GNSS掩星观测反演温度质量控制方法研究

Research of quality control method of gnss occultation observation inversion temperature

将中国区域划分成4个气候区,采用双权重标准差及设置相关系数阈值的方法,以中国120站的探空温度作为参考,将2014年的COSMIC、Metop-A、Metop-B掩星温度作为样本进行质量控制,同时也跟传统标准差法进行对比,并对质量控制结果进行了检验。结果显示,3种掩星之间的结果较为相似,不同气候区的温度具有不同的特征,从而划分不同的阈值区间,使质量控制更加准确。传统标准差法标识出的错误数据均可以由双权重标准差法标识出,且双权重法还能检测出传统标准差法无法检测出的错误点。并通过统计确定GNSS掩星温度与探空温度相关系数阈值为0.860 9,对可疑温度进行标识。从质量控制结果来看,错误温度的分布大多都是掩星温度与探空温度相差较大的点,剔除质量控制出的错误温度后,掩星温度与探空温度相关性得到明显改善,此方法适用于对GNSS掩星反演温度的质量控制,为气象服务提供更准确的观测资料。

The region of China was divided into four climate zones. The method of double weighted standard deviation and correlation coefficient threshold was adopted to take the sounding data of 120 stations in China as reference, the COSMIC, Metop-A, and Metop-B occultation inversion temperatures in 2014 were used as samples for quality control. It is also compared with the traditional standard deviation method, and the quality control results are tested. The results show that the three occultations are similar, and the temperature in different climatic regions has different characteristics, so that different threshold intervals are divided to make the quality control more accurate. The error data identified by the traditional standard deviation method can be identified by the double weight standard deviation method, and the double weight method can also detect error points that the traditional standard deviation method cannot detect, and the double weight standard deviation method is more suitable. Through statistical calculation, the correlation coefficient threshold of GNSS occultation inversion temperature and sounding temperature is determined to be 0.860 9, so as to determine whether the suspicious data is erroneous data. From the quality control results, the distribution of erroneous data is mostly the difference between the occultation temperature and the sounding temperature. After eliminating the erroneous data from the quality control, the correlation between the occultation inversion temperature and the sounding temperature is obvious improvement, data quality can be better improved, this method is applicable to the quality control of GNSS occultation inversion temperature, and provides more accurate observation data for weather analysis and numerical prediction.