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1.
The impact of mean dynamic topography on a sea-level anomaly assimilation in the South China Sea based on an eddy-resolving model 总被引：1，自引：0，他引：1
The sea-level anomaly (SLA) from a satellite altimeter has a high accuracy and can be used to improve ocean state estimation by assimilation techniques. However, the lack of an accurate mean dynamic topography (MDT) is still a bothersome issue in an ocean data assimilation. The previous studies showed that the errors in MDT have significant impacts on assimilation results, especially on the time-mean components of ocean states and on the time variant parts of states via nonlinear ocean dynamics. The temporal-spatial differences of three MDTs and their impacts on the SLA analysis are focused on in the South China Sea (SCS). The theoretical analysis shows that even for linear models, the errors in MDT have impacts on the SLA analysis using a sequential data assimilation scheme. Assimilation experiments, based on EnOI scheme and HYCOM, with three MDTs from July 2003 to June 2004 also show that the SLA assimilation is very sensitive to the choice of different MDTs in the SCS with obvious differences between the experimental results and observations in the centre of the SCS and in the vicinity of the Philippine Islands. A new MDT for assimilation of SLA data in the SCS was proposed. The results from the assimilation experiment with this new MDT show a marked reduction (increase) in the RMSEs (correlation coefficient) between the experimental and observed SLA. Furthermore, the subsurface temperature field is also improved with this new MDT in the SCS. 相似文献
2.
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
3.
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
4.
压力传感逆式回声仪(pressure-sensor-equipped inverted echo sounders,PIES)可以用来测量海底压力和声波从海底到海面的传播时间。海底压力和声波传播时间分别被用来估计水体质量变化(正压)和比容变化(斜压)对海面高度距平的贡献。对由PIES在日本海西南海域现场观测数据得到的海面高度距平(PIES SLA)与卫星高度计海面高度距平(Sat SLA)进行了比较研究。利用相关分析法,对PIES SLA和沿轨T/P卫星、沿轨ERS-2卫星测得的海面高度距平(TP SLA、ERS-2 SLA)进行了比较;对PIES SLA和AVISO网格化海面高度距平进行了比较,估计可能的误差来源,并分析PIES SLA正压部分和斜压部分对SLA的贡献。比较发现,PIES SLA和Sat SLA的相关系数较高,且均方根误差较小,并且对特定区域和特定站点产生误差可能的原因进行了进一步的探讨。通过研究,有以下结论:(1)相对于湾流和黑潮地区,这一区域正压部分对海面高度的贡献相对较大;(2)如果再考虑斜压变化对海面高度的贡献,PIES SLA和Sat SLA相关系数会有所提升;(3)在高能区PIES SLA和Sat SLA相关系数较高,符合得相对比较好。总的来说,在日本海地区,PIES SLA和Sat SLA相关系数较高,具有较高的一致性,能为我国海洋二号(HY-2)等卫星高度计的校验提供一种可靠的方式。该研究对于PIES的研发和设计以及对于PIES的布放位置的选择都有一定的借鉴意义。 相似文献
5.
北欧海比容高度及其与卫星高度计海表面高度异常的比较 总被引：2，自引：1，他引：1
In this study the steric height anomaly which is calculated from the hydrological data(EN3) is compared with the sea level anomaly derived from satellite altimetry in the Nordic Seas. The overall pattern of steric height is that it is higher in the margin area and lower in the middle area. The extreme values of steric height linear change from1993 to 2010 occur in the Lofoten Basin and off the Norwegian coast, respectively. Such a distribution may be partly attributed to the freshening trend of the Nordic Seas. The correlation between SLA(sea level anomaly) and SHA(steric height anomaly) is not uniform over the Nordic Seas. The time series of SLA and SHA agree well in the Lofoten Basin and northern Norwegian Basin, and worse in the northern Norwegian Sea, implying that the baroclinic effect plays a dominant role in most areas in the Norwegian Sea and the barotropic effect plays a dominant role in the northern Norwegian Sea. The weaker correlations between SLA and SHA in the Greenland and Iceland Seas lead a conclusion that the barotropic contribution is significant in these areas. The area-mean SHA over the entire Nordic Seas has similar amplitudes compared with the SLA during 1996–2002, but SHA has become lower than SLA, being less than half of SLA since 2006. 相似文献
6.
The impact of SARAL/AltiKa derived sea level anomaly (SLA) has been studied by assimilating it along with Jason-2 and Cryosat-2 SLA in the Princeton Ocean model (POM) using ensemble optimal interpolation (EnOI) technique. For isolating the extra benefit brought by SARAKL/Altika, a parallel run with assimilation of only Jason-2 and Cryosat-2 SLA has also been conducted. The importance of SARAL SLA in a data assimilative ocean prediction system has been evaluated with special emphasis on the improvement in thermocline depth, depth of the 20° isotherm, subsurface temperature and currents. Comparison with RAMA buoy has shown a positive impact of up to 13% for 20°C isotherm and up to 17% for thermocline depth after assimilating SARAL SLA. An overall improvement in temperature profile is also observed when compared with analogous profiles from RAMA buoys and Argo floats. Improvement in zonal currents away from the equator has also been noticed. 相似文献
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A comparison between 3DVAR and EnOI techniques for satellite altimetry data assimilation 总被引：3，自引：0，他引：3
Two conceptually different assimilation schemes, three dimensional variational (3DVAR) assimilation and Ensemble Optimum Interpolation (EnOI) are compared in the context of satellite altimetric data assimilation. Similarities and differences of the two schemes are briefly discussed and their impacts on the model simulation are investigated.With a tropical Pacific ocean model, two assimilation experiments of sea level anomaly (SLA) data from TOPEX/Poseidon are performed for 5 years from 1997 to 2001. Annual mean states of temperature and salinity fields are compared with analysis data and some independent observations. It is found that EnOI generally produces moderate improvements on both temperature and salinity fields, while changes induced by 3DVAR assimilation are strong and vary remarkably in different areas. For instance, 3DVAR tends to excessively modify the temperature field along the thermocline depth and even deteriorate the simulation, but it is more effective than EnOI below the thermocline depth. However, for the salinity field 3DVAR outperforms EnOI nearly for almost the whole layer. As the difference relative to the WOA01 analysis is compared, it is apparently reduced to below 0.3 psu in most areas in the 3DVAR experiment. On the other hand, the pattern of difference in the EnOI experiment resembles that of the simulation and the magnitude is only diminished to some extent. One advantage of EnOI is that it yields more consistent improvements even in areas where there are large model errors. It is more reliable than 3DVAR in such a sense. It is also revealed that the T–S relation plays a very important role in altimetric data assimilation. Further, the distinct performance of the two schemes can be partly accounted for by their inherent assumptions and settings. 相似文献
9.
对地球系统模式FIO-ESM同化实验中北极海冰模拟的评估 总被引：3，自引：0，他引：3
本文评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于集合调整Kalman滤波同化实验对1992-2013年北极海冰的模拟能力。结果显示:尽管同化资料只包括了全球海表温度和全球海面高度异常两类数据,而并没有对海冰进行同化,但实验结果能很好地模拟出与观测相符的北极海冰基本态和长期变化趋势,卫星观测和FIO-ESM同化实验所得的北极海冰覆盖范围在1992-2013年间的线性变化趋势分别为-7.06×105和-6.44×105 km2/(10a),同化所得的逐月海冰覆盖范围异常和卫星观测之间的相关系数为0.78。与FIO-ESM参加CMIP5(Coupled Model Intercomparison Project Phase 5)实验结果相比,该同化结果所模拟的北极海冰覆盖范围的长期变化趋势和海冰密集度的空间变化趋势均与卫星观测更加吻合,这说明该同化可为利用FIO-ESM开展北极短期气候预测提供较好的预测初始场。 相似文献
10.
基于ROMS模式的南海SST与SSH四维变分同化研究 总被引：1，自引：0，他引：1
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。 相似文献
11.
For ocean and climate research, it is essential to get long-term altimetric sea level data that is as accurate as possible. However, the accuracy of the altimetric data is frequently degraded in the interior of the Arctic Ocean due to the presence of seasonal or permanent sea ice. We have reprocessed ERS-1/2/Envisat satellite altimetry to develop an improved 20-year sea level dataset for the Arctic Ocean. We have developed both an along-track dataset and three-day gridded sea level anomaly (SLA) maps from September 1992 to April 2012. A major improvement in data coverage was gained by tailoring the standard altimetric editing criteria to Arctic conditions. The new reprocessed data has significant increased data coverage with between 4 and 10 times the amount of data in regions such as the Beaufort Gyre region compared with AVISO and RADS datasets. This allows for a more accurate estimation of sea level changes from satellite altimetry in the Arctic Ocean. The reprocessed dataset exhibit a mean sea level trend of 2.1 ± 1.3 mm/year (without Glacial Isostatic Adjustment correction) covering the Arctic Ocean between 66°N and 82°N with significant higher spatial coherency in the ice-covered regions than the RADS and DUACS datasets. 相似文献
12.
Mesoscale eddies, which are mainly caused by baroclinic effects in the ocean, are common oceanic phenomena in the Northwest Pacific Ocean and play very important roles in ocean circulation, ocean dynamics and material energy transport. The temperature structure of mesoscale eddies will lead to variations in oceanic baroclinity, which can be reflected in the sea level anomaly (SLA). Deep learning can automatically extract different features of data at multiple levels without human intervention, and find the hidden relations of data. Therefore, combining satellite SLA data with deep learning is a good way to invert the temperature structure inside eddies. This paper proposes a deep learning algorithm, eddy convolution neural network (ECN), which can train the relationship between mesoscale eddy temperature anomalies and sea level anomalies (SLAs), relying on the powerful feature extraction and learning abilities of convolutional neural networks. After obtaining the temperature structure model through ECN, according to climatic temperature data, the temperature structure of mesoscale eddies in the Northwest Pacific is retrieved with a spatial resolution of 0.25° at depths of 0–1 000 m. The overall accuracy of the ECN temperature structure is verified using Argo profiles at the locations of cyclonic and anticyclonic eddies during 2015–2016. Taking 10% error as the acceptable threshold of accuracy, 89.64% and 87.25% of the cyclonic and anticyclonic eddy temperature structures obtained by ECN met the threshold, respectively. 相似文献
13.
利用热带太平洋TAO浮标阵列评估全球高分辨率浪-潮-流耦合模式同化结果 总被引：3，自引：0，他引：3
In order to evaluate the assimilation results from a global high resolution ocean model, the buoy observations from tropical atmosphere ocean(TAO) during August 2014 to July 2015 are employed. The horizontal resolution of wave-tide-circulation coupled ocean model developed by The First Institute of Oceanography(FIOCOM model) is 0.1°×0.1°, and ensemble adjustment Kalman filter is used to assimilate the sea surface temperature(SST), sea level anomaly(SLA) and Argo temperature/salinity profiles. The simulation results with and without data assimilation are examined. First, the overall statistic errors of model results are analyzed. The scatter diagrams of model simulations versus observations and corresponding error probability density distribution show that the errors of all the observed variables, including the temperature, isotherm depth of 20°C(D20), salinity and two horizontal component of velocity are reduced to some extent with a maximum improvement of 54% after assimilation. Second, time-averaged variables are used to investigate the horizontal and vertical structures of the model results. Owing to the data assimilation, the biases of the time-averaged distribution are reduced more than70% for the temperature and D20 especially in the eastern Pacific. The obvious improvement of D20 which represents the upper mixed layer depth indicates that the structure of the temperature after the data assimilation becomes more close to the reality and the vertical structure of the upper ocean becomes more reasonable. At last,the physical processes of time series are compared with observations. The time evolution processes of all variables after the data assimilation are more consistent with the observations. The temperature bias and RMSE of D20 are reduced by 76% and 56% respectively with the data assimilation. More events during this period are also reproduced after the data assimilation. Under the condition of strong 2014/2016 El Ni?o, the Equatorial Undercurrent(EUC) from the TAO is gradually increased during August to November in 2014, and followed by a decreasing process. Since the improvement of the structure in the upper ocean, these events of the EUC can be clearly found in the assimilation results. In conclusion, the data assimilation in this global high resolution model has successfully reduced the model biases and improved the structures of the upper ocean, and the physical processes in reality can be well produced. 相似文献
14.
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
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16.
21世纪以来全球变暖进入停滞时期,研究表明,大量热量进入海洋深层是导致全球平均表面温度暂缓上升的主要原因。本文估计和研究了2002.4-2014.12间由热膨胀导致的海平面变化趋势,以此来探测海洋热含量的变化情况。研究使用GRACE重力卫星CSR RL05数据计算了全球海洋的水质量变化,并结合海平面异常数据,计算了由热量变化导致的海平面变化（Net SLA）。将Net SLA与Ishii温度数据计算的海洋热含量进行相关性分析后表明,Net SLA与海洋热含量存在高度相关性,相关系数最大值达0.95。考虑到海洋观测只能表现海洋上层2000m的热含量变化,而除去水质量变化的海平面变化则反映了整层海洋的热含量变化,是估计海洋增暖趋势快慢的有利工具。经计算得出,2002至2014年间南太平洋和南印度洋存在加速增暖趋势,而近年来南半球环状模的增强是导致其增暖的主要原因。 相似文献
17.
This paper studies sea level anomaly (SLA) behaviour in Malacca and Singapore straits which serve part of a major maritime trade route between Indian and Pacific Ocean using both observed data and numerical model. Spatio-temporal behaviour of SLA in the region is analyzed based on 15 years of in-situ and remote sensing data. Results show that SLA signatures can be distinctly different in the two straits, with vastly opposite behaviours during certain months. By further analyzing spatial dependency of observed SLA in the region, SLA in Malacca and Singapore straits are found to be under the influence of Indian Ocean and South China Sea, respectively. Based on this insight, a numerical model is built with the appropriate non-tidal forcing derived from meteorological model and satellite dataset to properly represent SLA in Malacca and Singapore straits with Root Mean Square Error of less than 10 cm. With this well calibrated model, the effect of different types of forcing on volume flux through the straits is investigated. Combined tidal and non-tidal forcing in the model gives 4 to 7 × 1011 m3 of annual net westward volume flux through the straits which is four to seven times higher than that of tidal forcing alone. Furthermore with this combined forcing, a distinct seasonal trend with westward net flow during northeast monsoon (November to March) and eastward net flow during southwest monsoon (May to September) can be observed through the straits in the model which is not observed with tidal forcing. The findings of this paper highlight the importance of these non-tidal forcing in the model to obtain accurate SLA and flow representation in the straits that is vital to environmental fate and transport modelling during operational forecast. 相似文献
18.
This study deals with the assimilation of TOPEX altimeter-derived sea level variability in a reduced-gravity model of the northwestern Indian Ocean. The assimilation has been done using variational assimilation method with adjoint technique. A cost function representing the misfit between the model and the data is minimized with the model equations acting as constraints. The initial conditions of the model are used as control parameters and the best-fit initial conditions are determined as a result of minimization carried out using a variable-storage quasi-Newton method. Experiment has been done with 10 days' cycle-averaged data of TOPEX. Assimilation for 10 days and 20 days has been performed and it has been seen that 20 days' assimilation of satellite data provides better results. 相似文献
19.
Climate teleconnections at monthly time scales in the Ligurian Sea inferred from satellite data 总被引：1，自引：0，他引：1
The existence and spatial distribution of possible teleconnections between the South Pacific and North Atlantic oceans and the Ligurian Sea (North-western Mediterranean) are investigated in the present paper. Teleconnections are searched by cross-correlating monthly spatio-temporal time series of 1.1 km resolution sea surface temperature (SST), and a 22.2 km resolution sea level anomaly (SLA), measured from satellite from March 1993 to August 1999, with two indices characterising the South Pacific and the North Atlantic variability: the Southern Oscillation (SO) and the North Atlantic Oscillation (NAO) indices, respectively. Concerning the variability induced by the North Atlantic Ocean, it is shown that it mostly influences the SLA field in the Ligurian Sea. Specifically, relevant anti-correlations between SLA and North Atlantic variability have been found in all the Ligurian sub-basin. As expected by geographical proximity, the effects of North Atlantic on the SLA field in the Ligurian Sea are instantaneous at monthly time scales. Instead, correlations between SST and NAO Index are found at time lag τ = 1 month in the southern part of the basin highlighting the memory of the ocean related to their heat capacity. Significant anti-correlations between SO Index and the SST field in the Ligurian Sea, were obtained at time lag τ = 4 months in the coastal areas of the sub-basin. Results also indicate that the impact of teleconnections in the area studied is not geographically uniform. 相似文献
20.
Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. 相似文献