星载雷达在评估地基天气雷达非降水回波识别算法效果中的应用

Use of Space-borne Radar Observations in Evaluating Ground Radar Non-precipitation Echo Identification Algorithm

非降水回波识别算法的效果直接影响着雷达数据后续应用(如定量降水估测)的结果,因此对其进行客观定量检验是很重要的。文中所用算法在SWAN系统雷达反射率因子质量控制算法基础上增加了晴空回波识别算法,使用回波延展度因子并增加高度限制来对晴空回波进行识别和去除。使用星载雷达反射率强度数据与非降水回波去除前后的地基雷达数据进行时空匹配,对非降水回波识别算法效果进行客观的直观和定量验证。参照星载雷达观测结果,文中算法针对与降水回波无混叠的超折射回波有很好的识别效果,效果优于存在降水与超折射混叠的情况,当降水回波中存在与超折射回波水平纹理相近的对流降水时,经算法处理后该部分回波会丢失部分信息。针对存在降水与超折射回波混叠的情况,将算法处理前后的地基雷达降水区域反射率因子分别与星载雷达数据进行比较,结果表明经算法处理后的数据更接近星载雷达观测值。评价结果可为算法适用性分析及改进提供依据。

The performance of non-precipitation echo identification algorithm directly affects the results of downstream applications(such as quantitative precipitation estimation). Therefore,it is important to evaluate the algorithm performance objectively and quantitatively. The algorithm used in this paper is based on the quality control algorithm used in SWAN system. The method of clear air echoes identification was added in the algorithm,the echo extension was used and the height limitation of the echoes was added to identify and remove the clear air echoes. The spatial and temporal matching between radar data from the Precipitation Radar(PR)on board the Tropical Rainfall Measuring Mission(TRMM)satellite and the ground-based radar(GR)before and after the non-precipitation echo removal was carried out to objectively assess the performance the algorithm visually and quantitatively. According to observations of PR,the algorithm has a good identifying performance for the AP echoes which is not aliased to the precipitation echoes,and the result is better than that AP echoes aliased to the precipitation echoes. Part of the convective echo information lost after processing by the algorithm because of the factor TDBZ. For the situation of precipitation aliased to AP echoes,the evaluation was carried out by comparing the PR reflectivity factor values with those of GR before and after the identifying. The result indicated that the observations after identifying was closer to those of PR. The evaluation results can provide basis for the applicability analysis and improvement of the identifying algorithm.