基于雷达组网拼图的定量降水估测算法优化及效果评估
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摘要
随着灾害性天气近年来的频繁发生，高时空分辨率的雷达定量降水估测信息，在暴雨、台风、洪水等灾害性天气的短时临近和精细化预警预报等工作中发挥了越来越重要的作用。
基于雷达三维组网拼图数据的定量降水估测（QPE:Quantitive PrecipationEstimation）算法业已在业务中得到初步应用，虽然表现出良好的稳定性和实时性，但目前并未开展较为全面的评估和分析工作。同时，Z-R关系、雷达波束部分遮挡和雷达-雨量计融合方法等因素，严重限制了雷达QPE的精度，在降水估测中，这些算法环节均待进一步优化改进。因此，本论文首先评估了基于雷达组网拼图的QPE算法的业务应用效果，同时，在Z-R关系、波束遮挡订正和融合方法环节使用新的技术手段对雷达QPE算法进行优化改进，以期提高雷达降水估测的精度。主要的工作和结论包括以下几个方面的内容：
（1）以雷达反射率因子廓线作为分析工具，评估了基于雷达组网拼图的QPE算法在杭州市气象局的业务应用效果。分析了算法在不同类型天气过程的适用能力，以及影响雷达降水估测的主要误差源：层状云过程中，如果完全遮挡和回波亮带效应并存，会导致雷达QPE的高估；回波云顶的偏弱雷达回波则会导致雷达QPE的低估；在梅雨锋和台风过程中，不同类型降水并存，这使单一动态Z-R关系拟合方案存在明显的不适用性；伴随飑线的强对流以及台风本身的非对称结构特征，都会严重降低雷达QPE的精度。
（2）针对多尺度天气过程中不同类型降水并存，提出了基于云团的分组Z-R关系拟合方案，用来改善雷达QPE初值场的精度。四种不同类型天气过程中效果评估对比表明，该方案在梅雨锋、台风、飑线和层状云降水过程中，均显著优于单一动态Z-R关系方案和简单分组Z-R关系拟合方案，可以有效提高雷达QPE初值场的精度，具有更好的降水估测效果和业务应用能力。
（3）针对雷达波束部分遮挡造成的雷达QPE低估，提出了基于雷达回波概率特征的雷达波束遮挡识别方案，并以单部雷达波束遮挡信息为先验知识，先剔除单部雷达部分遮挡区域内雷达回波，然后再组网拼图。不同天气过程的验证结果表明，使用新的数据处理方案，不仅可以显著提高雷达组网拼图数据的连续性，有效增强部分遮挡区域内的雷达回波强度，可以有效地降低部分遮挡对Z-R关系拟合的影响，提高部分遮挡区域内部、外部及整体的雷达QPE精度。
（4）针对雷达-雨量计融合方法的优化问题，提出了基于云团的分组Z-R关系和最优插值联合QPE方案，并对比了该方案与单一动态Z-R关系和最优插值联合QPE方案、简单分组Z-R关系和最优插值联合QPE方案，以及仅基于雨量计的IDW降水估测方案之间的相对精度，结果表明：基于云团的分组Z-R关系和最优插值联合QPE方案明显优于其他两种联合QPE方案；相比仅基于地面雨量计的IDW降水估测方案，三种不同的联合QPE方案，在梅雨锋、台风和飑线过程中，均表现出更高的降水估测精度；在层状云降水过程中，相比仅基于地面雨量计的IDW降水估测方案，基于单一动态Z-R关系方案或简单分组Z-R关系拟合方案与最优插值法的联合QPE方案，并未取得更优的降水估测效果，仅联合基于云团的分组Z-R关系拟合方案和最优插值法的融合方案，取得了与IDW法相当的降水估测精度。
With the frequent occurrence of the disastrous weather in the recent years, thesurface rainfall spatial distribution information with high resolution and high accuracyis more and more important in the short-term weather nowcasting and fine forecastingoperational work. The QPE (Quantitive Precipitation Estimation) algorithm based onmulti-radar mosaic has presented good stability and real-time performance in thepreliminary operational applications. However, it also suffers from the error sourcessuch as radar beam partial blockage, Z-R relationships and radar-gauge merge method,etc, which limited the accuracy of radar QPE seriously. This paper evaluates theoperational effectiveness of the multi-radar mosaic QPE and mainly optimizes andimproves on the key factors of the radar QPE algorithm using the new technologyprocessing scheme at the same time, the main work of the study and the main resultsincluded are presented in the following four sections:
(1) Evaluate and analyze the operational effectiveness, the applicability and mainerror souces of the multi-radar mosaic QPE algorithm，using the radar VPR (VerticalProfiles of Reflectivity) as the main means, with four different rainfall events. Theresults show that: with respect to the stratiform cloud precipitation, the coexistence ofabsolute blockage of the radar beam and bright-band is prone to overestimation of theradar QPE and the echoes from the top of the cloud is related to the underestimationof the radar QPE. The coexistence of different rainfall types makes uniform Z-Rrelationship obviously inapplicable in the in Meiyu front and typhoon, which resultsin serious local overestimation and underestimation of radar QPE. The severeconvection among the squall line and the asymmetry of typhoon are also importantreasons reducing the accuracy of the radar QPE.
(2) For the situation of the coexistence of different rainfall type in the multi-scaleweather events but the Z-R relationship is uniform, a new grouping fitting scheme,which is based on the storm identification, is proposed to improve the accuracy of theinitial field of radar QPE. With the evaluation and comparison using four differentrainfall events, it shows that the proposed fitting scheme is obviously superior to theuniform dynamical Z-R fitting scheme and simple-grouping Z-R fitting scheme and itcan derive more accurate radar QPE field, which makes it have better rainfallestimation effects and operational capabilities.
(3) For the situation of radar partial blockage effects which is related to the underestimation of the corresponding radar QPE, a partial blockage identificationmethod based on the probability features of radar echoes is proposed. With the help ofthe identification results of radar beam’s partial blockage, the weak radar echoes areeliminated before constructing the multi-radar mosaic. The evaluation and verificationresults using four different rainfall events show that: with the new data processingscheme, the discontinuity of multi-radar mosaic and the weak radar echoes can beeffectively enhanced and the influence on Z-R fitting scheme is reduced effectively,which further increases the accuracy of the radar QPE derived within and out of theregion affected, and the accuracy of radar QPE on the whole is also raised.
(4) For the optimization of radar-gauge merge algorithm, a new merge methodunited storm-based grouping Z-R relationships and optimal interpretation is proposed,and it is compared between the dynamical unique Z-R fitting scheme and optimalinterpretation scheme, the simple grouping Z-R fitting scheme and optimalinterpretation scheme, and the gauge-only IDW（Inverse Distance Weights）method.The results show that merge method with storm-based grouping Z-R relationships isobviously superior to the other two merge methods; the three merge methods are allsuperior to the gauge-only IDW method during the Meiyu front, typhoon and squallline. However, the latter two merge method show obvious inferiority to thegauge-only IDW method, only the merge method with storm-based grouping Z-Rrelationships is superior to the gauge-only IDW method.
基于雷达三维组网拼图数据的定量降水估测（QPE:Quantitive PrecipationEstimation）算法业已在业务中得到初步应用，虽然表现出良好的稳定性和实时性，但目前并未开展较为全面的评估和分析工作。同时，Z-R关系、雷达波束部分遮挡和雷达-雨量计融合方法等因素，严重限制了雷达QPE的精度，在降水估测中，这些算法环节均待进一步优化改进。因此，本论文首先评估了基于雷达组网拼图的QPE算法的业务应用效果，同时，在Z-R关系、波束遮挡订正和融合方法环节使用新的技术手段对雷达QPE算法进行优化改进，以期提高雷达降水估测的精度。主要的工作和结论包括以下几个方面的内容：
（1）以雷达反射率因子廓线作为分析工具，评估了基于雷达组网拼图的QPE算法在杭州市气象局的业务应用效果。分析了算法在不同类型天气过程的适用能力，以及影响雷达降水估测的主要误差源：层状云过程中，如果完全遮挡和回波亮带效应并存，会导致雷达QPE的高估；回波云顶的偏弱雷达回波则会导致雷达QPE的低估；在梅雨锋和台风过程中，不同类型降水并存，这使单一动态Z-R关系拟合方案存在明显的不适用性；伴随飑线的强对流以及台风本身的非对称结构特征，都会严重降低雷达QPE的精度。
（2）针对多尺度天气过程中不同类型降水并存，提出了基于云团的分组Z-R关系拟合方案，用来改善雷达QPE初值场的精度。四种不同类型天气过程中效果评估对比表明，该方案在梅雨锋、台风、飑线和层状云降水过程中，均显著优于单一动态Z-R关系方案和简单分组Z-R关系拟合方案，可以有效提高雷达QPE初值场的精度，具有更好的降水估测效果和业务应用能力。
（3）针对雷达波束部分遮挡造成的雷达QPE低估，提出了基于雷达回波概率特征的雷达波束遮挡识别方案，并以单部雷达波束遮挡信息为先验知识，先剔除单部雷达部分遮挡区域内雷达回波，然后再组网拼图。不同天气过程的验证结果表明，使用新的数据处理方案，不仅可以显著提高雷达组网拼图数据的连续性，有效增强部分遮挡区域内的雷达回波强度，可以有效地降低部分遮挡对Z-R关系拟合的影响，提高部分遮挡区域内部、外部及整体的雷达QPE精度。
（4）针对雷达-雨量计融合方法的优化问题，提出了基于云团的分组Z-R关系和最优插值联合QPE方案，并对比了该方案与单一动态Z-R关系和最优插值联合QPE方案、简单分组Z-R关系和最优插值联合QPE方案，以及仅基于雨量计的IDW降水估测方案之间的相对精度，结果表明：基于云团的分组Z-R关系和最优插值联合QPE方案明显优于其他两种联合QPE方案；相比仅基于地面雨量计的IDW降水估测方案，三种不同的联合QPE方案，在梅雨锋、台风和飑线过程中，均表现出更高的降水估测精度；在层状云降水过程中，相比仅基于地面雨量计的IDW降水估测方案，基于单一动态Z-R关系方案或简单分组Z-R关系拟合方案与最优插值法的联合QPE方案，并未取得更优的降水估测效果，仅联合基于云团的分组Z-R关系拟合方案和最优插值法的融合方案，取得了与IDW法相当的降水估测精度。
With the frequent occurrence of the disastrous weather in the recent years, thesurface rainfall spatial distribution information with high resolution and high accuracyis more and more important in the short-term weather nowcasting and fine forecastingoperational work. The QPE (Quantitive Precipitation Estimation) algorithm based onmulti-radar mosaic has presented good stability and real-time performance in thepreliminary operational applications. However, it also suffers from the error sourcessuch as radar beam partial blockage, Z-R relationships and radar-gauge merge method,etc, which limited the accuracy of radar QPE seriously. This paper evaluates theoperational effectiveness of the multi-radar mosaic QPE and mainly optimizes andimproves on the key factors of the radar QPE algorithm using the new technologyprocessing scheme at the same time, the main work of the study and the main resultsincluded are presented in the following four sections:
(1) Evaluate and analyze the operational effectiveness, the applicability and mainerror souces of the multi-radar mosaic QPE algorithm，using the radar VPR (VerticalProfiles of Reflectivity) as the main means, with four different rainfall events. Theresults show that: with respect to the stratiform cloud precipitation, the coexistence ofabsolute blockage of the radar beam and bright-band is prone to overestimation of theradar QPE and the echoes from the top of the cloud is related to the underestimationof the radar QPE. The coexistence of different rainfall types makes uniform Z-Rrelationship obviously inapplicable in the in Meiyu front and typhoon, which resultsin serious local overestimation and underestimation of radar QPE. The severeconvection among the squall line and the asymmetry of typhoon are also importantreasons reducing the accuracy of the radar QPE.
(2) For the situation of the coexistence of different rainfall type in the multi-scaleweather events but the Z-R relationship is uniform, a new grouping fitting scheme,which is based on the storm identification, is proposed to improve the accuracy of theinitial field of radar QPE. With the evaluation and comparison using four differentrainfall events, it shows that the proposed fitting scheme is obviously superior to theuniform dynamical Z-R fitting scheme and simple-grouping Z-R fitting scheme and itcan derive more accurate radar QPE field, which makes it have better rainfallestimation effects and operational capabilities.
(3) For the situation of radar partial blockage effects which is related to the underestimation of the corresponding radar QPE, a partial blockage identificationmethod based on the probability features of radar echoes is proposed. With the help ofthe identification results of radar beam’s partial blockage, the weak radar echoes areeliminated before constructing the multi-radar mosaic. The evaluation and verificationresults using four different rainfall events show that: with the new data processingscheme, the discontinuity of multi-radar mosaic and the weak radar echoes can beeffectively enhanced and the influence on Z-R fitting scheme is reduced effectively,which further increases the accuracy of the radar QPE derived within and out of theregion affected, and the accuracy of radar QPE on the whole is also raised.
(4) For the optimization of radar-gauge merge algorithm, a new merge methodunited storm-based grouping Z-R relationships and optimal interpretation is proposed,and it is compared between the dynamical unique Z-R fitting scheme and optimalinterpretation scheme, the simple grouping Z-R fitting scheme and optimalinterpretation scheme, and the gauge-only IDW（Inverse Distance Weights）method.The results show that merge method with storm-based grouping Z-R relationships isobviously superior to the other two merge methods; the three merge methods are allsuperior to the gauge-only IDW method during the Meiyu front, typhoon and squallline. However, the latter two merge method show obvious inferiority to thegauge-only IDW method, only the merge method with storm-based grouping Z-Rrelationships is superior to the gauge-only IDW method.
引文
陈良华.雷达定量估测降水技术在三峡流域面雨量估算中的应用[J].长江流域资源与环境.2012,21(Z1):148-152.
陈秋萍,余建华,杨林增,李建通,林文卿,赖荣钦,祖基煊.闽中北前汛期多普勒雷达定量估测降水分析[J].气象,2006,32(4):56-61.
迟竹屏,刘欣,陈金敏.春秋季层状云降水过程Z-I关系计算与讨论[J].气象,2000,26(1):35-37.
戴铁丕，张培昌等，确定Z-I关系的几种方法及其在定量测雨中的精度[J]，南京气象学院学报，(2)，1983，215-222.
邓雪娇,黄浩辉,吴兑.变分法在校准雷达定量估测降水中的应用[J].应用气象学报,2000,11(2):255-256.
东高红,刘黎平.雷达与雨量计联合估测降水的相关性分析[J].应用气象学报,2012a,23(01):30-39
东高红,刘黎平.雨量计密度对校准雷达估测降水的影响及单点对校准的贡献[J].气象,2012b,38(09):1042-1052
东高红,吕江津.不同校准方法检验雷达定量估测降水的效果对比[J].气象与环境学报,2012c,28(04):38-42
范其平,何宽科,李云,等.估算舟山区域内台风降水量分布的一种方法[J].科技通报，2009,25(1):24-28,34.
官莉,王振会,裴晓芳.雷达估测降水集成方法及其效果比较.气象科学,2004,24(1):104-111.
郭忠立,刘黎平.最优插值校准法定量估测降水及效果分析.成都信息工程学院学报[J],2011,26(2):197-200
高晓荣,梁建茵,李春晖.雷达定量降水估计技术及效果评估.热带气象学报[J],2012,28(1):77–88.
何宽科,范其平,李开奇,等,舟山地区台风降水Z-R关系研究及其应用[J].应用气象学报,2007,18(4):573~576.
冀春晓,陈联寿,徐祥德,等.多普勒雷达资料动态定量估测台风小时降水量的研究[J].热带气象学报,2008,24(2):147-155
冀春晓,薛根元,赵放.台风Rananim登陆期间地形对其江水和结构影响的数值模拟试验[J],2007,31(2):233-244
何会中,程明虎,周凤仙.0302号(鲸鱼)台风降水和水粒子空间分布的三维结构特征[J].大气科学,2006(03)
黄小玉,陈媛,熊毅,陈波,夏正龙,廖玉芳.基于漂移克里金融合雷达、雨量计定量估测降水研究[J].气象学报,2009,67(2):288-297
李建通,高守亭,郭林,等.2009.基于分步校准的区域降水量估测方法研究[J].大气科学,33(3):501-512
李建通,郭林,杨洪平.雷达-雨量计联合估测降水初值场形成方法探讨[J].大气科学,2005,29(6):1010–1020.
李建通,杨维生,郭林等.提高最优插值法测量区域降水量精度的探讨[J].大气科学,2000,24(2):263–270
李建通,张培昌.最优插值法用于天气雷达测定区域降水量[J].台湾海峡,15(3):255-259
梁卫芳,杨育强,林行,吴增茂,官莉.顺序卡尔曼滤波校准法雷达定量估测降水检验[J].气象,2008(Z1).
刘黎平,吴林林,杨引明.基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析[J].气象学报,2007,65(2):251-260
刘雨佳,陈洪滨,金德镇,等.2014.加密自动气象站雨量计资料的质量控制及其相关关系的研究[J].大气科学,38(1):159-170
江源,刘黎平,庄薇.多普勒天气雷达地物回波特征及其识别方法改进[J].应用气象学报,2009,20(2):203~213
马慧,万齐林,陈子通,等.基于Z-I关系和变分校正法改进雷达估测降水[J].热带气象学报,2008,(5):546-549
牟容,刘黎平,许小永,等.四维变分方法反演低层风场能力研究[J].气象,2007,33(1):11-18
钮学新,董加斌,杜惠良.华东地区台风降水及影响降水因素的气候分析[J].应用气象学报,2005,16(3):402–407.
钮学新,杜惠良,滕代高,等.影响登陆台风降水量的主要因素分析.暴雨灾害,2010,29(1):76–80
彭涛,宋星原,殷志远.雷达定量江水估算在水温模式汛期洪水预报中的应用试验[J].气象,2010,36(12):50-55
任启伟.应用天气雷达降雨的分布式流域水文模型径流模拟研究[D].广州：中山大学，2010:153-201.
邵末兰，戴铁丕.地物阻挡对雷达探测降水的影响分析及其校正方法[J].南京气象学院学报，1990,13(4):696-699.
谭学,刘黎平,范思睿.新一代天气雷达海浪回波特征分析和识别方法研究[J].气象学报,2013,71(5):962-975
田付友,程明虎,张亚萍,等.校准雨量计密度对雷达联合雨量计估测流域平均面雨量的影响[J].气象学报,2010,68(5):717-730
万齐林.气象遥感资料误差变分校准方案及应用试验[J].气象,2006,32(11):19-24.
王改利,刘黎平,阮征,等.基于雷达回波拼图资料的风暴识别、跟踪及临近预报技术[J].高原气象,2010,V29(6):1546-1555
王红艳,刘黎平,王改利,等.多普勒天气雷达三维数字组网系统开发及应用[J].应用气象学报,2009,20(2):214-224.
汪瑛,冯业荣,蔡锦辉,等.雷达定量降水动态分级Z-I关系估算方法.热带气象学报[J].2011.27(4):601-608.
伍静,刘黎平,尤卫红,等.应用雷达拼图数据估测降水试验[J].气象科技,2010,38(2):175-181.
肖艳姣,蒋义芳.风暴的自动识别,跟踪与预报[J].南京气象学院学报,1998,21(2):223-229.
肖艳娇,刘黎平.新一代天气雷达网资料的三维格点化及拼图方法研究[J].气象学报,2006,64(5):647-656.
肖艳姣,刘黎平,杨洪平.基于天气雷达网三维拼图的混合反射率因子生成技术[J].气象学报,2008,66(3):470-473
徐芬,慕熙昱,王卫芳,夏文梅,徐琪.分类型最优法在江苏沿江地区降水估测中的应用与讨论.气象科学,2013,33(1):51-58.
徐燕.卡尔曼滤波法在西峰雷达估测降水中的应用[J].干旱气象,2008,26(2):78-82
姚燕飞,程明虎,杨洪平,等.优化ZI关系及其在淮河流域面雨量测量中的应用.[J].气象,2007,33(6):37-43.
尹忠海,张沛源.利用卡尔曼滤波校准方法估算区域降水量[J].应用气象学报,2005,16(2):213-319.
张家国;王佐兵;力梅三峡地区气候Z-R关系[J].气象1999,(03).
张利平,张晓琳,王任超,等.雷达估算降雨的同化方法对比[J].武汉大学学报(工学版).2011,44(2):146-150
张宁,江志红,吴立广.江苏省自动站与基础站降水观测资料质量分析[J].大气科学学报,2010,33(5):606-614
张培昌,戴铁丕,伍志芳,等.1992.用变分方法校准数字化天气雷达测定区域降水量基本原理和精度[J].大气科学,16(2):248-256
张培昌,戴铁丕,王登炎,林炳干.最优化法求Z-I关系及其在测定降水量中的精度[J].气象科学,1992,(3).
张培昌,杜秉玉,戴铁丕.雷达气象学[M].北京:气象出版社,2000:175-182.
张志强,刘黎平,谢明元,等.CINRAD三维组网拼图产品显示系统[J].气象,2007,33(9):19-24.
庄薇,刘黎平,余燕群,等..雷达地物回波模糊逻辑识别法的改进及效果检验[J].气象学报,2012,70(3):576-584
庄薇,刘黎平,胡志群.青藏高原零度层亮带的识别订正方法及在雷达估测降水中的应用[J].气象,2013，39(8):1004–1013
赵放,冀春晓,任鸿翔,等.台风螺旋带回波识别追踪及结构分析技术应用[J].浙江气象.2007,29(1):15-21.
张亚萍,刘钧,夏文梅,等.雷达定量估测区域降水波束阻挡系数的计算[J].南京气象学院学报,2002,25(5):640-647.
郑媛媛,谢亦峰,吴林林,等.多普勒雷达定量估测降水的三种方法比较试验[J].热带气象学报,2004,20(3):192-197.
周彪,汤达章.雷达定量测量区域降水量的试验研究[J].南京气象学院学报,1998,21(1):152-159.
Ahnert, P. R., Krajewski,W. F., and Johnson, E. R.,1986. Kalman filter estimation of radar rainfall fieldbias[C]. In Preprints23th Conf. on Radar Met., JP33–JP37. AMS.
Anagnostou, E. N., and W. F. Krajewski,1999: Real-time radar rainfall estimation. Part II: Case study. J.Atmos. Oceanic Technol.,16,198–205.
Andrieu, H., J. D. Creutin, G. Delrieu, J. Leoussoff, and Y. Pointin,1989:Radar data processing forhydrology in the Cevennes region.Proc.
Andrieu, H., G. Delrieu, and J. D. Creutin,1995: Identification of vertical profiles of radar reflectivity forhydrological applications using an inverse method. Part II: Sensitivity analysis and case study. J. Appl.Meteor.,34,240–259.
Andrieu, H., J. D. Creutin, G. Delrieu, and D. Faure,1997: Use of a weather radar for the hydrology of amountainous area. Part I: Radar measurement interpretation. J. Hydrol.,193,1–25.
Atlas D, Ulbrich CW, Marks FD, Amitai E, Williams CR (1999) Systematic variation of drop size andradarrainfall relations. J Geophys Res Atmos104:6155–6169
Brandes, E. A.Optimizing rainfall estimates with the aid of RADAR, J. Appl. Meteorol.,1975,14,1339–1345.
Brandes EA, Vivekanandan J, Wilson JW (1999) A comparison of radar reflectivity estimates of rainfallfrom collocated radars. J Atmos Ocean Technol16:1264–1272
Balakrishnan N, Zrnic DS, Goldhirsh J, Rowland J (1989) Comparison of simulated rain rates fromdisdrometer data employing polarimetric radar algorithms. J Atmos Ocean Technol6:476–486
Battan LJ (1973) Radar observation of the atmosphere. The University of Chicago Press, Chicago
Bech, J., B. Codina, J. Lorente, and D. Bebbington,2003: The sensitivity of single polarization weatherradar beam blockage correction to variability in the vertical refractivity gradient. J. Atmos. OceanicTechnol.,20,845–855.
Bech J, Gjertsen U, Haase G (2007b) Modelling weather radar beam propagation and topographicalblockage at northern high latitudes. Quart J Royal Meteorol Soc133:1191–1204Bellon A, Lee G, Zawadzki I.2005.Error statistics of VPR corrections in stratiform precipitation. J ApplMeteorol44:998–1015
Calheiros RV, Zawadzki I (1987) Reflectivity-rain rate relationships for radar hydrology in Brazil. JClimate Appl Meteorol26:118–132
Campos E, Zawadzki I (2000) Instrumental uncertainties in Z–R relations. J Appl Meteorol39:1088–1102Chapon B, Delrieu G, Gosset M, Boudevillain B (2008) Variability of rain drop size distribution and itseffect on the Z–R relationship: a case study for intense Mediterranean rainfall. Atmos Res87:52–65
Chumchean S, Seed A, Sharma A.(2006) Correcting of real-time radar rainfall bias using a Kalmanfiltering approach. Journal of Hydrology317:1-2,123-137Online publication date:5-Feb-2006.
Ciach GJ, Krajewski WF (1999b) Radar-rain gauge comparisons under observational uncertainties. J ApplMeteorol38:1519–1525
Ciach GJ, Krajewski WF, Smith JA (1997) Comments on ‘‘The window probability matching method forrainfall measurements with radar’’. J Appl Meteorol36:243–246
Ciach GJ, Morrissey ML, Krajewski WF (2000) Conditional bias in radar rainfall estimation. J ApplMeteorol39:1941–1946
Ciach,G.J.,W. F. Krajewski, E. N. Anagnostou, M. L. Baeck, J. A. Smith, J. R. McCollum, and A. Kruger,1997: Radar rainfall estimation for ground validation studies of the Tropical Rainfall Measuring Mission. J.Appl. Meteor.,36,736–747.
Collier C G.1986a Accuracy of rainfall estimates by radar, Part I：Calibration by telemetering raingauges[J].Hydrology,83:207-223.
Collier C G.1986b Accuracy of rainfall estimates by radar，Part II：Comparison with raingauges[J].Hydrology,83:225-235.
Cifelli R, Williams CR, Rajopadhyaya DK, Avery SK, Gage KS, May PT (2000) Drop-size distributioncharacteristics in tropical mesoscale convective systems. J Appl Meteorol39:760–777
Collier CG.1999.The impact of wind drift on the utility of very high spatial resolution radar data overurban areas. Phys Chem Earth Part B-Hydrol Oceans Atmos24:889–893
Creutin, J. D., H. Andrieu, and D. Faure,1997: Use of a weather radar for the hydrology of a mountainousarea. Part II: Radar measurement validation. J. Hydrol.,193,26–44.
Creutin J D, Delrieu G, Lebel T.1998.Rian measurement by raingauge-radar combination: a geostatisticalapproach. J Atmos Oceanic,5:102-115
Cushmeer N (1999) WSR-88D rainfall estimates during tropical storm Nora. Western Region TechnicalAttachment, Las Vegas
Daley, R.,1991. Atmospheric Data Analysis. New York, Cambridge University Press.457pp.
Dalezios NR, Kouwen N.1990.Radar signal interpretation in warm season rainstorms. Nord Hydrol21:47–64
Delrieu, G., J. D. Creutin, and H. Andrieu,1995: Simulation of radar mountain returns using a digitizedterrain model. J. Atmos. Oceanic Technol.,12,1038–1049.
Delrieu, G., S. Caoudal, J. D. Creutin,1997: Feasibility of Using Mountain Return for the Correction ofGround-Based X-Band Weather Radar Data.J. Atmos. Oceanic Technol.,14,368–385.
Dinku, T., E. N. Anagnostou, and M. Borga,2002: Improving radar-based estimation of rainfall overcomplex terrain. J.Appl. Meteor.,41,1163–1178.
Eischeid JK, Baker CB, Karl T, Diaz HF.1995.The quality control of long-term climatological data usingobjective data analysis. Journal of Applied Meteorology34:2787–2795.
Eischeid JK, Pasteris PA, Diaz HF, Plantico MS, Lott NJ.2000. Creating a serially complete, national dailytime series of temperature and precipitation for the Western United States. Journal of Applied Meteorology39:1580–1591.
Faure D, Delrieu G, Tabary P, Du Chatelet JP, Guimera M (2005) Application of the hydrologic visibilityconcept to estimate rainfall measurement quality of two planned weather radars. Atmos Res77:232–246
Fulton, Richard A., Jay P. Breidenbach, Dong-Jun Seo, Dennis A. Miller, Timothy O’Bannon,1998: TheWSR-88D Rainfall Algorithm. Wea. Forecasting,13,377–395.
Gandin LS.1988.Complex quality control of meteorological observations. Monthly Weather Review116:1137–1156.
Germann U, and J Joss.2003.Operational measurement of precipitation in mountainous terrain. WeatherRadar: Principles and Advanced Applications, P. Meischner, Ed, Springer-Verlag:52–77.
Gunn KLS, Marshall JS.1955.The effect of wind shear on falling precipitation. J Meteorol12:339–349
Guttman N, Karl C, Reek T, Shuler V.1988.Measuring the performance of data validators. Bulletin of theAmerican Meteorological Society69(12):1448–1452.
Gabella M, Perona G. Simulation of the orographic influence on weather radar using a geometric-opticsapproach [J].Journal of Atmospheric and Oceanic Technology,1998,15(6):1485-1494.
Gabella M, Joss J, Perona G, Galli G (2001) Accuracy of rainfall estimates by two radars in the sameAlpine environment using gage adjustment. J Geophys Res Atmos106:5139–5150
Germann, U., and J. Joss,2003: Operational measurement of precipitation in mountainous terrain. WeatherRadar: Principles and Advanced Applications, P. Meischner, Ed., Springer-Verlag,52–77.
Gourley JJ, Calvert CM (2003) Automated detection of the bright band using WSR-88D data. WeatherForecast18:585–599
Haberlandt U. Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event[J]. Journal of Hydrology,2007,332(1/2):144-1571
Habib E, Aduvala AV, Meselhe EA.2008a.Analysis of radar-rainfall error characteristics and implicationsfor streamflow simulation uncertainty. Hydrol Sci J-J Des Sci Hydrol53:568–587
Habib E, Malakpet CG, Tokay A, Kucera PA.2008b.Sensitivity of streamflow simulations totemporalvariability and estimation of Z–R relationships. J Hydrol Eng13:1177–118
Houze,1993. R. A., Jr. Cloud Dynamics. M. Academic Press,573pp.
Hubbard KG.2001.Multiple station quality control procedures.Automated Weather Stations forApplications in Agriculture and Water Resources Management, AGM-3WMO./TD No.1074. High PLainsRegional Climate Center, Lincoln:248.
Hubbard KG, You J.2005. Sensitivity analysis of quality assurance using spatial regression approach–Acase Study of the maximum minimum air temperature. Journal of Atmospheric and Oceanic Technology22(10):1520–1530.
Joss, J., and R. Lee,1995: The application of radar–gauge comparisons to operational precipitation profilecorrections. J. Appl. Meteor.,34,2612–2630.
Koistinen, J., Puhakka, T.,1981: An improved spatial gauge-radar adjustment technique[C]. In Proc.20thConference on Radar Meteorology, pp.179-186. AMS
Krajewski, W. F.,1987. Cokriging radar-rainfall and rain gauge data. J. Geophys. Res.,92(d8),9571–9580.
Krajewski WF, Kruger A, Caracciolo C, Gole P, Barthes L, Creutin JD, Delahaye JY, Nikolopoulos EI,Ogden F, Vinson JP (2006a) DEVEX-disdrometer evaluation experiment: basic results and implications forhydrologic studies. Adv Water Resour29:311–325
Krajewski W F, Ntelekos A A, Goska R. A GIS-based methodology for the assessment of weather radarbeam blockages in mountainous regions: two examples from the USNEXRAD network [J]. Computers&Geosciences,2006,32(3):283-302.
Krajewski WF, Smith JA (1991) On the estimation of climatological Z–R relationships. J Appl Meteorol30:1436–1445
Kucera, P. A., W. F. Krajewski, and C. B. Young,2004: Radar beam occultation studies using GIS andDEM technology:An example study of Guam. J. Atmos. Oceanic Technol.,21,995–1006.
Lack SA, Fox NI.2005.Errors in surface rainfall rates retrieved from radar due to wind-drift. Atmos SciLett6:71–7
Lack SA, Fox NI.2007.An examination of the effect of wind-drift on radar-derived surface rainfallestimations. Atmos Res85:217–229
Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf,2007: An automated technique to qualitycontrol radar reflectivity data. J. Appl. Meteor. Climatol.,46,288-305.
Lakshmanan, V., J. Zhang, and K. Howard,2010: A technique to censor biological echoes in weather radarimages.J. Appl. Meteor. Climatol.,49,453–462.
Lee G, Zawadzki I (2005a) Variability of drop size distributions: noise and noise filtering in disdrometricdata. J Appl Meteorol44:634–652
Lee G, Zawadzki I (2005b) Variability of drop size distributions: time-scale dependence of the variabilityand its effects on rain estimation. J Appl Meteorol44:241–255
Lee GW, Zawadzki I (2006) Radar calibration by gage, disdrometer, and polarimetry: theoretical limitcaused by the variability of drop size distribution and application to fast scanning operational radar data. JHydrol28:83-97.
Marshall JS, Palmer WM (1948) The distribution of raindrops with size. J Meteorol5:165–166
Marshall JS, Hitschfeld W, Gunn KLS (1955) Advances in radar weather. In: Landsberg HE (ed) Advancesin geophysics, vol2. Academic Press Inc., New York
Maddox RA, Zhang J, Gourley JJ, Howard KW (2002) Weather radar coverage over the contiguous UnitedStates. Weather Forecast17:927–934
Maki M, Keenan TD, Sasaki Y, Nakamura K (2001) Characteristics of the raindrop size distribution intropical continental squall lines observed in Darwin, Australia. J Appl Meteorol40:1393–1412
Martner BE, Yuter SE, White AB, Matrosov SY, Kingsmill DE, Ralph FM (2008) Raindrop sizedistributions and rain characteristics in California coastal rainfall for periods with and without a radarbright band. J Hydrometeorol9:408–425
Wong M.C., Wong W.K., and Edwin S.T. Lai,2006: From SWIRLS to RAPIDS: Nowcast ApplicationsDevelopment in Hong Kong. PWS Workshop on Warnings of Real-Time Hazards by Using NowcastingTechnology, Sydney, Australia,9-13October2006.
Medlin JM, Kimball SK, Blackwell KG (2007) Radar and rain gauge analysis of the extreme rainfallduring hurricane Danny’s (1997) landfall. Mon Weather Rev135:1869–1888
Miriovsky BJ, Bradley AA, Eichinger WE, Krajewski WF, Kruger A, Nelson BR, Creutin J-D, LapetiteJ-M, Lee GW, Zawadzki I, Ogden FL (2004) An empirical study of small-scale variability of radarreflectivity using disdrometer observations. J Appl Meteorol43:106–118
Morin E, Krajewski WF, Goodrich DC, Gao XG, Sorooshian S (2003) Estimating rainfall intensities fromweather radar data: the scale-dependency problem. J Hydrometeorol4:782–797
Ninomiya K,Akiyama Y.Objective analysis of heavy rainfalls based on radar and gauge measurementJ.Meter Soc, Japan,1978,50:206-210
O’Bannon,T., Ahnert. P. A Study of the Precipitation Processing System on a Winter type OklahomaRainstorm [G]. Preprints of23rd Radar Meteorology Conf Boston: AM S,1986:99-1021
O’Bannon,T.,1997:Using a “terrain-based” hybrid scan to improve WSR-88D precipitationestimates.Preprints,28th Int. Conf. on Radar Meteorology,Austin, TX, Amer. Meteor. Soc.,506-507.
Pellarin T, Delrieu G, Saulnier GM, Andrieu H, Vignal B, Creutin JD (2002) Hydrologic visibility ofweather radar systems operating in mountainous regions: case study for the Ardeche Catchment (France). JHydrometeorol3:539–555
Robert A. Houze Jr.1989.Observed structure of mesoscale convective systems and implications forlarge-scale heating. Quart J Roy Meteor Soc,115:425–461.
Rosenfeld D, Wolff DB, Atlas D (1993) General probability-matched relations between radar reflectivityand rain rate. J Appl Meteorol32:50–72
Rosenfeld D, Wolff DB, Amitai E (1994) The window probability matching method for rainfallmeasurements with radar. J Appl Meteorol33:682–693
Rosenfeld D, Amitai E, Wolff DB (1995) Improved accuracy of radar WPMM estimated rainfall uponapplication of objective classification criteria. J Appl Meteorol34:212–223
Rosenfeld D, Amitai E (1998) Comparison of WPMM versus regression for evaluating Z–R relationships.J Appl Meteorol37:1241–1249
Roux, C, T. Lebel, C Depraetere, and H. Andrieu,1989: Simulation al'aide d'un modele nurnerique deterrain des echos de sol detectes par un radar meteorologique. Hydrol. Cont..4,139-149
Sasaki Y. Some basic formulas in numerical variation analysis [J]. Mon Wea Rev,1970,98:875-883
Sauvageot H (1994) Rainfall measurement by radar: a review. Atmos Res35:27–54
Schaake, J., A. Henkel, and S. Cong,2004: Application of PRISM climatologies for hydrologic modelingand forecasting in the western U.S. Preprints,18th Conf. on Hydrology, Seattle, WA, Amer. Meteor.Soc.,5.3.
Schroeder, J. L., W. S. Burgett, K. B. Haynie, I. Sonmez, G. D. Skwira, A. L. Doggett, J. W. Lipe.2005.TheWest Texas Mesonet: A Technical Overview. J. Atmos. Oceanic Technol.,22,211–222.
Seed A, Nichol J, Austin G, Stow CD, Bradley SG (1996) The impact of radar and rain gauge samplingerrors when calibrating a weather radar. Meteorol Appl3:43–52
Seo D J,Smith J A.1998a.Rainfall estimation using raingauges and radar A Bayesian approach:1.Derivation of estimators. Stoch Hydrol Hydraul,5:17-29
Seo D J,Smith J A.1998a.Rainfall estimation using raingauges and radar A Bayesian approach:2.Anapplication.Stoch. Stoch Hydrol Hydraul,5:31-44
Shafer MA, Fiebrich CA, Arndt DS, Fredrickson SE, Hughes TW.2000. Quality assurance procedures inthe Oklahoma mesonetwork. Journal of Atmospheric and Oceanic Technology17:474–494.
Sieck LC, Burges SJ, Steiner M (2007) Challenges in obtaining reliable measurements of pointrainfall.Water Resour Res43:23
Smith P L. On the minimum useful elevation angle for weather surveillance radar scans [J]. Journal ofAtmospheric and Oceanic Technology,1998,15(3):840-845.
Smith JA, Krajewski WF (1993) A modeling study of rainfall rate-reflectivity relationships. Water ResourRes29:2505–2514
Smith JA, De Veaux RD (1994) A stochastic model relating rainfall intensity to raindrop process. WaterResour Res30:651–664
Smith JA, Hui E, Steiner M, Baeck ML, Krajewski WF, Ntelekos AA (2009) Variability of rainfall rate andraindrop size distributions in heavy rain. Water Resour Res45:12
Steiner M, Smith JA (2000) Reflectivity, rain rate, and kinetic energy flux relationships based on raindropspectra. J Appl Meteorol39:1923–1940
Steiner M, Smith JA, Burges SJ, Alonso CV, Darden RW (1999) Effect of bias adjustment and rain gaugedata quality control on radar rainfall estimation. Water Resour Res35:2487–2503
Steiner M, Smith JA (2004) Scale dependence of radar-rainfall rates—An assessment based on raindropspectra. J Hydrometeorol5:1171–1180
Steiner, M., R. A. Houze Jr., and S. E. Yuter,1995: Climatological characterization of three-dimensionalstorm structure from operational radar and rain gauge data. J. Appl. Meteor.,34,1978–2007.
Sugier, J., J. Parent-du-Chatelet, P. Roquain, and A. Smith,2002: Detection and removal of clutter andanaprop in radar data using a statistical scheme based on echo fluctuation. Proc. Second European RadarConf., Delft, Netherlands, Copernicus GmbH,17–24.
Tabary, P.,2007: The new French operational radar rainfall product. Part I: Methodology. Wea. Forecasting,22,393–408.
Tabary, P.,2007: The new French operational radar rainfall product. Part II: Methodology. Wea.Forecasting,22,409–427.
Todini E.2001. Bayesian conditioning of RADAR to rain gauges.Hydrol Earth System Sci,5:255-232Tokay, Ali, David A. Short.1996.Evidence from Tropical Raindrop Spectra of the Origin of Rain fromStratiform versus Convective Clouds. Appl Meteor,35:355–371.
Tokay A, Kruger A, Krajewski WF (2001) Comparison of drop size distribution measurements by impactand optical disdrometers. J Appl Meteorol40:2083–2097
Tokay A, Short DA (1996) Evidence from tropical raindrop spectra of the origin of rain from stratiformversus convective clouds. J Appl Meteorol35:355–371
Tokay A, Bashor PG, Habib E, Kasparis T (2008) Raindrop size distribution measurements in tropicalcyclones. Mon Weather Rev136:1669–1685
Tokay A, Bashor PG, Wolff KR (2005) Error characteristics of rainfall measurements by collocatedJoss-Waldvogel disdrometers. J Atmos Ocean Technol22:513–527
Tuttle, J. D., and G. B. Foote,1990: Determination of boundary layer airflow from a single Doppler radar. J.Atmos. Oceanic Technol.,7,218–232.
Uijlenhoet R (2001) Raindrop size distributions and radar reflectivity-rain rate relationships for radarhydrology. Hydrol Earth Syst Sci5:615–627
Ulbrich CW, Atlas D (2002) On the separation of tropical convective and stratiform rains. J Appl Meteorol41:188–195
Uijlenhoet, Remko, Matthias Steiner, et al.2003.Variability of Raindrop Size Distributions in a Squall Lineand Implications for Radar Rainfall Estimation. Hydrometeor,4:43–61.
Vasiloff, Steven V, and Coauthors.2007.Improving QPE and Very Short Term QPF: An Initiative for aCommunity-Wide Integrated Approach. Bull Amer Meteor Soc,88:1899–1911.
Vieux BE, Bedient PB (1998) Estimation of rainfall for flood prediction from WSR-88D reflectivity: acase study,17–18October1994. Weather Forecast13:407–415
Vignal B, Galli G, Joss J, Germann U.2000.Three methods to determine profiles of reflectivity fromvolumetric radar data to correct precipitation estimates. J Appl Meteorol39:1715–1726
Vignal B, Krajewski WF.2001.Large-sample evaluation of two methods to correct range-dependent errorfor WSR-88D rainfall estimates. J Hydrometeorol2:490–504
Villarini G, Krajewski WF.2009.Sensitivity studies of the models of radar-rainfall uncertainties. J ApplMeteorol Climatol (Submitted)
Villarini, G.&Krajewski, W. F.2010.Review of the different sources of uncertainty in single polarizationradar-based estimates of rainfall. Surveys in Geophysics, Vol.31, pp.107-129, ISSN1573-0956
WANG Gaili,LIU Liping and DING Yuanyuan.2012.Improvement of Radar Quantitative PrecipitationEstimation Based on Real-Time Adjustments to Z-R Relationships and Inverse Distance WeightingCorrection Schemes. Adv Atmos Sci,29(3):575-584.
Wade CG.1987.A quality control program for surface mesometeorological data.Journal of Atmospheric andOceanic Technology4:435–453.
Ware, E. C.,2005:Corrections to radar-estimated precipitation using observed rain gauge data. M.S. thesis,Cornell University,87pp.
Westrick KJ, Mass CF, Colle BA (1999) The limitations of the WSR-88D radar network for quantitativeprecipitation measurement over the coastal western United States. Bull Am Meteorol Soc80:2289–2298
Williams CR, Kruger A, Gage KS, Tokay A, Cifelli R, Krajewski WF, Kummerow C (2000) Comparisonof simultaneous rain drop size distributions estimated from two surface disdrometers and a UHF profiles.Geophys Res Lett27:1763–1766
Willis PT, Tattelman P (1989) Drop-size distributions associated with intense rainfall. J Appl Meteorol28:3–15
Wilson J W, and E A Brandes,Radar Measurement of Rainfall—A Summary. Bull Amer Meteor Soc,1979,60:1048–1058.
Xu, Xiaoyong, Kenneth Howard, Jian Zhang.2008.An Automated Radar Technique for the Identification ofTropical Precipitation. Hydrometeor,9:885–902.
Young CB, Nelson BR, Bradley AA, Smith JA, Peters-Lidard CD, Kruger A, Baeck ML (1999) Anevaluation of NEXRAD precipitation estimates in complex terrain. J Geophys Res Atmos104:19691–19703
Yuter SE, Houze RA (1997) Measurements of raindrop size distributions over the Pacific warm pool andimplications for Z–R relations. J Appl Meteorol36:847–867
Zawadzki, I. Factors affecting the precision of radar measurements of rain. Preprints,22d Int. Conf. onRadar Meteorology, Zurich, Switzerland, Amer. Meteor. Soc.,1984,251-256.
Zhang Jian, Carrie Langston, Kenneth Howard.2008.Brightband Identification Based on Vertical Profilesof Reflectivity from the WSR-88D. J Atmos Oceanic Technol,25:1859–1872.
Zhang Jian,Youcun Qi.2010.A Real-Time Algorithm for the Correction of Brightband Effects inRadar-Derived QPE. J Hydrometeor,11:1157–1171.
Zhang Jian, and Coauthors.2011.National Mosaic and Multi-Sensor QPE (NMQ) System: Description,Results, and Future Plans. Bull Amer Meteor Soc,92:1321–1338.
Zhang Jian,Youcun Qi, David Kingsmill, Kenneth Howard.2012.Radar-Based Quantitative PrecipitationEstimation for the Cool Season in Complex Terrain: Case Studies from the NOAA HydrometeorologyTestbed. J. Hydrometeor,13:1836–1854.
Zhang, J., K. Howard, and J. J. Gourley,2005: Constructing three-dimensional multiple radar reflectivitymosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol.,22,30–42.
Zhang, J., K. C. Langston, K. Howard, and B. Clarke,2006b: Gap-filling in3D radar mosaic analysisusing vertical profile of reflectivity. Preprints,12th Conf. on Aviation, Range, and Aerospace Meteorology,Atlanta,GA, Amer. Meteor. Soc., CD-ROM,1.9.
Zhang, J, S. Wang, and B. Clarke,2004: WSR-88D reflectivity quality control using horizontal and verticalreflectivity structure. Preprints,11th Conf. on Aviation, Range, and Aerospace Meteorology, Hyannis, MA,Amer. Meteor. Soc.,5.4.
Zhang Yaping, Chen MingHun. Dynamical Weather Radar Beam Blockage Correction[C].DigitalManufacturing and Automation (ICDMA),2010International Conference.2010(1):67-70
Zipser, E. J., and K. R. Lutz.1944.The vertical profile of radar reflectivity of convective cells: A strongindicator of storm intensity and lightning probability. J Mon Wea Rev,122:1751–1759.
陈秋萍,余建华,杨林增,李建通,林文卿,赖荣钦,祖基煊.闽中北前汛期多普勒雷达定量估测降水分析[J].气象,2006,32(4):56-61.
迟竹屏,刘欣,陈金敏.春秋季层状云降水过程Z-I关系计算与讨论[J].气象,2000,26(1):35-37.
戴铁丕，张培昌等，确定Z-I关系的几种方法及其在定量测雨中的精度[J]，南京气象学院学报，(2)，1983，215-222.
邓雪娇,黄浩辉,吴兑.变分法在校准雷达定量估测降水中的应用[J].应用气象学报,2000,11(2):255-256.
东高红,刘黎平.雷达与雨量计联合估测降水的相关性分析[J].应用气象学报,2012a,23(01):30-39
东高红,刘黎平.雨量计密度对校准雷达估测降水的影响及单点对校准的贡献[J].气象,2012b,38(09):1042-1052
东高红,吕江津.不同校准方法检验雷达定量估测降水的效果对比[J].气象与环境学报,2012c,28(04):38-42
范其平,何宽科,李云,等.估算舟山区域内台风降水量分布的一种方法[J].科技通报，2009,25(1):24-28,34.
官莉,王振会,裴晓芳.雷达估测降水集成方法及其效果比较.气象科学,2004,24(1):104-111.
郭忠立,刘黎平.最优插值校准法定量估测降水及效果分析.成都信息工程学院学报[J],2011,26(2):197-200
高晓荣,梁建茵,李春晖.雷达定量降水估计技术及效果评估.热带气象学报[J],2012,28(1):77–88.
何宽科,范其平,李开奇,等,舟山地区台风降水Z-R关系研究及其应用[J].应用气象学报,2007,18(4):573~576.
冀春晓,陈联寿,徐祥德,等.多普勒雷达资料动态定量估测台风小时降水量的研究[J].热带气象学报,2008,24(2):147-155
冀春晓,薛根元,赵放.台风Rananim登陆期间地形对其江水和结构影响的数值模拟试验[J],2007,31(2):233-244
何会中,程明虎,周凤仙.0302号(鲸鱼)台风降水和水粒子空间分布的三维结构特征[J].大气科学,2006(03)
黄小玉,陈媛,熊毅,陈波,夏正龙,廖玉芳.基于漂移克里金融合雷达、雨量计定量估测降水研究[J].气象学报,2009,67(2):288-297
李建通,高守亭,郭林,等.2009.基于分步校准的区域降水量估测方法研究[J].大气科学,33(3):501-512
李建通,郭林,杨洪平.雷达-雨量计联合估测降水初值场形成方法探讨[J].大气科学,2005,29(6):1010–1020.
李建通,杨维生,郭林等.提高最优插值法测量区域降水量精度的探讨[J].大气科学,2000,24(2):263–270
李建通,张培昌.最优插值法用于天气雷达测定区域降水量[J].台湾海峡,15(3):255-259
梁卫芳,杨育强,林行,吴增茂,官莉.顺序卡尔曼滤波校准法雷达定量估测降水检验[J].气象,2008(Z1).
刘黎平,吴林林,杨引明.基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析[J].气象学报,2007,65(2):251-260
刘雨佳,陈洪滨,金德镇,等.2014.加密自动气象站雨量计资料的质量控制及其相关关系的研究[J].大气科学,38(1):159-170
江源,刘黎平,庄薇.多普勒天气雷达地物回波特征及其识别方法改进[J].应用气象学报,2009,20(2):203~213
马慧,万齐林,陈子通,等.基于Z-I关系和变分校正法改进雷达估测降水[J].热带气象学报,2008,(5):546-549
牟容,刘黎平,许小永,等.四维变分方法反演低层风场能力研究[J].气象,2007,33(1):11-18
钮学新,董加斌,杜惠良.华东地区台风降水及影响降水因素的气候分析[J].应用气象学报,2005,16(3):402–407.
钮学新,杜惠良,滕代高,等.影响登陆台风降水量的主要因素分析.暴雨灾害,2010,29(1):76–80
彭涛,宋星原,殷志远.雷达定量江水估算在水温模式汛期洪水预报中的应用试验[J].气象,2010,36(12):50-55
任启伟.应用天气雷达降雨的分布式流域水文模型径流模拟研究[D].广州：中山大学，2010:153-201.
邵末兰，戴铁丕.地物阻挡对雷达探测降水的影响分析及其校正方法[J].南京气象学院学报，1990,13(4):696-699.
谭学,刘黎平,范思睿.新一代天气雷达海浪回波特征分析和识别方法研究[J].气象学报,2013,71(5):962-975
田付友,程明虎,张亚萍,等.校准雨量计密度对雷达联合雨量计估测流域平均面雨量的影响[J].气象学报,2010,68(5):717-730
万齐林.气象遥感资料误差变分校准方案及应用试验[J].气象,2006,32(11):19-24.
王改利,刘黎平,阮征,等.基于雷达回波拼图资料的风暴识别、跟踪及临近预报技术[J].高原气象,2010,V29(6):1546-1555
王红艳,刘黎平,王改利,等.多普勒天气雷达三维数字组网系统开发及应用[J].应用气象学报,2009,20(2):214-224.
汪瑛,冯业荣,蔡锦辉,等.雷达定量降水动态分级Z-I关系估算方法.热带气象学报[J].2011.27(4):601-608.
伍静,刘黎平,尤卫红,等.应用雷达拼图数据估测降水试验[J].气象科技,2010,38(2):175-181.
肖艳姣,蒋义芳.风暴的自动识别,跟踪与预报[J].南京气象学院学报,1998,21(2):223-229.
肖艳娇,刘黎平.新一代天气雷达网资料的三维格点化及拼图方法研究[J].气象学报,2006,64(5):647-656.
肖艳姣,刘黎平,杨洪平.基于天气雷达网三维拼图的混合反射率因子生成技术[J].气象学报,2008,66(3):470-473
徐芬,慕熙昱,王卫芳,夏文梅,徐琪.分类型最优法在江苏沿江地区降水估测中的应用与讨论.气象科学,2013,33(1):51-58.
徐燕.卡尔曼滤波法在西峰雷达估测降水中的应用[J].干旱气象,2008,26(2):78-82
姚燕飞,程明虎,杨洪平,等.优化ZI关系及其在淮河流域面雨量测量中的应用.[J].气象,2007,33(6):37-43.
尹忠海,张沛源.利用卡尔曼滤波校准方法估算区域降水量[J].应用气象学报,2005,16(2):213-319.
张家国;王佐兵;力梅三峡地区气候Z-R关系[J].气象1999,(03).
张利平,张晓琳,王任超,等.雷达估算降雨的同化方法对比[J].武汉大学学报(工学版).2011,44(2):146-150
张宁,江志红,吴立广.江苏省自动站与基础站降水观测资料质量分析[J].大气科学学报,2010,33(5):606-614
张培昌,戴铁丕,伍志芳,等.1992.用变分方法校准数字化天气雷达测定区域降水量基本原理和精度[J].大气科学,16(2):248-256
张培昌,戴铁丕,王登炎,林炳干.最优化法求Z-I关系及其在测定降水量中的精度[J].气象科学,1992,(3).
张培昌,杜秉玉,戴铁丕.雷达气象学[M].北京:气象出版社,2000:175-182.
张志强,刘黎平,谢明元,等.CINRAD三维组网拼图产品显示系统[J].气象,2007,33(9):19-24.
庄薇,刘黎平,余燕群,等..雷达地物回波模糊逻辑识别法的改进及效果检验[J].气象学报,2012,70(3):576-584
庄薇,刘黎平,胡志群.青藏高原零度层亮带的识别订正方法及在雷达估测降水中的应用[J].气象,2013，39(8):1004–1013
赵放,冀春晓,任鸿翔,等.台风螺旋带回波识别追踪及结构分析技术应用[J].浙江气象.2007,29(1):15-21.
张亚萍,刘钧,夏文梅,等.雷达定量估测区域降水波束阻挡系数的计算[J].南京气象学院学报,2002,25(5):640-647.
郑媛媛,谢亦峰,吴林林,等.多普勒雷达定量估测降水的三种方法比较试验[J].热带气象学报,2004,20(3):192-197.
周彪,汤达章.雷达定量测量区域降水量的试验研究[J].南京气象学院学报,1998,21(1):152-159.
Ahnert, P. R., Krajewski,W. F., and Johnson, E. R.,1986. Kalman filter estimation of radar rainfall fieldbias[C]. In Preprints23th Conf. on Radar Met., JP33–JP37. AMS.
Anagnostou, E. N., and W. F. Krajewski,1999: Real-time radar rainfall estimation. Part II: Case study. J.Atmos. Oceanic Technol.,16,198–205.
Andrieu, H., J. D. Creutin, G. Delrieu, J. Leoussoff, and Y. Pointin,1989:Radar data processing forhydrology in the Cevennes region.Proc.
Andrieu, H., G. Delrieu, and J. D. Creutin,1995: Identification of vertical profiles of radar reflectivity forhydrological applications using an inverse method. Part II: Sensitivity analysis and case study. J. Appl.Meteor.,34,240–259.
Andrieu, H., J. D. Creutin, G. Delrieu, and D. Faure,1997: Use of a weather radar for the hydrology of amountainous area. Part I: Radar measurement interpretation. J. Hydrol.,193,1–25.
Atlas D, Ulbrich CW, Marks FD, Amitai E, Williams CR (1999) Systematic variation of drop size andradarrainfall relations. J Geophys Res Atmos104:6155–6169
Brandes, E. A.Optimizing rainfall estimates with the aid of RADAR, J. Appl. Meteorol.,1975,14,1339–1345.
Brandes EA, Vivekanandan J, Wilson JW (1999) A comparison of radar reflectivity estimates of rainfallfrom collocated radars. J Atmos Ocean Technol16:1264–1272
Balakrishnan N, Zrnic DS, Goldhirsh J, Rowland J (1989) Comparison of simulated rain rates fromdisdrometer data employing polarimetric radar algorithms. J Atmos Ocean Technol6:476–486
Battan LJ (1973) Radar observation of the atmosphere. The University of Chicago Press, Chicago
Bech, J., B. Codina, J. Lorente, and D. Bebbington,2003: The sensitivity of single polarization weatherradar beam blockage correction to variability in the vertical refractivity gradient. J. Atmos. OceanicTechnol.,20,845–855.
Bech J, Gjertsen U, Haase G (2007b) Modelling weather radar beam propagation and topographicalblockage at northern high latitudes. Quart J Royal Meteorol Soc133:1191–1204Bellon A, Lee G, Zawadzki I.2005.Error statistics of VPR corrections in stratiform precipitation. J ApplMeteorol44:998–1015
Calheiros RV, Zawadzki I (1987) Reflectivity-rain rate relationships for radar hydrology in Brazil. JClimate Appl Meteorol26:118–132
Campos E, Zawadzki I (2000) Instrumental uncertainties in Z–R relations. J Appl Meteorol39:1088–1102Chapon B, Delrieu G, Gosset M, Boudevillain B (2008) Variability of rain drop size distribution and itseffect on the Z–R relationship: a case study for intense Mediterranean rainfall. Atmos Res87:52–65
Chumchean S, Seed A, Sharma A.(2006) Correcting of real-time radar rainfall bias using a Kalmanfiltering approach. Journal of Hydrology317:1-2,123-137Online publication date:5-Feb-2006.
Ciach GJ, Krajewski WF (1999b) Radar-rain gauge comparisons under observational uncertainties. J ApplMeteorol38:1519–1525
Ciach GJ, Krajewski WF, Smith JA (1997) Comments on ‘‘The window probability matching method forrainfall measurements with radar’’. J Appl Meteorol36:243–246
Ciach GJ, Morrissey ML, Krajewski WF (2000) Conditional bias in radar rainfall estimation. J ApplMeteorol39:1941–1946
Ciach,G.J.,W. F. Krajewski, E. N. Anagnostou, M. L. Baeck, J. A. Smith, J. R. McCollum, and A. Kruger,1997: Radar rainfall estimation for ground validation studies of the Tropical Rainfall Measuring Mission. J.Appl. Meteor.,36,736–747.
Collier C G.1986a Accuracy of rainfall estimates by radar, Part I：Calibration by telemetering raingauges[J].Hydrology,83:207-223.
Collier C G.1986b Accuracy of rainfall estimates by radar，Part II：Comparison with raingauges[J].Hydrology,83:225-235.
Cifelli R, Williams CR, Rajopadhyaya DK, Avery SK, Gage KS, May PT (2000) Drop-size distributioncharacteristics in tropical mesoscale convective systems. J Appl Meteorol39:760–777
Collier CG.1999.The impact of wind drift on the utility of very high spatial resolution radar data overurban areas. Phys Chem Earth Part B-Hydrol Oceans Atmos24:889–893
Creutin, J. D., H. Andrieu, and D. Faure,1997: Use of a weather radar for the hydrology of a mountainousarea. Part II: Radar measurement validation. J. Hydrol.,193,26–44.
Creutin J D, Delrieu G, Lebel T.1998.Rian measurement by raingauge-radar combination: a geostatisticalapproach. J Atmos Oceanic,5:102-115
Cushmeer N (1999) WSR-88D rainfall estimates during tropical storm Nora. Western Region TechnicalAttachment, Las Vegas
Daley, R.,1991. Atmospheric Data Analysis. New York, Cambridge University Press.457pp.
Dalezios NR, Kouwen N.1990.Radar signal interpretation in warm season rainstorms. Nord Hydrol21:47–64
Delrieu, G., J. D. Creutin, and H. Andrieu,1995: Simulation of radar mountain returns using a digitizedterrain model. J. Atmos. Oceanic Technol.,12,1038–1049.
Delrieu, G., S. Caoudal, J. D. Creutin,1997: Feasibility of Using Mountain Return for the Correction ofGround-Based X-Band Weather Radar Data.J. Atmos. Oceanic Technol.,14,368–385.
Dinku, T., E. N. Anagnostou, and M. Borga,2002: Improving radar-based estimation of rainfall overcomplex terrain. J.Appl. Meteor.,41,1163–1178.
Eischeid JK, Baker CB, Karl T, Diaz HF.1995.The quality control of long-term climatological data usingobjective data analysis. Journal of Applied Meteorology34:2787–2795.
Eischeid JK, Pasteris PA, Diaz HF, Plantico MS, Lott NJ.2000. Creating a serially complete, national dailytime series of temperature and precipitation for the Western United States. Journal of Applied Meteorology39:1580–1591.
Faure D, Delrieu G, Tabary P, Du Chatelet JP, Guimera M (2005) Application of the hydrologic visibilityconcept to estimate rainfall measurement quality of two planned weather radars. Atmos Res77:232–246
Fulton, Richard A., Jay P. Breidenbach, Dong-Jun Seo, Dennis A. Miller, Timothy O’Bannon,1998: TheWSR-88D Rainfall Algorithm. Wea. Forecasting,13,377–395.
Gandin LS.1988.Complex quality control of meteorological observations. Monthly Weather Review116:1137–1156.
Germann U, and J Joss.2003.Operational measurement of precipitation in mountainous terrain. WeatherRadar: Principles and Advanced Applications, P. Meischner, Ed, Springer-Verlag:52–77.
Gunn KLS, Marshall JS.1955.The effect of wind shear on falling precipitation. J Meteorol12:339–349
Guttman N, Karl C, Reek T, Shuler V.1988.Measuring the performance of data validators. Bulletin of theAmerican Meteorological Society69(12):1448–1452.
Gabella M, Perona G. Simulation of the orographic influence on weather radar using a geometric-opticsapproach [J].Journal of Atmospheric and Oceanic Technology,1998,15(6):1485-1494.
Gabella M, Joss J, Perona G, Galli G (2001) Accuracy of rainfall estimates by two radars in the sameAlpine environment using gage adjustment. J Geophys Res Atmos106:5139–5150
Germann, U., and J. Joss,2003: Operational measurement of precipitation in mountainous terrain. WeatherRadar: Principles and Advanced Applications, P. Meischner, Ed., Springer-Verlag,52–77.
Gourley JJ, Calvert CM (2003) Automated detection of the bright band using WSR-88D data. WeatherForecast18:585–599
Haberlandt U. Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event[J]. Journal of Hydrology,2007,332(1/2):144-1571
Habib E, Aduvala AV, Meselhe EA.2008a.Analysis of radar-rainfall error characteristics and implicationsfor streamflow simulation uncertainty. Hydrol Sci J-J Des Sci Hydrol53:568–587
Habib E, Malakpet CG, Tokay A, Kucera PA.2008b.Sensitivity of streamflow simulations totemporalvariability and estimation of Z–R relationships. J Hydrol Eng13:1177–118
Houze,1993. R. A., Jr. Cloud Dynamics. M. Academic Press,573pp.
Hubbard KG.2001.Multiple station quality control procedures.Automated Weather Stations forApplications in Agriculture and Water Resources Management, AGM-3WMO./TD No.1074. High PLainsRegional Climate Center, Lincoln:248.
Hubbard KG, You J.2005. Sensitivity analysis of quality assurance using spatial regression approach–Acase Study of the maximum minimum air temperature. Journal of Atmospheric and Oceanic Technology22(10):1520–1530.
Joss, J., and R. Lee,1995: The application of radar–gauge comparisons to operational precipitation profilecorrections. J. Appl. Meteor.,34,2612–2630.
Koistinen, J., Puhakka, T.,1981: An improved spatial gauge-radar adjustment technique[C]. In Proc.20thConference on Radar Meteorology, pp.179-186. AMS
Krajewski, W. F.,1987. Cokriging radar-rainfall and rain gauge data. J. Geophys. Res.,92(d8),9571–9580.
Krajewski WF, Kruger A, Caracciolo C, Gole P, Barthes L, Creutin JD, Delahaye JY, Nikolopoulos EI,Ogden F, Vinson JP (2006a) DEVEX-disdrometer evaluation experiment: basic results and implications forhydrologic studies. Adv Water Resour29:311–325
Krajewski W F, Ntelekos A A, Goska R. A GIS-based methodology for the assessment of weather radarbeam blockages in mountainous regions: two examples from the USNEXRAD network [J]. Computers&Geosciences,2006,32(3):283-302.
Krajewski WF, Smith JA (1991) On the estimation of climatological Z–R relationships. J Appl Meteorol30:1436–1445
Kucera, P. A., W. F. Krajewski, and C. B. Young,2004: Radar beam occultation studies using GIS andDEM technology:An example study of Guam. J. Atmos. Oceanic Technol.,21,995–1006.
Lack SA, Fox NI.2005.Errors in surface rainfall rates retrieved from radar due to wind-drift. Atmos SciLett6:71–7
Lack SA, Fox NI.2007.An examination of the effect of wind-drift on radar-derived surface rainfallestimations. Atmos Res85:217–229
Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf,2007: An automated technique to qualitycontrol radar reflectivity data. J. Appl. Meteor. Climatol.,46,288-305.
Lakshmanan, V., J. Zhang, and K. Howard,2010: A technique to censor biological echoes in weather radarimages.J. Appl. Meteor. Climatol.,49,453–462.
Lee G, Zawadzki I (2005a) Variability of drop size distributions: noise and noise filtering in disdrometricdata. J Appl Meteorol44:634–652
Lee G, Zawadzki I (2005b) Variability of drop size distributions: time-scale dependence of the variabilityand its effects on rain estimation. J Appl Meteorol44:241–255
Lee GW, Zawadzki I (2006) Radar calibration by gage, disdrometer, and polarimetry: theoretical limitcaused by the variability of drop size distribution and application to fast scanning operational radar data. JHydrol28:83-97.
Marshall JS, Palmer WM (1948) The distribution of raindrops with size. J Meteorol5:165–166
Marshall JS, Hitschfeld W, Gunn KLS (1955) Advances in radar weather. In: Landsberg HE (ed) Advancesin geophysics, vol2. Academic Press Inc., New York
Maddox RA, Zhang J, Gourley JJ, Howard KW (2002) Weather radar coverage over the contiguous UnitedStates. Weather Forecast17:927–934
Maki M, Keenan TD, Sasaki Y, Nakamura K (2001) Characteristics of the raindrop size distribution intropical continental squall lines observed in Darwin, Australia. J Appl Meteorol40:1393–1412
Martner BE, Yuter SE, White AB, Matrosov SY, Kingsmill DE, Ralph FM (2008) Raindrop sizedistributions and rain characteristics in California coastal rainfall for periods with and without a radarbright band. J Hydrometeorol9:408–425
Wong M.C., Wong W.K., and Edwin S.T. Lai,2006: From SWIRLS to RAPIDS: Nowcast ApplicationsDevelopment in Hong Kong. PWS Workshop on Warnings of Real-Time Hazards by Using NowcastingTechnology, Sydney, Australia,9-13October2006.
Medlin JM, Kimball SK, Blackwell KG (2007) Radar and rain gauge analysis of the extreme rainfallduring hurricane Danny’s (1997) landfall. Mon Weather Rev135:1869–1888
Miriovsky BJ, Bradley AA, Eichinger WE, Krajewski WF, Kruger A, Nelson BR, Creutin J-D, LapetiteJ-M, Lee GW, Zawadzki I, Ogden FL (2004) An empirical study of small-scale variability of radarreflectivity using disdrometer observations. J Appl Meteorol43:106–118
Morin E, Krajewski WF, Goodrich DC, Gao XG, Sorooshian S (2003) Estimating rainfall intensities fromweather radar data: the scale-dependency problem. J Hydrometeorol4:782–797
Ninomiya K,Akiyama Y.Objective analysis of heavy rainfalls based on radar and gauge measurementJ.Meter Soc, Japan,1978,50:206-210
O’Bannon,T., Ahnert. P. A Study of the Precipitation Processing System on a Winter type OklahomaRainstorm [G]. Preprints of23rd Radar Meteorology Conf Boston: AM S,1986:99-1021
O’Bannon,T.,1997:Using a “terrain-based” hybrid scan to improve WSR-88D precipitationestimates.Preprints,28th Int. Conf. on Radar Meteorology,Austin, TX, Amer. Meteor. Soc.,506-507.
Pellarin T, Delrieu G, Saulnier GM, Andrieu H, Vignal B, Creutin JD (2002) Hydrologic visibility ofweather radar systems operating in mountainous regions: case study for the Ardeche Catchment (France). JHydrometeorol3:539–555
Robert A. Houze Jr.1989.Observed structure of mesoscale convective systems and implications forlarge-scale heating. Quart J Roy Meteor Soc,115:425–461.
Rosenfeld D, Wolff DB, Atlas D (1993) General probability-matched relations between radar reflectivityand rain rate. J Appl Meteorol32:50–72
Rosenfeld D, Wolff DB, Amitai E (1994) The window probability matching method for rainfallmeasurements with radar. J Appl Meteorol33:682–693
Rosenfeld D, Amitai E, Wolff DB (1995) Improved accuracy of radar WPMM estimated rainfall uponapplication of objective classification criteria. J Appl Meteorol34:212–223
Rosenfeld D, Amitai E (1998) Comparison of WPMM versus regression for evaluating Z–R relationships.J Appl Meteorol37:1241–1249
Roux, C, T. Lebel, C Depraetere, and H. Andrieu,1989: Simulation al'aide d'un modele nurnerique deterrain des echos de sol detectes par un radar meteorologique. Hydrol. Cont..4,139-149
Sasaki Y. Some basic formulas in numerical variation analysis [J]. Mon Wea Rev,1970,98:875-883
Sauvageot H (1994) Rainfall measurement by radar: a review. Atmos Res35:27–54
Schaake, J., A. Henkel, and S. Cong,2004: Application of PRISM climatologies for hydrologic modelingand forecasting in the western U.S. Preprints,18th Conf. on Hydrology, Seattle, WA, Amer. Meteor.Soc.,5.3.
Schroeder, J. L., W. S. Burgett, K. B. Haynie, I. Sonmez, G. D. Skwira, A. L. Doggett, J. W. Lipe.2005.TheWest Texas Mesonet: A Technical Overview. J. Atmos. Oceanic Technol.,22,211–222.
Seed A, Nichol J, Austin G, Stow CD, Bradley SG (1996) The impact of radar and rain gauge samplingerrors when calibrating a weather radar. Meteorol Appl3:43–52
Seo D J,Smith J A.1998a.Rainfall estimation using raingauges and radar A Bayesian approach:1.Derivation of estimators. Stoch Hydrol Hydraul,5:17-29
Seo D J,Smith J A.1998a.Rainfall estimation using raingauges and radar A Bayesian approach:2.Anapplication.Stoch. Stoch Hydrol Hydraul,5:31-44
Shafer MA, Fiebrich CA, Arndt DS, Fredrickson SE, Hughes TW.2000. Quality assurance procedures inthe Oklahoma mesonetwork. Journal of Atmospheric and Oceanic Technology17:474–494.
Sieck LC, Burges SJ, Steiner M (2007) Challenges in obtaining reliable measurements of pointrainfall.Water Resour Res43:23
Smith P L. On the minimum useful elevation angle for weather surveillance radar scans [J]. Journal ofAtmospheric and Oceanic Technology,1998,15(3):840-845.
Smith JA, Krajewski WF (1993) A modeling study of rainfall rate-reflectivity relationships. Water ResourRes29:2505–2514
Smith JA, De Veaux RD (1994) A stochastic model relating rainfall intensity to raindrop process. WaterResour Res30:651–664
Smith JA, Hui E, Steiner M, Baeck ML, Krajewski WF, Ntelekos AA (2009) Variability of rainfall rate andraindrop size distributions in heavy rain. Water Resour Res45:12
Steiner M, Smith JA (2000) Reflectivity, rain rate, and kinetic energy flux relationships based on raindropspectra. J Appl Meteorol39:1923–1940
Steiner M, Smith JA, Burges SJ, Alonso CV, Darden RW (1999) Effect of bias adjustment and rain gaugedata quality control on radar rainfall estimation. Water Resour Res35:2487–2503
Steiner M, Smith JA (2004) Scale dependence of radar-rainfall rates—An assessment based on raindropspectra. J Hydrometeorol5:1171–1180
Steiner, M., R. A. Houze Jr., and S. E. Yuter,1995: Climatological characterization of three-dimensionalstorm structure from operational radar and rain gauge data. J. Appl. Meteor.,34,1978–2007.
Sugier, J., J. Parent-du-Chatelet, P. Roquain, and A. Smith,2002: Detection and removal of clutter andanaprop in radar data using a statistical scheme based on echo fluctuation. Proc. Second European RadarConf., Delft, Netherlands, Copernicus GmbH,17–24.
Tabary, P.,2007: The new French operational radar rainfall product. Part I: Methodology. Wea. Forecasting,22,393–408.
Tabary, P.,2007: The new French operational radar rainfall product. Part II: Methodology. Wea.Forecasting,22,409–427.
Todini E.2001. Bayesian conditioning of RADAR to rain gauges.Hydrol Earth System Sci,5:255-232Tokay, Ali, David A. Short.1996.Evidence from Tropical Raindrop Spectra of the Origin of Rain fromStratiform versus Convective Clouds. Appl Meteor,35:355–371.
Tokay A, Kruger A, Krajewski WF (2001) Comparison of drop size distribution measurements by impactand optical disdrometers. J Appl Meteorol40:2083–2097
Tokay A, Short DA (1996) Evidence from tropical raindrop spectra of the origin of rain from stratiformversus convective clouds. J Appl Meteorol35:355–371
Tokay A, Bashor PG, Habib E, Kasparis T (2008) Raindrop size distribution measurements in tropicalcyclones. Mon Weather Rev136:1669–1685
Tokay A, Bashor PG, Wolff KR (2005) Error characteristics of rainfall measurements by collocatedJoss-Waldvogel disdrometers. J Atmos Ocean Technol22:513–527
Tuttle, J. D., and G. B. Foote,1990: Determination of boundary layer airflow from a single Doppler radar. J.Atmos. Oceanic Technol.,7,218–232.
Uijlenhoet R (2001) Raindrop size distributions and radar reflectivity-rain rate relationships for radarhydrology. Hydrol Earth Syst Sci5:615–627
Ulbrich CW, Atlas D (2002) On the separation of tropical convective and stratiform rains. J Appl Meteorol41:188–195
Uijlenhoet, Remko, Matthias Steiner, et al.2003.Variability of Raindrop Size Distributions in a Squall Lineand Implications for Radar Rainfall Estimation. Hydrometeor,4:43–61.
Vasiloff, Steven V, and Coauthors.2007.Improving QPE and Very Short Term QPF: An Initiative for aCommunity-Wide Integrated Approach. Bull Amer Meteor Soc,88:1899–1911.
Vieux BE, Bedient PB (1998) Estimation of rainfall for flood prediction from WSR-88D reflectivity: acase study,17–18October1994. Weather Forecast13:407–415
Vignal B, Galli G, Joss J, Germann U.2000.Three methods to determine profiles of reflectivity fromvolumetric radar data to correct precipitation estimates. J Appl Meteorol39:1715–1726
Vignal B, Krajewski WF.2001.Large-sample evaluation of two methods to correct range-dependent errorfor WSR-88D rainfall estimates. J Hydrometeorol2:490–504
Villarini G, Krajewski WF.2009.Sensitivity studies of the models of radar-rainfall uncertainties. J ApplMeteorol Climatol (Submitted)
Villarini, G.&Krajewski, W. F.2010.Review of the different sources of uncertainty in single polarizationradar-based estimates of rainfall. Surveys in Geophysics, Vol.31, pp.107-129, ISSN1573-0956
WANG Gaili,LIU Liping and DING Yuanyuan.2012.Improvement of Radar Quantitative PrecipitationEstimation Based on Real-Time Adjustments to Z-R Relationships and Inverse Distance WeightingCorrection Schemes. Adv Atmos Sci,29(3):575-584.
Wade CG.1987.A quality control program for surface mesometeorological data.Journal of Atmospheric andOceanic Technology4:435–453.
Ware, E. C.,2005:Corrections to radar-estimated precipitation using observed rain gauge data. M.S. thesis,Cornell University,87pp.
Westrick KJ, Mass CF, Colle BA (1999) The limitations of the WSR-88D radar network for quantitativeprecipitation measurement over the coastal western United States. Bull Am Meteorol Soc80:2289–2298
Williams CR, Kruger A, Gage KS, Tokay A, Cifelli R, Krajewski WF, Kummerow C (2000) Comparisonof simultaneous rain drop size distributions estimated from two surface disdrometers and a UHF profiles.Geophys Res Lett27:1763–1766
Willis PT, Tattelman P (1989) Drop-size distributions associated with intense rainfall. J Appl Meteorol28:3–15
Wilson J W, and E A Brandes,Radar Measurement of Rainfall—A Summary. Bull Amer Meteor Soc,1979,60:1048–1058.
Xu, Xiaoyong, Kenneth Howard, Jian Zhang.2008.An Automated Radar Technique for the Identification ofTropical Precipitation. Hydrometeor,9:885–902.
Young CB, Nelson BR, Bradley AA, Smith JA, Peters-Lidard CD, Kruger A, Baeck ML (1999) Anevaluation of NEXRAD precipitation estimates in complex terrain. J Geophys Res Atmos104:19691–19703
Yuter SE, Houze RA (1997) Measurements of raindrop size distributions over the Pacific warm pool andimplications for Z–R relations. J Appl Meteorol36:847–867
Zawadzki, I. Factors affecting the precision of radar measurements of rain. Preprints,22d Int. Conf. onRadar Meteorology, Zurich, Switzerland, Amer. Meteor. Soc.,1984,251-256.
Zhang Jian, Carrie Langston, Kenneth Howard.2008.Brightband Identification Based on Vertical Profilesof Reflectivity from the WSR-88D. J Atmos Oceanic Technol,25:1859–1872.
Zhang Jian,Youcun Qi.2010.A Real-Time Algorithm for the Correction of Brightband Effects inRadar-Derived QPE. J Hydrometeor,11:1157–1171.
Zhang Jian, and Coauthors.2011.National Mosaic and Multi-Sensor QPE (NMQ) System: Description,Results, and Future Plans. Bull Amer Meteor Soc,92:1321–1338.
Zhang Jian,Youcun Qi, David Kingsmill, Kenneth Howard.2012.Radar-Based Quantitative PrecipitationEstimation for the Cool Season in Complex Terrain: Case Studies from the NOAA HydrometeorologyTestbed. J. Hydrometeor,13:1836–1854.
Zhang, J., K. Howard, and J. J. Gourley,2005: Constructing three-dimensional multiple radar reflectivitymosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol.,22,30–42.
Zhang, J., K. C. Langston, K. Howard, and B. Clarke,2006b: Gap-filling in3D radar mosaic analysisusing vertical profile of reflectivity. Preprints,12th Conf. on Aviation, Range, and Aerospace Meteorology,Atlanta,GA, Amer. Meteor. Soc., CD-ROM,1.9.
Zhang, J, S. Wang, and B. Clarke,2004: WSR-88D reflectivity quality control using horizontal and verticalreflectivity structure. Preprints,11th Conf. on Aviation, Range, and Aerospace Meteorology, Hyannis, MA,Amer. Meteor. Soc.,5.4.
Zhang Yaping, Chen MingHun. Dynamical Weather Radar Beam Blockage Correction[C].DigitalManufacturing and Automation (ICDMA),2010International Conference.2010(1):67-70
Zipser, E. J., and K. R. Lutz.1944.The vertical profile of radar reflectivity of convective cells: A strongindicator of storm intensity and lightning probability. J Mon Wea Rev,122:1751–1759.
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