地基GPS的资料处理及在天气分析中的应用
|
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。
Water vapor is a major component of the atmosphere and is also a greenhouse gas. Although it is a minor component of the atmosphere, water vapor has considerable impact on local atmospheric conditions due to its uneven spatial distribution and rapidly shifting concentrations. The water vapor is a component that changes most in the atmosphere, whose changing scale is finer than the wind and temperature. One of the basic problems of meteorology and weather forecasting is obtaining accurate measurements of the distribution and variation of water vapor in the atmosphere.
First appearing in the1990s, global positioning system (GPS) techniques are new monitoring tools that provide mechanisms to estimate water vapor concentrations in the atmosphere. Due to high precision, high capacity, high spatial and temporal resolution, and ability for all-weather operation, and in obtaining water vapor data from the atmosphere at near real-time speeds, it has received much attention. National, sector, and local have invested substantial manpower and material resources in GPS techniques, and as a result, have accumulated a great quantity of original GPS data. However, the ground-based GPS water vapor monitoring network in China is still in its rudimentary stages and its detection techniques involve cross-integration of surveying and meteorological knowledge; several problems are yet to be further solved and studied.
Using GPS delay to determine precipitable water vapor (PWV) in the atmosphere entails several problems:too many processing links and complicated techniques, which restrain the effective use of the ground-based GPS data; the inversion process of water vapor data by GPS; it lacks a unified standard of modeling and application of weighted average temperature; in the processing of ground-based GPS data, GPS-PWV cannot be determined if either the original GPS data or the ground meteorological data is missing; GPS-PWV in weather analysis is restricted to research on changes of PWV itself; and researches on the deep-seated reasons for water vapor change is rare. In response to these questions, this paper carries out a deep analysis and study, and arrives at the following conclusions.
(1) The analysis determines that the two key problems in meteorological operation department's current techniques of water vapor inversion by ground-based GPS data are the usage of software in processing GPS data and integrated treatment in the operation;
(2) It focuses on the entire process of water vapor inversion by ground-based GPS data, qualitatively and quantitatively analyzes the main error sources, presents a solution to this error source, and calculates the range of error source reduction required to meet the accuracy demands of meteorological applications;
(3) The article uses mathematical methods and intelligent algorithms to propose two complementary schemes for addressing the lack of measurement of precipitable water vapor. Through principle component analysis, the selection problems of fitting factors are resolved, and through a sensitivity test, the structural composition of the neural network model that best fits precipitable water volume is obtained;
(4) As the fitting formula for the weighted average temperature is not unified, each region has its own problem of Tm calculation model. The article puts forward a Tm calculation model that can be applied in China, and this model achieves the requirement of having sufficient accuracy for use in meteorological applications;
(5) It conducts a systematic analysis of the changing characteristics of GPS-PWV in two strong rainfalls in Jiangsu and Chongqing, assiduously researches the dynamic and thermodynamic processes in line with NCEP reanalysis data, conventional sounding data, ground meteorological data, and WRF numerical simulation results, and reveals the deep-seated reasons for the water vapor change that is the internal reason for PWV change, as well as deepens the significance of GPS-PWV as forecast index;
(6) It uses GPS-PWV data for the first time to analyze the spatial distribution and cycle of GPS at Chengdu plain in autumn. The research establishes a negative correlation between PWV and the altitude, and reveals that PWV changes features in one quarter of a season, i.e., that mid-autumn is a turning point where PWV changes from more to less, where rainfall of Chengdu in autumn occurs mainly at night, and there is sufficient correspondence between the accumulation or release of the total amount of water vapor in atmosphere and the actual precipitation on ground, and where the precipitation peak usually appears earlier than the maximum of precipitation intensity.
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。
Water vapor is a major component of the atmosphere and is also a greenhouse gas. Although it is a minor component of the atmosphere, water vapor has considerable impact on local atmospheric conditions due to its uneven spatial distribution and rapidly shifting concentrations. The water vapor is a component that changes most in the atmosphere, whose changing scale is finer than the wind and temperature. One of the basic problems of meteorology and weather forecasting is obtaining accurate measurements of the distribution and variation of water vapor in the atmosphere.
First appearing in the1990s, global positioning system (GPS) techniques are new monitoring tools that provide mechanisms to estimate water vapor concentrations in the atmosphere. Due to high precision, high capacity, high spatial and temporal resolution, and ability for all-weather operation, and in obtaining water vapor data from the atmosphere at near real-time speeds, it has received much attention. National, sector, and local have invested substantial manpower and material resources in GPS techniques, and as a result, have accumulated a great quantity of original GPS data. However, the ground-based GPS water vapor monitoring network in China is still in its rudimentary stages and its detection techniques involve cross-integration of surveying and meteorological knowledge; several problems are yet to be further solved and studied.
Using GPS delay to determine precipitable water vapor (PWV) in the atmosphere entails several problems:too many processing links and complicated techniques, which restrain the effective use of the ground-based GPS data; the inversion process of water vapor data by GPS; it lacks a unified standard of modeling and application of weighted average temperature; in the processing of ground-based GPS data, GPS-PWV cannot be determined if either the original GPS data or the ground meteorological data is missing; GPS-PWV in weather analysis is restricted to research on changes of PWV itself; and researches on the deep-seated reasons for water vapor change is rare. In response to these questions, this paper carries out a deep analysis and study, and arrives at the following conclusions.
(1) The analysis determines that the two key problems in meteorological operation department's current techniques of water vapor inversion by ground-based GPS data are the usage of software in processing GPS data and integrated treatment in the operation;
(2) It focuses on the entire process of water vapor inversion by ground-based GPS data, qualitatively and quantitatively analyzes the main error sources, presents a solution to this error source, and calculates the range of error source reduction required to meet the accuracy demands of meteorological applications;
(3) The article uses mathematical methods and intelligent algorithms to propose two complementary schemes for addressing the lack of measurement of precipitable water vapor. Through principle component analysis, the selection problems of fitting factors are resolved, and through a sensitivity test, the structural composition of the neural network model that best fits precipitable water volume is obtained;
(4) As the fitting formula for the weighted average temperature is not unified, each region has its own problem of Tm calculation model. The article puts forward a Tm calculation model that can be applied in China, and this model achieves the requirement of having sufficient accuracy for use in meteorological applications;
(5) It conducts a systematic analysis of the changing characteristics of GPS-PWV in two strong rainfalls in Jiangsu and Chongqing, assiduously researches the dynamic and thermodynamic processes in line with NCEP reanalysis data, conventional sounding data, ground meteorological data, and WRF numerical simulation results, and reveals the deep-seated reasons for the water vapor change that is the internal reason for PWV change, as well as deepens the significance of GPS-PWV as forecast index;
(6) It uses GPS-PWV data for the first time to analyze the spatial distribution and cycle of GPS at Chengdu plain in autumn. The research establishes a negative correlation between PWV and the altitude, and reveals that PWV changes features in one quarter of a season, i.e., that mid-autumn is a turning point where PWV changes from more to less, where rainfall of Chengdu in autumn occurs mainly at night, and there is sufficient correspondence between the accumulation or release of the total amount of water vapor in atmosphere and the actual precipitation on ground, and where the precipitation peak usually appears earlier than the maximum of precipitation intensity.
引文
Askne J, Nordins H. Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci.,1987,22:379-386.
Baker H C, Dodson A H, Penna N T, et al. Ground-based GPS water vapour estimation:Potential for meteorological forecasting. Journal of Atmospheric and Solar-Terrestrial Physics,2001, 63:1305-1314.
Bevis M, Businger S, Herring T A, et al. GPS Meteorology:Remote sensing of atmospheric water vapor using the Global Positioning System. J. Geophys. Res.,1992,97:15787-15801.
Bevis M, Businger S, Chiswell S, et al. GPS Meteorology:Mapping Zenith Wet Delays onto Precipitable Water, Journal of Applied Meteorology,1994,33:379-386.
Bock O, Nuret M. Verification of NWP Model Analyses and Radiosonde Humidity Data with GPS Precipitable Water Vapor Estimates during AMMA. J. Weather and Forecasting,2009: 8(24):1085-1101.
Boudouris G., On the index of refraction of air, the absorption and dispersion of centimeter waves in gases. Res. Natl. Bur. Stand.,1963,67:631-684.
Braun J, Rocken C, ware R, et al. A technique for determining single satellite, single station information from double difference residuals. Eos, Trans. Amer. Geophy. Union.,1999,79: F183.
Businger S, Chiswell S, Bevis M, et al. The promise of GPS in atmospheric monitoring. Bulletin of the American Meteorological Society,1997,77(1):1-18.
Crook N. Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev.,1996,124:1767-1785.
Davis J L, Herring T A, Shapiro Ⅱ, et al. Geodesy by radio interferometry:Effects of atmospheric modeling errors on estimates of baseline length, Radio Sci.,1985,20:1593-1607
Dingman S L, Seely-Reynolds D M, Reynolds Ⅲ R C, Application of Kriging to estimate mean annual precipitation in a region of orographic influence. Water Resour. Bull.,1988,24: 329-339.
Duan J, Bevis M, Fang P, et al. GPS meteorology:direct estimation of the absolute value of precipitable water. J. Appl. Met.,1996,35:830-838.
Elgered G, Davis J L, Herring T A, et al. Geodesy by radio interferometry:water vapor radiometry for estimation of the wet dalay. J. Geophys. Res.,1991,96:6541-6555.
Elosegui, Davis P J, Gradinarsky L, et al. Sensing atmospheric structure using small scale space Geodetic Networks. J. Geophys. Res. Lett.,1999,26(16):2445-2448.
Emanuel K, Raymond D, Betts A, et al. Report of the first prospectus development team of the U.S. Weather Research Program to NOAA and the NSF.Bull. Amer. Meteor. Soc.,1995,76: 1194-1208.
Emardson T R, Derks J P H. On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere. Meteorol. Appl.,2000,7:61-68.
Emardson T R, Elegered G, Johansson J M, Three months of continuous monitoring of atmospheric water vapor with a network of GPS receivers. J. Geophys. Res.,1998,103: 1807-1820.
Emardson T R, Johansson J M. Spatial interpolation of the atmospheric water vapor content between sites in a ground-based GPS network. J. Geophysical Research Letters,1998,25(17): 3347-3350.
Foelsche U,G Kirchengast. Troposphere water vapor imaging by combination of ground-based and spaceborne GNSS sounding data. J.Geophys.Res.,2001,106:27221-27231.
Fontaine, B, Roucou P, Trzaska S, Atmospheric water cycle and moisture fluxes in the West African monsoon:Mean annual cycles and relationship using NCEP/NCAR reanalysis. Geophys. Res. Lett.,2003,30(3):1117.
Fujibe F. Diurnal variations of precipitation and thunder storm frequency in Japan in the warm season, Pap. Meteor. Geophys.,1988,39:79-94.
Gaffen D, Elliot W P, Robok A. Relationship between tropospheric water vapor and surface temperature as observed radiosondes. Geophys. Res. Lett.,1992,19:1839-1879.
Gradinarsky L P, Johansson J M, Bouma H R, et al. Climate monitoring using GPS. Phys Chen. Earth,2002,27:335-340.
Herring T, Shimada S. Estimation spatial Variations in atmospheric delays using GPS, Japanese conference on GPS Meteorology,1998.
Iwabuchi T, Shoji Y, Shimada S, et al. Tsukuba GPS dense net campaign observations: Comparison of the stacking maps of post-fit phase residuals estimated from three software packagesr. J. Meteor. Soc. Japan,2004,82(1B):315-330.
Jaldehag R T K, Johansson J M, Davis J L, et al. Geodesy using the Swedish Permanent GPS Network:Effects of snow accumulation on estimates of site positions. J. Geophys. Res. Lett., 1996,23,1601-1604.
Kiehl J T, Trenberth K E. Earth's annual global mean energy budget. Bull. Am. Meteor. Soc., 1997,78:197-208.
Kimura F, Kuwagata T, Horizontal heat fluxes over complex terrain computed using a simple mixed-layer model and a numerical model. J.Appl.Meteor.,1995,34:549-558.
Kou Y H, Gou Y R, Westerwater E R. Assimilation of precipitable water vapor into mesoscale numerical model. Mon. Wea. Rev.,1993,121:1215-1238.
Kou Y H, Zou X, Gou Y R. Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model, Part I:Moisture retrieval and sensitivity experiments. Mon. Wea. Rev.,1996,148:122-147.
Li G, Fujio K, Tomonori S, A composite analysis of diurnal cycle of GPS precipitable water vapor in central Japan during calm summer days. Theor. Appl. Clim.,2008,92(1-2):15-29.
Mendes V B, Langley R B. Tropospheric zenith delay prediction accuracy for high-precesion GPS positioning and navigation. Navigation,1999,46(1):25-33.
Niell A E. Global mapping functions for the atmosphere delay at radio wavelengths. J. Geophys. Res.,1996,101 (B2):3227-3246.
Niell A E, Coster A J, Solheim F S, et al. Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS and VLBI. J. Atmos. Ocean. Tech.2001,18: 830-850.
Reigber C. Water vapor monitoring for weather forecast. GPS World,2002,13(1):18-27.
Ross, R J, Rosenfeld S. Estimating mean weighted temperature of the atmosphere for global positioning system applications. J. Geophy. Res.,1997,102:21719-21730.
Rocken C R, Ware R H, Van H T, et al. Sensing atmospheric water vapor with the Global Positioning System. Geophys. Res. Lett.,1993,23(20):2631-2634.
Rocken C R, Van H T, Johnson M, et al. GPS/STORM-GPS sensing of atmospheric water vapor for meteorology. J. Atmos. Ocean. Tech.,1995,12:468-478.
Rocken C R, Anthes R, Exner M, et al. Analysis and validation of GPS/MET data in the neutral atmosphere. J. Geophys. Res.,1997,102(D25):29849-29866.
Saastamoinen J. Atmospheric correction for the troposphere and stratosphere in radio ranging of satellites.1972, American Geophysical Union, Washington D. C.
Schupler B R. The response of GPS antennas-How design, environment, and frequency affect what you see. J. Phys. Chem. Earth,2001:26A:605-611.
Seko H, Nakamura H, Shoji Y, et al. The Meso-y scale Water Va-por Distribution Associated with a Thunderstorm Calculated from a Dense Network of GPS Receivers. J. Meteo. Soc. Japan, 2004,82(1B):569-586.
Shuanggen J, Li Z, Cha J. Integrated Water Vapor Field and Multiscale Variations over China from GPS Measurements. J. Appl. Meteor. Clim.,2008a,47:3008-3015.
Shuanggen J, Luo O F, Gleason S. Characterization of diurnal cycles in ZTD from a decade of global GPS observations. J.Geod.,2008b,83:537-545.
Sibylle V, Reinhard D, Axel R, et al. Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade. Journal of Climate.2010,23:1675-1695.
Smith T L, Benjamin S G, Shwartz B E, et al. Using GPS-IWV in a 4-D data assimilation system. Earth Plants Space.2000,52:921-926.
Soden, B J, Lanzante J R.. An assessment of satellite and radiosonde climatologies of upper-tropospheric water vapor. J. Climate,1996,9:1235-1250.
Stull, R B. Meteorology for Scientists and Engineers-A Technical Companion Book. West Publishing,1995,528 pp.
Takagi T, Kimura F, Diurnal variation of GPS precipitable water vapor at Lhasa in premonsoon and moosoon periods. J. Meteor. Soc. Japan.,2001,78:175-180.
Tsuda T, Heki K, Miyazaki S, et al. GPS meteorology project of Japanexploring frontier of geodesy. Earth Planets Space,1998,50(1):1-5.
Van B, Joel, Aubagnac J, et al. Comparison of Near-Real Time Estimates of Integrated Water Vapor Derived with GPS, Radiosondes, and Microwave Radiometer. J. Atmos. Oceanic Technol.,2005,22:201-210.
Vey S, Dietrich R, Fritsche M, et al. Influence of mapping function parameters on global GPS network analyses:Comparisons between NMF and IMF. J. Geophys. Res. Lett.,2006,33, L01814,doi:10.1029/2005GL024361.
Wang J, Zhang L, Dai A. Global estimates of water vapor weighted mean temperature of the atmosphere for GPS applications. J. Geophys. Res.,2005,110, D21101, doi:10.1029/2005 JD006215.
Ware R, Exner M, Feng D, et al. GPS sounding of the atmosphere from low earth orbit: preliminary results. Bulletin of the American Meteorological Society,1997,77(1):19-40.
Ware R, Fulker D W, Stein S A. SuomiNet:A real-time national GPS network for atmospheric research and education. Bull. Am. Meteor. Soc.,2000,81:677-694.
Wei M, Peng T, Li N, et al. On the Earth Rotation in Relation to Subtropical Anticyclone. Scientific Inquiry,2008,9 (2):115-122.
Zhai, Eskridge R E. Atmospheric water vapor over China. J. Clima.,1997,10:2643-2652.
伯晓晨,李涛,刘路,等.MATLAB工具箱应用指南.北京：电子工业出版社,2000.
毕研盟,毛节泰,刘晓阳,等.应用地基GPS遥感倾斜路径方向大气水汽总量.地球物理学报,2006,49(2)：335-342.
曹丽青,余锦华,葛朝霞.华北地区大气水汽含量特征及其变化趋势.水科学进展,2005,16(3)：439-443.
曹云昌,方宗义,李成才,等.利用GPS和云图资料监测北京地区中小尺度降水的研究.高原气象,2005,24(1)：1-96.
曹云昌,方宗义,夏青.地基GPS探测应用研究进展.南京气象学院学报.2004,27(4)：565-572.
陈娇娜,李国平,黄诗文.华西秋雨天气过程中GPS遥感的水汽总量的演变特征.应用气象学报,2009a,20(6)：753-759.
陈娇娜,李国平,郝丽萍.成都地区秋、冬季地基GPS遥感的可降水量的时空变化分析.气 象科学,2009b,29(5)：682-686.
陈明.Linux基础与应用.2005,北京：清华大学出版社.
初东,王刚.GPS天线相位中心偏差的数学模型.测绘工程,2000,9(4)：55-57.
楚艳丽,郭英华,张朝林,等.地基GPS水汽资料在北京“7.10”暴雨过程研究中的应用.气象,2007,33(12)：16-22.
党亚民,王权,冯金涛.利用GPS资料反演大气水汽含量的研究.测绘科技动态,1999,3：2-5.
丁金才.GPS气象学及其应用.2009,北京：气象出版社.
丁继新,成英燕,王权,等.利用GPS技术遥感大气对流层水汽含量的研究.测绘科学,2002,27(2)：16-19.
杜坤.多尺度资料在强对流天气预报中的应用.2011,南京信息工程大学硕士论文.
葛茂荣,刘经南PC-GAMIT软件及其应用.测绘通报,1997,2：21-23.
谷晓平,王长耀,王汶.GPS水汽遥感中的大气干延迟局地订正模型研究.热带气象学报,2004,20(6)：697-703.
谷晓平,王长耀,蒋国华,等.地基GPS遥感大气水汽含量及在气象上的应用.气象科学,2005,25(5)：543-549.
谷晓平,王长耀,吴登秀.GPS水汽遥感中的大气加权平均温度的变化特征及局地算式研究.气象科学,2005,25(1)：79-83.
郭洁,李国平,黄丁发.基于40年探空资料的川渝地区对流层加权平均温度及其局地建模.武汉大学学报(信息科学版),2008,33(专辑)：43-46.
郭洁,李国平.川渝地区大气可降水量的气候特征以及与地面水汽量的关系.自然资源学报,2009,24(2)：344-350.
何平,徐宝祥,周秀骥.地GPS反演大气水汽总量的初步试验.应用气象学报,2002,13(2)：179-183.
金慧华,白征东,过静珺,等.地基GPS反演水汽影响因素分析.测绘科学,2008,33(4)：65-67.
李炳华,唐宇辉,黎守德.全球卫星定位系统(GPS)水汽数据在暴雨临近预报中的发展和应用.2005,中国气象学会2005年学术年会,苏州.
李成才,毛节泰.GPS地基遥感大气水汽总量分析.应用气象学报,1998,9(4)：470-477.
李国翠,李国平,连志鸾,等.不同云系降水过程中GPS可降水量的特征.高原气象,2008,27(5)：1066-1073.
李国翠,李国平,刘凤辉,等.华北地区水汽总量特征及其与地面水汽压关系.热带气象学报,2009,25(4)：488-494.
李国平,黄丁发.GPS遥感区域大气水汽总量研究回顾与展望.气象科技,2004,32(4)：201-205.
李国平,黄丁发.GPS气象学研究及应用的进展与前景.气象科学,2005,25(6)：651-661.
李国平,黄丁发,刘碧全.地基GPS遥感的成都地区夏季可降水量的日循环合成分析.水科学进展,2006,17(2)：160-163.
李国平.地基GPS遥感大气可降水量及其在气象中的应用研究.2007,西南交通大学博士学位论文.
李国平,陈娇娜,黄丁发,等.地基GPS水汽实时监测系统及其气象业务应用,武汉大学学报(信息科学版),2009,34(11)：1328-1331.
李国平,黄丁发,郭洁,等.2010,地基GPS气象学,北京：科学出版社.
李建国,毛节泰,李建国,等.使用全球定位系统遥感水汽分布原理和中国东部地区加权“平均温度”的回归分析.气象学报,1999,157(3)：283-293.
李军,高咏梅GAMIT、GIPSY和BERNESE软件解算结果的比较研究.全球定位系统,2010(3)：5-9.
李延兴,徐宝祥,胡新康,等.应用地基GPS技术遥感大气柱水汽量的实验研究.应用气象学报,2001,12(1)：61-68.
梁丰,李成才,王迎春,等.应用区域地基全球定位系统观测分析北京地区大气总水汽量.大气科学,2003,27(2)：236-243.
梁宏,刘晶淼,李世奎.青藏高原及周边地区大气水汽资源分布和季节变化特征分析.自然资源学报,2006,21(4)：526-534.
梁辉MATLAB制作图形界面的应用.佳木斯大学学报(自然科学版),2003,21(4)：402-406.
林文鹏,石婧,王长耀.滨江流域GPS降水预报系统的研究与实现.长江科学院院报,2005,22(3)：25-28.
刘炎雄.地基GPS技术遥感香港地区大气水汽含量.武汉测绘科技大学学报.1999,28(3)： 245-248.
刘炎雄,陈永奇IZHB.可降水份的误差分析.武汉测绘科技大学学报,2000a,25(2)：105-107.
刘炎雄,陈永奇.GPS气象学中垂直干分量延时的精确确定.测绘学报.2000b,29(2)：172-180.
刘焱雄,陈永奇,刘经南.利用地面气象观测资料确定对流层加权平均温度.武汉测绘科技大学学报,2000c,25(5)：400-403.
罗建军,杨琦.2005,MATLAB7教程.北京：电子工业出版社.
吕弋培,殷海涛,黄丁发等.成都地区大气平均温度建模及其在GPS/PWV计算中的应用研究.测绘科学,2008,33(4)：103-105.
毛健,魏峰远.GAMIT在红旗5.0 LINUX操作系统上的安装和使用.山西建筑,2008,3：354-355.
孟妙志.K指数在暴雨分析预报中的应用.气象,2003,29(8)：封2-封3.
欧阳首承,陈刚毅,林益.信息数字化与预测.2009,北京：气象出版社.
曲建光,魏旭东,王泽民,等.在高海拔地区Saastamonien与Hopfield模型推算水汽含量差异的研究.武汉大学学报(信息科学版),2003,28(4)：397-399.
施晓红,周佳.精通GUI图形界面编程.2003,北京：北京大学出版社.
宋淑丽,严文耀,丁金才,等.上海GPS综合应用网对2002年长江三角洲地区入海过程的监测.天文学进展,2003,21(2)：180-184.
宋淑丽,朱文耀,丁金才,等.上海GPS综合应用网对可降水量的实时监测及其改进数值预报初始场的试验.地球物理学报,2004,47(4)：631-638.
王皓,李国平,陈娇娜.运用GAMIT10.34解算成都地区的GPS可降水量.成都信息工程学院学报,2009,24(5)：478-483.
王皓,李国平.成都地基的GPS水汽监测系统建设与应用.地球信息科学学报,2011,13(2)：213-218.
王留朋,过静珺,金慧华,等GAMIT在LINUX操作系统上的安装和使用.全球定位系统,2005,6：43-46.
王天应,施闯.GAMIT在PC机上的安装和使用.地理空间信息,2007,5：116-118.
王小亚,朱文耀,严豪健,等.地面GPS探测大气可降水量的初步结果.大气科学,1999,23 (5)：605-612.
王新龙,李亚峰.GPS定位中4种对流层延迟修正模型适应性分析.电光与控制,2008,15(11)：5-9.
王勇,柳林涛,郝晓光.准实时地基GPS可降水量的解算方案与可靠性研究.热带气象学报,2007,23(2)：177-181.
王勇,柳林涛,刘根友.基于水汽辐射计与GPS湿延迟的对比研究.大地测量与地球动力学,2005,25(4)：110-113.
王志强,李军.GAMIT使用指南.全球定位系统,2002,2：36-39.
邬熙娟,高俊强,黄燕.小区域GPS网GAMIT解算结果与IGS站选取的关系.南京工业大学学报(自然科学版),2010,32(4)：96-99.
杨光林,刘晶淼,毛节泰.西藏地区水汽GPS遥感分析.气象科技,2002,30(5)：266-272.
杨景梅,邱金桓.我国可降水量同地面水汽压的经验表达式.大气科学,1996,20(5)：620-626.
杨景梅,邱金桓.用地面湿度参量计算我国整层大气可降水量及有效水汽含量方法的研究.大气科学,2002,26(1)：9-22.
叶其欣,杨露华,丁金才,等GPS-PWV资料在强对流天气系统中的特征分析.暴雨灾害,2008,27(2)：141-148.
袁野,王成章,蒋年冲,等.不同云天条件下水汽含量特征及其变化分析.气象科学,2005,31(4)：394-398.
袁招洪,丁金才,陈永林.中尺度数值预报模式预报水汽与GPS观测的比较研究.大气科学,2004,28(3)：435-440.
张学文.可降水量与地面水汽压力的关系.气象,2004,30(2)：9-11.
张朝林,陈敏,Kuo Y H,等.“00.7”北京特大暴雨模拟中气象资料同化作用的评估.气象学报,2005,63(3)：922-932.
翟丽萍.华北暴雪和暴雨的大气能量结构研究.2012,南京信息工程大学硕士论文.
赵芬,吴志勇,陆桂华.塔里木河流域空中水汽状况分析.中国科技论文在线(http://www.paper.edu.cn.),2008:1-8.
Baker H C, Dodson A H, Penna N T, et al. Ground-based GPS water vapour estimation:Potential for meteorological forecasting. Journal of Atmospheric and Solar-Terrestrial Physics,2001, 63:1305-1314.
Bevis M, Businger S, Herring T A, et al. GPS Meteorology:Remote sensing of atmospheric water vapor using the Global Positioning System. J. Geophys. Res.,1992,97:15787-15801.
Bevis M, Businger S, Chiswell S, et al. GPS Meteorology:Mapping Zenith Wet Delays onto Precipitable Water, Journal of Applied Meteorology,1994,33:379-386.
Bock O, Nuret M. Verification of NWP Model Analyses and Radiosonde Humidity Data with GPS Precipitable Water Vapor Estimates during AMMA. J. Weather and Forecasting,2009: 8(24):1085-1101.
Boudouris G., On the index of refraction of air, the absorption and dispersion of centimeter waves in gases. Res. Natl. Bur. Stand.,1963,67:631-684.
Braun J, Rocken C, ware R, et al. A technique for determining single satellite, single station information from double difference residuals. Eos, Trans. Amer. Geophy. Union.,1999,79: F183.
Businger S, Chiswell S, Bevis M, et al. The promise of GPS in atmospheric monitoring. Bulletin of the American Meteorological Society,1997,77(1):1-18.
Crook N. Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev.,1996,124:1767-1785.
Davis J L, Herring T A, Shapiro Ⅱ, et al. Geodesy by radio interferometry:Effects of atmospheric modeling errors on estimates of baseline length, Radio Sci.,1985,20:1593-1607
Dingman S L, Seely-Reynolds D M, Reynolds Ⅲ R C, Application of Kriging to estimate mean annual precipitation in a region of orographic influence. Water Resour. Bull.,1988,24: 329-339.
Duan J, Bevis M, Fang P, et al. GPS meteorology:direct estimation of the absolute value of precipitable water. J. Appl. Met.,1996,35:830-838.
Elgered G, Davis J L, Herring T A, et al. Geodesy by radio interferometry:water vapor radiometry for estimation of the wet dalay. J. Geophys. Res.,1991,96:6541-6555.
Elosegui, Davis P J, Gradinarsky L, et al. Sensing atmospheric structure using small scale space Geodetic Networks. J. Geophys. Res. Lett.,1999,26(16):2445-2448.
Emanuel K, Raymond D, Betts A, et al. Report of the first prospectus development team of the U.S. Weather Research Program to NOAA and the NSF.Bull. Amer. Meteor. Soc.,1995,76: 1194-1208.
Emardson T R, Derks J P H. On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere. Meteorol. Appl.,2000,7:61-68.
Emardson T R, Elegered G, Johansson J M, Three months of continuous monitoring of atmospheric water vapor with a network of GPS receivers. J. Geophys. Res.,1998,103: 1807-1820.
Emardson T R, Johansson J M. Spatial interpolation of the atmospheric water vapor content between sites in a ground-based GPS network. J. Geophysical Research Letters,1998,25(17): 3347-3350.
Foelsche U,G Kirchengast. Troposphere water vapor imaging by combination of ground-based and spaceborne GNSS sounding data. J.Geophys.Res.,2001,106:27221-27231.
Fontaine, B, Roucou P, Trzaska S, Atmospheric water cycle and moisture fluxes in the West African monsoon:Mean annual cycles and relationship using NCEP/NCAR reanalysis. Geophys. Res. Lett.,2003,30(3):1117.
Fujibe F. Diurnal variations of precipitation and thunder storm frequency in Japan in the warm season, Pap. Meteor. Geophys.,1988,39:79-94.
Gaffen D, Elliot W P, Robok A. Relationship between tropospheric water vapor and surface temperature as observed radiosondes. Geophys. Res. Lett.,1992,19:1839-1879.
Gradinarsky L P, Johansson J M, Bouma H R, et al. Climate monitoring using GPS. Phys Chen. Earth,2002,27:335-340.
Herring T, Shimada S. Estimation spatial Variations in atmospheric delays using GPS, Japanese conference on GPS Meteorology,1998.
Iwabuchi T, Shoji Y, Shimada S, et al. Tsukuba GPS dense net campaign observations: Comparison of the stacking maps of post-fit phase residuals estimated from three software packagesr. J. Meteor. Soc. Japan,2004,82(1B):315-330.
Jaldehag R T K, Johansson J M, Davis J L, et al. Geodesy using the Swedish Permanent GPS Network:Effects of snow accumulation on estimates of site positions. J. Geophys. Res. Lett., 1996,23,1601-1604.
Kiehl J T, Trenberth K E. Earth's annual global mean energy budget. Bull. Am. Meteor. Soc., 1997,78:197-208.
Kimura F, Kuwagata T, Horizontal heat fluxes over complex terrain computed using a simple mixed-layer model and a numerical model. J.Appl.Meteor.,1995,34:549-558.
Kou Y H, Gou Y R, Westerwater E R. Assimilation of precipitable water vapor into mesoscale numerical model. Mon. Wea. Rev.,1993,121:1215-1238.
Kou Y H, Zou X, Gou Y R. Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model, Part I:Moisture retrieval and sensitivity experiments. Mon. Wea. Rev.,1996,148:122-147.
Li G, Fujio K, Tomonori S, A composite analysis of diurnal cycle of GPS precipitable water vapor in central Japan during calm summer days. Theor. Appl. Clim.,2008,92(1-2):15-29.
Mendes V B, Langley R B. Tropospheric zenith delay prediction accuracy for high-precesion GPS positioning and navigation. Navigation,1999,46(1):25-33.
Niell A E. Global mapping functions for the atmosphere delay at radio wavelengths. J. Geophys. Res.,1996,101 (B2):3227-3246.
Niell A E, Coster A J, Solheim F S, et al. Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS and VLBI. J. Atmos. Ocean. Tech.2001,18: 830-850.
Reigber C. Water vapor monitoring for weather forecast. GPS World,2002,13(1):18-27.
Ross, R J, Rosenfeld S. Estimating mean weighted temperature of the atmosphere for global positioning system applications. J. Geophy. Res.,1997,102:21719-21730.
Rocken C R, Ware R H, Van H T, et al. Sensing atmospheric water vapor with the Global Positioning System. Geophys. Res. Lett.,1993,23(20):2631-2634.
Rocken C R, Van H T, Johnson M, et al. GPS/STORM-GPS sensing of atmospheric water vapor for meteorology. J. Atmos. Ocean. Tech.,1995,12:468-478.
Rocken C R, Anthes R, Exner M, et al. Analysis and validation of GPS/MET data in the neutral atmosphere. J. Geophys. Res.,1997,102(D25):29849-29866.
Saastamoinen J. Atmospheric correction for the troposphere and stratosphere in radio ranging of satellites.1972, American Geophysical Union, Washington D. C.
Schupler B R. The response of GPS antennas-How design, environment, and frequency affect what you see. J. Phys. Chem. Earth,2001:26A:605-611.
Seko H, Nakamura H, Shoji Y, et al. The Meso-y scale Water Va-por Distribution Associated with a Thunderstorm Calculated from a Dense Network of GPS Receivers. J. Meteo. Soc. Japan, 2004,82(1B):569-586.
Shuanggen J, Li Z, Cha J. Integrated Water Vapor Field and Multiscale Variations over China from GPS Measurements. J. Appl. Meteor. Clim.,2008a,47:3008-3015.
Shuanggen J, Luo O F, Gleason S. Characterization of diurnal cycles in ZTD from a decade of global GPS observations. J.Geod.,2008b,83:537-545.
Sibylle V, Reinhard D, Axel R, et al. Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade. Journal of Climate.2010,23:1675-1695.
Smith T L, Benjamin S G, Shwartz B E, et al. Using GPS-IWV in a 4-D data assimilation system. Earth Plants Space.2000,52:921-926.
Soden, B J, Lanzante J R.. An assessment of satellite and radiosonde climatologies of upper-tropospheric water vapor. J. Climate,1996,9:1235-1250.
Stull, R B. Meteorology for Scientists and Engineers-A Technical Companion Book. West Publishing,1995,528 pp.
Takagi T, Kimura F, Diurnal variation of GPS precipitable water vapor at Lhasa in premonsoon and moosoon periods. J. Meteor. Soc. Japan.,2001,78:175-180.
Tsuda T, Heki K, Miyazaki S, et al. GPS meteorology project of Japanexploring frontier of geodesy. Earth Planets Space,1998,50(1):1-5.
Van B, Joel, Aubagnac J, et al. Comparison of Near-Real Time Estimates of Integrated Water Vapor Derived with GPS, Radiosondes, and Microwave Radiometer. J. Atmos. Oceanic Technol.,2005,22:201-210.
Vey S, Dietrich R, Fritsche M, et al. Influence of mapping function parameters on global GPS network analyses:Comparisons between NMF and IMF. J. Geophys. Res. Lett.,2006,33, L01814,doi:10.1029/2005GL024361.
Wang J, Zhang L, Dai A. Global estimates of water vapor weighted mean temperature of the atmosphere for GPS applications. J. Geophys. Res.,2005,110, D21101, doi:10.1029/2005 JD006215.
Ware R, Exner M, Feng D, et al. GPS sounding of the atmosphere from low earth orbit: preliminary results. Bulletin of the American Meteorological Society,1997,77(1):19-40.
Ware R, Fulker D W, Stein S A. SuomiNet:A real-time national GPS network for atmospheric research and education. Bull. Am. Meteor. Soc.,2000,81:677-694.
Wei M, Peng T, Li N, et al. On the Earth Rotation in Relation to Subtropical Anticyclone. Scientific Inquiry,2008,9 (2):115-122.
Zhai, Eskridge R E. Atmospheric water vapor over China. J. Clima.,1997,10:2643-2652.
伯晓晨,李涛,刘路,等.MATLAB工具箱应用指南.北京：电子工业出版社,2000.
毕研盟,毛节泰,刘晓阳,等.应用地基GPS遥感倾斜路径方向大气水汽总量.地球物理学报,2006,49(2)：335-342.
曹丽青,余锦华,葛朝霞.华北地区大气水汽含量特征及其变化趋势.水科学进展,2005,16(3)：439-443.
曹云昌,方宗义,李成才,等.利用GPS和云图资料监测北京地区中小尺度降水的研究.高原气象,2005,24(1)：1-96.
曹云昌,方宗义,夏青.地基GPS探测应用研究进展.南京气象学院学报.2004,27(4)：565-572.
陈娇娜,李国平,黄诗文.华西秋雨天气过程中GPS遥感的水汽总量的演变特征.应用气象学报,2009a,20(6)：753-759.
陈娇娜,李国平,郝丽萍.成都地区秋、冬季地基GPS遥感的可降水量的时空变化分析.气 象科学,2009b,29(5)：682-686.
陈明.Linux基础与应用.2005,北京：清华大学出版社.
初东,王刚.GPS天线相位中心偏差的数学模型.测绘工程,2000,9(4)：55-57.
楚艳丽,郭英华,张朝林,等.地基GPS水汽资料在北京“7.10”暴雨过程研究中的应用.气象,2007,33(12)：16-22.
党亚民,王权,冯金涛.利用GPS资料反演大气水汽含量的研究.测绘科技动态,1999,3：2-5.
丁金才.GPS气象学及其应用.2009,北京：气象出版社.
丁继新,成英燕,王权,等.利用GPS技术遥感大气对流层水汽含量的研究.测绘科学,2002,27(2)：16-19.
杜坤.多尺度资料在强对流天气预报中的应用.2011,南京信息工程大学硕士论文.
葛茂荣,刘经南PC-GAMIT软件及其应用.测绘通报,1997,2：21-23.
谷晓平,王长耀,王汶.GPS水汽遥感中的大气干延迟局地订正模型研究.热带气象学报,2004,20(6)：697-703.
谷晓平,王长耀,蒋国华,等.地基GPS遥感大气水汽含量及在气象上的应用.气象科学,2005,25(5)：543-549.
谷晓平,王长耀,吴登秀.GPS水汽遥感中的大气加权平均温度的变化特征及局地算式研究.气象科学,2005,25(1)：79-83.
郭洁,李国平,黄丁发.基于40年探空资料的川渝地区对流层加权平均温度及其局地建模.武汉大学学报(信息科学版),2008,33(专辑)：43-46.
郭洁,李国平.川渝地区大气可降水量的气候特征以及与地面水汽量的关系.自然资源学报,2009,24(2)：344-350.
何平,徐宝祥,周秀骥.地GPS反演大气水汽总量的初步试验.应用气象学报,2002,13(2)：179-183.
金慧华,白征东,过静珺,等.地基GPS反演水汽影响因素分析.测绘科学,2008,33(4)：65-67.
李炳华,唐宇辉,黎守德.全球卫星定位系统(GPS)水汽数据在暴雨临近预报中的发展和应用.2005,中国气象学会2005年学术年会,苏州.
李成才,毛节泰.GPS地基遥感大气水汽总量分析.应用气象学报,1998,9(4)：470-477.
李国翠,李国平,连志鸾,等.不同云系降水过程中GPS可降水量的特征.高原气象,2008,27(5)：1066-1073.
李国翠,李国平,刘凤辉,等.华北地区水汽总量特征及其与地面水汽压关系.热带气象学报,2009,25(4)：488-494.
李国平,黄丁发.GPS遥感区域大气水汽总量研究回顾与展望.气象科技,2004,32(4)：201-205.
李国平,黄丁发.GPS气象学研究及应用的进展与前景.气象科学,2005,25(6)：651-661.
李国平,黄丁发,刘碧全.地基GPS遥感的成都地区夏季可降水量的日循环合成分析.水科学进展,2006,17(2)：160-163.
李国平.地基GPS遥感大气可降水量及其在气象中的应用研究.2007,西南交通大学博士学位论文.
李国平,陈娇娜,黄丁发,等.地基GPS水汽实时监测系统及其气象业务应用,武汉大学学报(信息科学版),2009,34(11)：1328-1331.
李国平,黄丁发,郭洁,等.2010,地基GPS气象学,北京：科学出版社.
李建国,毛节泰,李建国,等.使用全球定位系统遥感水汽分布原理和中国东部地区加权“平均温度”的回归分析.气象学报,1999,157(3)：283-293.
李军,高咏梅GAMIT、GIPSY和BERNESE软件解算结果的比较研究.全球定位系统,2010(3)：5-9.
李延兴,徐宝祥,胡新康,等.应用地基GPS技术遥感大气柱水汽量的实验研究.应用气象学报,2001,12(1)：61-68.
梁丰,李成才,王迎春,等.应用区域地基全球定位系统观测分析北京地区大气总水汽量.大气科学,2003,27(2)：236-243.
梁宏,刘晶淼,李世奎.青藏高原及周边地区大气水汽资源分布和季节变化特征分析.自然资源学报,2006,21(4)：526-534.
梁辉MATLAB制作图形界面的应用.佳木斯大学学报(自然科学版),2003,21(4)：402-406.
林文鹏,石婧,王长耀.滨江流域GPS降水预报系统的研究与实现.长江科学院院报,2005,22(3)：25-28.
刘炎雄.地基GPS技术遥感香港地区大气水汽含量.武汉测绘科技大学学报.1999,28(3)： 245-248.
刘炎雄,陈永奇IZHB.可降水份的误差分析.武汉测绘科技大学学报,2000a,25(2)：105-107.
刘炎雄,陈永奇.GPS气象学中垂直干分量延时的精确确定.测绘学报.2000b,29(2)：172-180.
刘焱雄,陈永奇,刘经南.利用地面气象观测资料确定对流层加权平均温度.武汉测绘科技大学学报,2000c,25(5)：400-403.
罗建军,杨琦.2005,MATLAB7教程.北京：电子工业出版社.
吕弋培,殷海涛,黄丁发等.成都地区大气平均温度建模及其在GPS/PWV计算中的应用研究.测绘科学,2008,33(4)：103-105.
毛健,魏峰远.GAMIT在红旗5.0 LINUX操作系统上的安装和使用.山西建筑,2008,3：354-355.
孟妙志.K指数在暴雨分析预报中的应用.气象,2003,29(8)：封2-封3.
欧阳首承,陈刚毅,林益.信息数字化与预测.2009,北京：气象出版社.
曲建光,魏旭东,王泽民,等.在高海拔地区Saastamonien与Hopfield模型推算水汽含量差异的研究.武汉大学学报(信息科学版),2003,28(4)：397-399.
施晓红,周佳.精通GUI图形界面编程.2003,北京：北京大学出版社.
宋淑丽,严文耀,丁金才,等.上海GPS综合应用网对2002年长江三角洲地区入海过程的监测.天文学进展,2003,21(2)：180-184.
宋淑丽,朱文耀,丁金才,等.上海GPS综合应用网对可降水量的实时监测及其改进数值预报初始场的试验.地球物理学报,2004,47(4)：631-638.
王皓,李国平,陈娇娜.运用GAMIT10.34解算成都地区的GPS可降水量.成都信息工程学院学报,2009,24(5)：478-483.
王皓,李国平.成都地基的GPS水汽监测系统建设与应用.地球信息科学学报,2011,13(2)：213-218.
王留朋,过静珺,金慧华,等GAMIT在LINUX操作系统上的安装和使用.全球定位系统,2005,6：43-46.
王天应,施闯.GAMIT在PC机上的安装和使用.地理空间信息,2007,5：116-118.
王小亚,朱文耀,严豪健,等.地面GPS探测大气可降水量的初步结果.大气科学,1999,23 (5)：605-612.
王新龙,李亚峰.GPS定位中4种对流层延迟修正模型适应性分析.电光与控制,2008,15(11)：5-9.
王勇,柳林涛,郝晓光.准实时地基GPS可降水量的解算方案与可靠性研究.热带气象学报,2007,23(2)：177-181.
王勇,柳林涛,刘根友.基于水汽辐射计与GPS湿延迟的对比研究.大地测量与地球动力学,2005,25(4)：110-113.
王志强,李军.GAMIT使用指南.全球定位系统,2002,2：36-39.
邬熙娟,高俊强,黄燕.小区域GPS网GAMIT解算结果与IGS站选取的关系.南京工业大学学报(自然科学版),2010,32(4)：96-99.
杨光林,刘晶淼,毛节泰.西藏地区水汽GPS遥感分析.气象科技,2002,30(5)：266-272.
杨景梅,邱金桓.我国可降水量同地面水汽压的经验表达式.大气科学,1996,20(5)：620-626.
杨景梅,邱金桓.用地面湿度参量计算我国整层大气可降水量及有效水汽含量方法的研究.大气科学,2002,26(1)：9-22.
叶其欣,杨露华,丁金才,等GPS-PWV资料在强对流天气系统中的特征分析.暴雨灾害,2008,27(2)：141-148.
袁野,王成章,蒋年冲,等.不同云天条件下水汽含量特征及其变化分析.气象科学,2005,31(4)：394-398.
袁招洪,丁金才,陈永林.中尺度数值预报模式预报水汽与GPS观测的比较研究.大气科学,2004,28(3)：435-440.
张学文.可降水量与地面水汽压力的关系.气象,2004,30(2)：9-11.
张朝林,陈敏,Kuo Y H,等.“00.7”北京特大暴雨模拟中气象资料同化作用的评估.气象学报,2005,63(3)：922-932.
翟丽萍.华北暴雪和暴雨的大气能量结构研究.2012,南京信息工程大学硕士论文.
赵芬,吴志勇,陆桂华.塔里木河流域空中水汽状况分析.中国科技论文在线(http://www.paper.edu.cn.),2008:1-8.