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基于ROMS和4DVAR的沿轨与网格化SSH数据同化效果评价 总被引:1,自引:1,他引:0
Remote sensing products are significant in the data assimilation of an ocean model. Considering the resolution and space coverage of different remote sensing data, two types of sea surface height(SSH) product are employed in the assimilation, including the gridded products from AVISO and the original along-track observations used in the generation. To explore their impact on the assimilation results, an experiment focus on the South China Sea(SCS) is conducted based on the Regional Ocean Modeling System(ROMS) and the four-dimensional variational data assimilation(4 DVAR) technology. The comparison with EN4 data set and Argo profile indicates that, the along-track SSH assimilation result presents to be more accurate than the gridded SSH assimilation, because some noises may have been introduced in the merging process. Moreover, the mesoscale eddy detection capability of the assimilation results is analyzed by a vector geometry–based algorithm. It is verified that, the assimilation of the gridded SSH shows superiority in describing the eddy's characteristics, since the complete structure of the ocean surface has been reconstructed by the original data merging. 相似文献
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中尺度涡旋是海洋中典型的中尺度现象,是海洋中能量传递的运输者,中尺度涡识别与提取是物理海洋学研究的重要内容之一,而中尺度涡自动发现算法是最基础的用于寻找与分析中尺度涡的工具。中尺度涡旋探测工作的数据来源主要为卫星高度计数据融合出的SLA数据,该数据可以客观的描述海洋表层高度状态。中尺度涡表示为SLA闭合等值线所包围的局部等值区域,涡旋识别需要从SLA数据中提取出稳定的闭合等值线结构。针对基于SLA数据中的中尺度涡探测的特点,本文提出了一种新的基于聚类方法的中尺度涡自动识别算法,通过对SLA数据集的分割与筛选将中尺度涡区域与背景区域分离,后建立区域内联系并将其映射到SLA地图上来提取中尺度涡结构。本文算法解决了传统探测算法中参数设定的敏感性问题,不需要进行稳定性测试,算法适应性增强。算法中加入了涡旋筛选机制,保证了结果的涡旋结构的稳定性,提高了识别准确率。在此基础上,本文选取了西北太平洋及中国南海地区进行了中尺度涡探测实验,实验结果展示出了本文算法在较传统算法提高算法效率的同时,也保持着较高的算法稳定性,可以在稳定识别各个单涡结构的同时识别稳定的多涡结构。 相似文献
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联合Jason-1/2、T/P、Envisat、ERS-1/2、Geosat等多代卫星测高数据计算中国近海及邻域(0°~42°N,100°~140°E)2′×2′重力异常。对卫星测高数据分别进行共线处理和自交叉点平差,并以T/P卫星测高数据为基准进行多星数据联合平差,有效削弱了卫星测高数据的时变影响和不协调性;利用逆Vening-Meinesz公式计算重力异常,与船测重力相比,均方根误差为5.4mGal。结果表明,通过引入高精度的卫星测高数据,结合多项平差处理手段,提高了海洋重力异常的计算精度。 相似文献
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