The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relative...The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.展开更多
天气雷达对中小尺度灾害性天气具有较强的监测预警能力,对研究中小尺度对流系统的云雨结构、理解降水内部的热力学和动力学过程有很大的帮助。单站点地基雷达受到诸如电磁波衰减、地物干扰等影响,在探测上存在一些限制。为了扩大天气雷...天气雷达对中小尺度灾害性天气具有较强的监测预警能力,对研究中小尺度对流系统的云雨结构、理解降水内部的热力学和动力学过程有很大的帮助。单站点地基雷达受到诸如电磁波衰减、地物干扰等影响,在探测上存在一些限制。为了扩大天气雷达探测区域,需要采用多部天气雷达组网联合探测。然而雷达组网的各雷达之间没有进行统一标定,影响雷达网资料一致性、组网拼图,以及使雷达资料在数值模式同化的应用中受到限制。本文以TRMM(Tropical Rainfall Measuring Mission)卫星搭载的经过精确标定的测雨雷达PR(Precipitation Radar)数据产品作为标准参照源,订正地基雷达GR(Ground-based Radar)的反射率因子偏差。为了减小PR与GR之间观测值对比的不一致性,利用最佳配对数据对比法(ABCD, Available Best Comparable Dataset法),对2008年1月至2014年9月间,江苏省六部地基雷达(南京、常州、连云港、南通、徐州、盐城)的反射率因子值进行订正。最后对方法的应用范围、存在的问题及未来展望进行了讨论。展开更多
Using super resolution direction of arrival（DOA） estimation algorithm to reduce the resolution angle is an effective method for passive radar seeker（PRS） to antagonize non-coherent radar decoy.Considering the powe...Using super resolution direction of arrival（DOA） estimation algorithm to reduce the resolution angle is an effective method for passive radar seeker（PRS） to antagonize non-coherent radar decoy.Considering the power and correlation property between radar and non-coherent decoy,an improved subspace DOA estimation method based on traditional subspace algorithm is proposed.Because this new method uses the invariance property of noise subspace,compared with traditional MUSIC algorithm,it shows not only better resolution in condition of closely spaced sources,but also superior performance in case of different power or partially correlated sources.Using this new method,PRS can distinguish radar and non-coherent decoy with good performance.Both the simulation result and the experimental data confirm the performance of the method.展开更多
基金supported by funding from the Natural Science Foundation of Jiangsu Province (Grant No. BK20171457)the 2013 Special Fund for Meteorological Scientific Research in the Public Interest (Grant No. GYHY201306078)+1 种基金the National Natural Science Foundation of China (Grant No. 41301399)Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.
文摘天气雷达对中小尺度灾害性天气具有较强的监测预警能力,对研究中小尺度对流系统的云雨结构、理解降水内部的热力学和动力学过程有很大的帮助。单站点地基雷达受到诸如电磁波衰减、地物干扰等影响,在探测上存在一些限制。为了扩大天气雷达探测区域,需要采用多部天气雷达组网联合探测。然而雷达组网的各雷达之间没有进行统一标定,影响雷达网资料一致性、组网拼图,以及使雷达资料在数值模式同化的应用中受到限制。本文以TRMM(Tropical Rainfall Measuring Mission)卫星搭载的经过精确标定的测雨雷达PR(Precipitation Radar)数据产品作为标准参照源,订正地基雷达GR(Ground-based Radar)的反射率因子偏差。为了减小PR与GR之间观测值对比的不一致性,利用最佳配对数据对比法(ABCD, Available Best Comparable Dataset法),对2008年1月至2014年9月间,江苏省六部地基雷达(南京、常州、连云港、南通、徐州、盐城)的反射率因子值进行订正。最后对方法的应用范围、存在的问题及未来展望进行了讨论。
文摘Using super resolution direction of arrival（DOA） estimation algorithm to reduce the resolution angle is an effective method for passive radar seeker（PRS） to antagonize non-coherent radar decoy.Considering the power and correlation property between radar and non-coherent decoy,an improved subspace DOA estimation method based on traditional subspace algorithm is proposed.Because this new method uses the invariance property of noise subspace,compared with traditional MUSIC algorithm,it shows not only better resolution in condition of closely spaced sources,but also superior performance in case of different power or partially correlated sources.Using this new method,PRS can distinguish radar and non-coherent decoy with good performance.Both the simulation result and the experimental data confirm the performance of the method.
文摘中国新一代天气雷达可以进行较大范围降水的定量估测,但相邻站雷达之间的资料可能存在不一致性而影响组网应用效果。将热带降雨测量卫星TRMM(Tropical Rainfall Measuring Mission)上携带的降水雷达PR(Precipitation Radar)作为统一的参照,针对2010年5-8月份长江中下游降水天气期间PR与苏南三部(南京、常州、南通)雷达的7次匹配事件的资料进行一致性分析,并利用经质量控制后得到的比较适宜于对比分析的数据集建立订正关系,进行偏差订正,分析订正效果,并详细比较其中两次事件订正前后地基雷达数据之间的差异。结果表明:(1)南京雷达反射率因子强度比常州雷达低3.5 d B左右,常州比南通低0.9 d B左右,3 km高度的回波强度拼图存在明显不连续;(2)将TRM M PR作为参照,利用预处理后的雷达观测数据样本,计算得出南京、常州、南通各地基雷达与PR的差值,并进行偏差订正后,南京与常州、常州与南通之间的反射率因子差值减小成为0.3和0.2,拼图效果也明显改善。
