本文關(guān)鍵詞： 臺(tái)風(fēng) 海洋資料同化 集合最優(yōu)插值 膨脹系數(shù) 拖曳系數(shù)　出處：《中國(guó)氣象科學(xué)研究院》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：至今海洋資料同化研究多針對(duì)一般天氣條件的情況進(jìn)行,缺少在臺(tái)風(fēng)條件下的相關(guān)海洋資料同化研究。臺(tái)風(fēng)作為最強(qiáng)大的天氣系統(tǒng)對(duì)海洋的強(qiáng)迫有其特殊性,其攪拌和抽吸作用可影響到較深的海洋溫鹽流結(jié)構(gòu)。因此,臺(tái)風(fēng)條件下的集合最優(yōu)插值(En OI)同化分析可能與一般天氣條件下存在差異,尤其是對(duì)次表層及其以深的海洋分析,至今這方面的研究少見(jiàn)。另外,臺(tái)風(fēng)條件下一般缺少海洋觀測(cè)資料,本文利用國(guó)家973項(xiàng)目“上層海洋對(duì)臺(tái)風(fēng)的響應(yīng)和調(diào)制機(jī)理研究”在南海北部布設(shè)的強(qiáng)化觀測(cè)浮標(biāo)陣列資料,而2014年第15號(hào)臺(tái)風(fēng)“海鷗”恰好經(jīng)過(guò)該浮標(biāo)列陣中心,它為本文開展資料同化分析提供了有利條件。本文從中科院大氣物理研究所引進(jìn)集合最優(yōu)插值En OI程序,并根據(jù)POM模式模擬的靜態(tài)樣本與臺(tái)風(fēng)條件下觀測(cè)資料計(jì)算出新的海溫離散度膨脹系數(shù),通過(guò)同化衛(wèi)星海表面溫度SST資料,結(jié)合南海浮標(biāo)觀測(cè)陣列資料的驗(yàn)證,對(duì)臺(tái)風(fēng)條件下同化以后的南海海洋海溫分析場(chǎng)進(jìn)行深入研究。結(jié)果表明,臺(tái)風(fēng)條件下膨脹系數(shù)與EnOI原有的膨脹系數(shù)存在一定的差別,主要表現(xiàn)出0-200米之內(nèi)膨脹系數(shù)隨深度增大較快,量值較大,從200米至350米膨脹系數(shù)數(shù)值有下降趨勢(shì)更加迅速。改進(jìn)后的集合最優(yōu)插值同化方法有效提高了臺(tái)風(fēng)影響下的上層海洋海溫分析場(chǎng)準(zhǔn)確性,與浮標(biāo)觀測(cè)對(duì)比,改進(jìn)后的集合最優(yōu)插值同化方法獲得的上層海洋海溫分析場(chǎng)比未經(jīng)同化的模式背景場(chǎng)和原有膨脹系數(shù)方案計(jì)算的海溫分析場(chǎng),其均方根誤差分別減小了0.49℃和0.11℃。另一方面,為了進(jìn)一步提高對(duì)上層海洋的模式模擬準(zhǔn)確性,為臺(tái)風(fēng)海氣耦合模式提供更加合理的動(dòng)量交換耦合方案,本文應(yīng)用一個(gè)分粒徑段的海洋飛沫函數(shù)給出一個(gè)新的海面拖曳系數(shù)DC計(jì)算方案,它體現(xiàn)了臺(tái)風(fēng)高風(fēng)速條件下海洋飛沫層使海面拖曳系數(shù)數(shù)值減小特點(diǎn)。論文以2014年第15號(hào)臺(tái)風(fēng)“海鷗”經(jīng)過(guò)南海強(qiáng)化觀測(cè)區(qū)域時(shí)段作為個(gè)例,應(yīng)用POM三維海洋環(huán)流模式進(jìn)行數(shù)值模擬試驗(yàn),結(jié)果發(fā)現(xiàn)低風(fēng)速情況下,考慮海洋飛沫因素后的DC與傳統(tǒng)計(jì)算方案數(shù)值相近,在高風(fēng)速情況下,考慮海洋飛沫因素后的C_D方案與模式傳統(tǒng)計(jì)算方案不同,表現(xiàn)出隨風(fēng)速增長(zhǎng)趨緩,直至隨風(fēng)速略有下降現(xiàn)象。與傳統(tǒng)拖曳系數(shù)方案相比,采用新的拖曳系數(shù)方案后,模擬的臺(tái)風(fēng)條件下上層海洋的溫度降溫幅度、混合層深度加深幅度、溫躍層強(qiáng)度減弱程度都略有減弱,這些模擬特征與觀測(cè)事實(shí)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，

本文編號(hào)：1524925