万蓉,郑国光,于胜杰,付志康. 2013. 基于观测约束的地基犌犘犁三维水汽层析技术研究[J]. 气象学报, 71(2):318-331, doi:10.11676/qxxb2013.026
基于观测约束的地基犌犘犁三维水汽层析技术研究
A study of the ground-based GPS 3D water vapor tomography with radiosonde vertical constraining
投稿时间:2012-07-11  修订日期:2012-12-14
DOI:10.11676/qxxb2013.026
中文关键词:  全球定位系统  三维水汽层析  观测约束  水汽密度
英文关键词:GPS  3D Water vapor tomography  Obscrvations constraining  Water vapor density
基金项目:自然科学基金项目(40575011);863研究专项(2012AA120902);社会公益研究专项(2004DIB3J117)和中国气象局武汉暴雨研究所2007、2008年修购专项。
作者单位
万蓉 南京信息工程大学, 南京, 210044
中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室, 武汉, 430074 
郑国光 南京信息工程大学, 南京, 210044
中国气象局, 北京, 100081 
于胜杰 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室, 武汉, 430074 
付志康 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室, 武汉, 430074 
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中文摘要:
      全球定位系统(GPS)卫星信号穿过大气层时发生的偏折和延迟, 可以用来反演信号传播路径上的大气水汽总量。为 获取区域高精度的大气水汽三维分布, 借助分布密集的地基GPS观测网及其斜路径水汽观测, 建立新的观测约束层析模型, 提出以高斯函数为水平约束, 区域逐月多年探空观测为垂直约束, 即以平均量为先验值, 以标准偏差为权重矩阵的新方法;并 在层析算法中引入地面观测, 以提高整体尤其是低层反演精度。三维水汽层析网格模型基于长江中游鄂东区域的22站地基 GPS加密网搭建, 实时解算系统可逐时输出三维水汽产品。三维湿折射度和水汽密度可以分别由斜路径的湿延迟总量和水 汽总量观测反演获得。以2010年开展的汛期联合加密探空观测为参照, 对三维层析的总体反演精度、低层反演精度、层析区 域中心与边缘反演精度进行了对比和分析。结果显示:整体样本检验三维水汽密度平均偏差为-0.63g/m3,标准偏差为 1.22g/m3,与探空相关系数为0.98;水汽密度与探空资料的相关比湿折射度与探空资料的相关好;对于不同层析区域, 中心 区域观测元数量较为丰富, 使得位于中心的层析精度好于整体和边缘;加入低层观测的层析结果与探空的相关比未加低层观 测时的好, 低层观测的加入提高了层析与探空的相关, 减小了低层层析标准差、区域中心和2km 以上层析的均差, 有效提高 了反演精度;低层观测的加入对整体标准差的影响, 可能与加剧观测方程中长度矩阵元素间的量级差异有关。
英文摘要:
      The atmospheric water vapor can be retrieved exactly on certain scales by the deflection and delay of the Navigation Satcllite Timing And Ranging Global Position System(GPS) satellite signal along signal path. Based on the water vapor obscr-vations along the signal paths over the density ground-based GPS network,a new GPS tomography model has been established by using a Gauss function as horizontal constraint and the radiosondc(RS) observation vertical constraint, which includes RS mean values of 4 years as priori information and RS standard deviation as the weight matrix to get the regional high-accuracy three-dimensional (3D) distribution of the atmospheric water vapor. The observations on the ground surface have been used in the tomography formulation to improve the tomographic result accuracy in the overall region, especially the low-level. A 3D water vapor tomographic grid has been designed based on the GPS observation network with 22 GPS sites in the eastern part of Hubei Province in the middle reaches of the Yangtze River. The quasi-real-time retrieval system can provide the hourly output of 3D water vapor density. The 3D wet refraction and water vapor density can be retrieved from the observations of Slant Wet Delay(SWD) and Slant Water Vapor(SWV), respectively. The accuracy of the retrieval results in the 3D tomography domain of the overall, the low-level, the center and the edge has been analyzed by comparing to the RS observations during the flood season of 2010. The results show that for overall sample tests the 3D water vapor density average deviation is -0.63 g/m3, the standard deviation is 1.22 g/m3, and the correlation coefficient with RS is 0.98. The correlation between the retrieval results for water vapor density and soundings is better than that between those for the wet refraction and RS ohservations, and the correlation hctween RS ohscrvations and the results of water vapor density after joining the ground surface observations is better than those without joining the ground surface observations in the tomography. For the rich observations path of GPS, the accuracy at the center of the tomography domain is better than that in the overall and at the marginal domain. Combined with the ground surface observations, the 3D water vapor retrieval accuracy is improved with enhancing the correlation between tomographic results and RS observations, reducing the standards deviation of tomographic results in low-level of the region, and the average deviation of tomographic results in the center and the layer above 2 km of the region. The magnitude order difference between the elements in the signal length matrix due to the ground surface observation being used in tomography could disturb the stability of the observation equations and thus increase the overall standard deviation.
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