自动站实时数据质量分析及质控算法改进

Quality Analysis of Real-Time AWS Data and Algorithm Improvement of Quality Control

自动气象站实时资料对于气象预警、决策服务、预报验证等方面具有重要意义,为确保自动站实时资料的科学性和准确性,开展实时资料质量控制工作十分必要。对综合气象观测系统运行监控平台中国家级自动气象站2013年的数据质量情况进行了统计和分析,并基于自动站历史数据研究了特殊天气事件发生时气象要素的变化规律以及要素之间的内部一致性,提出了高湿事件和单站天气突变事件的判断条件以改进自动站质量控制算法,最后给出了新算法的应用情况,结果表明改进后的质控算法有效地降低了数据误判率,取得了较好的控制效果。

The real-time data of Automatic Meteorological Station （AWS） is of great significance to meteorological warning, decision-making service, and forecast verification. Developing the quality control of real-time data is necessary to ensure the rationality and accuracy of real-time data of AWS＇s. The data quality of national AWS＇ s from the Atmospheric observing System Operation and Monitoring platform （ASOM） in 2013 is statistically analyzed, and the variation regularities and internal consistency of meteorological elements when special weather happens are studied on the basis of historical data of AWS ＇s, and then criterions for judging high-humidity and sudden weather change events are proposed to improve the data quality control algorithm of AWS＇ s. The application results of the new algorithms indicate that the improved data quality control algorithms can reduce the misiudgment rate effectively and achieve goodcontrol effect.