本文選題：高光譜紅外資料　切入點：衛(wèi)星資料同化　出處：《南京信息工程大學(xué)》2016年碩士論文

【摘要】：近年來暴雨、冰雹等自然災(zāi)害頻發(fā),給人們生活帶來不便,給社會經(jīng)濟(jì)帶來了巨大的損失。高光譜紅外傳感器可提供高分辨率的大氣垂直溫度和濕度數(shù)據(jù),引入該數(shù)據(jù)已證明可以很大程度的改善全球模式的精度。對于區(qū)域模式而言,同化高光譜紅外資料仍然存在很多問題待解決�，F(xiàn)階段國內(nèi)有很多將AIRS數(shù)據(jù)同化到區(qū)域模式中研究,而對于歐洲中心的高光譜紅外IASI數(shù)據(jù),國內(nèi)研究較少,僅對海上臺風(fēng)有少數(shù)同化模擬研究。本文利用三維變分同化方法,結(jié)合中尺度WRF模式,探究IASI數(shù)據(jù)同化對區(qū)域模式暴雨模擬的改進(jìn)效果。首先針對2014年6月一個月的預(yù)報結(jié)果,利用美國NMC方法統(tǒng)計研究區(qū)域的背景誤差協(xié)方差。利用統(tǒng)計得到的背景誤差協(xié)方差,針對2014年6月25～28的暴雨個例設(shè)計了兩組試驗。第一組試驗為確定IASI資料具體的同化波段,對比分別同化常規(guī)觀測數(shù)據(jù)、IASI溫度探測波段、IASI濕度探測波段以及IASI所有波段,比較各組IASI試驗同化結(jié)果。第二組試驗根據(jù)第一組得到的結(jié)論同化IASI溫度探測波段,并與同化AMSUA、MHS、 HIRS4、ATOVS試驗進(jìn)行對比,分析幾種資料對江淮暴雨預(yù)報效果改進(jìn)的影響。得到主要結(jié)論為：(1)針對模擬區(qū)域采用美國NMC方法統(tǒng)計背景誤差協(xié)方差,可得到非平衡溫度和假相對濕度是局地性很強(qiáng)的量,而流函數(shù)和非平衡速度勢受邊界層影響較大。(2)從第一部分試驗可知在代價函數(shù)和梯度圖上證明同化IASI溫度探測波段試驗更易達(dá)到收斂；對初始場改進(jìn)更合理；降水落區(qū)和降水強(qiáng)度的模擬與實況降水最為接近,說明針對本次個例而言,同化溫度探測波段比同化濕度探測波段更合理。因此針對本次個例主要選擇同化IASI溫度探測波段。(3)同化IASI溫度探測波段試驗與其他同化方案相比,進(jìn)入同化系統(tǒng)內(nèi)的數(shù)據(jù)點更多、對初始場改進(jìn)效果明顯、降水模擬和TS評分上結(jié)果也較好、誤差分析中某些高度均方根誤差小于其他方案。體現(xiàn)了IASI資料的應(yīng)用價值。(4)對比同化AMSUA、MHS、ATOVS三個試驗方案,發(fā)現(xiàn)在降水場模擬、誤差分析上會出現(xiàn)同化AMSU A和MHS方案結(jié)果較好,而同化ATOVS (AMSU A+MHS+HIRS4)方案效果較差。原因很可能是：同化多個數(shù)據(jù)可能帶來更大的觀測誤差,相互抵消正效應(yīng),反而導(dǎo)致結(jié)果變差。
[Abstract]:In recent years, heavy rain, hail and other natural disasters have brought inconvenience to people's life and great loss to social economy.Hyperspectral infrared sensor can provide high resolution atmospheric vertical temperature and humidity data, which has been proved to greatly improve the accuracy of the global model.For the regional model, there are still many problems to be solved in assimilation of hyperspectral infrared data.At present, there are many studies on assimilation of AIRS data into regional models in China, but there are few studies on hyperspectral infrared IASI data in the center of Europe, and only a few assimilation simulation studies on offshore typhoons.In this paper, using three-dimensional variational assimilation method and mesoscale WRF model, the improved effect of IASI data assimilation on regional model rainstorm simulation is explored.Based on the forecast results of June 2014, the background error covariance of the region is analyzed by using the NMC method in the United States.Based on the statistical background error covariance, two sets of experiments were designed for the rainstorm case on June 25, 2014.In order to determine the specific assimilation band of the IASI data, the first group of experiments compared the assimilation data of the conventional observation data, the temperature detection band and the humidity detection band and all the IASI bands, and compared the assimilation results of each group of IASI experiments.According to the conclusions of the first group, the second group of experiments assimilated the IASI temperature detection band, and compared with the assimilation IASI UAHS, HIRS4 / ATOVS test, and analyzed the influence of several kinds of data on the improvement of the forecast effect of Jianghuai rainstorm.The main conclusion is: 1) for the simulated region, the background error covariance is calculated by using the American NMC method, and the non-equilibrium temperature and the pseudo-relative humidity are found to be highly localized.However, the current function and the unbalanced velocity potential are greatly influenced by the boundary layer.) from the first part of the experiment, it is proved that the assimilation IASI temperature detection band experiment is more easy to converge, and the improvement of the initial field is more reasonable in the cost function and gradient diagram.The simulation of precipitation fall area and precipitation intensity is most close to the actual precipitation, which shows that the assimilation temperature detection band is more reasonable than the assimilation humidity detection band.Therefore, in this case, we mainly select assimilation IASI temperature detection band. 3) compared with other assimilation schemes, the assimilation IASI temperature detection band experiment has more data points entering the assimilation system, and the improvement effect on the initial field is obvious.The results of precipitation simulation and TS score are also good, and some RMS errors in error analysis are smaller than those in other schemes.The reason may be that assimilation of multiple data may result in greater observation errors and counteract positive effects, which may result in worse results.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：P457.6;P412.27
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本文編號：1709807