台风条件下的海洋资料集合最优插值同化分析

发布时间：2018-02-22 17:39

  本文关键词： 台风 海洋资料同化 集合最优插值 膨胀系数 拖曳系数　出处：《中国气象科学研究院》2017年硕士论文　论文类型：学位论文

【摘要】：至今海洋资料同化研究多针对一般天气条件的情况进行,缺少在台风条件下的相关海洋资料同化研究。台风作为最强大的天气系统对海洋的强迫有其特殊性,其搅拌和抽吸作用可影响到较深的海洋温盐流结构。因此,台风条件下的集合最优插值(En OI)同化分析可能与一般天气条件下存在差异,尤其是对次表层及其以深的海洋分析,至今这方面的研究少见。另外,台风条件下一般缺少海洋观测资料,本文利用国家973项目“上层海洋对台风的响应和调制机理研究”在南海北部布设的强化观测浮标阵列资料,而2014年第15号台风“海鸥”恰好经过该浮标列阵中心,它为本文开展资料同化分析提供了有利条件。本文从中科院大气物理研究所引进集合最优插值En OI程序,并根据POM模式模拟的静态样本与台风条件下观测资料计算出新的海温离散度膨胀系数,通过同化卫星海表面温度SST资料,结合南海浮标观测阵列资料的验证,对台风条件下同化以后的南海海洋海温分析场进行深入研究。结果表明,台风条件下膨胀系数与EnOI原有的膨胀系数存在一定的差别,主要表现出0-200米之内膨胀系数随深度增大较快,量值较大,从200米至350米膨胀系数数值有下降趋势更加迅速。改进后的集合最优插值同化方法有效提高了台风影响下的上层海洋海温分析场准确性,与浮标观测对比,改进后的集合最优插值同化方法获得的上层海洋海温分析场比未经同化的模式背景场和原有膨胀系数方案计算的海温分析场,其均方根误差分别减小了0.49℃和0.11℃。另一方面,为了进一步提高对上层海洋的模式模拟准确性,为台风海气耦合模式提供更加合理的动量交换耦合方案,本文应用一个分粒径段的海洋飞沫函数给出一个新的海面拖曳系数DC计算方案,它体现了台风高风速条件下海洋飞沫层使海面拖曳系数数值减小特点。论文以2014年第15号台风“海鸥”经过南海强化观测区域时段作为个例,应用POM三维海洋环流模式进行数值模拟试验,结果发现低风速情况下,考虑海洋飞沫因素后的DC与传统计算方案数值相近,在高风速情况下,考虑海洋飞沫因素后的C_D方案与模式传统计算方案不同,表现出随风速增长趋缓,直至随风速略有下降现象。与传统拖曳系数方案相比,采用新的拖曳系数方案后,模拟的台风条件下上层海洋的温度降温幅度、混合层深度加深幅度、温跃层强度减弱程度都略有减弱,这些模拟特征与观测事实更加接近。
[Abstract]:Up to now, the research on ocean data assimilation is mostly focused on the general weather conditions, and there is a lack of relevant ocean data assimilation studies under typhoon conditions. Typhoon, as the most powerful weather system, has its particularity in forcing the ocean. The mixing and pumping can affect the structure of the deep ocean temperature and salt current. Therefore, the assimilation analysis of the optimal set interpolation en OI under typhoon conditions may be different from that under normal weather conditions, especially for the subsurface layer and its deep ocean analysis. In addition, there is a general lack of ocean observation data under typhoon conditions. In this paper, the enhanced observation buoy array data are deployed in the northern part of the South China Sea using the National 973 Project "study on the response and Modulation Mechanism of the Upper Sea to Typhoon". On 2014, Typhoon No. 15 "seagull" passed the center of the buoy array, which provided favorable conditions for data assimilation analysis in this paper. In this paper, a set optimal interpolation program, en OI, is introduced from the Institute of Atmospheric Physics of the Chinese Academy of Sciences. Based on the static sample simulated by POM model and the observed data under typhoon condition, the new SST dispersion expansion coefficient is calculated. By assimilating SST data of satellite sea surface temperature, the data of South China Sea buoy observation array are verified. The sea surface temperature analysis field of the South China Sea after assimilation under typhoon condition is studied. The results show that the expansion coefficient under typhoon condition is different from the original expansion coefficient of EnOI. It mainly shows that the coefficient of expansion increases rapidly with the depth within 0-200 meters, and the value is larger. The numerical value of expansion coefficient from 200 m to 350 m is decreasing more rapidly. The improved ensemble optimal interpolation assimilation method can effectively improve the accuracy of the upper sea surface temperature analysis field under the influence of typhoon, and compare with the observation of buoy. Compared with the model background field without assimilation and the SST field calculated by the original expansion coefficient scheme, the root mean square error (RMS) of the improved set optimal interpolation assimilation method is reduced by 0.49 鈩，

本文编号：1524925