In this paper, we propose a parallel data assimilation module based on ensemble optimal interpolation （EnOI）. We embedded the method into the full-spectral third-generation wind-wave model, WAVEWATCH III Version 3.1...In this paper, we propose a parallel data assimilation module based on ensemble optimal interpolation （EnOI）. We embedded the method into the full-spectral third-generation wind-wave model, WAVEWATCH III Version 3.14, producing a wave data assimilation system. We present our preliminary experiments assimilating altimeter significant wave heights （SWH） using the EnOI-based wave assimilation system. Waters north of 15°S in the Indian Ocean and South China Sea were chosen as the target computational domain, which was two-way nested into the global implementation of the WAVEWATCH III. The wave model was forced by six-hourly ocean surface wind velocities from the cross-calibrated multi-platform wind vector dataset. The assimilation used along-track SWH data from the Jason-2 altimeter. We evaluated the effect of the assimilation on the analyses and hindcasts, and found that our technique was effective. Although there was a considerable mean bias in the control SWHs, a month-long consecutive assimilation reduced the bias by approximately 84% and the root mean-square error （RMSE） by approximately 65%. Improvements in the SWH RMSE for both the analysis and hindcast periods were more significant in July than January, because of the monsoon climate. The improvement in model skill persisted for up to 48 h in July. Furthermore, the SWH data assimilation had the greatest impact in areas and seasons where and when the sea-states were dominated by swells.展开更多
When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relatin...When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies （SLA） from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3DVAR. Correlations of temperature with the reanalysis data increased with EnOI by 0.1 0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3DVAR in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3DVAR generally reduced the correlations by 0.1 0.3. Though both methods were able to reduce the model-data difference in climatological sense, 3DVAR appears to have degraded the simulated variability, especially during E1 Nifio-Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.展开更多
基于ROMS(Regional Ocean Modeling System)模式建立一个南海北部集合最优插值的同化系统,并且利用2008年夏季SCOPE(Northern South China Sea Coastal Oceanographic Process Experiment)航次的温盐数据以及航次期间逐日OSTIA(Operatio...基于ROMS(Regional Ocean Modeling System)模式建立一个南海北部集合最优插值的同化系统,并且利用2008年夏季SCOPE(Northern South China Sea Coastal Oceanographic Process Experiment)航次的温盐数据以及航次期间逐日OSTIA(Operational Sea Surface Temperature and Sea Ice Analysis)数据进行同化试验。试验结果表明:同化较好地改善了海表温度的模拟,加强了南海北部的上升流,尤其是加强了珠江冲淡水的模拟,垂向定量的分析表明,温度整层都得到改善,表层改善达到30%,盐度在80 m以上得到明显改善,表层改善40%。此外,针对近岸卫星SST(sea surface temperature)和航次SST的不协调性问题以及不同观测数量对同化结果的影响,利用敏感性同化试验进行了初步探讨,结果显示:相对于剔除40 m以浅,同化所有区域内卫星SST资料能显著减小近岸区域的SST均方根误差(约51%);加密用于同化的SST数据量,如由每隔5个格点调整为每隔3个格点选取观测数据,也能在此基础上再减小SST的均方根误差(约9.1%),但二者的SST分布形态均与观测吻合。展开更多
在海洋数据同化领域,集合最优插值方法中,矩阵求逆过程所使用的奇异值分解(singular value decomposition,SVD)十分耗时。对集合最优插值中逆矩阵的求逆过程进行优化,分别使用LU分解、Choleskey分解、QR分解来替代SVD分解。首先,通过LU...在海洋数据同化领域,集合最优插值方法中,矩阵求逆过程所使用的奇异值分解(singular value decomposition,SVD)十分耗时。对集合最优插值中逆矩阵的求逆过程进行优化,分别使用LU分解、Choleskey分解、QR分解来替代SVD分解。首先,通过LU分解(Choleskey分解或QR分解)得到相应的三角矩阵(或正交矩阵);然后,利用分解后的矩阵来实现相关逆矩阵的计算。由于LU分解、Choleskey分解、QR分解的算法复杂度都远小于SVD分解,因此改进后的同化程序能得到大幅度的性能提升。数值结果表明,所采用的三种矩阵分解方法相比于SVD分解,都能将集合最优插值的计算效率提升至少两倍以上。值得一提的是,在四种矩阵分解中Choleskey分解使得整个同化程序的性能达到了最优。展开更多
基金Supported by the National Special Research Fund for Non-Profit Marine Sector(Nos.201005033,201105002)the National High Technology Research and Development Program of China(863 Program)(No.2012AA091801)+1 种基金the National Natural Science Foundation of China(No.U1133001)the NSFC-Shandong Joint Fund for Marine Science Research Centers(No.U1406401)
文摘In this paper, we propose a parallel data assimilation module based on ensemble optimal interpolation （EnOI）. We embedded the method into the full-spectral third-generation wind-wave model, WAVEWATCH III Version 3.14, producing a wave data assimilation system. We present our preliminary experiments assimilating altimeter significant wave heights （SWH） using the EnOI-based wave assimilation system. Waters north of 15°S in the Indian Ocean and South China Sea were chosen as the target computational domain, which was two-way nested into the global implementation of the WAVEWATCH III. The wave model was forced by six-hourly ocean surface wind velocities from the cross-calibrated multi-platform wind vector dataset. The assimilation used along-track SWH data from the Jason-2 altimeter. We evaluated the effect of the assimilation on the analyses and hindcasts, and found that our technique was effective. Although there was a considerable mean bias in the control SWHs, a month-long consecutive assimilation reduced the bias by approximately 84% and the root mean-square error （RMSE） by approximately 65%. Improvements in the SWH RMSE for both the analysis and hindcast periods were more significant in July than January, because of the monsoon climate. The improvement in model skill persisted for up to 48 h in July. Furthermore, the SWH data assimilation had the greatest impact in areas and seasons where and when the sea-states were dominated by swells.
基金supportedby National Natural Science Foundation of China(GrantNos.41176014and41075064)the Key Technologies R&D Program of China(Grant No.2011BAC03B02)
文摘When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies （SLA） from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3DVAR. Correlations of temperature with the reanalysis data increased with EnOI by 0.1 0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3DVAR in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3DVAR generally reduced the correlations by 0.1 0.3. Though both methods were able to reduce the model-data difference in climatological sense, 3DVAR appears to have degraded the simulated variability, especially during E1 Nifio-Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.
文摘基于ROMS(Regional Ocean Modeling System)模式建立一个南海北部集合最优插值的同化系统,并且利用2008年夏季SCOPE(Northern South China Sea Coastal Oceanographic Process Experiment)航次的温盐数据以及航次期间逐日OSTIA(Operational Sea Surface Temperature and Sea Ice Analysis)数据进行同化试验。试验结果表明:同化较好地改善了海表温度的模拟,加强了南海北部的上升流,尤其是加强了珠江冲淡水的模拟,垂向定量的分析表明,温度整层都得到改善,表层改善达到30%,盐度在80 m以上得到明显改善,表层改善40%。此外,针对近岸卫星SST(sea surface temperature)和航次SST的不协调性问题以及不同观测数量对同化结果的影响,利用敏感性同化试验进行了初步探讨,结果显示:相对于剔除40 m以浅,同化所有区域内卫星SST资料能显著减小近岸区域的SST均方根误差(约51%);加密用于同化的SST数据量,如由每隔5个格点调整为每隔3个格点选取观测数据,也能在此基础上再减小SST的均方根误差(约9.1%),但二者的SST分布形态均与观测吻合。
文摘在海洋数据同化领域,集合最优插值方法中,矩阵求逆过程所使用的奇异值分解(singular value decomposition,SVD)十分耗时。对集合最优插值中逆矩阵的求逆过程进行优化,分别使用LU分解、Choleskey分解、QR分解来替代SVD分解。首先,通过LU分解(Choleskey分解或QR分解)得到相应的三角矩阵(或正交矩阵);然后,利用分解后的矩阵来实现相关逆矩阵的计算。由于LU分解、Choleskey分解、QR分解的算法复杂度都远小于SVD分解,因此改进后的同化程序能得到大幅度的性能提升。数值结果表明,所采用的三种矩阵分解方法相比于SVD分解,都能将集合最优插值的计算效率提升至少两倍以上。值得一提的是,在四种矩阵分解中Choleskey分解使得整个同化程序的性能达到了最优。
