高时空分辨率自动站温度观测资料自主质量控制研究

Independent Quality Control of High Spatiotemporal Resolution Surface Temperature Observations from Automatic Stations

中国地面自动站观测系统的建设日趋完善,目前已建成六万多个自动气象观测站点。严格的质量控制是自动站资料有效应用的前提条件,然而如何分辨高分辨率自动站资料中的局地小尺度天气信息和错误资料导致的局地变化特征一直是自动站质量控制的难点。本研究在分析地面温度空间尺度特征和误差分布特征的基础上,建立了仅依赖观测资料的EOF(Empirical Orthogonal Function)地面温度观测质量控制方法。研究利用2022年1月和5月的地面自动站温度观测资料,进行了质量控制试验,并对比了质量控制前后自动站观测资料和CRA40(CMA's global atmospheric Re-Analysis)资料中的地面气温差异特征。结果表明:建立的观测资料自主质量控制方法可以有效地识别错误观测资料,很好地避免了背景场误差、陡峭地形和局部天气变化对质量控制的影响,质量控制后的自动站温度与CRA40资料的地面温度差异明显减小,空间相关性也有明显提高,证明质量控制能够有效剔除错误资料并提高自动站资料的空间连续性。

The construction of automatic meteorological observation stations in China has been continuously improved.Currently,more than 60,000 automatic meteorological observation stations have been built,providing abundant information of surface meteorological variables for weather and climate research.However,the practical application of ground automatic station data has always been constrained by high uncertainty in the quality of observation data.Strict quality control is a prerequisite for the effective application of automatic station data,but the high spatiotemporal resolution characteristics of automatic station observations bring more difficulties to quality control researches.How to accurately distinguish local small-scale weather information and local variation caused by erroneous data in high-resolution automatic station data has always been a difficult point in the research of quality control methods for spatiotemporal resolution automatic station data.On the basis of analyzing the spatial correlation scale and error characteristics of surface temperature,this study established a quality control method for temperatures from surface automatic station based on EOF(Empirical Orthogonal Function)analysis method,which only relies on observation data.The study conducted quality control experiments using surface automatic station temperature observations from January to May 2022,and compared the differences in surface temperature between the automatic station observation data and the Chinese reanalysis data CRA40(CMA's global atmospheric Re-Analysis)before and after quality control.The results indicate that the established autonomous quality control method for observation data can effectively identify erroneous observation data,relying solely on the observation data itself,effectively avoiding the impact of background errors on quality control effectiveness.The quality control sub regions determined on the basis of correlation scale analysis further enhance the quality control method's ability to identify small-scale temperature changes in observation data,effectively preserving the reject of temperature extremum data corresponding to extreme events in small areas,the number of quality control modes determined by actual data characteristics can well separate the principal and residual terms of the observed data,significantly improving the accuracy of erroneous extreme value recognition.Further introducing sliding detection methods and overlap rejection standards can also retain as much valuable observation data as possible in areas with steep terrain.The quality control results of 1 month data show that the new quality control method can obviously and stably improve the spatial correlation coefficient between the surface temperature of automatic station data and the corresponding variable of CRA40(CMA's global atmospheric Reanalysis)reanalysis data,and the average deviation is also reduced.Although the average data rejection rate is only about 8%,the spatial correlation coefficient can reach a maximum increase of about 0.02,which fully proves that proposed quality control method can effectively eliminate erroneous data and improve the spatial continuity of automatic station data.