风廓线雷达与天气雷达风廓线数据的融合及应用

摘要

风廓线雷达与多普勒天气雷达风廓线产品均可以获取高时间分辨率的高空风信息,但两种遥感测风的探测原理及时空代表性不同.在对风廓线雷达进行质量控制处理、剔除降水粒子空间不均匀分布对数据可信度影响之后,根据风廓线雷达与天气雷达风廓线数据探测原理差异,进行不同时间代表性的风廓线数据的空间匹配试验,确定与天气雷达风廓线数据进行融合的风廓线雷达数据最优时间分辨率,结果为1h.利用2015年7月北京南郊观象台的探空、风廓线雷达、天气雷达测风数据进行三种高空风的一致性比对,结果表明三种测风数据具有较好的一致性,均方根误差分别为2.3和2.5 m· s-1;60、30以及6 min不同时间代表性风廓线雷达数据与天气雷达风廓线数据之间的均方根误差分别为2.6、2.8及3.1m·s-1,60 min数据的融合效果最佳,低空尤其明显.利用广东省2014年5月的风廓线雷达观测网以及天气雷达网风廓线数据进行了高空风场的融合分析试验,融合分析场提供了更为丰富的高空中尺度水平风场信息,低空的涡旋更加明显.%Upper-level wind data can be derived from wind-profiling radar (WPR) network and Doppler radar network.In order to apply upper-level wind data comprehensively to make full use of radar network in the field of weather analysis and numerical forecast,comparison and integration analysis of wind profiles for WPR and weather radar are conducted.After evaluating the utility and feasibility of WPR data and VAD wind profiles (VWP) by comparing with vertical profiles of horizontal winds provided from rawinsonde located in the southern suburbs of Beijing in July 2015,this paper analyzes differences of temporal and spatial representation of the two upper-level wind data.Furthermore,different periods of WPR data are compared with VWP data to decide the optimal time resolution for integration analysis of the two kinds of wind data.Then,integration analysis is conducted by merging wind component measurements for WPR network and Doppler radar network of Guangdong Province in May 2014.The results indicate that WPR and rawinsonde data are in good agreement with root mean square error (RMSE) 2.3 m · s-1,and VWP data concides with RMSE 2.5 m · s-1.RMSE of 60 min averaged WPR data and VWP data is the lowest by comparing with that of 30 min averaged data and 6 min averaged data,and the optimal time scale for integration analysis is 60 min.Objective integration analysis field of wind data derived from WPR network and Doppler radar network can enrich the mesoscale wind field information,especially at low level.