长江流域起伏地形下降水量分布精细化气候估算模型研究
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摘要
本文首先分别构建了降水量背景场理论估算模型和地形抬升降水增量理论估算模型,并在此基础上,结合统计方法建立了起伏地形下降水量空间分布精细化混合估算模型。融合常规气象站点资料、数字高程模型资料和NCEP再分析资料,在GIS平台上计算得到1km×1km分辨率的长江流域起伏地形下降水量精细化空间分布。本文得到下面几点结论：
1、构建的降水量背景场估算模型模拟了长江流域降水量背景场年总量和各月总量的空间分布,结果分析表明：无论是年总量还是各月总量,模型估算结果的时空分布总体规律与各地降水气候特征吻合都较好。该理论模型用于降水量背景场的估算是可行的,计算结果可靠。
2、坡向修正因子模拟结果可以很好的表征迎风坡、背风坡的地形特征。地形抬升降水增量无论是年总量还是各月总量,模型估算结果的时空分布与各山区地形水汽特征吻合很好,各类地形特征明显,充分表征地形和水汽条件的共同作用。
3、结合统计方法,完成模型系数估算,最终得到的起伏地形下降水量精细化混合估算模型,其模拟结果体现了水汽、风速、风向各项气象要素在不同的地理位置和地形特征下综合影响的结果,与各地各季降水气候和地形气候一致。剔除个别由于数据精度问题引起误差的台站后,各项误差指标都较好,各月和全年的相对误差均在20%以内。因此基于水汽辐合与地形抬升物理机制的模型理论依据明确,结果可靠。
4、为了比较说明上述混合模型的优劣,本文构建了基于坡度坡向修正因子的回归模型。修正回归模型无法表征水汽输送及其辐合和降水的物理关联,因此各个季节水汽输送主风带方向的变化导致的迎风坡、背风坡的降水量时空分布差异表现并不明显,从这个方面来说混合模型比修正回归模型模拟地形降水量效果更好。但是由于物理方程形式和气象数据空间精度(水平和垂直空间)所限,混合模型模拟结果还有很多缺陷,如海拔高度因子由于和NCEP数据耦合计算,导致精度降低,因而海拔对降水的影响表现得没有修正回归模型显著,需要进一步完善模型、探索新方案和新数据,提高模拟精度。
5、DEM数据在模型估算中提供了精细化的各项地形因子,GIS软件为地形因子和气象因子的栅格化耦合解算提供了高效的工作平台,并为模拟结果的可视化制图提供了有力的工具。GIS技术和数据在气候要素精细化估算和可视化方面的作用强大,并有待更好更深入的开发和利用。
The paper firstly built two theoretical models to estimate respectively the precipitation background field and the precipitation increment caused by orographic lifting. And then, a fine-scale mixed models was established combining with the statistical methods, based on that, the spatial distribution of precipitation over the rugged terrain in Yangtze river basin was estimated with1km×1km resolution on GIS integrated the ground-based station data, digital elevation model (DEM) and NCEP reanalysis data. This paper obtained the following conclusions:
1. The precipitation background field theoretical model can provide the distribution of annual and monthly precipitation background field quantity in Yangtze river basin. The annual and monthly precipitation background field distribution could describe the climate distribution regular pattern and characteristics of precipitation. So this theoretical model was considered practicable and dependable.
2. The simulation result of correction factor for aspect could finely describe topographic characteristics of windward slopes and leeward slope. The annual and monthly precipitation increment quantity caused by orographic lifting could describe the topoclimate distribution regular pattern and characteristics of precipitation and could characterize the effect of moisture and topography conditions.
3. Combined with statistical method, the model coefficient was estimated. Ultimately, the fine-scale mixed models was established to estimate the spatial distribution of precipitation over the rugged terrain in Yangtze river basin which could reflect the comprehensive influence of meteorological elements including water vapour, wind speed and wind direction. Getting rid of the individual stations which errors was caused by data precision, the annual and monthly relative errors were all below20%. So the mixed model based on physical mechanism has reliable theory and results.
4. To explain the quality of the mixed model, this paper built a regression model based on the correction factor of slope and aspect. The corrected regression model could not indicate the physical connection between water vapor transmission and convergence with precipitation. Because of the variation of direction of water vapor transmission in each season, the difference of spatio-temporal distribution of precipitation with windward slope and leeward slope was not obvious. So the mixture model was better than corrected regression model in the simulation of terrain precipitation. But owing to the limitation of the physical equation form and the space precision(horizontal and vertical space) of meteorological data, the simulation results of mixture model also has some deficiency. For instance, because of the coupling calculation with NCEP data, the accuracy of altitude factor was lower, therefore, the influence of elevation to precipitation was less remarkable than the corrected regression model. So it was necessary to further perfect model, explore new solutions and new data, and improve the simulation accuracy.
5. In the model, DEM data provided the intensification terrain factors. GIS software provided an efficient work platform for the grid coupling of terrain factors and meteorological factors. It also provided a powerful tool for the visual mapping of simulated results. The function of GIS technology and GIS data was powerful in the refining estimation of climate elements and visualization. In addition, GIS technology and GIS data needed to be better further development and utilization.
1、构建的降水量背景场估算模型模拟了长江流域降水量背景场年总量和各月总量的空间分布,结果分析表明：无论是年总量还是各月总量,模型估算结果的时空分布总体规律与各地降水气候特征吻合都较好。该理论模型用于降水量背景场的估算是可行的,计算结果可靠。
2、坡向修正因子模拟结果可以很好的表征迎风坡、背风坡的地形特征。地形抬升降水增量无论是年总量还是各月总量,模型估算结果的时空分布与各山区地形水汽特征吻合很好,各类地形特征明显,充分表征地形和水汽条件的共同作用。
3、结合统计方法,完成模型系数估算,最终得到的起伏地形下降水量精细化混合估算模型,其模拟结果体现了水汽、风速、风向各项气象要素在不同的地理位置和地形特征下综合影响的结果,与各地各季降水气候和地形气候一致。剔除个别由于数据精度问题引起误差的台站后,各项误差指标都较好,各月和全年的相对误差均在20%以内。因此基于水汽辐合与地形抬升物理机制的模型理论依据明确,结果可靠。
4、为了比较说明上述混合模型的优劣,本文构建了基于坡度坡向修正因子的回归模型。修正回归模型无法表征水汽输送及其辐合和降水的物理关联,因此各个季节水汽输送主风带方向的变化导致的迎风坡、背风坡的降水量时空分布差异表现并不明显,从这个方面来说混合模型比修正回归模型模拟地形降水量效果更好。但是由于物理方程形式和气象数据空间精度(水平和垂直空间)所限,混合模型模拟结果还有很多缺陷,如海拔高度因子由于和NCEP数据耦合计算,导致精度降低,因而海拔对降水的影响表现得没有修正回归模型显著,需要进一步完善模型、探索新方案和新数据,提高模拟精度。
5、DEM数据在模型估算中提供了精细化的各项地形因子,GIS软件为地形因子和气象因子的栅格化耦合解算提供了高效的工作平台,并为模拟结果的可视化制图提供了有力的工具。GIS技术和数据在气候要素精细化估算和可视化方面的作用强大,并有待更好更深入的开发和利用。
The paper firstly built two theoretical models to estimate respectively the precipitation background field and the precipitation increment caused by orographic lifting. And then, a fine-scale mixed models was established combining with the statistical methods, based on that, the spatial distribution of precipitation over the rugged terrain in Yangtze river basin was estimated with1km×1km resolution on GIS integrated the ground-based station data, digital elevation model (DEM) and NCEP reanalysis data. This paper obtained the following conclusions:
1. The precipitation background field theoretical model can provide the distribution of annual and monthly precipitation background field quantity in Yangtze river basin. The annual and monthly precipitation background field distribution could describe the climate distribution regular pattern and characteristics of precipitation. So this theoretical model was considered practicable and dependable.
2. The simulation result of correction factor for aspect could finely describe topographic characteristics of windward slopes and leeward slope. The annual and monthly precipitation increment quantity caused by orographic lifting could describe the topoclimate distribution regular pattern and characteristics of precipitation and could characterize the effect of moisture and topography conditions.
3. Combined with statistical method, the model coefficient was estimated. Ultimately, the fine-scale mixed models was established to estimate the spatial distribution of precipitation over the rugged terrain in Yangtze river basin which could reflect the comprehensive influence of meteorological elements including water vapour, wind speed and wind direction. Getting rid of the individual stations which errors was caused by data precision, the annual and monthly relative errors were all below20%. So the mixed model based on physical mechanism has reliable theory and results.
4. To explain the quality of the mixed model, this paper built a regression model based on the correction factor of slope and aspect. The corrected regression model could not indicate the physical connection between water vapor transmission and convergence with precipitation. Because of the variation of direction of water vapor transmission in each season, the difference of spatio-temporal distribution of precipitation with windward slope and leeward slope was not obvious. So the mixture model was better than corrected regression model in the simulation of terrain precipitation. But owing to the limitation of the physical equation form and the space precision(horizontal and vertical space) of meteorological data, the simulation results of mixture model also has some deficiency. For instance, because of the coupling calculation with NCEP data, the accuracy of altitude factor was lower, therefore, the influence of elevation to precipitation was less remarkable than the corrected regression model. So it was necessary to further perfect model, explore new solutions and new data, and improve the simulation accuracy.
5. In the model, DEM data provided the intensification terrain factors. GIS software provided an efficient work platform for the grid coupling of terrain factors and meteorological factors. It also provided a powerful tool for the visual mapping of simulated results. The function of GIS technology and GIS data was powerful in the refining estimation of climate elements and visualization. In addition, GIS technology and GIS data needed to be better further development and utilization.
引文
[1]Ahnert P R, Krajewski W F, Johnson E R. Kalman filter estimation of radar-rainfall field bias//Preprints,23rd Conf.on Radar Meteorology, AMS,1986:33-37.
[2]Ashraf M, Loftis J C, Hubbard K G. Application of geostatistics to evaluate partial station networks. Agric. for. Meteorol.,1997,84:225-271.
[3]Beyer,H.G, G.Czeplak,U. Terzenbach and L.Wald, Assessment of the method used to construct clearness index maps for the new European solar radiation Atlas(ESRA). Solar Energy,1977,61 (6):389-397.
[4]Bhargava M, Danard M. Application of optimum interpolation to the analysis of precipitation in complex terrain. J. Appl.Meteor.,1994,33:508-518.
[5]Brandes E.Optimizing rainfall estimates with the aid of radar. J. Appl.Meteor.,1975,14:1339-1345.
[6]Christopher Daly, Ronald P.Neilson, Donald L.Phillips, A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology,1993,33:140-158.
[7]Frouin R, Deschamps P Y, Lecomte P.Determination from Space of Atmospheric Total Water Vapor Amounts by Differential Absorption Near940 nm:Theory and Airborne Verification.App Meteoro,1990,29:448-460.
[8]Frouin R,Deschamps P Y,Lecomte P. Detemination from space of atmospheric total water vapor amounts by differential absorption near 940 nm:Theory and airborne verification. Journal of Applied Meteorology.1990.29(6):448-460.
[9]Gao B C, Goetz A F H.Column Atmospheric Water Vapor and Vegetation Liquid Water Retrievals from Airborne Imaging Spectrometer Data.Geophys Res,1990, 95:3549-3564.
[10]Germann U, Joss J.Spatial continuity of Alpine precipitation. Phys. Chem. Earth(B),2000,25:903-908.
[11]Goovaerts P. Comparison of Geostatistical Methods for Estimating the Areal Average Climatological Rainfall Mean Using Data on Precipitation and Topography. Int. J. Climate.2000,18:1031-1047.
[12]Gottschalk L,Batchvarova E,Gryning S E et al.Scale aggregation-comparison of flux estimates from NOPEX.Agri.For.Meteorol.,1999,98-99:103-119.
[13]Grant W Petty. The Status of Satellite-Based Rainfall Estimation over Land. REMOTE SENS.ENVIRON.1995,51:125-137.
[14]Hulme,M.,D.Conway,PD.Jones,T.Jiang, E.M.Barrow and C. Turney,A1961-1990 climatology for EuroPe for climate change modeling and impact applications. Int. J.of climatology,1995,15:1333-1364.
[15]Hutchinson M F. Interpolation of Rainfall Data with Thin Plate Smoothing Splines-Part Ⅱ:Analysis of Topographic Dependence.Journal of Geographic Information and Decision Analysis,1998,2:152-167.
[16]Hutchinson,M.F.,BoothT.H.,McMahon L.P and NinH.A.,Estimating monthly mean values of daily total solar radiation for Australia. Solar Energ,1984,32:277-290.
[17]Ian A N alder, Ro ssW W ein. Spat ial interpo lat ion of climat icNo rmals:test of a new method in the Canadian bo real fo rest. A gric. Fo r. M etero 1.,1998,92: 211-255.
[18]I.Poeeard, S.Janicot, P.Camberlin. Comparasion of rainfall structures between NCEP/NCAR reanalyses and observed data over tropical Africa. Climate Dynamics.2000,16(12):897-915.
[19]Kleespies T J,McMillin L M.Retrieval of precipitable water from observations in the split window over varying surface temperature. Journal of Applied Meteorology.1990.29:851-826.
[20]Krajewski W F.Cokriging radar-rainfall and rain gauge data.J.Geophys.Res.,1987, 92:9571-9580.
[21]Kravchenko A, ZHANG RenDuo,Jung Y K. Estimation of Mean Annual Precipitation in Wyoming Using Geo statistical Analysis. Proceedings of American Geophysical Union 16th Annual Hydrology Days,1996,271-282.
[22]Manabu Kanda. GPS Meteorology:Ground-Based and Space-Borne Applications, Proceedings of GPS meteorology.Tsukuba, Japan,2001,3-12.
[23]Nalder I A,Wein R W.Spatial interpolation of climate normals:test of a new method in the Canadian boreal forest.Agric.For.Meteorol.,1998,92:211-225.
[24]Naoum S, Tsanis I K. Orographic precipitation modeling with multiple linear regression. Journal of Hydrologic Engineering,2004,9:79-102.
[25]NIELSEN R Hecht. Theory of the back propagation neural network. Proc of IJCNN,1989,11:593-603.
[26]Ollinger S V, Aber J D, Federer C A, et al. Modeling physical and chemical climate of the northeastern United States for a geographic information system. Gen. Tech. Rep,1995,191.
[27]I.Poccard, S Janicot, P.Camberlin. Comparasion of rainfall struetures between NCEP/NCAR reanalyses and observed data over tropical Africa. Climate Dynamics.2000,16(12):897-915.
[28]Price D T, Mckenney D W, Nalder I A et al. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric.For.Meteorol.,2000,101:81-94.
[29]Reagan JA, Pilewskie PA, Scott-Fleming IC, eta. Extrapolation of Earth-based Solar Irradiance Measurements to Exoatmospheric Levels for Broad-band and Selected Absorption band Observations.IEEE Transactions on Geo-science and Remote Sensing,1987,25(6):647-653.
[30]Reid, P.A., P, D.Jones, O.Brown et al. Assessment of the reliability of NCEP circulation data and relationship with surface by direct comparison with station based data. Climate Researeh.2001,17(3):247-261.
[31]Sasaki Y.Some basic formalisms in numerical variation analysis.Mon.Wea.Rev.,1970,98:875-883.
[32]Schlussel P,Emery W.Atmospheric water vapor over ocean from special sensor microwaveP imager observations.International Journal of Remote Sensing.1990.11:753-766.
[33]Seo D J, Krajewski W F, Azimi-Zonooz A, et al. Stochastic interpolation of rainfall data from rain gages and radar using cokriging 2. Results. Water Resour. Res.,1990b,26:915-924.
[34]Seo D J, Krajewski W F, Bowles D S. Stochastic interpolation of rainfall data from rain gauges and radar using cokriging 1.Design of experiments. Water Resour. Res.,1990a,26:469-477.
[35]Seo D J. Real-time estimation of rainfall fields using rain gauge data under fractional coverage conditions. J. Hydrol.,1998,208:25-36.
[36]Sevruk B, Nevenic M. The geography and topography effects on the area pattern of precipitation in a Amall Prealpline Basin. Wat. Sci. Tech.1998.37:163-170.
[37]Shafiqur Rehman, Saleem G Ghori, Spatial estimation of global solar radiation using geostatistics. Renewable Energy,2000,21:583-605.
[38]Smiths, S.R., Legler, D.M., Verzone, K.V. Quantifying Uncertainties in NCEP Reanalyses Using High-Quality Research Vessel Observations. Journal of Climate.2001,14(20):4062-4072.
[39]Suckling, p.w, Estimating daily solar radiation values in selected mid-latitude regions by extra-polating measurements from nearby stations. Solar Energy,1985,35:491-499.
[40]Tanguay M, Robert A. An efficient optimum interpolation analysis scheme. Atmos. Ocean,1990,28:365-377.
[41]Wilson J W, Brandes E A. Radar measurement of rainfall—A summary. Bull. Amer. Meteor. Soc.,1979,60 (9):1048-1058.
[42]Woltling G, Bouvier C, Dan loux J, et al. Regionalization of extreme precipitation distribution using the principal components of the topographical environment. Journal of Hydrology.2000,233:86-101.
[43]Yoram J Kaufman, Gao Bo Cai.Remote Sensing of Water Vaporin the Near IR from EOS/MODIS.IEEE Transactions on Geo-science and Remote Sensing,1992, 5(30):871-884.
[44]Zhang R H. Relations of water vapor transport from Indian monsoon with that over east Asia and the summer rainfall in China. Adv Atmos Sci.2002.18:21-37.
[45]白爱娟,方建刚,张科翔TRMM卫星资料对陕西及周边地区夏季降水的探测.灾害学,2008,23(2)：41-45.
[46]毕宝贵,徐晶,林建.面雨量计算方法及其在海河流域的应用.气象.2003,29(8)：39-42.
[47]蔡福,于慧波,矫玲玲,等.降水要素空间插值精度的比较.资源科学,2006,28(6)：73-79.
[48]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.
[49]曹云昌,方宗义,夏青.GPS遥感的大气可降水量与局地降水关系的初步分析.应用气象学报,2005,16(1)：54-59.
[50]陈贺,李原园,杨志峰,等.地形因素对降水分布影响的研究.水土保持研究,2007,14(1)：119-122.
[51]陈洪滨,王普才等.从SSM/I亮温反演海洋上大气可降水量.遥感技术与应用,1998,3：1-6.
[52]陈洪滨,吴北婴,章文星等.从红外太阳透过率反演大气可降水量的研究.大气科学,1996,20(5)：628-632.
[53]陈晓峰,刘纪远,张增祥,等.利用GIS方法建立山区温度分布模型.中国图像图形学报,1998,3(3)：235-238.
[54]陈艳英,高阳华,等.面雨量空间扩展估算方法.气象科技,2010,38(1)：9-14.
[55]崔彩霞,杨青,赵玲等.MODIS近红外大气可降水含量与乌鲁木齐地基GPS水汽数据的对比分析.2008,02(1)：01-04.
[56]范广洲,吕世华.地形对华北地区夏季降水影响的数值模拟研究.高原气象,1999,18(4)：659-667.
[57]封志明,杨艳昭,丁晓强,等.气象要素空间插值方法优化.地理研究,2004,23(3)：357-364.
[58]付晓辉,肖稳安,龙利民,等.数值预报产品在长江干流段面雨量概率预报中的释用.长江流域资源与环境,2006,15(4)：537-540.
[59]傅抱璞.地形和海拔高度对降水的影响.地理学报,1992,47(4)：302-314.
[60]傅抱璞.山地气候.北京：科学出版社,1983.
[61]谷晓平,王长耀,袁淑杰.GA-BP神经网络模型在流域面雨量预报的应用研究.热带气象学报,2006,22(3)：248-252.
[62]郭迎春.太行山燕山山地降水推算方法研究.地理学与国土研究,1994,10(3)：35-39.
[63]黄家洁,万幼川,刘良明.MODIS的特性及其应用.2003,01(4)：20-23.
[64]黄杏元,马劲松等,地理信息系统概论.高等教育出版社,2008：38-39.
[65]蒋耿明,刘荣高,牛铮MODIS 1B影像几何纠正方法研究及软件实现.遥感学报,2004,(8)：158-163.
[66]蒋忠信.山地降水垂直分布模式讨论.地理研究,1988,7(1)：73-78.
[67]康红文,谷湘潜,祝从文.我国中部和南部地区降水再循环率评估.大气科学,2004,28(6)：892-900.
[68]康红文,谷湘潜,付翔,徐祥德.我国北方地区降水再循环率的初步评估.应用气象学报,2005,16(2)：139-147.
[69]李才媛,宋清翠,金琪.短期强降水面雨量预报与T213产品的天气学释用.气象,2003,29(3)：27-31.
[70]李国平,黄丁发,等.成都地区地基GPS观测网遥感大气可降水量的初步试验.武汉大学学报,2006,31(12)：1-6.
[71]李红林,李万彪MODIS近红外资料反演大气水汽总含量.北京大学学报·自然科学版.2008.44(1)：121-128.
[72]李建通,高守亭,郭林,等.基于分步校准的区域降水量估测方法研究.大气科学,2009,33(3)：501-512.
[73]李建通,杨维生,郭林,等.2000.提高最优插值法测量区域降水量精度的探讨.大气科学,24(2)：263-270.
[74]李丽娟,王娟,李海滨.无定河流域降雨量空间变异性研究.地理研究,2002,21(4)：434-440
[75]李柳霞.MODIS影像数据预处理技术研究.中国农业大学农业·遥感与土地信息系统专业硕士论文.2004.
[76]李霞,张广兴.天山可降水量和降水转化率的研究.中国沙漠,2003,23(5)：510-513.
[77]李新,程国栋,陈贤章,等.任意条件下太阳辐射模型的改进.科学通报,1999(5)：993-998.
[78]李新,程国栋,卢玲.空间内插方法比较.地球科学进展,2000,15(3)：260-265.
[79]李新,程国栋,卢玲.青藏高原气温分布的空间插值方法比较.高原气象,2003,22(6)：565-573.
[80]连志鸾,李国翠.石家庄的云、降水和水汽特征.气象科技,2005,33(增刊)：21-26.
[81]梁树献,刘小虎,等.距离加权平均法计算流域平均面雨量.新世纪气象科技创新与大气科学发展——中国气象学会2003年年会"03.7淮河大水的水文气象学问题”分会论文集.2003.315-317.
[82]廖菲,洪延超,郑国光.地形对降水的影响研究概述.气象科技,2007,35(3)：309-316.
[83]林必元,张维恒.地形对降水影响的研究.北京：气象出版社,1996.
[84]林炳干,张培昌,顾松山.1997.天气雷达测定区域降水量方法的改进与比较.南京气象学院学报,1997,20(3)：334-340.
[85]林之光,地形降水气候学.科学出版社,1995：91
[86]刘鹏,傅云飞,冯沙,等.中国南方地基雨量计观测与星载测雨雷达探测降水的比较分析.气象学报,2010,68(6).822-835.
[87]刘玉洁,杨忠东.MODIS遥感信息处理原理与算法.北京：科学出版社,2001：66-74.
[88]卢毅敏,岳天祥,陈传法,等.中国区域年降水空间分布高精度曲面建模.自然资源学报,2010,25(7)：1194-1205.
[89]罗琦,李栋梁,张杰.青藏高原东部雨季降水量分布模型的建立.干旱区研究,2007,24(6)：766-772.
[90]毛克彪,覃志豪.用MODIS影像反演环渤海地区的大气水汽含量.遥感信息应用技术,2004,24(4)：47-49.
[91]孟宪红,吕世华,张堂堂.MODIS近红外水汽产品的检验、改进及初步应用—以黑河流域金塔绿洲为例.红外与毫米波学报.2007.26(2)：107-111.
[92]穆振侠,等.基于TRMM TMI的天山西部山区降水研究.干旱区资源与环境,2010,24(7)：115-119.
[93]潘永地,方庆文.有关面雨量研究综述.贵州气象,2004,5(28)：3-7.
[94]彭乃志,傅抱璞.我国地形与暴雨的若干气候统计分析.气象科 学,1995,15(3)：288-292.
[95]邱冰等.基于MODIS数据的降水估算在博斯腾湖流域的应用.干旱区研究,2010,27(5)：675-679.
[96]邱新法,仇月萍,曾燕.重庆山地月平均气温分布式模拟研究.地球科学进展,2009,24(6)：621-628.
[97]权维俊,陈洪滨,肖稳安MODIS与NCEP大气可降水量资料的比较分析.南京气象学院学报,2004,27(2)：169-177.
[98]史玉光,孙照渤,杨青.新疆区域面雨量分布特征及其变化规律.应用气象学报,2008,19(3)：326-332.
[99]舒守娟,王元,储惠芸,等.地理和地形影响下我国区域的气温空间分布.南京大学学报(自然科学),2009,45(3)：334-342.
[100]舒守娟.中国区域地理_地形因子对降水分布影响的估算和分析,地球物理学报,2007,50(6)：1704-1712,
[101]苏宏新,桑卫国.山地小气候模拟研究进展.植物生态学报,2002,26(增刊)：107-114.
[102]苏立娟.内蒙古地区云、降水和可降水量的时空分布、变化趋势及其与气候因子的关系.兰州大学,2005,12：1-10.
[103]孙佳,江灏,等.黑河流域气候平均降水的精细化分布及总量计算.冰川冻土,2011,32(2)：318-324.
[104]田红,郭品文,陆维松.夏季水汽输送特征及其与中国降水异常的关系.热带气象学报,2002,25(4)：496-502.
[105]汪璇,吕家恪,刘洪斌,等.基于GIS的重庆农业气候资源空间分布精细模拟研究.中国农业通报,2009,25(14)：256-262.
[106]王成刚,吴宝俊.BP网络在鲁西南地区西南涡降水量级预报中的应用试验.气象科学,1999,19(2)：158-165.
[107]王菱.华北山区年降水量的推算和分布特征.地理学报,1996,51(2)：164-171.
[108]王菱.华北山区坡地方位和海拔高度对降水的影响.地理科学,1996,16(2)：150-158.
[109]王守荣,黄荣辉,丁一汇,等.分布式水文-土壤-植被模式的改进及气候水文 off-line模拟试验.气象学报,2002,60(3)：290-300.
[110]王正兴.MODIS数据产品与应用.第一部分MODIS概况,全国生态环境监测与评价技术培训班.2008年4月9日.
[111]伍立群,李学辉.高山地区年降水量随高程变化分析.云南地理环境研究,2004,16(2)4-7.
[112]武汉大学MODIS卫星数据地面接收站http://modis.whu.edu.cn/chinese /context/modis.php2008年10月26日访问.
[113]熊秋芬,王丽,郑启松,等.三峡区间面雨量预报方法及其预报试验.气象,2000,26(10)：19-23.
[114]徐晶,林建,毕宝贵,等.七大江河流域面雨量计算方法及应用.气象,2001,27(11)：13-51.
[115]徐晶,姚学祥.流域面雨量估算技术综述.气象,2007,7(33)：15-21.
[116]徐影,丁一汇,赵宗慈.美国NCEP/NCAR近50年全球在分析资料在我国气候变化研究中可信度的初步分析.应用气象学报.2001,12(3)：337-347.
[117]闫育华,赖洪年.利用降水平均递增率求山地最大降水高度.地理研究,1987,6(1)：62-67.
[118]杨红梅,葛润生,徐宝祥.用单站探空资料分析对流层汽柱水汽总量.气象,1998,24(9)：8-11.
[119]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)：10-22.
[120]杨青,史玉光,袁玉江,等.基于DEM的天山山区气温和降水序列推算方法研究.冰川冻土,2006,28：337-340.
[121]杨青,赵玲,等MODIS近红外大气可降水含量与乌鲁木齐地基GPS水汽数据的对比分析.2008,02(1)：01-04.
[122]尹忠海,张沛源.2005.利用卡尔曼滤波校准方法估算区域降水量.应用气象学报,16(2)：213-219.
[123]喻洁,喻家龙.山地降水垂直分布三参数高斯模式及其应用.地理研究,1996,15(4)：82-86.
[124]曾燕,邱新法,刘昌明等.基于DEM的黄河流域天文辐射空间分布.地理学报,2003,58(6)：810-816.
[125]曾燕,邱新法,刘绍民.起伏地形下天文辐射分布式估算模型[J].地球物理学报.2005,48(5)：1028-1033.
[126]曾燕,邱新法,刘昌明,等.起伏地形下黄河流域太阳直接辐射分布式模拟.地理学报.2005,60(4)：680-687.
[127]张强,张杰,孙国武,等.祁连山山区空中水汽分布特征研究.气象学报,2007,65(4)：633-643.
[128]赵登忠,张万昌,刘三超.基于DEM的地理要素PRISM空间内插研究.地理科学,2004,24(2)：205-211.
[129]赵坤,葛文忠,刘国庆,等.2005.在雷达测雨和洪水预报中自适应卡尔曼滤波法的应用.高原气象,24(6)：956-965.
[130]赵天保,艾丽坤,冯景明NCEP再分析资料和中国站点观测资料的分析与比较.气候与环境研究.2004,9(2)：278-294.
[131]周锁铨,薛根元,周丽峰,等.基于GIS降水空间分析的逐步插值方法.气象学报,2006,64(1)：100-111.
[132]周天军,张学洪,王绍武.全球水循环的海洋分量研究.气象学报,1999,57(3)：264-282.
[133]周晓霞,丁一汇,王盘兴.夏季亚洲季风区的水汽输送及其对中国降水的影响.气象学报.2006.66(1)：59-70.
[134]朱红芳,王东勇,朱鹏飞,等.GRAPES模式在淮河流域面雨量预报中的应用.气象,2007,33(3)：76-82.
[135]朱乾根,天气学原理和方法.气象学出版社,2000：319.
[136]朱文明.基于MODIS大气水汽的时间变化及其与气象因子的相关分析.南京师范大学硕士论文,2007.
[2]Ashraf M, Loftis J C, Hubbard K G. Application of geostatistics to evaluate partial station networks. Agric. for. Meteorol.,1997,84:225-271.
[3]Beyer,H.G, G.Czeplak,U. Terzenbach and L.Wald, Assessment of the method used to construct clearness index maps for the new European solar radiation Atlas(ESRA). Solar Energy,1977,61 (6):389-397.
[4]Bhargava M, Danard M. Application of optimum interpolation to the analysis of precipitation in complex terrain. J. Appl.Meteor.,1994,33:508-518.
[5]Brandes E.Optimizing rainfall estimates with the aid of radar. J. Appl.Meteor.,1975,14:1339-1345.
[6]Christopher Daly, Ronald P.Neilson, Donald L.Phillips, A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology,1993,33:140-158.
[7]Frouin R, Deschamps P Y, Lecomte P.Determination from Space of Atmospheric Total Water Vapor Amounts by Differential Absorption Near940 nm:Theory and Airborne Verification.App Meteoro,1990,29:448-460.
[8]Frouin R,Deschamps P Y,Lecomte P. Detemination from space of atmospheric total water vapor amounts by differential absorption near 940 nm:Theory and airborne verification. Journal of Applied Meteorology.1990.29(6):448-460.
[9]Gao B C, Goetz A F H.Column Atmospheric Water Vapor and Vegetation Liquid Water Retrievals from Airborne Imaging Spectrometer Data.Geophys Res,1990, 95:3549-3564.
[10]Germann U, Joss J.Spatial continuity of Alpine precipitation. Phys. Chem. Earth(B),2000,25:903-908.
[11]Goovaerts P. Comparison of Geostatistical Methods for Estimating the Areal Average Climatological Rainfall Mean Using Data on Precipitation and Topography. Int. J. Climate.2000,18:1031-1047.
[12]Gottschalk L,Batchvarova E,Gryning S E et al.Scale aggregation-comparison of flux estimates from NOPEX.Agri.For.Meteorol.,1999,98-99:103-119.
[13]Grant W Petty. The Status of Satellite-Based Rainfall Estimation over Land. REMOTE SENS.ENVIRON.1995,51:125-137.
[14]Hulme,M.,D.Conway,PD.Jones,T.Jiang, E.M.Barrow and C. Turney,A1961-1990 climatology for EuroPe for climate change modeling and impact applications. Int. J.of climatology,1995,15:1333-1364.
[15]Hutchinson M F. Interpolation of Rainfall Data with Thin Plate Smoothing Splines-Part Ⅱ:Analysis of Topographic Dependence.Journal of Geographic Information and Decision Analysis,1998,2:152-167.
[16]Hutchinson,M.F.,BoothT.H.,McMahon L.P and NinH.A.,Estimating monthly mean values of daily total solar radiation for Australia. Solar Energ,1984,32:277-290.
[17]Ian A N alder, Ro ssW W ein. Spat ial interpo lat ion of climat icNo rmals:test of a new method in the Canadian bo real fo rest. A gric. Fo r. M etero 1.,1998,92: 211-255.
[18]I.Poeeard, S.Janicot, P.Camberlin. Comparasion of rainfall structures between NCEP/NCAR reanalyses and observed data over tropical Africa. Climate Dynamics.2000,16(12):897-915.
[19]Kleespies T J,McMillin L M.Retrieval of precipitable water from observations in the split window over varying surface temperature. Journal of Applied Meteorology.1990.29:851-826.
[20]Krajewski W F.Cokriging radar-rainfall and rain gauge data.J.Geophys.Res.,1987, 92:9571-9580.
[21]Kravchenko A, ZHANG RenDuo,Jung Y K. Estimation of Mean Annual Precipitation in Wyoming Using Geo statistical Analysis. Proceedings of American Geophysical Union 16th Annual Hydrology Days,1996,271-282.
[22]Manabu Kanda. GPS Meteorology:Ground-Based and Space-Borne Applications, Proceedings of GPS meteorology.Tsukuba, Japan,2001,3-12.
[23]Nalder I A,Wein R W.Spatial interpolation of climate normals:test of a new method in the Canadian boreal forest.Agric.For.Meteorol.,1998,92:211-225.
[24]Naoum S, Tsanis I K. Orographic precipitation modeling with multiple linear regression. Journal of Hydrologic Engineering,2004,9:79-102.
[25]NIELSEN R Hecht. Theory of the back propagation neural network. Proc of IJCNN,1989,11:593-603.
[26]Ollinger S V, Aber J D, Federer C A, et al. Modeling physical and chemical climate of the northeastern United States for a geographic information system. Gen. Tech. Rep,1995,191.
[27]I.Poccard, S Janicot, P.Camberlin. Comparasion of rainfall struetures between NCEP/NCAR reanalyses and observed data over tropical Africa. Climate Dynamics.2000,16(12):897-915.
[28]Price D T, Mckenney D W, Nalder I A et al. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric.For.Meteorol.,2000,101:81-94.
[29]Reagan JA, Pilewskie PA, Scott-Fleming IC, eta. Extrapolation of Earth-based Solar Irradiance Measurements to Exoatmospheric Levels for Broad-band and Selected Absorption band Observations.IEEE Transactions on Geo-science and Remote Sensing,1987,25(6):647-653.
[30]Reid, P.A., P, D.Jones, O.Brown et al. Assessment of the reliability of NCEP circulation data and relationship with surface by direct comparison with station based data. Climate Researeh.2001,17(3):247-261.
[31]Sasaki Y.Some basic formalisms in numerical variation analysis.Mon.Wea.Rev.,1970,98:875-883.
[32]Schlussel P,Emery W.Atmospheric water vapor over ocean from special sensor microwaveP imager observations.International Journal of Remote Sensing.1990.11:753-766.
[33]Seo D J, Krajewski W F, Azimi-Zonooz A, et al. Stochastic interpolation of rainfall data from rain gages and radar using cokriging 2. Results. Water Resour. Res.,1990b,26:915-924.
[34]Seo D J, Krajewski W F, Bowles D S. Stochastic interpolation of rainfall data from rain gauges and radar using cokriging 1.Design of experiments. Water Resour. Res.,1990a,26:469-477.
[35]Seo D J. Real-time estimation of rainfall fields using rain gauge data under fractional coverage conditions. J. Hydrol.,1998,208:25-36.
[36]Sevruk B, Nevenic M. The geography and topography effects on the area pattern of precipitation in a Amall Prealpline Basin. Wat. Sci. Tech.1998.37:163-170.
[37]Shafiqur Rehman, Saleem G Ghori, Spatial estimation of global solar radiation using geostatistics. Renewable Energy,2000,21:583-605.
[38]Smiths, S.R., Legler, D.M., Verzone, K.V. Quantifying Uncertainties in NCEP Reanalyses Using High-Quality Research Vessel Observations. Journal of Climate.2001,14(20):4062-4072.
[39]Suckling, p.w, Estimating daily solar radiation values in selected mid-latitude regions by extra-polating measurements from nearby stations. Solar Energy,1985,35:491-499.
[40]Tanguay M, Robert A. An efficient optimum interpolation analysis scheme. Atmos. Ocean,1990,28:365-377.
[41]Wilson J W, Brandes E A. Radar measurement of rainfall—A summary. Bull. Amer. Meteor. Soc.,1979,60 (9):1048-1058.
[42]Woltling G, Bouvier C, Dan loux J, et al. Regionalization of extreme precipitation distribution using the principal components of the topographical environment. Journal of Hydrology.2000,233:86-101.
[43]Yoram J Kaufman, Gao Bo Cai.Remote Sensing of Water Vaporin the Near IR from EOS/MODIS.IEEE Transactions on Geo-science and Remote Sensing,1992, 5(30):871-884.
[44]Zhang R H. Relations of water vapor transport from Indian monsoon with that over east Asia and the summer rainfall in China. Adv Atmos Sci.2002.18:21-37.
[45]白爱娟,方建刚,张科翔TRMM卫星资料对陕西及周边地区夏季降水的探测.灾害学,2008,23(2)：41-45.
[46]毕宝贵,徐晶,林建.面雨量计算方法及其在海河流域的应用.气象.2003,29(8)：39-42.
[47]蔡福,于慧波,矫玲玲,等.降水要素空间插值精度的比较.资源科学,2006,28(6)：73-79.
[48]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.
[49]曹云昌,方宗义,夏青.GPS遥感的大气可降水量与局地降水关系的初步分析.应用气象学报,2005,16(1)：54-59.
[50]陈贺,李原园,杨志峰,等.地形因素对降水分布影响的研究.水土保持研究,2007,14(1)：119-122.
[51]陈洪滨,王普才等.从SSM/I亮温反演海洋上大气可降水量.遥感技术与应用,1998,3：1-6.
[52]陈洪滨,吴北婴,章文星等.从红外太阳透过率反演大气可降水量的研究.大气科学,1996,20(5)：628-632.
[53]陈晓峰,刘纪远,张增祥,等.利用GIS方法建立山区温度分布模型.中国图像图形学报,1998,3(3)：235-238.
[54]陈艳英,高阳华,等.面雨量空间扩展估算方法.气象科技,2010,38(1)：9-14.
[55]崔彩霞,杨青,赵玲等.MODIS近红外大气可降水含量与乌鲁木齐地基GPS水汽数据的对比分析.2008,02(1)：01-04.
[56]范广洲,吕世华.地形对华北地区夏季降水影响的数值模拟研究.高原气象,1999,18(4)：659-667.
[57]封志明,杨艳昭,丁晓强,等.气象要素空间插值方法优化.地理研究,2004,23(3)：357-364.
[58]付晓辉,肖稳安,龙利民,等.数值预报产品在长江干流段面雨量概率预报中的释用.长江流域资源与环境,2006,15(4)：537-540.
[59]傅抱璞.地形和海拔高度对降水的影响.地理学报,1992,47(4)：302-314.
[60]傅抱璞.山地气候.北京：科学出版社,1983.
[61]谷晓平,王长耀,袁淑杰.GA-BP神经网络模型在流域面雨量预报的应用研究.热带气象学报,2006,22(3)：248-252.
[62]郭迎春.太行山燕山山地降水推算方法研究.地理学与国土研究,1994,10(3)：35-39.
[63]黄家洁,万幼川,刘良明.MODIS的特性及其应用.2003,01(4)：20-23.
[64]黄杏元,马劲松等,地理信息系统概论.高等教育出版社,2008：38-39.
[65]蒋耿明,刘荣高,牛铮MODIS 1B影像几何纠正方法研究及软件实现.遥感学报,2004,(8)：158-163.
[66]蒋忠信.山地降水垂直分布模式讨论.地理研究,1988,7(1)：73-78.
[67]康红文,谷湘潜,祝从文.我国中部和南部地区降水再循环率评估.大气科学,2004,28(6)：892-900.
[68]康红文,谷湘潜,付翔,徐祥德.我国北方地区降水再循环率的初步评估.应用气象学报,2005,16(2)：139-147.
[69]李才媛,宋清翠,金琪.短期强降水面雨量预报与T213产品的天气学释用.气象,2003,29(3)：27-31.
[70]李国平,黄丁发,等.成都地区地基GPS观测网遥感大气可降水量的初步试验.武汉大学学报,2006,31(12)：1-6.
[71]李红林,李万彪MODIS近红外资料反演大气水汽总含量.北京大学学报·自然科学版.2008.44(1)：121-128.
[72]李建通,高守亭,郭林,等.基于分步校准的区域降水量估测方法研究.大气科学,2009,33(3)：501-512.
[73]李建通,杨维生,郭林,等.2000.提高最优插值法测量区域降水量精度的探讨.大气科学,24(2)：263-270.
[74]李丽娟,王娟,李海滨.无定河流域降雨量空间变异性研究.地理研究,2002,21(4)：434-440
[75]李柳霞.MODIS影像数据预处理技术研究.中国农业大学农业·遥感与土地信息系统专业硕士论文.2004.
[76]李霞,张广兴.天山可降水量和降水转化率的研究.中国沙漠,2003,23(5)：510-513.
[77]李新,程国栋,陈贤章,等.任意条件下太阳辐射模型的改进.科学通报,1999(5)：993-998.
[78]李新,程国栋,卢玲.空间内插方法比较.地球科学进展,2000,15(3)：260-265.
[79]李新,程国栋,卢玲.青藏高原气温分布的空间插值方法比较.高原气象,2003,22(6)：565-573.
[80]连志鸾,李国翠.石家庄的云、降水和水汽特征.气象科技,2005,33(增刊)：21-26.
[81]梁树献,刘小虎,等.距离加权平均法计算流域平均面雨量.新世纪气象科技创新与大气科学发展——中国气象学会2003年年会"03.7淮河大水的水文气象学问题”分会论文集.2003.315-317.
[82]廖菲,洪延超,郑国光.地形对降水的影响研究概述.气象科技,2007,35(3)：309-316.
[83]林必元,张维恒.地形对降水影响的研究.北京：气象出版社,1996.
[84]林炳干,张培昌,顾松山.1997.天气雷达测定区域降水量方法的改进与比较.南京气象学院学报,1997,20(3)：334-340.
[85]林之光,地形降水气候学.科学出版社,1995：91
[86]刘鹏,傅云飞,冯沙,等.中国南方地基雨量计观测与星载测雨雷达探测降水的比较分析.气象学报,2010,68(6).822-835.
[87]刘玉洁,杨忠东.MODIS遥感信息处理原理与算法.北京：科学出版社,2001：66-74.
[88]卢毅敏,岳天祥,陈传法,等.中国区域年降水空间分布高精度曲面建模.自然资源学报,2010,25(7)：1194-1205.
[89]罗琦,李栋梁,张杰.青藏高原东部雨季降水量分布模型的建立.干旱区研究,2007,24(6)：766-772.
[90]毛克彪,覃志豪.用MODIS影像反演环渤海地区的大气水汽含量.遥感信息应用技术,2004,24(4)：47-49.
[91]孟宪红,吕世华,张堂堂.MODIS近红外水汽产品的检验、改进及初步应用—以黑河流域金塔绿洲为例.红外与毫米波学报.2007.26(2)：107-111.
[92]穆振侠,等.基于TRMM TMI的天山西部山区降水研究.干旱区资源与环境,2010,24(7)：115-119.
[93]潘永地,方庆文.有关面雨量研究综述.贵州气象,2004,5(28)：3-7.
[94]彭乃志,傅抱璞.我国地形与暴雨的若干气候统计分析.气象科 学,1995,15(3)：288-292.
[95]邱冰等.基于MODIS数据的降水估算在博斯腾湖流域的应用.干旱区研究,2010,27(5)：675-679.
[96]邱新法,仇月萍,曾燕.重庆山地月平均气温分布式模拟研究.地球科学进展,2009,24(6)：621-628.
[97]权维俊,陈洪滨,肖稳安MODIS与NCEP大气可降水量资料的比较分析.南京气象学院学报,2004,27(2)：169-177.
[98]史玉光,孙照渤,杨青.新疆区域面雨量分布特征及其变化规律.应用气象学报,2008,19(3)：326-332.
[99]舒守娟,王元,储惠芸,等.地理和地形影响下我国区域的气温空间分布.南京大学学报(自然科学),2009,45(3)：334-342.
[100]舒守娟.中国区域地理_地形因子对降水分布影响的估算和分析,地球物理学报,2007,50(6)：1704-1712,
[101]苏宏新,桑卫国.山地小气候模拟研究进展.植物生态学报,2002,26(增刊)：107-114.
[102]苏立娟.内蒙古地区云、降水和可降水量的时空分布、变化趋势及其与气候因子的关系.兰州大学,2005,12：1-10.
[103]孙佳,江灏,等.黑河流域气候平均降水的精细化分布及总量计算.冰川冻土,2011,32(2)：318-324.
[104]田红,郭品文,陆维松.夏季水汽输送特征及其与中国降水异常的关系.热带气象学报,2002,25(4)：496-502.
[105]汪璇,吕家恪,刘洪斌,等.基于GIS的重庆农业气候资源空间分布精细模拟研究.中国农业通报,2009,25(14)：256-262.
[106]王成刚,吴宝俊.BP网络在鲁西南地区西南涡降水量级预报中的应用试验.气象科学,1999,19(2)：158-165.
[107]王菱.华北山区年降水量的推算和分布特征.地理学报,1996,51(2)：164-171.
[108]王菱.华北山区坡地方位和海拔高度对降水的影响.地理科学,1996,16(2)：150-158.
[109]王守荣,黄荣辉,丁一汇,等.分布式水文-土壤-植被模式的改进及气候水文 off-line模拟试验.气象学报,2002,60(3)：290-300.
[110]王正兴.MODIS数据产品与应用.第一部分MODIS概况,全国生态环境监测与评价技术培训班.2008年4月9日.
[111]伍立群,李学辉.高山地区年降水量随高程变化分析.云南地理环境研究,2004,16(2)4-7.
[112]武汉大学MODIS卫星数据地面接收站http://modis.whu.edu.cn/chinese /context/modis.php2008年10月26日访问.
[113]熊秋芬,王丽,郑启松,等.三峡区间面雨量预报方法及其预报试验.气象,2000,26(10)：19-23.
[114]徐晶,林建,毕宝贵,等.七大江河流域面雨量计算方法及应用.气象,2001,27(11)：13-51.
[115]徐晶,姚学祥.流域面雨量估算技术综述.气象,2007,7(33)：15-21.
[116]徐影,丁一汇,赵宗慈.美国NCEP/NCAR近50年全球在分析资料在我国气候变化研究中可信度的初步分析.应用气象学报.2001,12(3)：337-347.
[117]闫育华,赖洪年.利用降水平均递增率求山地最大降水高度.地理研究,1987,6(1)：62-67.
[118]杨红梅,葛润生,徐宝祥.用单站探空资料分析对流层汽柱水汽总量.气象,1998,24(9)：8-11.
[119]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)：10-22.
[120]杨青,史玉光,袁玉江,等.基于DEM的天山山区气温和降水序列推算方法研究.冰川冻土,2006,28：337-340.
[121]杨青,赵玲,等MODIS近红外大气可降水含量与乌鲁木齐地基GPS水汽数据的对比分析.2008,02(1)：01-04.
[122]尹忠海,张沛源.2005.利用卡尔曼滤波校准方法估算区域降水量.应用气象学报,16(2)：213-219.
[123]喻洁,喻家龙.山地降水垂直分布三参数高斯模式及其应用.地理研究,1996,15(4)：82-86.
[124]曾燕,邱新法,刘昌明等.基于DEM的黄河流域天文辐射空间分布.地理学报,2003,58(6)：810-816.
[125]曾燕,邱新法,刘绍民.起伏地形下天文辐射分布式估算模型[J].地球物理学报.2005,48(5)：1028-1033.
[126]曾燕,邱新法,刘昌明,等.起伏地形下黄河流域太阳直接辐射分布式模拟.地理学报.2005,60(4)：680-687.
[127]张强,张杰,孙国武,等.祁连山山区空中水汽分布特征研究.气象学报,2007,65(4)：633-643.
[128]赵登忠,张万昌,刘三超.基于DEM的地理要素PRISM空间内插研究.地理科学,2004,24(2)：205-211.
[129]赵坤,葛文忠,刘国庆,等.2005.在雷达测雨和洪水预报中自适应卡尔曼滤波法的应用.高原气象,24(6)：956-965.
[130]赵天保,艾丽坤,冯景明NCEP再分析资料和中国站点观测资料的分析与比较.气候与环境研究.2004,9(2)：278-294.
[131]周锁铨,薛根元,周丽峰,等.基于GIS降水空间分析的逐步插值方法.气象学报,2006,64(1)：100-111.
[132]周天军,张学洪,王绍武.全球水循环的海洋分量研究.气象学报,1999,57(3)：264-282.
[133]周晓霞,丁一汇,王盘兴.夏季亚洲季风区的水汽输送及其对中国降水的影响.气象学报.2006.66(1)：59-70.
[134]朱红芳,王东勇,朱鹏飞,等.GRAPES模式在淮河流域面雨量预报中的应用.气象,2007,33(3)：76-82.
[135]朱乾根,天气学原理和方法.气象学出版社,2000：319.
[136]朱文明.基于MODIS大气水汽的时间变化及其与气象因子的相关分析.南京师范大学硕士论文,2007.