本文選題：資料同化 + WRF模式��； 參考：《中國氣象科學(xué)研究院》2017年碩士論文

【摘要】：通常情況下,由于背景誤差協(xié)方差的不確定、觀測誤差或模式本身誤差等因素,資料同化過程所得到的結(jié)果不能在動力上達(dá)到較好的平衡性,因此為了減小或消除分析場中存在的虛假的高頻振蕩,氣象學(xué)者引入了初始化的概念,近年來,數(shù)值濾波初始化(Digital Filter Initialization,DFI)被廣泛應(yīng)用于氣象預(yù)報業(yè)務(wù)系統(tǒng)中,DFI方法是通過設(shè)置截斷波數(shù)來進(jìn)行濾波,但它的物理意義并不明確;另一種正在發(fā)展的在同化過程中阻尼高頻噪音的方法是通過引入物理約束條件,如引入模式約束的同化方案(Model Constrained-3DVar),它在同化過程中極小化了模式變量的時間傾向來得到最優(yōu)分析場。在本研究中,基于模式約束方案的一種物理濾波初始化方法(Physical Filter Initialization,PFI)應(yīng)用于WRF模式四維變分同化系統(tǒng)中,分別進(jìn)行單點(diǎn)試驗和真實(shí)降水個例的同化和模擬,通過對比傳統(tǒng)4DVar和PFI-4DVar試驗結(jié)果的差異,來驗證PFI方案的合理性和有效性。主要結(jié)論如下:1、傳統(tǒng)4DVar同化過程中在加入了觀測信息后,由于背景誤差協(xié)方差的不確定性等因素,引起分析場存在高頻振蕩,這個高頻振蕩對模式積分預(yù)報會產(chǎn)生一定影響,并隨積分時間不斷向外擴(kuò)散;PFI-4DVar在同化過程中,以模式的動力和物理過程作為弱約束條件,使得同化所得分析場中各變量之間有較好的平衡性和協(xié)調(diào)性,即有效抑制了同化所得分析場中出現(xiàn)的高頻振蕩。2、PFI-4DVar方法同化所得分析場中模式變量之間都能具有較優(yōu)的流依賴特性,并能在積分過程中始終保持這種流依賴特征發(fā)展;初始時刻變量間相關(guān)性和協(xié)調(diào)性較好,并且在積分過程中,變量間始終保持相互協(xié)調(diào),沒有出現(xiàn)高頻振蕩隨積分時間向外傳播的現(xiàn)象。3、傳統(tǒng)4DVar和PFI-4DVar同化所得分析場作為初始條件均能有效提高短時降水預(yù)報的準(zhǔn)確性,而值得一提的是PFI-4DVar可以更有效預(yù)報前六小時降水,無論從降水落區(qū)或是評分效果看,前六小時的降水預(yù)報都有一定提高。這說明由于加入了模式作為弱約束條件,減小了分析場中的高頻振蕩,使得分析場變量間協(xié)調(diào)并合理,進(jìn)一步導(dǎo)致模式積分過程中初始調(diào)整時間大大縮短,即減少了模式spin-up時間,從而提高了預(yù)報初期的準(zhǔn)確性。4、在弱化BE矩陣作用和影響范圍的前提下,在傳統(tǒng)BE矩陣的作用下,觀測信息和背景場誤差存在不平衡地、較大范圍地在背景場中向外傳遞的現(xiàn)象,導(dǎo)致分析場中變量之間不協(xié)調(diào);同時初始時刻高度場和風(fēng)場主要為氣壓梯度力和加速度的平衡,直至積分60min后高度場增量和風(fēng)場增量之間開始逐漸建立起地轉(zhuǎn)關(guān)系。初步說明了PFI-4DVar方法在傳遞背景場誤差和觀測信息時,能更加遵循模式中物理和動力模型,同時能更好地協(xié)調(diào)變量之間關(guān)系,進(jìn)而得到一個平衡的分析場,從而縮短模式積分的spin-up時間。同時也初步證實(shí)了PFI方案有部分代替背景誤差協(xié)方差矩陣,充當(dāng)同化過程中傳遞觀測信息的功能。
[Abstract]:Generally, because of the uncertainty of the covariance of the background error, the error of the observation or the error of the pattern itself, the results obtained by the data assimilation process can not achieve a better balance in the dynamics. Therefore, in order to reduce or eliminate the false high frequency oscillation in the analysis field, the meteorologist introduced the concept of initialization, in recent years, Digital Filter Initialization (DFI) is widely used in the weather forecasting business system. The DFI method is filtered by setting the truncated wavenumber, but its physical meaning is not clear; another developing method of damping high frequency noise in the process of assimilation is by introducing physical constraints, such as citation. In this study, a physical filtering initialization method based on schema constraints (Physical Filter Initialization, PFI) is applied to the WRF model four-dimensional variational assimilation system, in the assimilation process, which minimizes the time tendencies of the pattern variables in the assimilation process. Through the assimilation and Simulation of single point test and real precipitation case, the rationality and effectiveness of the PFI scheme are verified by comparing the differences between the traditional 4DVar and PFI-4DVar test results. The main conclusions are as follows: 1, after the observation information was added to the traditional 4DVar assimilation process, the uncertainties of the background error covariance were caused by the factors such as the uncertainty of the background error covariance. There is a high frequency oscillation in the analysis field. This high frequency oscillation will have a certain influence on the model integral prediction and spread out with the integration time. In the process of assimilation, PFI-4DVar takes the dynamic and physical processes of the model as a weak constraint condition, so that there is a better balance and coordination among the variables in the analysis field of assimilation, that is to say, it is effective. The high frequency oscillation.2 appeared in the analysis field of assimilation is suppressed. The model variables in the analysis field of the PFI-4DVar method assimilation can have better flow dependence, and can always keep the flow dependence in the integration process; the correlation and coordination between the initial time variables is better, and in the integration process, the variables are among the variables. It is always consistent with each other, and there is no phenomenon of high frequency oscillation spreading out with integral time.3. The traditional 4DVar and PFI-4DVar assimilation analysis field can effectively improve the accuracy of short-time precipitation prediction as the initial condition. It is worth mentioning that PFI-4DVar can be more effective in pre reporting the first six hours of precipitation, whether from precipitation area or evaluation. As a result, the precipitation forecast in the first six hours has been improved to a certain extent. This shows that the addition of the model as a weak constraint condition reduces the high frequency oscillation in the analysis field, makes the analysis of the field variables coordinated and reasonable, and further leads to a large reduction in the initial adjustment time in the model integration process, that is, reducing the time of the model spin-up and thus increasing the time of the model. The accuracy of the early prediction is.4. Under the premise of weakening the effect and the influence range of the BE matrix, under the effect of the traditional BE matrix, the observation information and the background field error are unbalance, and the phenomenon of the larger range in the background field leads to the disharmony between the variables in the analysis field; at the same time, the initial time height field and the wind field are mainly as a result. The balance between the pressure gradient force and the acceleration is gradually established between the increment of the height field and the increment of the wind field after the integration of 60min. It is shown preliminarily that when the PFI-4DVar method passes the background field error and the observation information, the physical and dynamic models in the model can be more followed, and the relationship between the variables can be better coordinated, and then the relationship between the variables can be better coordinated. To a balanced analysis field, the spin-up time of the model integral is shortened, and it is also preliminarily proved that the PFI scheme has partially replaced the background error covariance matrix, which serves as the function of transmitting observation information in the process of assimilation.
【學(xué)位授予單位】：中國氣象科學(xué)研究院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P456.7

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