Wind-Profiling Radar Data Processing And Merging And Application For Radar Network Wind Profiles

Wind profiling radar is an important component of equipments observing upper-level winds, which has high temporal resolution and can observe upper-level winds automatically and continually.Wind profiling radar network can make up the shortage of wind information data that conventional sounding system provides.From 1980s, America and Japan established Wind profiling radar network, which was applied in monitoring disastrous thunderstorm as well as numerical model assimilation and forecast.In recent years, wind profiling radar(WPR) network in our country is under a rapid development. To take advantage of the network measurements in weather analysis and numerical prediction, make full aware of quality factors and improve the current processing algorithm for WPR data is of great importance.Many factors affect the quality of horizontal wind data detected by WPR, especially system error and meteorological background. According to five-beam WPR, a new method for examining system error from radar Doppler measurements is described. As for meteorological background, wind filed is assumed homogenous when it is detected by WPR, and the accuracy of horizontal wind data will be reduced when the assumption is not satisfied. During the period of precipitation, Scattering caused by raindrops is much stronger than turbulence detected by WPR. And the assumption of homogenous wind breaks down easily for the reason that fall terminal velocity of precipitation particles changes rapid in space when convective precipitation happens, which is a important problem for WPR data quality control algorithm.Upper level wind data with high time resolution can be derived from wind profiling radar (WPR) as well as Doppler radar VAD wind profile(VWP), but principle and temporal and spatial representation of both the wind data are different. We firstly reduce the impact from uneven distribution of precipitation particles to make sure WPR data is credible during rainfall.Besides, quality control is also conducted on VWP data. Then according to differences between the two kinds of wind data, different period time of averaged WPR data are compared with VWP data to decide the optimal time scale for integration analysis.After evaluating the utility and feasibility of WPR and VWP data by comparing with vertical profiles of horizontal winds provided from radiosonde located in the southern suburbs of Beijing in July 2015,differences of temporal and spatial representation of the two upper level wind data are analysed.Combined with 10 radars of Guangdong WPR network, evaluation of the new methods for processing basic data is analyzed from March to May,2014. Results show that all the 10 radars in Guangdong WPR network, including 8 boundary radars(LC),1 troposphere Ⅰ radar(PA) and 1 troposphere Ⅱ radar(PB), meet the designed requirements respectively in terms of the maximum height of credible data in clear air, which is 3km for LC radar,6km for PB radar and 10km for PA radar. Further more, there are no large system errors in the 10 radars except that the examining consequence is unsatisfactory during 1km-2km for PA radar. It is necessary to consider the atmospheric inhomogeneities that may cause great errors especially when it rains heavily, and consensus averaged wind is superior to simple averaged wind in median and high heights.Therefore, a improved algorithm according to examination of atmospheric inhomogeneities and consensus average is proposed to obtain hourly averaged winds. It is proved that winds obtained from the improved algorithm show better representation than the currently used data during precipitation, and better quality as the stand deviation of differences between each two independent zonal winds and meridional winds are all close to 1m/s.WPR and radiosonde data are in good agreement with root mean square error 2.3m/s, as well as VWP data and root mean square error is 2.5m/s. Root mean square error of one hour averaged WPR data(OOBS) and VWP data is lowest, especially at low level, by comparing with that of half an hour averaged data(HOBS) and 6min averaged data(ROBS), and the values are 2.6m/s,2.8m/s,3.1m/s. As such, research of integration analysis is conducted by merging wind component measurements for WPR network and Doppler radar network of Guangdong Province in July 2014. Objective integration analysis field of wind data derived from WPR network and Doppler radar network can enrich the meso-scale wind field information.