【摘要】：背景誤差相關(guān)結(jié)構(gòu)的確定是影響海浪同化效果的關(guān)鍵因素之一。集合Kalman濾波是一種較為成熟的同化方法,其可以對(duì)背景誤差進(jìn)行實(shí)時(shí)更新和動(dòng)態(tài)估計(jì),現(xiàn)已廣泛應(yīng)用于海洋和大氣領(lǐng)域的研究。本文基于MASNUM-WAM海浪模式,分別采用靜態(tài)樣本集合Kalman濾波和EAKF方法,針對(duì)2014年全球海域開展海浪數(shù)據(jù)同化實(shí)驗(yàn),同化資料為Jason-2衛(wèi)星高度計(jì)數(shù)據(jù),利用Saral衛(wèi)星高度計(jì)資料對(duì)同化實(shí)驗(yàn)結(jié)果進(jìn)行檢驗(yàn)。結(jié)果表明,兩組同化方案均有效提高了海浪模式的模擬水平,EAKF方案在風(fēng)場(chǎng)變化較大的西風(fēng)帶區(qū)域表現(xiàn)顯著優(yōu)于靜態(tài)樣本集合Kalman濾波方案,但總體上兩者相差不大。綜合考慮計(jì)算成本和同化效果,靜態(tài)樣本集合Kalman濾波方案更適用于海浪業(yè)務(wù)化預(yù)報(bào)。
[Abstract]:The determination of background error correlation structure is one of the key factors affecting the wave assimilation effect. Ensemble Kalman filtering is a mature assimilation method, which can update and estimate background errors in real time. It has been widely used in the research of ocean and atmosphere. Based on the MASNUM-WAM wave model, the static sample set Kalman filter and EAKF method are used to carry out wave data assimilation experiments for the global sea area in 2014. The assimilation data are Jason-2 satellite altimeter data. The data of Saral satellite altimeter are used to test the results of assimilation experiment. The results show that both groups of assimilation schemes can effectively improve the simulation level of wave model and the performance of EAKF scheme is significantly better than that of the static sample set Kalman filtering scheme in the westerly zone where the wind field changes greatly, but there is no significant difference between the two schemes on the whole. Considering the computational cost and assimilation effect, the static sample set Kalman filtering scheme is more suitable for wave operational prediction.
【作者單位】： 中國海洋大學(xué)海洋與大氣學(xué)院;國家海洋局第一海洋研究所海洋環(huán)境與數(shù)值模擬研究室;海洋國家實(shí)驗(yàn)室區(qū)域海洋動(dòng)力學(xué)與數(shù)值模擬功能實(shí)驗(yàn)室;
【基金】：國家高技術(shù)研究發(fā)展計(jì)劃-南海及周邊海域風(fēng)浪流耦合同化精細(xì)化數(shù)值預(yù)報(bào)與信息服務(wù)系統(tǒng)項(xiàng)目,2013AA09A506號(hào) 國家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目,2016YFC1402001號(hào),2016YFC1402004號(hào)
【分類號(hào)】：P731.33
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本文編號(hào)：2119783