基于CloudSat卫星对地基云雷达反射率因子的校准

地基云雷达是云的重要探测手段，但随着运行时间的增加，雷达发射机、接收器等参数的变化，会使观测数据产生漂移偏差，从而对云物理特性的反演产生显著影响，因此云雷达数据的校准是一个重要的基础问题。针对KAZR（Ka Band Zenith Radar，K波段云雷达）云雷达特征，本文在Pavlos等提出的雷达数据校准方法基础上进行改进，优化了对弱云和降水的信号识别，利用CloudSat星载雷达观测的反射率因子，气体衰减校正等数据，对兰州大学半干旱气候环境监测站（Semi Arid Climate and Environment Observatory of Lanzhou University，SACOL）KAZR云雷达2013年8月至2017年5月反射率因子数据进行了校准，建立了KAZR雷达反射率因子46个月的历史资料校准数据库，并对校准周期的变化进行了对比分析。校准数据库的建立对SACOL站云的长期观测研究具有重要意义，同时为不同波段地基雷达的对比增加了可行性。

Calibration of Ka-Band Zenith Radar Reflectivity Factor by Using CloudSat

Cloud radar is a powerful instrument for cloud detection. However, the parameters of the radar transmitter and receiver system can drift during operation, which will cause the overall offset error of the observation data and affect the accuracy of cloud physical property retrieval. Therefore, cloud radar data need to be periodically calibrated properly. This paper uses the radar data calibration method proposed by Pavlos et al., which optimizes weak clouds and precipitation signal recognition. It also uses CloudSat satellite borne radar observations of reflectivity factors, gas attenuation correction and other data to improve the data quality of the Ka band Zenith Radar (KAZR) deployed at the Semi arid Climate and Environment Observatory site (35.57°N, 104.08°E) of Lanzhou University (SACOL). It has established a 46 month historical data calibration from August 2013 to May 2017 for the KAZR radar reflectivity. That means calibration of cloud radar reflectivity data is essential for obtaining accurate cloud macro and micro physical properties, providing a solid foundation for long term clouds study at the SACOL site.