共查询到18条相似文献，搜索用时 140 毫秒
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利用变分同化技术,将船舶报资料与海表面温度短期数值预报模式有机结合,实现了渤、黄、东海的海表面温度短期数值预报。本预报模式利用伴随方法实现了预报模式的全局优化,不但最大限度地利用了船舶报资料,而且初始温度场的调整由自动的数值迭代过程来实现。在渤、黄、东海海域,4个季节的典型月份的SST连续1个月的24h后报结果与船舶报资料均方差均降至0.8℃以下。同化后海表面温度初始场的绝均差较同化前有显著下降。与以前所用的客观分析方法比较的结果表明,伴随同化的预报精度明显高于客观分析方法。 相似文献
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本文将AMSR-E卫星微波遥感海表温资料运用到渤黄东海海表面温度短期数值预报模式当中.数值预报模式利用伴随方法实现了预报模式的初值场优化.微波遥感海表温资料与海表面温度短期数值预报模式有机结合后的试验结果表明:将预报结果和船舶报资料进行比对时,将遥感资料引入到数据同化的结果要明显优于仅同化船舶报资料的结果,且均方差大部... 相似文献
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针对数值模式和统计学习方法在海表面温度(SST)建模中的不足,将长短时记忆循环神经网络(LSTM-RNN)应用于SST的建模。使用研究海区24 a月平均的SST和太阳辐射、风场、蒸发降水等物理参数,通过LSTM-RNN构建西太平洋研究海区SST时间序列变化模型,用于预报研究海区下个月SST。建立了两个模型model1和model2,model1仅使用SST数据作为model2的对照,model2使用SST和其他物理参数。结果表明:model2在验证数据中的MAE为0. 15℃,RMSE为0. 19℃,相关性系数为0. 978,和model1相比总体准确性提升31%,表明LSTM-RNN应用于SST建模是可行的; LSTM-RNN可以建立其他物理参数与SST的关系,从而显著提升海水表面温度模型的准确性。 相似文献
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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
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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
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日本信息服务中心自1985年以来就利用卫星测得的海表面温度(SST)来预报捕鱼区的位置.从NOAA极轨卫星上AVHRR获得的数据通过GOES-TAP数据传播系统,并与其它有关的各种数据相结合,形成海表面温度变化的成像.这些成像用来预报捕鱼区,这些预报很快传送到捕鱼队. 相似文献
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The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space. 相似文献
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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. 相似文献
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A new method of assimilating sea surface height (SSH) data into ocean models is introduced and tested. Many features observable by satellite altimetry are approximated by the first baroclinic mode over much of the ocean, especially in the lower (but non-equatorial) and mid latitude regions. Based on this dynamical trait, a reduced-dynamics adjoint technique is developed and implemented with a three-dimensional model using vertical normal mode decomposition. To reduce the complexity of the variational data assimilation problem, the adjoint equations are based on a one-active-layer reduced-gravity model, which approximates the first baroclinic mode, as opposed to the full three-dimensional model equations. The reduced dimensionality of the adjoint model leads to lower computational cost than a traditional variational data assimilation algorithm. The technique is applicable to regions of the ocean where the SSH variability is dominated by the first baroclinic mode. The adjustment of the first baroclinic mode model fields dynamically transfers the SSH information to the deep ocean layers. The technique is developed in a modular fashion that can be readily implemented with many three-dimensional ocean models. For this study, the method is tested with the Navy Coastal Ocean Model (NCOM) configured to simulate the Gulf of Mexico. 相似文献
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多普勒雷达资料同化在台风“桑美”预报中的应用研究 总被引：4，自引：2，他引：2
本文以2006年超强台风"桑美"为个例,考察了同化雷达径向风观测资料对台风初始场和预报场的改进作用。首先对沿海新一代多普勒天气雷达的径向风观测资料进行了去噪音、退模糊等一系列的质量控制,进一步利用美国国家大气研究中心开发的中尺度数值模式WRFV3.5及其三维变分同化系统WRF-3DVAR,每30min循环同化雷达径向风观测资料。结果表明:同化多普勒雷达径向风观测资料后,对台风在模式中的初始位置进行了很好的修正,同时对台风区的动力和热力结构均有较好的调整。两组同化试验对于台风的路径、强度、降水等预报要优于控制试验,并且对背景误差协方差尺度化因子优化调整可以更有效地吸收雷达观测资料并提供更多的中小尺度信息。 相似文献
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Correcting the errors in the initial conditions and wind stress in storm surge simulation using an adjoint optimal technique 总被引：1，自引：0，他引：1
An adjoint data assimilation methodology is applied to the Princeton Ocean Model and is evaluated by obtaining “optimal” initial conditions, sea surface forcing conditions, or both for coastal storm surge modelling. By prescribing different error sources and setting the corresponding control variables, we performed several sets of identical twin experiments by assimilating model-generated water levels. The experiment results show that, when the forecasting errors are caused by the initial (or surface boundary) conditions, adjusting initial (or surface boundary) conditions accordingly can significantly improve the storm surge simulation. However, when the forecasting errors are caused by surface boundary (or initial) conditions, data assimilation targeting improving the initial (or surface boundary) conditions is ineffective. When the forecasting errors are caused by both the initial and surface boundary conditions, adjusting both the initial and surface boundary conditions leads to the best results. In practice, we do not know whether the errors are caused by initial conditions or surface boundary conditions, therefore it is better to adjust both initial and surface boundary conditions in adjoint data assimilation. 相似文献
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OSTIA数据在中国近海业务化环流模型中的同化应用 总被引：3，自引：0，他引：3
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. 相似文献
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利用伴随法优化非线性潮汐模型的开边界条件Ⅱ.黄海、东海潮汐资料的同化试验 总被引：12，自引：4，他引：12
本研究基于最优控制理论,采用变分数据同化法,通过建立伴随模型,把观测资料同化到陆架海域潮汐数值模型中去,优化开边界条件,以便提高数值预报的精度.潮汐模型的控制方程为考虑平流项、非线性底摩擦和侧向涡动粘性项的非线性浅水方程组.在第Ⅰ部分建立伴随模型和进行“孪生”数值试验的基础上,给出利用验潮站的水位资料以及TOPEX/Poseidon卫星测高数据在黄海、东海进行变分数据同化试验的数值结果.试验表明利用上述资料对模型进行变分同化校正是可行的. 相似文献
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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