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一种有限区域海表温度预报模式——Ⅰ.动力学部分 总被引：6，自引：1，他引：6
本文探索性地以建立短期SST业务数值预报模式为目的,从中国海域的海洋学特征出发,给出一个物理上合理、业务可行的上混合层二维原始方程预报模式。它包括低频流(黑潮及其分支)方程组、由漂流方程和热力学平流方程组成的动力方程组、从热力学方程中“割裂”出来的模式物理学方程三个组成部分。本模式用于3—5天SST预报,同时也给出混合层平均漂流的预报。由于本文属创新性研究,讨论内容较多,故将文章分作两部分。在部分Ⅰ仅限于讨论模式建立的物理依据以及模式的动力学方面的内容。 相似文献
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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
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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
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针对数值预报和人工经验预报在近岸定点表层海温(sea surface temperature, SST)预报中预报准确度不高,将近岸台站定点SST预报转换为多元时间序列预测任务,应用卷积神经网络(convolutional neural networks, CNN)构建近岸台站定点SST时间序列变化模型,对近岸台站每日最高海温、最低海温、平均海温进行预报,并与人工经验方法和长短期记忆网络(long short-termmemory,LSTM)方法进行对比试验。结果显示,在测试数据中相比人工经验预报,CNN方法全年日最高海温预报平均绝对误差(mean absolute error, MAE)为0.36℃,平均下降0.14℃,均方根误差(root mean squared error, RMSE)为0.49℃,平均下降0.21℃,日最低海温预报MAE为0.36℃,平均下降0.17℃, RMSE为0.63℃,平均下降0.24℃,日平均海温预报MAE为0.30℃, RMSE为0.47℃,预报性能和LSTM模型预报性能相当。研究表明CNN应用于近岸SST预报具有可行性,能够有效地提高SST预报准... 相似文献
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热带印度洋SST的日变化幅度受到大气季节内振荡(Madden-Julian Oscillation,MJO)的调制,其在MJO对流最强(弱)位相达到极小(大)值,并且在MJO对流增强位相显著强于其对流减弱位相。本文利用逐时的再分析海表通量强迫一维海洋混合层模式,定量地诊断了MJO事件中SST日变化的差异成因。结果表明,SST日变化在MJO对流最强与最弱位相的显著差异主要是由短波辐射的季节内变化所致(40%),其次是风应力(38%)和潜热通量(14%),其他要素的影响较小。而SST日变化在MJO对流增强与减弱位相所呈现的不对称特征,主要是由纬向风应力的不对称性所致,这是MJO扰动结构与背景环流相互作用的结果。 相似文献
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尝试了一种新的通过自回归模型进行模式预报订正的方法:通过1992—2006年的卫星遥感和NERSC-HYCOM模式模拟的SST资料,计算模式15 a的历史误差序列,建立自回归模型AR(p),来估算2007年和2008年两年的误差,并用来对输出的模式资料进行修订,对修订后的模式预报结果进行检验。可以得出:经过数据订正以后,模式SST的均方根误差明显减小,相关系数增大,模式误差在很大程度上得到消减,订正效果明显;同时,订正效果也存在时间和空间上的差异,不同时刻,不同海域订正效果不尽相同。该方法计算过程简单,计算量小,便于实现。总体而言,利用本文方法对模式SST的预报订正具有很强的操作性和可应用性。 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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基于系统构建工作[1],开展北印度洋风浪流数值预报系统后报和准业务化预报,并利用2013年9月—2014年3月共6个月的资料对预报结果进行了统计检验。结果显示北印度洋风浪流数值预报业务运行稳定可靠,大气模式(WRF)72 h预报的500 hPa位势高度距平相关系数达到89%,海浪模式(SWAN)的72 h有效波高预报的相对误差低于20%,海流模式(ROMS)的72 h海表温度预报的均方根误差在0.5℃左右;同时对2013年10月期间孟加拉湾的超级气旋风暴"PHAILIN"的预报结果进行了分析。该风、浪、流预报系统能够较好地预报"PHAILIN"的移动路径、最低气压及相应的海浪和海流过程。该系统的试运行和检验分析结果,对建立新一代海洋环境数值预报系统具有一定借鉴意义。 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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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
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Evaluation of the Diurnal Warming of Sea Surface Temperature Using Satellite-Derived Marine Meteorological Data 总被引：2，自引：0，他引：2
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. 相似文献
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介绍了红外辐射计和微波辐射计测量海表面温度的原理，分析了它们各自在反演海表面温度时的差异。在全球范围的海表面温度的遥感蛉测中，红外辐射计和微波辐射计的遥感精度受到多种因素影响。传感器本身的噪音、算法反演精度、传感器分辨率、搭载卫星的全球覆盖率等自身因素使辐射计的探测资料产生差别：大气状况、海面风速、测量海洋不同深度海水的表征温度等外界因子也同时影响着红外辐射计和微波辐射计的遥感精度。了解红外波段和微波波段的辐射计在各方面的优劣，有助于发挥各自特长，有效提高卫星监测海表面温度的精度。 相似文献
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A. Birol Kara Charlie N. Barron Paul J. Martin Lucy F. Smedstad Robert C. Rhodes 《Ocean Modelling》2006,11(3-4):376-398
A 1/8° global version of the Navy Coastal Ocean Model (NCOM) is used for simulation of upper-ocean quantities on interannual time scales. The model spans the global ocean from 80°S to a complete Arctic cap, and includes 19 terrain-following σ- and 21 fixed z-levels. The global NCOM assimilates three-dimensional (3D) temperature and salinity fields produced by the Modular Ocean Data Assimilation System (MODAS) which generates synthetic temperature and salinity profiles based on ocean surface observations. Model-data intercomparisons are performed to measure the effectiveness of NCOM in predicting upper-ocean quantities such as sea surface temperature (SST), sea surface salinity (SSS) and mixed layer depth (MLD). Subsurface temperature and salinity are evaluated as well. An extensive set of buoy observations is used for this validation. Where possible, the model validation is performed between year-long time series obtained from the model and time series from the buoys. The statistical analyses include the calculation of dimensionless skill scores (SS), which are positive if statistical skill is shown and equal to one for perfect SST simulations. Model SST comparisons with year-long SST time series from all 83 buoys give a median SS value of 0.82. Model subsurface temperature comparisons with the year-long subsurface temperature time series from 24 buoys showed that the model is able to predict temperatures down to 500 m reasonably well, with positive SS values ranging from 0.18 to 0.97. Intercomparisons of MLD reveal that the model MLD is usually shallower than the buoy MLD by an average of about 15 m. Annual mean SSS and subsurface salinity biases between the model and buoy values are small. A comparison of SST between NCOM and a satellite-based Pathfinder data set demonstrates that the model has a root-mean-square (RMS) SST difference of 0.61 °C over the global ocean. Spatial variations of kinetic energy fields from NCOM show agree with historical observations. Based on these results, it is concluded that the global NCOM presented in this paper is able to predict upper-ocean quantities with reasonable accuracy for both coastal and open ocean locations. 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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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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使用美国夏威夷大学发展的中等复杂程度海洋模式(IOM)在给定表面强迫条件下模拟了热带大西洋上层海洋年际和年代际变率的时空结构.利用NCEP的41a(1958～1998年)逐月平均表面资料作为强迫场,积分海洋模式41a作为控制试验,并利用模式分别做动量(风应力)通量和热量通量无异常变化的平行试验,与控制试验作比较.对3组试验模拟上层海洋变率状况的比较,并按年际和年代际时间尺度分别分析,揭示表面风应力和热通量异常对海表面温度和温跃层深度变化的影响,并比较了其影响的相对重要性.结果表明模式成功地模拟出了热带大西洋上层海洋的变率.模式模拟的海表面温度年际变化主要表现为弱ENSO型,年代际变化表现为南、北大西洋变化相反的偶极子型.在年际时间尺度上,热力强迫和动力强迫对海表温度变化都有贡献,其中赤道外海表面温度异常(SSTA)变化主要由热通量异常引起,而近赤道SSTA的变化主要由动量异常强迫引起.在年代际时间尺度上,热通量强迫的作用远比动量强迫重要.模式不仅能够模拟SST在年际和年代际时间尺度上的变率,还能够模拟温跃层深度在年际和年代际时间尺度上的变率.年际和年代际时间尺度上,温跃层深度的变率主要由动量异常决定,热通量异常强迫的贡献很小. 相似文献
19.
Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas 总被引：3，自引：2，他引：1
An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas(OCFS-C) is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China's seas has been updated to(1/24)° from the global model with(1/2)°resolution. Besides, daily remote sensing sea surface temperature(SST) data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth(MLD) and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores(SS) is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value(more than 0.90). Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean. 相似文献