地基GPS的资料处理及在天气分析中的应用
发布时间：2018-09-19 08:38
【摘要】：水汽是大气的一种主要成分,也是一种温室气体,尽管在大气中的含量很少,但是其在大气中的变化却十分剧烈。其空间分布极不均匀,时间变化也极其迅速,是大气中变化最大的一种成分,并且其变化尺度比风速、气温要精细得多。气象学和天气预报的基本问题之一就是要较为准确地测量大气中水汽的分布及其变化情况。 运用GPS技术估算大气中的水汽含量是20世纪90年代兴起的一种极有潜力、实用价值很大的一种新方法或监测技术,由于其在获取大气水汽时具有高精度、高容量、高时空分辨率、全天候、近实时等诸多优点,因此受到了气象工作者的广泛重视。国家、部门和地方为此投入了大量的人力和物力,并已获取、积累了大量的GPS原始资料,但目前我国地基GPS水汽监测网建设还处于起步阶段,加之该探测技术涉及测量学与气象学知识的交叉融合,存在着很多需要解决及进一步研究的问题。 通过GPS延迟量反演大气可降水量具有处理环节多、技术较复杂等特点,它们制约了地基GPS资料的有效使用；在GPS反演水汽过程中对于加权平均温度的建模以及应用缺乏统一的标准；地基GPS数据处理过程中,存在GPS原始数据或地面气象数据中有一方数据缺失,就无法计算出GPS可降水量(GPS-PWV)的问题；利用GPS-PWV在天气气候分析中多局限于PWV自身变化的研究,对水汽变化的深层次缘由研究较少。针对以上这些问题,本文开展了深入的分析与研究,主要得到了以下一些结论： (1)通过自身的实践与应用,较全面地提出了解决目前气象业务部门运用地基GPS反演水汽技术中存在的两个关键性问题,即：GPS数据处理软件的使用问题以及业务化运行中的一体化处理问题； (2)以地基GPS反演水汽的整个流程为主线,定性地分析了其中存在的主要误差源,定量地计算出了允许误差的范围,进一步提升了GPS反演水汽的精度问 (3)利用数学方法和智能算法分别提出了两种可降水量缺测时的简便插补方案,通过主成分分析法解决了拟合因子的选择问题,通过敏感性试验得到了拟合可降水量神经网络模型的结构组成； (4)对于加权平均温度存在的拟合公式不统一,各区域有不同的Tm计算模型的问题,提出了适用于我国的通用Tm计算模型,并达到了气象应用的精度要求； (5)对江苏和重庆地区两次强降雨过程中GPS-PWV的变化特征进行了细致分析,并结合NCEP再分析资料、常规探空资料、卫星云图资料、地面气象资料以及wrf数值模拟结果等对过程中的动力、热力过程进行了详细剖析,揭示出了水汽变化的深层次缘由,深化了GPS-PWV作为预报指标的意义； (6)利用GPS-PWV资料首次对成都地区秋季可降水量的空间分布与循环周期进行了分析,研究得到了PWV与海拔高度间存在负相关关系,PWV季节内存在1/4季的变化特征,十月中旬是PWV由多到少的转折期,成都地区秋季降水过程主要集中在夜间,大气水汽总量的累积或释放与地面实际降水量有着较好的对应关系,可降水量的峰值出现的时间一般提前于降水强度极大值出现的时间等结论。
[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.
【学位授予单位】：南京信息工程大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：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.
【学位授予单位】：南京信息工程大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：P228.4;P412
【参考文献】
相关期刊论文 前10条
1 金慧华;白征东;过静s，
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