共查询到20条相似文献，搜索用时 31 毫秒
1.
Brown W.S. Gangopadhyay A. Bub F.L. Zhitao Yu Strout G. Robinson A.R. 《Oceanic Engineering, IEEE Journal of》2007,32(4):807-822
The basic elements of a prototype operational data assimilation modeling system that can provide near-real-time information on the ocean water property and circulation environment in the Gulf of Maine (GOM)/Georges Bank (GB) region are described in this paper. This application of the Harvard Ocean Prediction System (HOPS, Harvard University, Cambridge, MA) model includes development of protocols for the following: 1) the production of model initial fields from an objective blending of climatological and feature model (FM) hydrographic data with fishing-boat-measured bottom temperature data, 2) the ldquowarm startrdquo of the model to produce reasonably realistic initial model fields, 3) converting real-time Fleet Numerical Meteorological and Oceanographic Center (FNMOC, Monterey, CA) model nowcast and forecast winds and/or National Data Buoy Center (NDBC, Stennis Space Center, MS) operational wind measurements to model wind stress forcing fields, and 4) the assimilation of satellite-derived sea surface temperature (SST). These protocols are shown herein to evolve the initial model fields, which were dominated by climatological data, toward more dynamically balanced, realistic fields. Thus, the model nowcasts, with the assimilation of one SST field, are well positioned to produce reasonably realistic ocean fields within a few model days (MDs). 相似文献
2.
中国海及邻近海域卫星观测资料同化试验 总被引：4，自引：0，他引：4
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%。 相似文献
3.
4.
南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统研制 总被引：2，自引：1，他引：1
本文主要介绍了南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统的研制概况。预报区域为99°E~135°E，15°S~45°N，包括渤海、黄海、东海和南海及其周边海域。为了给耦合预报模式提供较准确的预报初始场，在预报开始之前，分别进行了海浪模式和海洋模式的前24小时同化后报模拟。海浪模式和海洋模式都采用了集合调整Kalman滤波同化方法，海浪模式同化了Jason-2有效波高数据；海洋模式同化了SST数据、MADT数据和ARGO剖面数据。为了改进海洋温度和盐度的模拟，我们在海洋模式的垂向混合方案中引入波致混合和内波致混合的作用。预报系统的运行主要包括两个阶段，首先海浪模式和海洋模式进行了2014年1月至2015年10月底的同化后报模拟，强迫场源自欧洲气象中心的六小时的再分析数据产品。然后耦合预报系统将同化后报模拟的结果作为初始场进行了14个月的耦合预报。预报产品包括大气产品（气温、风速风向、气压等）、海浪产品（有效波高和波向等）、海流产品（温度、盐度和海流等）。一系列观测资料的检验比较表明该大气-海浪-海洋耦合精细化数值预报系统的预报结果较为可靠，可以为南海及周边海洋资源开发和安全保障提供数据和信息产品服务。 相似文献
5.
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。 相似文献
6.
Bias correction of sea surface temperature retrospective fore-casts in the South China Sea 总被引：2，自引：1，他引：1
Guijun Han Jianfeng Zhou Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou 《海洋学报(英文版)》2022,41(2):41-50
Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South... 相似文献
7.
In this study, we evaluate the performance of the recently developed incremental strong constraint 4-dimensional variational (4DVAR) data assimilation applied to the Yellow Sea (YS) using the Regional Ocean Modeling System (ROMS). Two assimilation experiments are compared: assimilating remote-sensed sea surface temperature (SST) and both the SST and in-situ profiles measured by shipboard CTD casts into a regional ocean modeling from January to December of 2011. By comparing the two assimilation experiments against a free-run without data assimilation, we investigate how the assimilation affects the hydrographic structures in the YS. Results indicate that the SST assimilation notably improves the model behavior at the surface when compared to the non-assimilative free-run. The SST assimilation also has an impact on the subsurface water structure in the eastern YS; however, the improvement is seasonally dependent, that is, the correction becomes more effective in winter than in summer. This is due to a strong stratification in summer that prevents the assimilation of SST from affecting the subsurface temperature. A significant improvement to the subsurface temperature is made when the in-situ profiles of temperature and salinity are assimilated, forming a tongue-shaped YS bottom cold water from the YS toward the southwestern seas of Jeju Island. 相似文献
8.
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. 相似文献
9.
一个高分辨率太平洋-印度洋海盆环流模式的初步结果 总被引：1，自引：0，他引：1
利用LASG/IAP发展的一个0.25°×0.25°高分辨率太平洋-印度洋海盆环流模式，初步分析了模式在太平洋区域的模拟结果，并与海洋同化资料以及前人的研究结果作比较，检验此模式对该区域平均气候态、年际变化的模拟能力。分析表明，模式较好地再现了海表温度（SST）分布、赤道温跃层和纬向流结构、赤道流系分布形态、海表高度以及正压流函数空间分布特征；同时，对显著的El Ni?o和La Ni?a事件的模拟等方面与Simple Ocean Data Assimilation（SODA）2.0.2版本结果相近。此外，模式模拟北赤道流（NEC）分叉点位置的季节和年际变化以及吕宋海峡流量的年际变化与已有研究结果基本一致。进一步分析还发现，在年际尺度上，NEC分叉点位置和吕宋海峡流量与ENSO密切相关。 相似文献
10.
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。 相似文献
11.
海洋表层温度对台风"蔷薇"路径和强度预测精度的影响 总被引：1，自引：0，他引：1
基于中尺度大气模式WRF(Weather Research and Forecasting Model),首先对2007年3次船舶辐射通量观测进行模拟,以检验WRF对长波和短波辐射通量的模拟能力,结果表明使用中国近海海洋环境数值预报系统环流模式POM(Princeton Ocean Model)模拟的高时空分辨率的海洋表层温度能够显著改进短波辐射通量的模拟,而对长波辐射通量模拟的改进不明显。然后,将业务化运行的中国近海海洋环境数值预报系统后报的逐时海洋表面温度(SST)作为WRF底边界条件,对2008年15号强台风"蔷薇"(Jangmi)过程进行了数值后报试验。结果表明,与使用NCEP/NCAR的SST试验后报的台风中心位置偏差相比,使用高时空分辨率的SST能够较为显著地改善"蔷薇"的路径模拟,台风中心位置模拟偏差减少11%,尤其在台风减弱阶段,台风中心位置模拟偏差减少37%。台风强度在台风发展的不同阶段对下垫面SST的变化敏感性不同。台风路径附近的海表面温度下降会导致海洋向大气输送的热量减少从而减弱台风强度。 相似文献
12.
基于中尺度大气模式WRF(Weather Research and Forecasting Model),首先对2007年3次船舶辐射通量观测进行模拟,以检验WRF对长波和短波辐射通量的模拟能力,结果表明使用中国近海海洋环境数值预报系统环流模式POM(Princeton Ocean Model)模拟的高时空分辨率的海洋表层温度能够显著改进短波辐射通量的模拟,而对长波辐射通量模拟的改进不明显。然后,将业务化运行的中国近海海洋环境数值预报系统后报的逐时海洋表面温度(SST)作为WRF底边界条件,对2008年15号强台风"蔷薇"(Jangmi)过程进行了数值后报试验。结果表明,与使用NCEP/NCAR的SST试验后报的台风中心位置偏差相比,使用高时空分辨率的SST能够较为显著地改善"蔷薇"的路径模拟,台风中心位置模拟偏差减少11%,尤其在台风减弱阶段,台风中心位置模拟偏差减少37%。台风强度在台风发展的不同阶段对下垫面SST的变化敏感性不同。台风路径附近的海表面温度下降会导致海洋向大气输送的热量减少从而减弱台风强度。 相似文献
13.
Aart C. Voorrips 《Coastal Engineering》1999,37(3-4)
Spectral observations from pitch-and-roll buoys have been assimilated in a North Sea wave model, in order to study their impact on the wave analysis and forecast. The assimilation is based on Optimal Interpolation (OI) of a limited number of characteristic spectral parameters. In a case study, the propagation of the corrections through the model domain is followed, and it is clarified for which wave conditions the data assimilation has the largest influence on the forecast: this is especially the case for swell waves with long travel times between the assimilation site and the location where validation is carried out. A 1-year test has been carried out in which an analysis and subsequent forecast were produced four times a day. From a statistical analysis of the results a modest but systematic improvement of the 12-h forecast is found. When only swell cases are selected, the impact is more pronounced. It is argued that for shelf seas like the North Sea, more progress is to be expected from extension of the ‘conventional' observations network (buoys and wave radars) than from satellite measurements. 相似文献
14.
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
15.
根据预报值具有最小方差这一要求,详细推导了融合法在观测数据为一维、多维和维数不同的情况下的具体同化表达形式,同时还给出了不同情况下与同化表达式相对应的预报误差公式.利用这些公式,可以用融合法处理常见的海洋观测数据的同化问题.在陆架海模式HAMSOM基础上,以4月份的渤海海表温度为例,我们验证了同化公式的正确性,并给出了同化后较好的同化结果。最后将融合法的同化结果与卡尔曼滤波同化结果进行了对比.比较表明,融合法使用起来更简单,且能有效地处理常见的海洋观测数据. 相似文献
16.
南海及周边海域风浪流耦合同化精细化数值预报与信息服务系统简介 总被引：1，自引：0，他引：1
面向社会需求,建立覆盖南海及周边海域的高分辨率风-浪-流耦合同化数值预报与信息服务系统。系统包含耦合同化数值预报模式、海洋动力环境数据库与可视化平台两部分。其中,耦合同化数值预报模式由中尺度大气数值预报模式、海浪数值预报模式和区域海洋环流数值模式,在C-Coupler耦合器中进行耦合,引入集合调整Kalman滤波同化模块,在耦合预报前进行大气、海浪和海流的同化后报模拟,为耦合预报模式提供更为精确的初始场。预报结果经海洋动力环境数据库和可视化平台处理后,通过二维和三维可视化展示,向用户提供直观的南海及周边海域海洋环境预报产品。 相似文献
17.
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.
Qi Shu Fangli Qiao Jiping Liu Zhenya Song Zhiqiang Chen Jiechen Zhao Xunqiang Yin Yajuan Song 《海洋学报(英文版)》2021,40(10):65-75
To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) are assimilated into this system, using the method of localized error subspace transform ensemble Kalman ?lter (LESTKF). Five-year (2014–2018) Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast. Since sea ice thickness in the PIOMAS is updated in time, it is a good choice for data assimilation to improve sea ice prediction skills in the near-real-time Arctic sea ice seasonal prediction. 相似文献
20.
We propose an improvement of the algorithm of joint assimilation of the data on climatic temperature, salinity, and altimetric
sea level in a model of circulation. Unlike the previous works, the variances of the forecast errors of temperature and salinity
and the cross-covariance functions of of the forecast errors of salinity-level and temperature-level depend on the dynamics
of waters. It is shown that the structure of the fields of cross-covariance functions in the upper mixed layer is formed by
the vertical turbulent diffusion of the variances of forecast errors of temperature and salinity. At greater depths, these
statistical characteristics are mainly determined by the vertical advection. We compared the results of calculations with
and without taking into account the dynamics of the statistical characteristics. The analysis of the influence of the dynamics
of these characteristics makes it possible to reconstruct the mutually adapted climatic fields of temperature, salinity, and
horizontal and vertical current velocities in the Black Sea with the assimilation of data in the numerical model in each time
step.
Translated from Morskoi Gidrofizicheskii Zhurnal, No. 4, pp. 18–31, July–August, 2008. 相似文献