本文選題：全球?qū)Ш较到y(tǒng) + 水汽��； 參考：《武漢大學(xué)》2016年博士論文

【摘要】：水汽是大氣中含量最為豐富的一種溫室氣體,在維持地球適宜生命生存的溫度環(huán)境和水文循環(huán)中扮演著至關(guān)重要的角色。水汽在大氣中含量的分布與變化深刻地影響著全球各地的氣候環(huán)境和天氣特征。然而不同于大氣中的其他溫室氣體(如二氧化碳),水汽在大氣中的含量具有復(fù)雜的空間和時間變化,因此難以精確測定、建模及預(yù)報。中國位于世界上最大的大陸板塊(歐亞大陸板塊)和最大的海洋(太平洋)交接處,地域跨度大且地形變化復(fù)雜,氣候類型豐富,天氣變化多端,因此中國區(qū)域上空的水汽含量分布與變化具有其獨(dú)特性和復(fù)雜性。一些大尺度的氣候現(xiàn)象(如厄爾尼諾和東亞季風(fēng)等)對中國區(qū)域大氣狀況影響顯著,加之每年頻發(fā)的自然災(zāi)害天氣(如臺風(fēng)和暴雨等)的發(fā)生、發(fā)展和消亡均與水汽含量的變化息息相關(guān)。因此對中國區(qū)域上空水汽分布和變化的研究和監(jiān)測對進(jìn)一步地理解中國區(qū)域氣候變化特征、推動中國區(qū)域氣候研究及改進(jìn)天氣預(yù)報等方面有十分重要的科學(xué)價值和現(xiàn)實(shí)意義。地基GNSS水汽反演技術(shù)是一種利用GNSS衛(wèi)星信號在大氣中的傳播延遲來反演大氣中水汽含量的技術(shù),相比于其他水汽觀測手段,地基GNSS具有高精度、全天候、低成本、高時間分辨率且長時間測量結(jié)果一致性好的優(yōu)勢。本文以中國陸態(tài)網(wǎng)GNSS基準(zhǔn)站觀測數(shù)據(jù)為主,綜合利用地面氣象站記錄、氣象再分析資料和radiosonde資料等,從中國區(qū)域上空水汽分布特征、各種時間尺度周期信號、水汽變化主導(dǎo)因素、水汽變化與其他氣候天氣現(xiàn)象關(guān)聯(lián)、對radiosonde資料和氣象再分析資料水汽誤差評定、對流層相關(guān)模型建模以及地基GNSS在極端災(zāi)害天氣研究和預(yù)報中的應(yīng)用等方面進(jìn)行細(xì)致深入的研究。主要的研究成果和內(nèi)容有：(1)采用武漢大學(xué)開發(fā)的PANDA軟件PPP模塊處理了中國陸態(tài)網(wǎng)1999～2015年的GNSS觀測數(shù)據(jù),獲得了中國區(qū)域各測站上空該時段的ZTD時間序列,平均精度約為3.9mm(與IGS事后ZTD產(chǎn)品精度相當(dāng))。由于陸態(tài)網(wǎng)測站氣象觀測不完整,論文綜合利用了GNSS測站處的氣象觀測、.GNSS測站周邊氣象站氣壓記錄和氣象再分析資料獲取各GNSS測站處的氣壓,并基于氣象再分析資料獲取各測站上空的水汽加權(quán)平均溫度Tm,兩者誤差RMS分別為0.7hPa和1.8K。將ZTD轉(zhuǎn)換為PW,并根據(jù)PW誤差估計(jì)模型估計(jì)得到PW產(chǎn)品的平均誤差約為0.98mm。(2)基于所獲PW產(chǎn)品,研究了中國區(qū)域1999～2015年間水汽的地理分布和變化：水汽的平均含量與地表氣溫分布有很強(qiáng)的正相關(guān),在華南地區(qū)含量最為豐富(PW約為50mm),而在青藏高原地區(qū)最低(PW小于20mm)；周年變化振幅和半周年變化振幅峰值分別在長江中下游區(qū)域和華北平原,而最小值分別在青藏高原和西南地區(qū)；天變化方面,水汽在一天當(dāng)中的峰值時刻相對較為固定,而最低值時刻隨四季有所變化。(3)基于所獲PW產(chǎn)品,通過分析實(shí)測PW與僅考慮溫度變化的模型PW之間的相關(guān)性,研究了不同氣候類型區(qū)域水汽變化的主導(dǎo)因素(熱力學(xué)或動力學(xué)因素)。結(jié)果表明南海南沙附近水汽變化幾乎僅由動力學(xué)因素主導(dǎo),而亞熱帶和溫帶季風(fēng)氣候區(qū)域主要由熱力學(xué)因素所主導(dǎo)；而其他區(qū)域兩種因素的作用相當(dāng)。通過分析中國沿海地區(qū)測站水汽長期變化信號與SOI指數(shù)之間的相關(guān)性,研究了水汽變化與ENSO現(xiàn)象的關(guān)聯(lián),結(jié)果表明了ENSO現(xiàn)象主要對中國熱帶地區(qū)上空水汽含量的長期變化有較直接的影響。(4)基于所獲PW產(chǎn)品,對中國區(qū)域1999～2015年期間的radiosonde水汽觀測資料按類型(GZZ2、GTS1、GTS1-1和GTS1-2)進(jìn)行了誤差評估。結(jié)果顯示早期廣泛使用的GZZ2類型測量的水汽普遍偏濕,而后期更換的GTS1和GTS1-1普遍偏干,GTS1-2未表現(xiàn)出明顯干濕偏差。因此,同一radiosonde測站由于儀器的更換會給基于radiosonde歷史觀測資料獲得的水汽長期變化趨勢估值引入虛假的下降信號。氣象再分析資料(ERA-Interim)由于同化了未經(jīng)修復(fù)的radiosonde資料,其水汽長期變化趨勢估值同樣受到了不一致問題的影響。(5)利用ERA-Interim產(chǎn)品,充分考慮了模型復(fù)雜性和模型精度,建立了CPT模型,相比于目前國際上通用的GPT和GPT2(5°和1°)模型,在中國區(qū)域先驗(yàn)氣壓誤差RMS由5.92、5.14和5.04hPa減小為3.76hPa,先驗(yàn)溫度誤差RMS由5.95、4.25和4.14K減小為4.07K。而相比于ITG模型,CPT模型參數(shù)個數(shù)減少了一半。建立了中國區(qū)域先驗(yàn)Tm模型和Tm-Ts轉(zhuǎn)換模型,相比于Bevis轉(zhuǎn)換模型和GPT2先驗(yàn)?zāi)Ｐ?Tm誤差RMS從4.45和4.19K減小為3.81和2.97K。(6)構(gòu)建了中國區(qū)域?qū)崟rZTD格網(wǎng)產(chǎn)品,能夠?yàn)橹袊鴧^(qū)域任意測站提供平均精度約為1.5cm的實(shí)時ZTD先驗(yàn)改正,可顯著加快實(shí)時PPP用戶的收斂速度：對于BDS實(shí)時PPP用戶,三維方向收斂時間可縮短20%,高程方向更加明顯(可縮短約50%)。(7)針對2016年6月23日江蘇阜寧超強(qiáng)龍卷風(fēng)事件,使用地基GNSS觀測資料分析了此次事件發(fā)生前后測站上空ZTD的時空變化特征。利用WRF3DVAR數(shù)據(jù)同化系統(tǒng),討論了地基GNSS觀測在改善此次事件發(fā)生的天氣背景相關(guān)變量(累積降雨量)短期預(yù)報可靠性的作用。
[Abstract]:Water vapor is the most abundant greenhouse gas in the atmosphere, which plays a vital role in maintaining the temperature and hydrological cycle of the earth's suitable life. The distribution and change of water vapor in the atmosphere profoundly affect the climate and weather characteristics around the world. However, it is different from other greenhouse gases. Gas (such as carbon dioxide), the content of water vapor in the atmosphere has complex spatial and temporal variations, so it is difficult to accurately determine, model and predict. China is located at the junction of the largest continental plate (Eurasian plate) and the largest ocean (Pacific) in the world, with large spans and complex topographic changes, rich climate types and weather changes. The distribution and variation of water vapor content over China's region has its unique characteristics and complexity. Some large scale climate phenomena, such as the El Nino and East Asian monsoon, have a significant impact on the regional atmospheric conditions in China, and the occurrence of frequent natural disasters (such as typhoon and rainstorm), development and extinction are all with water vapor. Therefore, it is of great scientific and practical significance to study and monitor the distribution and change of water vapor over the region of China. It is of great scientific and practical significance to further understand the characteristics of regional climate change in China, promote the study of regional climate and improve the weather forecast in China. The technology of GNSS water vapor inversion is a kind of use of GNSS Wei. In comparison with other methods of water vapor observation, the foundation GNSS has the advantages of high precision, all-weather, low cost, high time resolution and good consistency of the long time measurement results. This paper uses the observation data of the Chinese terrestrial network GNSS datum station as the main method, and makes comprehensive use of the ground gas. Image station records, meteorological reanalysis data and radiosonde data, from the characteristics of water vapor distribution over the region of China, a variety of time scale periodic signals, water vapor change leading factors, water vapor changes associated with other climate weather phenomena, water vapor error assessment for radiosonde data and meteorological reanalysis data, modeling of tropospheric correlation models and ground The main research results and contents are as follows: (1) the PANDA software PPP module developed by Wuhan University has been used to deal with the GNSS observation data of China land network for 1999~2015 years, and the ZTD time series over the period of each station in the central region is obtained. The average accuracy is about 3.9mm (equivalent to the IGS ZTD product precision). Because the meteorological observation of the land network station is incomplete, the paper comprehensively uses the meteorological observation at the GNSS station, and obtains the gas pressure at the GNSS stations at the meteorological stations around the.GNSS station, and obtains the above stations on the basis of the meteorological reanalysis data. The weighted average temperature of water vapor is Tm, and the error RMS is 0.7hPa and 1.8K. to convert ZTD to PW respectively. According to the PW error estimation model, the average error of PW product is estimated to be 0.98mm. (2) based on the PW products obtained, and the geographical distribution and change of water vapor in China region for 1999~2015 years are studied: the average content of water vapor and the surface temperature are divided. It has a strong positive correlation. In Southern China, the most abundant (PW is 50mm), and the lowest (PW less than 20mm) in the Qinghai Tibet Plateau; the amplitude and peak amplitude peak of the annual variation amplitude and the half year anniversary are respectively in the middle and lower reaches of the Yangtze River and the North China Plain respectively, and the minimum values are in the Qinghai Xizang Plateau and the southwest region respectively. The peak time is relatively fixed and the minimum time varies with the four seasons. (3) based on the correlation between the measured PW and the model PW only considering the temperature change, the dominant factors (thermodynamic or kinetic factors) in different climate types are studied based on the obtained PW products. The results show that the water near Nansha in the South China Sea is water. The variation of steam is dominated by the dynamic factors, while the subtropical and temperate monsoon climate regions are dominated by the thermodynamic factors, while the two factors in other regions are the same. By analyzing the correlation between the long change signals of water vapor and the SOI index in the coastal areas of China, the correlation between the water vapor change and the ENSO phenomenon is studied. The results show that the ENSO phenomenon has a direct impact on the long-term changes in the water vapor content over the tropical regions of China. (4) based on the PW products obtained, the radiosonde water vapor observation data for the period of 1999~2015 years in China were evaluated by type (GZZ2, GTS1, GTS1-1 and GTS1-2). The results showed the early widely used GZZ2 type measurement. The amount of water vapor is generally wet, and the later replacement of GTS1 and GTS1-1 is generally dry, and the GTS1-2 does not show obvious dry and wet deviations. Therefore, the same radiosonde station will introduce a false descent signal to the estimation of the long term variation trend of water vapor based on the historical observation data based on radiosonde. The meteorological reanalysis data (ERA-Interim) Because of the assimilation of unrepaired radiosonde data, the estimation of the long-term variation trend of water vapor is also affected by the inconsistency. (5) using the ERA-Interim products, the model complexity and model accuracy are fully considered, and the CPT model is established. Compared to the current international GPT and GPT2 (5 and 1 degrees) model, the regional transcendental gas is in China. The pressure error RMS is reduced from 5.92,5.14 and 5.04hPa to 3.76hPa, and the prior temperature error RMS is reduced from 5.95,4.25 and 4.14K to 4.07K., and the number of CPT model parameters is reduced by half. The regional priori Tm model and the Tm-Ts conversion model in China are established. Compared with the transition model and the prior model, the error decreases from 4.45 and decreases. Small for 3.81 and 2.97K. (6), a Chinese regional real-time ZTD grid product is built, which can provide a real time ZTD prior correction of an average precision of about 1.5cm for any regional station in China. It can significantly speed up the convergence rate of real-time PPP users. For BDS real-time PPP users, the convergence time of three-dimensional direction can be shortened by 20% and the direction of elevation is more obvious (can be shortened approximately. 50%) (7) according to the super Tornado Event in Funing, Jiangsu, Jiangsu in June 23, 2016, the temporal and spatial variation characteristics of ZTD over the station before and after the event were analyzed by using the ground-based observation data. The WRF3DVAR data assimilation system was used to discuss the short-term weather background related variables (accumulated rainfall) in the improvement of this event. The function of predicting reliability.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：P228.4;P412

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