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1.
在海雾的短时临近预报中,初始场的水汽凝结状态扮演着重要角色。为了改进初始场的云水含量,本文提出直接同化雾体云水信息的思路。针对2011年5月一次大范围的黄海海雾,借助EnKF (Ensemble Kalman Filter)方法,尝试进行了极轨卫星反演云水路径数据的同化试验。结果表明:(1)通过利用EnKF将云水混合比增加到背景场和分析场的控制变量中,构建云水观测数据与背景场之间的关系,实现云水路径数据的直接同化是可行的;(2)同化云水路径可显著改善海面气温与湿度状态,大幅提高海雾预报效果;(3)EnKF能够基于集合体动态统计流依赖的背景误差协方差是其取得良好同化效果的主要原因。值得指出的是,受集合样本误差的影响,需要特别关注云水含量与风之间的相关关系。 相似文献
2.
基于适应性观测误差和温盐偏差控制的EnKF温盐廓线资料同化试验 总被引：1，自引：0，他引：1
通过开展2008年夏季南海北部开放航次CTD的温盐廓线数据资料同化试验,本文采取了观测误差适应的方法来防止EnKF滤波发散问题;同时,从背景误差协方差和温盐模式偏差关系入手,在同化中引入温盐控制来减小模式偏差对同化结果的影响。对于改进的同化方案进行了试验验证,并用卫星高度计观测数据,OSCAR流速数据,走航ADCP数据作为独立观测数据检验。结果证明新的EnKF同化策略能够有效地减小温盐均方根误差。同时整个同化系统能有效地改善高度场和流场的模拟。 相似文献
3.
中国近海现场海洋观测系统设计评估 总被引：1，自引：0，他引：1
中国科学院正在发展一个在中国近海(包括黄海、东海和南海)现场海洋观测系统。观测系统包括3个沿岸观测站点、4个近海离岸浮标和由观测船只按固定航线做的船舶观测断面。观测站点、浮标和断面的位置已经预先确定,这个计划在2008-2011实施。利用基于卡尔曼理论的样本集合方法对这样一个能够监测大尺度的季节和年季变率的观测系统设计进行了评估。根据卡尔曼滤波理论,用集合样本的方法能够给出经过同化这个观测系统位置的观测资料后能够减少多少分析误差和分析场的不确定性。用2个来自不同模式、不同分辨率的模式的结果作为集合样本来计算静态的背景误差协方差,这2套样本分别是来自分辨率是0.5°×0.5°的模式同化结果和高分辨0.125°×0.125°的模式结果。由这2个不同资料得到的结果是一致的。发现来自3个近岸和4个离岸浮标得到的观测能够有效地减少SST在渤海、黄海、东海和南海中部的分析误差。然而在越南东部和台湾东部海域,分析误差减少的百分比相对要小。最后,给出了中国近海最优的观测位置序列设计。 相似文献
4.
现代海洋/大气资料同化方法的统一性及其应用进展 总被引：9，自引：3，他引：9
海洋／大气资料同化的理论基础是用数值模式作为动力学强迫对观测信息进行提炼，或者说，从包含观测误差(噪声)的空间分布不均匀的实测资料中依据动力系统自身的演化规律(动力学方程或模式)来确定海洋／大气系统状态的最优估计。本文对主要的现代海洋／大气资料同化方法，包括最优插值(()ptimal Interpolation，简称()Ⅰ)、变分方法(3—Dimensional Variational和4—Dimensional Variational，分别简称3DVAR和4DVAR)和滤波方法(Filtering)的原理、算法设计和实际应用进行系统地回顾，并对这些资料同化方法的优缺点进行分析和讨论。在滤波框架下，所有的现代资料同化方法都被统一了：()Ⅰ和3DVAR是不随时间变化的滤波器，4DVAR和卡曼滤波是线性滤波器，即非线性滤波的退化情形；而集合滤波能构建非线性的滤波器，因为集合在某种程度上体现了系统的非高斯信息。一个非线性滤波器的主要优点是能计算和应用随时间变化的各阶误差统计距，如误差协方差矩阵。将非线性滤波器计算的随时间变化的误差协方差矩阵引入到（）Ⅰ或4DVAR中，也许能实质性地改进这些传统方法。在实际应用中，方法的优劣可能取决于所选用的数值模式和可获得的计算资源，因此需针对不同的问题选取不同的资料同化方法。由于各种资料同化方法具有统一性，因此可建立测试系统来评价这些方法，从而对各种方法获得更深入的理解，改进现有的资料同化技术，并提高人们对海洋／大气环境的预测能力。 相似文献
5.
2002/03年厄尔尼诺事件,是暖海温中心出现在赤道中太平洋区域的一种新型厄尔尼诺,即中太平洋型厄尔尼诺。本文基于一个厄尔尼诺预测系统,利用三组回报试验来详细区分海洋表层和次表层初始状态对预报2002/03年中太平洋型厄尔尼诺事件的作用,并由此来探寻对预报厄尔尼诺演变过程最有利的初始条件。回报试验分为三组:(1)仅同化海表温度观测(sea surface temperature;简称SST)来优化海洋表层初始状态(Assim_SST);(2)仅同化海表高度观测(sea level;简称SL)来更新海洋次表层初始状态(Assim_SL);(3)同时同化SST和SL观测来一起更新海洋表层和次表层初始状态(Assim_SST+SL)。回报试验结果表明,三种不同的初始条件都可以使模式提前一年成功地预报2002/03年厄尔尼诺事件,并且"Assim_SST+SL"回报试验的效果最好。三组回报试验结果间的对比表明:海洋表层和次表层初始状态均对成功地预报该事件有重要作用,但其作用分别集中在事件发展的不同阶段。精确的海洋表层初始状态更容易激发模式预报出一次厄尔尼诺事件,而更合理的海洋次表层初始状态则能有效地提高厄尔尼诺事件预报的强度。 相似文献
6.
国家气候中心全球海洋资料四维同化系统在热带太平洋的结果初步分析 总被引：6，自引：0，他引：6
介绍了国家气候中心(NCC)“全球海洋资料四维同化系统”(简称NCC-GODAS).该系统包含观测资料预处理系统、插值分析系统和所应用的动力模式.插值分析系统采用四维同化技术方案,在时间上设置一个4周的窗口,将此窗口之内的观测资料以一定的权重插入插值分析系统,在空间上采用三维变分方案.海洋动力模式为“九五”期间LASG研制的L30T63OGCM 1 0版本.文中考察了该系统从1982年到2003年3月在热带太平洋的部分同化分析结果,并与NCEP的再分析资料和EMC/NCEP的太平洋区域海洋资料同化系统的结果进行了对比分析.结果显示,该系统的同化结果(如SST,SSTA,Nino指数,次表层海温变化等)与NCEP的同期同化结果具有很好的一致性.同时,该系统得出的海洋同化资料气候场与海洋模式气候场相比,有显著的改进.表明该系统具有较好的同化能力,其同化结果可为海-气耦合模式进行季节和跨季节的气候预测业务提供可信的海洋初始场资料,同时还可以为相关研究提供海洋分析场. 相似文献
7.
《热带海洋学报》2016,(1)
采用国际海—气综合数据集(The International Comprehensive Ocean-Atmosphere Data Set,ICOADS)船舶观测资料及简单海洋同化分析数据(Simple Ocean Data Assimilation,SODA),研究了厄尔尼诺(El Ni?o-Southern Oscillation,ENSO)和印度洋海盆模态(Indian Ocean Basin,IOB)对南海海表温度(sea surface temperature,SST)影响的年代际变化,并着重讨论了不同时期海洋平流输送对SST异常的影响。结果表明,ENSO事件对南海SST的影响呈现显著的年代际变化特征;在1870~2007年期间,扣除资料较少的时期,有4个显著不同的时段,分别是1892~1915年、1930~1940年、1960~1983年、1984~2007年。在1950年之前的两个时段,南海在ENSO期间出现一次显著的增暖,而在1950年之后的两个时段中,南海出现了两次显著的增暖。除第一个时段外,其余三个时段ENSO发展期冬季大气潜热及短波辐射异常是导致南海增暖的主要原因,海洋平流作用较弱;而在最近的两个时段中,海洋平流对ENSO消亡年夏季南海增暖有重要影响。不同时段海洋平流对南海增暖贡献的差异说明ENSO及IOB对南海区域气候的影响具有明显的年代际变化特征。 相似文献
8.
海洋状态场的历史变化过程对其分布状态有重要影响。在观测资料稀疏的情况下，合理利用历史观测资料能够为海洋数据同化提供大量有效信息。然而在目前的顺序资料同化过程中，往往只同化当前时刻的观测数据，没有考虑到历史观测资料对当前状态的约束。四维变分虽然可以体现变量在时间维度的演变过程，但引入伴随方程会增加计算代价。本文基于集合最优平滑同化算法（Ensemble Optimal Smoothing,EnOS）探讨了一种在数据同化中加入历史观测资料的简易可行方案，其能够根据历史观测数据估计当前状态，并进行单点同化实验和区域同化实验来验证该方案的有效性。实验结果表明，将历史观测资料引入到同化过程中可以把控时间演变趋势，减小分析数据与真实值之间的偏差，更有效地消除数值模式误差，提高同化质量。 相似文献
9.
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。 相似文献
10.
对一个热带太平洋海气耦合ENSO集合数值预测系统进行历史后报检验。该系统模式为一个中等复杂程度的耦合模式,其中大气部分为统计模式,海洋部分为动力模式。扰动初始场通过集合卡尔曼滤波同化获得,并引入了模式误差,由一阶马尔科夫随机微分方程生成。集合预报样本数为100个。系统从1993—2011年共19 a的每个月都开始起报,每个预报样本都分别向后预报12个月。给出了El Nino指数时间序列预报结果检验、个例预报检验,以及El Nino指数和热带太平洋SSTA的相关系数和均方根误差检验,计算了模式的平均系统误差,并分析了其不同季节的预报性能。结果表明,系统具有较强的ENSO预测能力,能够提前6~12个月给出有参考价值的预报。 相似文献
11.
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. 相似文献
12.
Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter 总被引：2，自引：0，他引：2
Gwang-Ho Seo Byoung-Ju Choi Yang-Ki Cho Young Ho Kim Sangil Kim 《Ocean Science Journal》2010,45(4):225-242
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
13.
The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction. 相似文献
14.
This study compares two regional eddy resolving ocean reanalysis systems, based on the ensemble Kalman filter (EnKF) and ensemble optimal interpolation (EnOI), focusing on data assimilation aspects. Both systems are configured for the Tasman Sea using the same ocean model with 0.1° resolution and commonly available observations of satellite altimetry, sea surface temperature and subsurface temperature and salinity. The primary goals are to quantify the difference in performance of the EnKF and EnOI and investigate how important this difference might be from an oceanographic perspective. We find that both systems generally constrain mesoscale circulation in the region, with some exceptions for the East Australian Current separation region, the most energetic and chaotic part of the domain. Overall, the EnKF is found to consistently outperform the EnOI, producing on average 9–21% smaller innovations. The EnKF also has better forecast skill relative to the persisted analysis than the EnOI. For SST the EnKF forecast outperforms persisted analysis by about 17%, which indicates that the surface circulation is mainly constrained. The EnKF and EnOI are shown to produce qualitatively different increments of unobserved or sparsely observed variables; however, we find only moderate improvements of the EnKF over EnOI in subsurface temperature fields when compared against withheld XBT observations. We attribute this lack of a major improvement in subsurface reconstruction to the inability of the EnKF to linearly constrain the system due to initialisation shock, model error caused by open boundaries, and possibly insufficient observations. 相似文献
15.
气候模式中海洋数据同化对热带降水偏差的影响 总被引：1，自引：1，他引：0
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%。 相似文献
16.
Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models. Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio–Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Niño Southern Oscillation (ENSO) via teleconnection.
相似文献17.
The Localized Weighted Ensemble Kalman Filter(LWEnKF) is a new nonlinear/non-Gaussian data assimilation(DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height(AT-SSH), swath sea surface temperature(S-SST) and in-situ temperature and salinity(T/S) profiles are assimilated using the LWEnKF in the northern South China ... 相似文献
18.
An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Ni?o was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions. 相似文献
19.
厄尔尼诺和南方涛动(ENSO)是仅次于季节变化的最强年际气候变率信号,对全球气候和天气产生重要影响。准确、及时、有效地预报ENSO事件的发生和演变具有重大的实用意义。以中国科学院海洋研究所冠名的中等复杂程度海气耦合模式(IOCAS ICM),每月定期进行ENSO实时预报试验。IOCAS ICM实时预报结果目前收录于美国哥伦比亚大学国际气候研究所(IRI),以作进一步的集成分析和应用。该模式的大气部分是一个描述对海表温度(SST)年际异常响应的风应力异常经验模式,海洋部分包括了动力海洋模块、SST距平模块(嵌套于动力海洋模块中)和次表层上卷海温(T_e)距平模块三部分。IOCAS ICM的特点之一是开发了次表层海温反算优化这一创新技术,可有效改进热带太平洋SST异常的模拟和预报。IOCAS ICM和其他海气耦合模式的最新预报结果(以2017年9月为初条件)表明,2017年年末热带太平洋会处于一个SST冷异常态,最大变冷中心集中在赤道东太平洋,但并不足以达到拉尼娜(La Ni?a)事件的水平,SST冷异常可能会在2018年春季逐渐减弱,转化为中性状态。此外,本文还对四维变分资料同化方法(4D-Var)以及条件非线性最优扰动方法(CNOP)在IOCAS ICM中的应用进行了讨论。 相似文献