【摘要】：海洋模式和氣候系統(tǒng)模式在實際應(yīng)用過程中往往存在較大的偏差,亟需利用較為成熟的數(shù)據(jù)同化方法在數(shù)值模擬過程中有效結(jié)合觀測信息,得到更合理的模擬結(jié)果或通過改進初值場提高預(yù)測的精度。在目前海洋與氣候研究的主要的數(shù)據(jù)同化方法中,集合調(diào)整Kalman濾波(EAKF)同化方法不需要擾動觀測,可以充分保留數(shù)值模式的先驗信息,其計算和存儲方面的需求也相對較少,適合用于開展海洋和氣候系統(tǒng)模式的數(shù)據(jù)同化。本文從方法實現(xiàn)的角度詳細(xì)闡述了EAKF方法的基本理論和相關(guān)假定,討論了EAKF同化方法的串行實現(xiàn)、并行實現(xiàn)、觀測數(shù)據(jù)處理和集合樣本處理等過程,建立了EAKF同化模塊,隨后將其應(yīng)用于區(qū)域海洋模式、全球海洋模式和海氣耦合模式中。本文首先在基于POM建立的西北太平洋環(huán)流模式中,通過EAKF同化模塊開展了2005年到2009年的Argo資料的集合濾波同化實驗。為對比分析區(qū)域海洋模式數(shù)據(jù)同化的效果,本研究設(shè)計了3組數(shù)值實驗：控制實驗(單模式運行,無數(shù)據(jù)同化)、集合自由發(fā)散實驗(集合運行,無數(shù)據(jù)同化)和EAKF同化實驗(集合運行,Argo數(shù)據(jù)同化)。該區(qū)域海洋模式將不同年份的初始場作為2005年不同模式樣本的初始場,實現(xiàn)集合模式運行,開展集合自由發(fā)散實驗和EAKF同化實驗。自由發(fā)散實驗的集合樣本分布情況的分析表明：由于模式對初始場的適應(yīng)過程,集合模擬結(jié)果在開始幾個月內(nèi)出現(xiàn)集合樣本分布有所減少,但隨后穩(wěn)定在一定范圍。這種構(gòu)造集合初始場的方法應(yīng)用在區(qū)域模式中,所有集合樣本具有一定的發(fā)散性,可用來開展準(zhǔn)確的集合濾波同化。經(jīng)過EAKF同化后的集合樣本分布相比無同化的自由發(fā)散實驗略小,但仍保持了一定的量值,對后續(xù)的濾波同化過程不會造成不良影響。通過分析SST的集合模擬結(jié)果相對特定參考點的相關(guān)系數(shù),模式背景誤差協(xié)方差表現(xiàn)出較強的各向異性特征。為考察同化的效果,所有實驗的結(jié)果與衛(wèi)星觀測SST、GTSPP溫鹽剖面數(shù)據(jù)和衛(wèi)星高度計觀測數(shù)據(jù)等進行了細(xì)致的對比。誤差統(tǒng)計結(jié)果顯示：同化結(jié)果相對衛(wèi)星SST的誤差比同化前在整體上減少,平均減少量為10%左右：相比獨立于Argo數(shù)據(jù)的GTSPP溫鹽剖面觀測,同化后的溫度和鹽度誤差比同化前均有大幅減小,相對控制實驗和自由發(fā)散實驗的誤差減少最大百分比分別達到85%和80%。同化前后的模擬結(jié)果與衛(wèi)星高度計觀測海面高度數(shù)據(jù)的對比顯示：同化過程還增強了模式對海洋中尺度活動能力模擬能力,這一改進在黑潮及其延伸體附近,以及10°N斷面上尤為突出。在基于MOM4建立的全球大洋環(huán)流模式中,開展了2008年的Argo浮標(biāo)數(shù)據(jù)的EAKF同化,對比分析了4組同化實驗與控制實驗(未同化)的實驗結(jié)果。初步同化實驗(實驗1)中初始溫度場的擾動采用了1.0℃的振幅對上層海洋進行擾動,且無集合樣本擴展,其實驗結(jié)果表明：通過Argo數(shù)據(jù)同化后的溫度(鹽度)在上400m(500m)水層偏差顯著減小,然而這些偏差在更深水層增大;SST的誤差在上半年的減小值明顯高于其余時段。為了考察同化的改善作用在不同深度和不同時段的差異,本文設(shè)計了3個敏感性實驗。其中2個實驗用于分析不同垂向擾動的敏感性：擾動深度(實驗2)和擾動振幅(實驗3)。實驗2采用了整層水柱的擾動,擾動振幅仍為1.0℃,實驗結(jié)果表明：模擬溫度、鹽度的偏差在整個水體中均得到減小。實驗3采用較小擾動振幅(0.1℃),相比實驗2說明合適的擾動振幅也非常重要。實驗4采用了集合樣本擴展,其擴展系數(shù)則是通過一系列的數(shù)值實驗的敏感性分析所得到的5%。與其它3個實驗相比,實驗4的同化性能有了較大的提高。綜合上述實驗結(jié)果,我們認(rèn)為：對于初始場的擾動應(yīng)考慮模式的所有分層：合適的擾動振幅對EAKF同化具有重要作用;最優(yōu)集合樣本擴展系數(shù)的選擇有助于提高EAKF同化的效果�；趪液Ｑ缶值谝缓Ｑ笱芯克厍蛳到y(tǒng)模式(FIO-ESM),采用微擾動法構(gòu)建了集合初始場,開展了衛(wèi)星SST和SLA等數(shù)據(jù)的EAKF同化實驗。本研究利用氣候系統(tǒng)模式數(shù)據(jù)同化后的海洋模式、大氣模式、海冰模式、陸面模式和海浪模式等分量模式的同化結(jié)果重構(gòu)了1992-2013年的氣候再分析數(shù)據(jù),并從整體上對重構(gòu)的再分析數(shù)據(jù)進行了評估。本研究采用了ERA-Interim再分析數(shù)據(jù)集、EN4溫鹽再分析數(shù)據(jù)集、GPCP降水?dāng)?shù)據(jù)集、AVISO沿軌道觀測海浪有效波高等多種數(shù)據(jù),對FIO-ESM同化再分析數(shù)據(jù)行了對比分析,結(jié)果顯示：重構(gòu)的再分析數(shù)據(jù)可以成功再現(xiàn)1992-2013年間上層海洋、大氣運動和水汽分布、海冰變化、海浪氣候態(tài)分布等方面的氣候特征。在進一步研究中,該數(shù)據(jù)將用于開展氣候分析和未來氣候預(yù)測,提高我們對氣候變化的認(rèn)知水平
[Abstract]:There are often large deviations between ocean model and climate system model in the actual application process. It is urgent to use more mature data assimilation method to effectively combine observation information in the numerical simulation process to obtain more reasonable simulation results or to improve the prediction accuracy by improving the initial value field. In data assimilation method, ensemble-adjusted Kalman filter (EAKF) assimilation method does not need perturbation observation, can fully retain the prior information of numerical model, and its calculation and storage requirements are relatively small. It is suitable for data assimilation of ocean and climate system models. The basic theory and related hypotheses are discussed. The serial implementation, parallel implementation, observation data processing and set sample processing of EAKF assimilation method are discussed. The EAKF assimilation module is established and then applied to the regional ocean model, the global ocean model and the air-sea coupling model. In order to compare and analyze the effect of regional ocean model data assimilation, three groups of numerical experiments were designed: control experiment (single mode operation, no data assimilation), aggregate free divergence experiment (aggregate operation, no data assimilation) and EAKF experiment (aggregate operation, no data assimilation). Assimilation experiment (collective operation, Argo data assimilation). The regional ocean model takes the initial field of different years as the initial field of different model samples in 2005, realizes the collective mode operation, carries out the collective free divergence experiment and EAKF assimilation experiment. The results of ensemble simulation show that the distribution of ensemble samples decreases in the first few months, but then stabilizes in a certain range. This method of constructing the initial field of ensemble is applied to the regional model. All the ensemble samples have certain divergence and can be used to carry out accurate ensemble filtering assimilation. The sample distribution is slightly smaller than that of the non-assimilated free-divergence experiment, but it still keeps a certain amount of value, which will have no adverse effect on the subsequent filtering assimilation process. As a result, all the experimental results were compared with SST, GTSPP temperature and salinity profiles and satellite altimeter data in detail. The error statistics showed that the error of the assimilation results was less than that of the satellite SST before assimilation, and the average reduction was about 10%. Compared with the GTSPP temperature and salinity profiles independent of Argo data, the error of the assimilation results was less than that of the satellite SST before assimilation. The results show that the temperature and salinity errors of the assimilated model are greatly reduced compared with those of the pre-assimilated model, and the maximum errors of relative control experiment and free divergence experiment are 85% and 80% respectively. In the global oceanic circulation model based on MOM4, EAKF assimilation of Argo buoy data in 2008 was carried out, and the results of four groups of Assimilation Experiments and control experiments (not assimilated) were compared and analyzed. The results show that the deviations of temperature (salinity) in the upper 400 m (500 m) water layer after assimilation of Argo data are significantly reduced, but these deviations increase in the deeper water layer; the deviations of SST in the first half of the year are significantly higher than those in the deeper water layer. In order to investigate the difference of the improvement effect of assimilation at different depths and different periods, three sensitivity experiments were designed. Two of them were used to analyze the sensitivity of different vertical disturbances: disturbance depth (experiment 2) and disturbance amplitude (experiment 3). In experiment 2, the disturbance of the whole water column was used, and the disturbance amplitude was still 1.0 C. The results show that the deviation of salinity decreases with the simulated temperature in the whole water body. In experiment 3, a small disturbance amplitude (0.1 C) is used, and the appropriate disturbance amplitude is also very important compared with experiment 2. In experiment 4, the set sample expansion is used, and the expansion coefficient is 5% obtained by a series of numerical experiments. Comparing with the three experiments, the assimilation performance of Experiment 4 has been greatly improved. Based on the above experimental results, we consider that all the layers of the model should be considered for the initial field perturbations: the appropriate amplitude of the perturbation plays an important role in EAKF assimilation; the selection of the optimal set of sample expansion coefficients helps to improve the effect of EAKF assimilation. The Earth System Model (FIO-ESM) of the First Institute of Oceanography, Jiahai Oceanic Administration, was used to construct a set of initial fields and to carry out EAKF Assimilation Experiments of satellite SST and SLA data. In this study, the assimilation of ocean model, atmospheric model, sea ice model, land surface model and ocean wave model was carried out using the assimilated data of climate system model. Results The climate reanalysis data from 1992 to 2013 were reconstructed and the reconstructed reanalysis data were evaluated as a whole. The data of ERA-Interim reanalysis data set, EN4 thermohaline reanalysis data set, GPCP precipitation data set, AVISO along-track observation of wave effective wave height were used in this study. The results show that the reconstructed reanalysis data can successfully reproduce the climatic characteristics of the upper ocean, atmospheric movement and water vapor distribution, sea ice changes, and wave climatic distribution during 1992-2013. Knowledge level
【學(xué)位授予單位】：中國海洋大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：P73;P435

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