基于多源数据融合的降水数据质量控制技术研究

【目的】降水为非连续性观测要素,利用常规的质量控制方法无法很好地检查降水观测数据的正确性,因此需要开展自动气象站降水观测数据质量控制技术研究。【方法】提出一种基于雷达、卫星等多源数据融合的地面自动观测降水数据质量控制方法,研究基于标准Z-R关系的雷达定量降水估测产品加工算法,生成宁夏全区地面自动观测站1 h雷达定量估测降水量,建立基于雷达、卫星估测降水产品的自动气象站降水观测数据质量控制技术流程,开展自动气象站降水观测数据质量控制,并对质量控量结果进行分析评估。【结果】（1）对于国家级站,多源数据融合的地面自动观测降水数据质控方法与MDOS对降水量数据的质控均为“正确”的一致率较高,平均达到94.88%;对于区域站,均为“正确”的一致率明显比国家级站低,平均达到51.57%;（2）多源数据融合的地面自动观测降水数据质控方法质控出“可疑”和“错误”数据明显比MDOS多,对“可疑”和“错误”数据的检出率更高;（3）多源数据融合的地面自动观测降水数据质控方法对错误降水数据的质量控制较MDOS质量控制更符合实际。【结论】多源数据融合的地面自动观测降水数据质控方法可以较好地解决宁夏降水要素自动观测数据的质量问题,为降水数据质量控制提供技术和理论支撑。

Research on Precipitation Data Quality Control Technology Based on Multi-source Data Fusion

Precipitation is a discontinuous observation element, and the accuracy of precipitation observation data cannot be well checked by conventional quality control methods, so we carried out the study on quality control technology of precipitation observation data from automatic weather stations (AWSs). In the study, a method for quality control of precipitation data of AWSs was proposed based on multi-source data fusion such as the data of radar and satellite. A processing algorithm for radar quantitative precipitation estimation products based on standard Z-R relationship was studied, and the one-hour radar quantitative precipitation estimation of AWSs observations in Ningxia was generated. The technical process of quality control of precipitation data of AWSs based on the radar and satellite precipitation estimation products was established. Moreover, the quality control of precipitation data of AWSs was conducted, and the results were evaluated. The results show that: (1) For national stations, the quality control method of AWSs precipitation data based on multi-source data fusion and the quality control of precipitation data using MDOS are both "correct" with a higher consistency rate, reaching 94.88% on average. For regional stations, the consistency rate of "correct" is significantly lower than that of national stations, 51.57% on average. (2) The quality control method of AWSs precipitation data based on multi-source data fusion produces more "suspicious" and "wrong" data than MDOS, and the detection rate of "suspicious" and "wrong" data is higher. (3) The quality control method of AWSs precipitation data based on multi-source data fusion is more realistic than MDOS quality control for the quality control of erroneous precipitation data. Therefore, the quality control method of AWSs precipitation data with multi-source data fusion can better solve the quality problem of AWSs precipitation data in our district, and provide technical and theoretical support for the quality control of precipitation data in our district.