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类型 基础研究 预答辩日期 2018-05-19
开始(开题)日期 2015-06-03 论文结束日期 2017-12-23
地点 交通学院大楼3楼会议室 论文选题来源 国家自然科学基金项目     论文字数 6.5 (万字)
题目 GNSS对流层湿延迟及其加权平均温度研究
主题词 GNSS, 水汽,对流层天顶湿延迟,加权平均温度,神经网络
摘要 全球导航卫星系统(Global Navigation Satellite System, GNSS)是一种能够为在地球表面或近地空间的任何地点的用户提供全天候的三维坐标、时间和速度信息的空基无线电导航定位系统。由于大气折射的作用,当GNSS的信号在大气层传播时候会产生对流层延迟误差,该误差是GNSS定位中一项重要的误差源。而水汽是对流层的一种主要成分,其含量在对流层中很少,但是随着时间变化很快,空间分布极不均匀,其变化尺度比气温、风速要精细得多,在大气运动中扮演着重要角色。水汽对全球卫星导航系统(GNSS)的信号传播产生对流层天顶湿延迟(ZWD)误差;另一方面GNSS估算出来的ZWD误差可以转换成可降水量(PWV),而在ZWD-PWV的转换过程需要一个中间参数即,加权平均温度(Tm)。ZWD以及Tm是与水汽紧密联系的两个参数。由于水汽多变性和随机性的特征,相对于区域性ZWD以及Tm而言,全球范围的ZWD和Tm研究和建模在GNSS研究中是具有挑战性的课题。本文以全球范围内的ZWD和Tm为研究对象,主要研究内容和结论如下: (1)基于前人研究的基础上,以测站的气象参数、坐标参数和时间参数为输入变量构造一种ZWD模型(NN-ZWD模型),讨论其在全球精度。以全球范围的130个站作为建模站点的VMF1对流层产品作为建模数据并以163个站的探空数据的作为测试数据,结果显示NN-ZWD模型和传统的Saastamoinen模型或者Hopfield模型精度相当。 (2)优化了基于神经网络的ZWD模型。为了提高NN-ZWD模型精度,笔者考虑了水汽垂直变化特征并引入水汽梯度因子(λ)作为建模变量,将GPT2w模型提供的水汽梯度因子(λGPT2w)作为近似值。并在基础上提出优化基于神经网络的ZWD模型(NN-ZWD-R模型),讨论了NN-ZWD-R模型全球范围的精度。结果表明NN-ZWD-R模型要比NN-ZWD模型提高约16.9 %左右。此外,NN-ZWD-R模型并具有较好的适用性:在不同纬度、不同高度和不同季节具有可以保持较好精度。 (3)研究了Tm与测站气象参数的关系。Tm与测站气象参数存一定的相关性:Tm与测站的温度(TS)是极强相关的;Tm与测站的水汽压力(eS)是强相关的,而与测站的大气压力(PS)几乎是不相关的。Tm-TS和Tm-eS的相关性特征表现出一定的地理分布规律:它们的相关性大致随着纬度的增加相关性增强。 (4)研究了基于Tm时间序列在时域的变化特征。首先,利用Lomb-Scargle周期图法研究对Tm进行频谱分析研究周期性特征。通过全球范围内不同气候区域的10个样本站发现Tm存在年周期特征,可能会存在半年周期特征,其他周期特征并不明显。再次,时间序列的周期项、趋势项和随机项构造了Tm时间序列模型。在热带地区,Tm的季节变化往往是不规则的;每年的变化或季节变化既可以是Tm的主要周期特征。在非热带地区,Tm的季节变化表现出一定的规则的性:其季节变化的主要是年变化特征影响;在南半球年初始相位大约一月而北半球的年初始相位一般在七月;Tm时间序列模型的最大值和最小值通常分别在夏季和冬季。从全球来看,Tm在长期内有大约0.22 K/decade增长的趋势,Tm趋势是在的所有测试地点在北半球高纬度地区是最明显的。Tm季节模型的高模型误差在中纬度和高纬度地区,而Tm时间序列模型误差最大值在极地地区。 (5)提出了一种基于神经网络的多参数的Tm模型。根据本文第(3)部分和第(4)部分研究内容,融合了Tm与测站的气象参数关系和Tm时间序列的特征,并首次将神经网络模型作为建模工具,设计了2种全球范围内适用多参数Tm模型,即NN-1模型和NN-2模型。NN-1模型是适合于用户有能力获取实测的TS和eS的情况,NN-2是适合于用户仅可以获取实测TS的情况。在全球范围内,NN-1模型和NN-2模型精度相当,其精度均高于GPT2w、BTm、GTm、GTm-I和PTm-I等5种传统的模型。在同等的使用条件下,NN-1模型的精度比PTm-I模型的精度提高约为11.1 %;而NN-2模型的精度比GTm-I模型的精度提高约为17.9 %。并且NN-1模型和NN-2模型具有良好适用性:在长期时间内,不同的季节,不同的高度和不同的纬度这两个模型均能保持较高的精度。
英文题目 Study on tropospheric wet delay and weighted mean temperature in GNSS
英文主题词 GNSS,water vapor,zenith wet delay,weighted mean temperature,neural network
英文摘要 Global Navigation Satellite System (GNSS) is a space-based radio Navigation and Positioning System, which can provide any location, time and speed information on the earth’s surface or near-earth space users with all-weather three-dimensional coordinates. Due to the atmospheric refraction, the tropospheric delay error is generated and is an important error source in GNSS positioning when GNSS signals are propagated in the atmosphere. Water vapor is a major component of the troposphere, but it changes quickly as the time. Water vapor has a uneven spatial distribution and its variation scale is larger than the temperature and the wind. Water vapor plays an important role in the atmospheric movement. The signal transmission of the global satellite navigation system (GNSS) is caused by the water vapor transmission. On the other hand, the estimated ZWD error from GNSS can be converted to precipitable water vapor (PWV), while the conversion process of ZWD-PWV requires a parameter, i.e. the weighted average temperature (Tm). ZWD and Tm are two parameters that are closely related to water vapor. Because of the characteristics of water vapor variability and randomness, studying the global ZWD and Tm are challenging in GNSS research compared to the regional ZWD and Tm. In this paper, ZWD and Tm on the global scale are studied. The main research contents and conclusions are as follows: (1)Based on some previous research, a ZWD model (NN-ZWD model) is constructed by using the meteorological parameters, coordinate parameters and time parameters at the station. Site with 130 sites around the globe as the modeling of VMF1 troposphere products as modeling data and we use the sounding data of 163 sites as the test data. But the results show that the NN-ZWD are not better than the traditional Saastamoinen model or the Hopfield model. (2)The ZWD model based on neural network was refined. In order to improve the NN-ZWD model, the author takes into account the water vapor vertical variation characteristics and the introduction of moisture gradient factor (λ) as model variables, will provide the moisture gradient factor GPT2w model (λ-GPT2w) as approximation values. On the basis of this, the ZWD model (NN-ZWD-R model) based on neural network is proposed to discuss the accuracy of nn-zwd-r model worldwide. The results show that nn-zwd-r model is about 16.9% higher than NN-ZWD model. In addition, NN-ZWD-R model has a good applicability: it can maintain good accuracy at different latitudes, different heights and different seasons. (3)The relationship between Tm and the site meteorological parameters was also studied. Tm is related to the site meteorological parameters Tm is strongly related to the temperature (TS) of the station. Tm is strongly related to the water vapor pressure (eS) of the station, and the atmospheric pressure (PS) of the station is almost irrelevant. The correlation characteristics of Tm-TS and Tm-eS show a certain geographical distribution pattern: their correlation increases roughly with the increase of latitude. (4)The variation characteristics of Tm time series in time domain were studied. Firstly, the periodic characteristics of spectrum analysis of Tm were studied by using the lomb-scargle periodogram. The annual cycle characteristics of Tm are found in 10 sample sites in different climatic regions around the world, which may be characterized by the semi-annual cycle, and the other cycle characteristics are not obvious. Again, the periodic terms, trend items and random items were used construct the Tm time series model. In tropical regions, the seasonal variation of Tm is often irregular; annul seasonal variation can be a major cyclical feature of Tm. In non-tropical regions, the seasonal variation of Tm shows certain rules: the change of seasons is mainly influenced by the characteristics of the year. In the southern hemisphere, the initial phase of the phase is about January and the annual phase of the northern hemisphere is generally in July. The maximum and minimum values of the Tm time series model are usually in summer and winter respectively. Globally, Tm has a long-term trend of around 0.22 K/decade growth, and the Tm trends are the most visible in high latitudes in the northern hemisphere. The high model error of Tm season model are found in mid-latitude and high latitudes, while the maximum errors of Tm time series model are in the polar region. (5)Two multi-parameter Tm model based on the neural network were proposed. According to the research content (3) and (4), the author combined of the relationship between Tm and meteorological parameters of the station and Tm time series characteristics, and then the neural network model for the first time as a modeling tool, design for more than two globally applicable parameter Tm model. Thus, Two feedforward neural network (FFNN) models (the NN-1 and the NN-2) were established by a combination of the Tm seasonal variations and its relationship with surface meteorological elements. The NN-1 is used with measurements of both surface temperature TS and surface vapor pressure eS, while the NN-2 is only used with measurements of only TS. Globally, the NN-1 model and NN-2 model have the same accuracy, and their accuracies are higher than that of 5 traditional models, such as GPT2w, BTm, GTm, GTm-I and PTm-I. Under the same conditions, the accuracy of the NN-1 model is about 11.1 % higher than that of the PTm-I model. The accuracy of NN-2 model is about 17.9 % higher than that of GTm-I model. The NN-1 model and the NN-2 model have good applicability: in the long term, different seasons, different heights and different latitudes can maintain high precision.
学术讨论
主办单位时间地点报告人报告主题
东南大学交通学院 2014年7月1日 交通学院四楼会议室 丁茂华 深度学习算法
同济大学 2014年7月28日至8月1日 上海同济大学 杨元喜 院士 Academician in Science
东南大学交通学院 2015年7月19日 交通学院四楼会议室 丁茂华 基于神经网络的ZWD模型的优化
东南大学交通学院 2016年06月30日 交通学院四楼会议室 丁茂华 Tm时空变化特征研究
东南大学交通学院 2016年12月20日 交通学院四楼会议室 丁茂华 基于神经网络的多参数Tm模型
东南大学研工部 2017年5月21日 东南大学礼东二楼报告厅 房建成 院士 量子传感技术的发展与展望
东南大学交通学院 2017年4月18日 交通学院四楼会议室 金双根 研究员 GNSS遥感—最近进展及将来机会
东南大学仪器科学与工程学院 2017年7月11日 东南大学仪器科学与工程学院 孟晓林 教授 Multi-sensor integration with high definition map for autonomous driving
     
学术会议
会议名称时间地点本人报告本人报告题目
第九届移动测量技术国际学术交流大会(MMT2015) 2015年11月8日-11月12日 澳大利亚悉尼 新南威尔士大学 Study on comparison of zenith tropospheric delay correction models
2016年全国博士生学术论坛(测绘科学与技术) 2016年11月5日-11月6日 中国徐州 中国矿业大学 增强GPT2w模型精度分析
     
代表作
论文名称
A further contribution to the seasonal variation of weighted mean temperature
一种优化的基于神经网络的经验ZTD模型
基于GGOS Atmosphere数据的ZHD模型精度分析
A neural network model for predicting weighted mean temperature
 
答辩委员会组成信息
姓名职称导师类别工作单位是否主席备注
李明峰 正高 教授 博导 南京工业大学
史玉峰 正高 教授 博导 南京林业大学
高成发 正高 教授 博导 东南大学
于先文 副高 副教授 博导 东南大学
翁永玲 正高 教授 博导 东南大学
      
答辩秘书信息
姓名职称工作单位备注
王军 其他 讲师 东南大学