测值,在航次断面及其附近海域同化结果误差较小;另一方面,由于航次资料站点相对较少,在无观测数据的海域模式同化不是很理想,有待改进.此方法可为南海大型航次及其他资料同化提供一个再分析资料的实验性参考.     

海洋预报中的初值形成问题：以SST初始场为例

海洋初值的形成,是开展海洋数值预报的关键问题之一,在我国的海洋预报研究中,它还是一个新课题。本文从海洋观测现状出发,对海洋初值形成的主要环节及其有关理论和处理方法进行了初探,它包括大洋资料选取、资料质量控制、客观分析、初值化及四维同化等5个方面。作为举例,对SST(海表温度)模式的初值形成方法进行了阐述,实际应用表明该方法是稳定而有效的,尽管是初步的。     

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.     

Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation

A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.     

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.     

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.     

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%。     

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。     

Evaluation of the Diurnal Warming of Sea Surface Temperature Using Satellite-Derived Marine Meteorological Data

In order to produce a high-quality sea surface temperature (SST) data set, the daily amplitude of SST (ΔSST) should be accurately known. The purpose of this study was to evaluate the diurnal variation of sea surface temperature in a simple manner. The authors first simulated ΔSST with a one-dimensional numerical model using buoy-observed meteorological data and satellite-derived solar radiation data. When insolation is strong, the model-simulated 1-m-depth ΔSST becomes much smaller than the in situ value as wind speed decreases. By forcibly mixing the sea surface layer, the model ΔSST becomes closer to the in situ value. It can be considered that part of this difference is due to the turbulence induced by the buoy hull. Then, on the assumption that the model results were reliable, the authors derived a regression equation to evaluate ΔSST at the skin and 1-m depth from daily mean wind speed (U) and daily peak solar radiation (PS). ΔSST is approximately proportional to In(U) and (PS)², and the skin ΔSST estimated by the equation is not inconsistent with in situ observation results reported in past studies. The authors prepared maps of PS and U using only satellite data, and demonstrated the ΔSST evaluation over a wide area. The result showed that some wide patchy areas where the skin ΔSST exceeds 3.0 K can appear in the tropics and the mid-latitudes in summer. This revised version was published online in July 2006 with corrections to the Cover Date.     

Assimilation of historical SST data for long-term ENSO retrospective forecasts

In this study, the assimilation of historic SST (sea surface temperature) data was performed for long-term ENSO hindcasts. The emphasis was placed on the design of background error covariance (BEC) that dominates the transfer of SST information to the subsurface. Four different data-assimilation schemes, based on Optimal Interpolation (OI) algorithm, were proposed, and compared in terms of ENSO simulation and prediction skills for the period from 1876 to 2000.It was found that the data-assimilation scheme that has a three-dimensional BEC constructed from model simulations forced by observed wind stress can effectively correct the second-layer temperature in the SST assimilation and lead to the best ENSO prediction skill. Further analysis for the long-term hindcasts shows that the prediction skills have a striking decadal/interdecadal variability similar to that found in other models. These results provide a fundamental basis for the further study of ENSO predictability.     

数据同化在浅海温度同步性调查中的应用

建立了一个功能较为完善的四维同化系统,解决了温度调查资料中存在的同步性问题。为了检验同化方法和模式程序的正确性,以及讨论同步性误差的校正效果,从同化结果与锚系调查数据、同化结果与遥感数据、比对断面均方根误差和海表平均温度4个方面进行了探讨,结果表明:1)同化结果与锚系调查数据对比显示,改进参数的NMC方法(来自美国国家气象中心)能够适用于长江口外海域中的温度四维同化,同化温度结果与锚系调查数据在变化趋势上一致,在数值上最大差异不超过0.4℃;2)同化结果与遥感数据对比显示,同化结果能够将调查资料推演到完整的时空范围,并具有较好的变化趋势和精度;3)根据比对断面均方根误差分析,调查资料中两个比对断面温度间的均方差误差为3.8℃,而同化结果与实测数据的均方根误差低于0.6℃,说明同化方法有效地降低了调查资料中的同步性误差;4)海表平均温度分析显示,同化结果能够避免调查不同步引起的各种温度斑块,并且其温度分布和锋面结构能够更如实地反映实际情况。