多普勒雷达资料业务应用研究
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摘要
本论文基于我国新近建成的新一代多普勒雷达探测网,针对其业务应用中迫切需要解决的一些问题进行了探讨,主要研究内容如下:
首先设计了一种基于反射率因子三维结构的奇异回波自动剔除方案,该方案利用三个物理因子(雷达回波的顶高ECHOtop、反射率因子的空间可变性SPIN及其垂直梯度vertGRAD)来实现对奇异回波的识别和剔除。接着介绍了WSR—88D速度退模糊算法,当算法首次处理时,如果无法寻找到第一个用于比较的有效的邻近速度值,那么算法误差较大。鉴于此,我们在对开始径向上的头几个速度数据进行检验时,增加了一个额外的处理,用环境风在相应径向上的分量作临界控制,以避免出现不恰当的退模糊处理。
其次,本文分析了国内外当前业务中使用的几种风暴自动识别算法,并设计出一种能够描述风暴对流发展强弱的新方案。WSR—88D Build 7.0风暴算法(B7SI)采用了风暴具有三维结构的思想,识别时利用多个阈值检验其强度和连续性。WSR—88D Build 9.0风暴算法(B9SI)在此基础上增加了多阈值、核抽取、以及相近单体处理等多项新技术,以解决成簇、成串排列的风暴或多个风暴相距较近时造成的误差。文中设计出的第三种方法(CSI)在降低B9SI反射率因子识别阈值的基础上,利用模糊逻辑技术对B9SI输出结果和雷达基资料做处理,以计算描述风暴对流发展强弱的对流指数(CI)。CSI首先提取一组描述风暴对流性特征的物理量,包括反射率因子纹理结构(Texture)、反射率因子空间变化率(SIGN和VertGrad)、垂直积分含水量(VIL)和径向速度标准方差(SDVE),并分配权重;其次,利用每一个物理量的统计结果,结合其物理意义,设计出相应的隶属函数,以计算风暴与该物理量描述的对流性特征相匹配的概率;最后对多个概率值进行加权平均即得对流指数。
再次,我们开发出了一套具有高时空分辨率的雷达区域拼图系统,以实现对中尺度天气系统的实时监测。该系统输出产品能够给出中尺度天气系统的完整结构,其时间序列分析还可以给出整个中尺度系统的移动趋势;同时在区域拼图系统输出产中划分空间二维阵列,并应用TREC技术,以获取中尺度天气系统内的水平流场。
最后,还设计了一种基于模糊逻辑技术的、适用于我国新一代雷达的回波分类方案,它包含了PDA、APDA、ICADA和SCDA四种回波探测算法(其中SCDA算法仍在发展之中),即在雷达基资料最低层(0.5度)反射率因子PPI上提取降水回波、奇异回波、昆虫晴空回波以及海浪回波信息,供预报员和其他雷达衍生产品使用。
Firstly, This study reviews past attempts to mitigate ground clutter contamination of radar data resulting from anomalous signal propagation, and presents a new algorithm for radar data quality control. The new automated procedure has been developed that makes use of the three-dimensional reflectivity structure. In particular, the vertical extent of radar echoes, their spatial variability, and vertical gradient of intensity are evaluated. Then, the paper aims at Doppler velocity dealiasing. A operational algorithm provided by WSR-88D is introduced. Because of its defaults, an additional process is applied to the first gates.Secondly, three storm automatic identification algorithms by Doppler radar are discussed. The first one provided by WSR—88D Build 7.0 (B7SI) tests the intensity and continuity of the objective echoes to build three-dimensional storms. When storms are merging, splitting, or clustered closely, errors may occur in B7SI. The second algorithm (B9SI) is part of the Build 9.0 Radar Products Generator of the WSR-88D system. It uses multiple thresholds of reflectivity, designs the technique of cell nucleus extraction, and processes the close storms. So B9SI is capable of identifying embedded cells in multi-cellular storms. But, B9SI can't give information on storm convection strength, because texture and gradient of reflectivity are not calculated and radial velocity data are not used. Then, the third algorithm (CSI) is addressed detailedly. By using fuzzy logic technique, CSI processes radar base data and the output of B9SI, in which the levels of the seven reflectivity thresholds are lowered, to obtain storm convection index. For the CSI algorithm, a set of features is combined to describe the convective characteristics of storm, and each feature is given a weight. These features include texture and gradient of reflectivity, VIL, and standard deviation of radial velocity. Then, the likelihood values that the features match the objective storm are calculated by the linear membership functions, which are from the feature field histograms of the historical data. Finally, the convection index is the weighted average of all the likelihood values.Thirdly, the basic technique, functions and key parameters of the Regional CINRAD Mosaic System is documented in this paper. And it is introduced that the system was applied to the severe convection weather and typhoon detecting and warning in Guangdong province recently. The
horizontal wind field in the meso-scale weather system can be derived by applying the TREC technique to mosaic products.Finally, a fuzzy logic radar echo classifying scheme for CIN-98SA is designed, and it consists of four data fusion algorithms including the AP Detection Algorithm(APDA), the Precipitation Detection Algorithm(PDA), the Insect Clear Air Detection Algorithm(ICADA) and the Sea Clutter Detection Algorithm(SCDA). For each algorithm, a set of features is combined to describe the characteristics of the objective echo type, and every feature is given a weight. These features include mean, median, standard deviation, texture in reflectivity, radial velocity or spectrum field. Then, the likelihood values that the features match the objective echo type are calculated by the linear membership functions, which are from the feature field histograms of the history data. By comparing the chosen threshold and the weighted average of the likelihood values, whether the observed echoes are the objective type is decided.
首先设计了一种基于反射率因子三维结构的奇异回波自动剔除方案,该方案利用三个物理因子(雷达回波的顶高ECHOtop、反射率因子的空间可变性SPIN及其垂直梯度vertGRAD)来实现对奇异回波的识别和剔除。接着介绍了WSR—88D速度退模糊算法,当算法首次处理时,如果无法寻找到第一个用于比较的有效的邻近速度值,那么算法误差较大。鉴于此,我们在对开始径向上的头几个速度数据进行检验时,增加了一个额外的处理,用环境风在相应径向上的分量作临界控制,以避免出现不恰当的退模糊处理。
其次,本文分析了国内外当前业务中使用的几种风暴自动识别算法,并设计出一种能够描述风暴对流发展强弱的新方案。WSR—88D Build 7.0风暴算法(B7SI)采用了风暴具有三维结构的思想,识别时利用多个阈值检验其强度和连续性。WSR—88D Build 9.0风暴算法(B9SI)在此基础上增加了多阈值、核抽取、以及相近单体处理等多项新技术,以解决成簇、成串排列的风暴或多个风暴相距较近时造成的误差。文中设计出的第三种方法(CSI)在降低B9SI反射率因子识别阈值的基础上,利用模糊逻辑技术对B9SI输出结果和雷达基资料做处理,以计算描述风暴对流发展强弱的对流指数(CI)。CSI首先提取一组描述风暴对流性特征的物理量,包括反射率因子纹理结构(Texture)、反射率因子空间变化率(SIGN和VertGrad)、垂直积分含水量(VIL)和径向速度标准方差(SDVE),并分配权重;其次,利用每一个物理量的统计结果,结合其物理意义,设计出相应的隶属函数,以计算风暴与该物理量描述的对流性特征相匹配的概率;最后对多个概率值进行加权平均即得对流指数。
再次,我们开发出了一套具有高时空分辨率的雷达区域拼图系统,以实现对中尺度天气系统的实时监测。该系统输出产品能够给出中尺度天气系统的完整结构,其时间序列分析还可以给出整个中尺度系统的移动趋势;同时在区域拼图系统输出产中划分空间二维阵列,并应用TREC技术,以获取中尺度天气系统内的水平流场。
最后,还设计了一种基于模糊逻辑技术的、适用于我国新一代雷达的回波分类方案,它包含了PDA、APDA、ICADA和SCDA四种回波探测算法(其中SCDA算法仍在发展之中),即在雷达基资料最低层(0.5度)反射率因子PPI上提取降水回波、奇异回波、昆虫晴空回波以及海浪回波信息,供预报员和其他雷达衍生产品使用。
Firstly, This study reviews past attempts to mitigate ground clutter contamination of radar data resulting from anomalous signal propagation, and presents a new algorithm for radar data quality control. The new automated procedure has been developed that makes use of the three-dimensional reflectivity structure. In particular, the vertical extent of radar echoes, their spatial variability, and vertical gradient of intensity are evaluated. Then, the paper aims at Doppler velocity dealiasing. A operational algorithm provided by WSR-88D is introduced. Because of its defaults, an additional process is applied to the first gates.Secondly, three storm automatic identification algorithms by Doppler radar are discussed. The first one provided by WSR—88D Build 7.0 (B7SI) tests the intensity and continuity of the objective echoes to build three-dimensional storms. When storms are merging, splitting, or clustered closely, errors may occur in B7SI. The second algorithm (B9SI) is part of the Build 9.0 Radar Products Generator of the WSR-88D system. It uses multiple thresholds of reflectivity, designs the technique of cell nucleus extraction, and processes the close storms. So B9SI is capable of identifying embedded cells in multi-cellular storms. But, B9SI can't give information on storm convection strength, because texture and gradient of reflectivity are not calculated and radial velocity data are not used. Then, the third algorithm (CSI) is addressed detailedly. By using fuzzy logic technique, CSI processes radar base data and the output of B9SI, in which the levels of the seven reflectivity thresholds are lowered, to obtain storm convection index. For the CSI algorithm, a set of features is combined to describe the convective characteristics of storm, and each feature is given a weight. These features include texture and gradient of reflectivity, VIL, and standard deviation of radial velocity. Then, the likelihood values that the features match the objective storm are calculated by the linear membership functions, which are from the feature field histograms of the historical data. Finally, the convection index is the weighted average of all the likelihood values.Thirdly, the basic technique, functions and key parameters of the Regional CINRAD Mosaic System is documented in this paper. And it is introduced that the system was applied to the severe convection weather and typhoon detecting and warning in Guangdong province recently. The
horizontal wind field in the meso-scale weather system can be derived by applying the TREC technique to mosaic products.Finally, a fuzzy logic radar echo classifying scheme for CIN-98SA is designed, and it consists of four data fusion algorithms including the AP Detection Algorithm(APDA), the Precipitation Detection Algorithm(PDA), the Insect Clear Air Detection Algorithm(ICADA) and the Sea Clutter Detection Algorithm(SCDA). For each algorithm, a set of features is combined to describe the characteristics of the objective echo type, and every feature is given a weight. These features include mean, median, standard deviation, texture in reflectivity, radial velocity or spectrum field. Then, the likelihood values that the features match the objective echo type are calculated by the linear membership functions, which are from the feature field histograms of the history data. By comparing the chosen threshold and the weighted average of the likelihood values, whether the observed echoes are the objective type is decided.
引文
[1] 张陪昌,杜秉玉,戴铁丕.雷达气象学.北京:气象出版社,2001
[2] Gossard E E. Refractive index variance and its height distribution in different air masses. Radio Sci., 1977,12, 89-105
[3] Cook J. A sensitivity study of weather data inaccuracies on evaporation duct height algorithms. Radio Sci., 1991, 26, 731-746
[4] Babin S M. Surface duct height distributions for Wallops Island, Virginia, 1985-1994. J. Appl. Meteor., 1996, 35, 86-93
[5] Fabry F, C Frush, I Zawadzki, et al. On the extraction of near-surface index of refraction using radar phase measurements from ground targets. J. Atmos. Oceanic Technol., 1997, 14, 978-987
[6] Skolnik M I. Introduction to Radar Systems. McGraw-Hill International Editions, 1980, 581 PP
[7] Puzzo D C, S W Troxel, M A Meister, et al. Appendix Ⅰ: Methods for filtering clutter as a result of anomalous propagation. ASR-9 Weather Channel Test Report. Project Rep. ATC-165, Lincoln Laboratory, MIT, Lexington, MA, 1989, 155-170
[8] Smith P L. Siting considerations for weather radars. Preprints, 15th Radar Meteorology Conf., Champaign-Urbana, IL, Amer. Meteor. Soc., 1972, 99-100
[9] Mann D R, J E Evans, and M W Merritt. Clutter suppression for low altitude wind shear detection by Doppler weather radars. Preprints, 23d Conf. on Radar Meteorology, Vol. 1, Snowmass, CO, Amer. Meteor. Soc., 1986, R9-R13
[10] Joss J, and H Wessels. Ground clutter suppression for weather radar data. COST Tech. Rep. 1990,73/WD/130,6pp
[11] Moores W, and T W Harrold. Estimating the distribution and intensity of ground clutter. Preprints, 16th Radar Meteorology Conf., Houston, TX, Amer. Meteor. Soc, 1975, 370-373
[12] Hogg W D. Quality control and analysis of an archive of digital radar data. Preprints, 18th Conf. on Radar Meteorology, Atlanta, GA, Amer. Meteor. Soc, 1978, 150-154
[13] Delrieu G, J D Creutin, and H Andrieu. Simulation of radar mountain returns using a digitized terrain model. J. Atmos. Oceanic Technol., 1995, 12, 1038-1049
[14] Archibald E. Enhanced clutter processing for the UK weather radar network. Phys. Chem. Earth, 2000, 25B, 823-828
[15] Johnson G N, P L Smith, F E Nathanson, et al. An analysis of techniques for dealing with anomalous propagation. Preprints, 16th Radar Meteorology Conf., Houston, TX, Amer. Meteor. Soc, 1975, 374-377
[16] Pratte J F, R J Keeler, R Gagnon, et al. Clutter processing during anomalous propagation conditions. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc, 1995,139-141
[17] Fabry F, C Frush, I Zawadzki, et al. On the extraction of near-surface index of refraction using radar phase measurements from ground targets. J. Atmos. Oceanic Technol., 1997,14, 978-987
[18] Atkinson N C, and P K James. Quality control of data from overlapping weather radars. Preprints, 25th Int. Conf. on Radar Meteorology, Paris, France, Amer. Meteor. Soc, 1995, 206-209
[19] Klingle-Wilson, D E Mann, and R Boldi. An algorithm to remove anomalous propagation clutter returns from ASR-9 weather channel data using pencil beam radar data. Preprints, Sixth Conf. on Aviation Weather Systems, Dallas, TX, Amer. Meteor. Soc, 1995, 366-371
[20] Pamment J A, and B J Conway. Objective identification of echoes due to anomalous propagation in weather radar data. J. Atmos. Oceanic Technol., 1998, 15, 98-113
[21] Pratte J F, R Gagnon, and R Cornelius. Ground clutter characteristics and residue mapping. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc, 1993, 50-52
[22] Harrison D L, S J Driscoll, and M Kitchen. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteor. 2000, Appl., 7,135-144
[23] Joss J, R W Lee. The application of radar-gauge comparisons to operational precipitation profile corrections. J. Appl. Meteor., 1995, 34, 2612-2630
[24] Fulton R A, J P Breidenbach, D J Seo, et al. The WSR-88D rainfall algorithm. Wea. Forecasting, 1998, 13, 377-395
[25] VanAndel J. Radar echo classifier algorithm development using Python. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc, 2001,704-706
[26] Collier C G. COST-75 final report—A summary. Phys. Chem. Earth, 2000,25B, 817-822
[2] Gossard E E. Refractive index variance and its height distribution in different air masses. Radio Sci., 1977,12, 89-105
[3] Cook J. A sensitivity study of weather data inaccuracies on evaporation duct height algorithms. Radio Sci., 1991, 26, 731-746
[4] Babin S M. Surface duct height distributions for Wallops Island, Virginia, 1985-1994. J. Appl. Meteor., 1996, 35, 86-93
[5] Fabry F, C Frush, I Zawadzki, et al. On the extraction of near-surface index of refraction using radar phase measurements from ground targets. J. Atmos. Oceanic Technol., 1997, 14, 978-987
[6] Skolnik M I. Introduction to Radar Systems. McGraw-Hill International Editions, 1980, 581 PP
[7] Puzzo D C, S W Troxel, M A Meister, et al. Appendix Ⅰ: Methods for filtering clutter as a result of anomalous propagation. ASR-9 Weather Channel Test Report. Project Rep. ATC-165, Lincoln Laboratory, MIT, Lexington, MA, 1989, 155-170
[8] Smith P L. Siting considerations for weather radars. Preprints, 15th Radar Meteorology Conf., Champaign-Urbana, IL, Amer. Meteor. Soc., 1972, 99-100
[9] Mann D R, J E Evans, and M W Merritt. Clutter suppression for low altitude wind shear detection by Doppler weather radars. Preprints, 23d Conf. on Radar Meteorology, Vol. 1, Snowmass, CO, Amer. Meteor. Soc., 1986, R9-R13
[10] Joss J, and H Wessels. Ground clutter suppression for weather radar data. COST Tech. Rep. 1990,73/WD/130,6pp
[11] Moores W, and T W Harrold. Estimating the distribution and intensity of ground clutter. Preprints, 16th Radar Meteorology Conf., Houston, TX, Amer. Meteor. Soc, 1975, 370-373
[12] Hogg W D. Quality control and analysis of an archive of digital radar data. Preprints, 18th Conf. on Radar Meteorology, Atlanta, GA, Amer. Meteor. Soc, 1978, 150-154
[13] Delrieu G, J D Creutin, and H Andrieu. Simulation of radar mountain returns using a digitized terrain model. J. Atmos. Oceanic Technol., 1995, 12, 1038-1049
[14] Archibald E. Enhanced clutter processing for the UK weather radar network. Phys. Chem. Earth, 2000, 25B, 823-828
[15] Johnson G N, P L Smith, F E Nathanson, et al. An analysis of techniques for dealing with anomalous propagation. Preprints, 16th Radar Meteorology Conf., Houston, TX, Amer. Meteor. Soc, 1975, 374-377
[16] Pratte J F, R J Keeler, R Gagnon, et al. Clutter processing during anomalous propagation conditions. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc, 1995,139-141
[17] Fabry F, C Frush, I Zawadzki, et al. On the extraction of near-surface index of refraction using radar phase measurements from ground targets. J. Atmos. Oceanic Technol., 1997,14, 978-987
[18] Atkinson N C, and P K James. Quality control of data from overlapping weather radars. Preprints, 25th Int. Conf. on Radar Meteorology, Paris, France, Amer. Meteor. Soc, 1995, 206-209
[19] Klingle-Wilson, D E Mann, and R Boldi. An algorithm to remove anomalous propagation clutter returns from ASR-9 weather channel data using pencil beam radar data. Preprints, Sixth Conf. on Aviation Weather Systems, Dallas, TX, Amer. Meteor. Soc, 1995, 366-371
[20] Pamment J A, and B J Conway. Objective identification of echoes due to anomalous propagation in weather radar data. J. Atmos. Oceanic Technol., 1998, 15, 98-113
[21] Pratte J F, R Gagnon, and R Cornelius. Ground clutter characteristics and residue mapping. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc, 1993, 50-52
[22] Harrison D L, S J Driscoll, and M Kitchen. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteor. 2000, Appl., 7,135-144
[23] Joss J, R W Lee. The application of radar-gauge comparisons to operational precipitation profile corrections. J. Appl. Meteor., 1995, 34, 2612-2630
[24] Fulton R A, J P Breidenbach, D J Seo, et al. The WSR-88D rainfall algorithm. Wea. Forecasting, 1998, 13, 377-395
[25] VanAndel J. Radar echo classifier algorithm development using Python. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc, 2001,704-706
[26] Collier C G. COST-75 final report—A summary. Phys. Chem. Earth, 2000,25B, 817-822