Quality Factors and Processing Algorithm for Wind Profiling Radar Data

In recent years, wind profiling radar (WPR) network in China is under rapid development. To take advantage of the network measurements in weather analysis and numerical prediction, it's of great significance to make full aware of quality factors and improve the current processing algorithm for WPR data.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 proposed. As for meteorological background, wind filed is assumed homogenous when it is detected by WPR, and the accuracy of horizontal wind data will decline 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 cause that fall terminal velocity of precipitation particles changes rapidly in space when convective precipitation happens, which is a significant problem for WPR data quality control algorithm.However, two independent wind profiles can be measured with a five-beam WPR and differences between measured zonal winds and meridional winds can reflect errors caused by the inhomogeneity of wind field. In order to reduce such errors, all observations are examined to make sure data detected under circumstances where the wind filed is extremely inhomogenous are deleted. Besides, different averaging methods, such as consensus average and simple average, used to calculate hourly averaged winds also affect the accuracy of it and comparisons are conducted on two averaging methods.Combined with 10 radars of Guangdong WPR network, evaluation of the new methods for processing basic data is analyzed from March to May in 2014. Results show that 10 radars in Guangdong WPR network, including 8 boundary radars (LC), 1 troposphere radar Ⅰ(PA) and 1 troposphere radar Ⅱ(PB), meet the designed requirements respectively in terms of the maximum height of credible data in clear air, which is 3 km for LC radar, 6 km for PB radar and 10 km for PA radar. Furthermore, there are no large system errors in 10 radars except that the examining consequence is unsatisfactory during 1-2 km 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, an 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, as the stand deviation of differences between two independent measured zonal wind values and meridional wind values are both close to 1 m·s^-1.