地基GPS的資料處理及在天氣分析中的應(yīng)用
發(fā)布時(shí)間：2018-09-19 08:38
【摘要】：水汽是大氣的一種主要成分,也是一種溫室氣體,盡管在大氣中的含量很少,但是其在大氣中的變化卻十分劇烈。其空間分布極不均勻,時(shí)間變化也極其迅速,是大氣中變化最大的一種成分,并且其變化尺度比風(fēng)速、氣溫要精細(xì)得多。氣象學(xué)和天氣預(yù)報(bào)的基本問(wèn)題之一就是要較為準(zhǔn)確地測(cè)量大氣中水汽的分布及其變化情況。 運(yùn)用GPS技術(shù)估算大氣中的水汽含量是20世紀(jì)90年代興起的一種極有潛力、實(shí)用價(jià)值很大的一種新方法或監(jiān)測(cè)技術(shù),由于其在獲取大氣水汽時(shí)具有高精度、高容量、高時(shí)空分辨率、全天候、近實(shí)時(shí)等諸多優(yōu)點(diǎn),因此受到了氣象工作者的廣泛重視。國(guó)家、部門(mén)和地方為此投入了大量的人力和物力,并已獲取、積累了大量的GPS原始資料,但目前我國(guó)地基GPS水汽監(jiān)測(cè)網(wǎng)建設(shè)還處于起步階段,加之該探測(cè)技術(shù)涉及測(cè)量學(xué)與氣象學(xué)知識(shí)的交叉融合,存在著很多需要解決及進(jìn)一步研究的問(wèn)題。 通過(guò)GPS延遲量反演大氣可降水量具有處理環(huán)節(jié)多、技術(shù)較復(fù)雜等特點(diǎn),它們制約了地基GPS資料的有效使用；在GPS反演水汽過(guò)程中對(duì)于加權(quán)平均溫度的建模以及應(yīng)用缺乏統(tǒng)一的標(biāo)準(zhǔn)；地基GPS數(shù)據(jù)處理過(guò)程中,存在GPS原始數(shù)據(jù)或地面氣象數(shù)據(jù)中有一方數(shù)據(jù)缺失,就無(wú)法計(jì)算出GPS可降水量(GPS-PWV)的問(wèn)題；利用GPS-PWV在天氣氣候分析中多局限于PWV自身變化的研究,對(duì)水汽變化的深層次緣由研究較少。針對(duì)以上這些問(wèn)題,本文開(kāi)展了深入的分析與研究,主要得到了以下一些結(jié)論： (1)通過(guò)自身的實(shí)踐與應(yīng)用,較全面地提出了解決目前氣象業(yè)務(wù)部門(mén)運(yùn)用地基GPS反演水汽技術(shù)中存在的兩個(gè)關(guān)鍵性問(wèn)題,即：GPS數(shù)據(jù)處理軟件的使用問(wèn)題以及業(yè)務(wù)化運(yùn)行中的一體化處理問(wèn)題； (2)以地基GPS反演水汽的整個(gè)流程為主線(xiàn),定性地分析了其中存在的主要誤差源,定量地計(jì)算出了允許誤差的范圍,進(jìn)一步提升了GPS反演水汽的精度問(wèn) (3)利用數(shù)學(xué)方法和智能算法分別提出了兩種可降水量缺測(cè)時(shí)的簡(jiǎn)便插補(bǔ)方案,通過(guò)主成分分析法解決了擬合因子的選擇問(wèn)題,通過(guò)敏感性試驗(yàn)得到了擬合可降水量神經(jīng)網(wǎng)絡(luò)模型的結(jié)構(gòu)組成； (4)對(duì)于加權(quán)平均溫度存在的擬合公式不統(tǒng)一,各區(qū)域有不同的Tm計(jì)算模型的問(wèn)題,提出了適用于我國(guó)的通用Tm計(jì)算模型,并達(dá)到了氣象應(yīng)用的精度要求； (5)對(duì)江蘇和重慶地區(qū)兩次強(qiáng)降雨過(guò)程中GPS-PWV的變化特征進(jìn)行了細(xì)致分析,并結(jié)合NCEP再分析資料、常規(guī)探空資料、衛(wèi)星云圖資料、地面氣象資料以及wrf數(shù)值模擬結(jié)果等對(duì)過(guò)程中的動(dòng)力、熱力過(guò)程進(jìn)行了詳細(xì)剖析,揭示出了水汽變化的深層次緣由,深化了GPS-PWV作為預(yù)報(bào)指標(biāo)的意義； (6)利用GPS-PWV資料首次對(duì)成都地區(qū)秋季可降水量的空間分布與循環(huán)周期進(jìn)行了分析,研究得到了PWV與海拔高度間存在負(fù)相關(guān)關(guān)系,PWV季節(jié)內(nèi)存在1/4季的變化特征,十月中旬是PWV由多到少的轉(zhuǎn)折期,成都地區(qū)秋季降水過(guò)程主要集中在夜間,大氣水汽總量的累積或釋放與地面實(shí)際降水量有著較好的對(duì)應(yīng)關(guān)系,可降水量的峰值出現(xiàn)的時(shí)間一般提前于降水強(qiáng)度極大值出現(xiàn)的時(shí)間等結(jié)論。
[Abstract]:Water vapor is a major component of the atmosphere and a greenhouse gas. Although its content in the atmosphere is very small, its change in the atmosphere is very intense. Its spatial distribution is extremely uneven, and the time change is extremely rapid. Water vapor is the most variable component in the atmosphere, and its change scale is much more fine than wind speed and temperature. One of the basic problems in science and weather forecasting is to measure the distribution and variation of water vapor in the atmosphere more accurately.
Estimation of atmospheric water vapor content using GPS technology is a new method or monitoring technology with great potential and practical value. Because of its high accuracy, high capacity, high spatial and temporal resolution, all-weather, near real-time and many other advantages, it has been widely used by meteorologists. The state, departments and localities have invested a great deal of manpower and material resources in this regard, and have acquired and accumulated a large number of GPS raw data. However, the construction of the ground-based GPS water vapor monitoring network in China is still in its infancy. In addition, the detection technology involves the cross-integration of Surveying and meteorological knowledge, there are many problems to be solved and further studied. Problem.
The retrieving of Atmospheric Precipitable Water by GPS delay is characterized by many processing steps and complicated techniques, which restrict the effective use of ground-based GPS data; there is no uniform standard for the modeling and application of weighted average temperature in the retrieving of water vapor by GPS; there is GPS raw data or ground air in the processing of ground-based GPS data The problem of GPS-PWV can not be calculated because one part of the image data is missing. The study of PWV is mostly confined to the study of its own changes in weather and climate analysis by using GPS-PWV, and the deep-seated causes of water vapor change are seldom studied. Some conclusions:
(1) Through its own practice and application, two key problems in the application of ground-based GPS to retrieve water vapor in meteorological departments are put forward comprehensively, namely, the usage of GPS data processing software and the integration of GPS data processing in operational operation.
(2) Taking the whole process of ground-based GPS inversion of water vapor as the main line, the main error sources are analyzed qualitatively, and the allowable error range is calculated quantitatively, which further improves the accuracy of GPS inversion of water vapor.
(3) Two simple interpolation schemes for missing precipitable water are proposed by using mathematical method and intelligent algorithm. The problem of selecting fitting factors is solved by principal component analysis. The structure of neural network model for fitting precipitable water is obtained by sensitivity test.
(4) For the problem that the fitting formulas of weighted mean temperature are not uniform and there are different Tm calculation models in different regions, a general Tm calculation model suitable for our country is put forward, which meets the accuracy requirement of meteorological application.
(5) The variation characteristics of GPS-PWV during the two heavy rainfall processes in Jiangsu and Chongqing are analyzed in detail. Combined with NCEP reanalysis data, conventional sounding data, satellite cloud map data, ground meteorological data and WRF numerical simulation results, the dynamic and thermal processes in the process are analyzed in detail, revealing the deep layer of water vapor variation. Second, it deepens the significance of GPS-PWV as a prediction index.
(6) Using GPS-PWV data, the spatial distribution and cycle period of autumn precipitable water in Chengdu area were analyzed for the first time. The negative correlation between PWV and altitude was found. The PWV seasonal variation characteristics were found in 1/4 of the season. The mid-October was the turning period of PWV from more to less. The autumn precipitation process in Chengdu area was mainly concentrated at night. The accumulation or release of the total atmospheric water vapor has a good corresponding relationship with the actual precipitation on the ground, and the peak time of the precipitable water is generally earlier than the time of the maximum precipitation intensity.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：P228.4;P412
[Abstract]:Water vapor is a major component of the atmosphere and a greenhouse gas. Although its content in the atmosphere is very small, its change in the atmosphere is very intense. Its spatial distribution is extremely uneven, and the time change is extremely rapid. Water vapor is the most variable component in the atmosphere, and its change scale is much more fine than wind speed and temperature. One of the basic problems in science and weather forecasting is to measure the distribution and variation of water vapor in the atmosphere more accurately.
Estimation of atmospheric water vapor content using GPS technology is a new method or monitoring technology with great potential and practical value. Because of its high accuracy, high capacity, high spatial and temporal resolution, all-weather, near real-time and many other advantages, it has been widely used by meteorologists. The state, departments and localities have invested a great deal of manpower and material resources in this regard, and have acquired and accumulated a large number of GPS raw data. However, the construction of the ground-based GPS water vapor monitoring network in China is still in its infancy. In addition, the detection technology involves the cross-integration of Surveying and meteorological knowledge, there are many problems to be solved and further studied. Problem.
The retrieving of Atmospheric Precipitable Water by GPS delay is characterized by many processing steps and complicated techniques, which restrict the effective use of ground-based GPS data; there is no uniform standard for the modeling and application of weighted average temperature in the retrieving of water vapor by GPS; there is GPS raw data or ground air in the processing of ground-based GPS data The problem of GPS-PWV can not be calculated because one part of the image data is missing. The study of PWV is mostly confined to the study of its own changes in weather and climate analysis by using GPS-PWV, and the deep-seated causes of water vapor change are seldom studied. Some conclusions:
(1) Through its own practice and application, two key problems in the application of ground-based GPS to retrieve water vapor in meteorological departments are put forward comprehensively, namely, the usage of GPS data processing software and the integration of GPS data processing in operational operation.
(2) Taking the whole process of ground-based GPS inversion of water vapor as the main line, the main error sources are analyzed qualitatively, and the allowable error range is calculated quantitatively, which further improves the accuracy of GPS inversion of water vapor.
(3) Two simple interpolation schemes for missing precipitable water are proposed by using mathematical method and intelligent algorithm. The problem of selecting fitting factors is solved by principal component analysis. The structure of neural network model for fitting precipitable water is obtained by sensitivity test.
(4) For the problem that the fitting formulas of weighted mean temperature are not uniform and there are different Tm calculation models in different regions, a general Tm calculation model suitable for our country is put forward, which meets the accuracy requirement of meteorological application.
(5) The variation characteristics of GPS-PWV during the two heavy rainfall processes in Jiangsu and Chongqing are analyzed in detail. Combined with NCEP reanalysis data, conventional sounding data, satellite cloud map data, ground meteorological data and WRF numerical simulation results, the dynamic and thermal processes in the process are analyzed in detail, revealing the deep layer of water vapor variation. Second, it deepens the significance of GPS-PWV as a prediction index.
(6) Using GPS-PWV data, the spatial distribution and cycle period of autumn precipitable water in Chengdu area were analyzed for the first time. The negative correlation between PWV and altitude was found. The PWV seasonal variation characteristics were found in 1/4 of the season. The mid-October was the turning period of PWV from more to less. The autumn precipitation process in Chengdu area was mainly concentrated at night. The accumulation or release of the total atmospheric water vapor has a good corresponding relationship with the actual precipitation on the ground, and the peak time of the precipitable water is generally earlier than the time of the maximum precipitation intensity.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：P228.4;P412
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 金慧華;白征東;過(guò)靜s，
本文編號(hào)：2249615
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