Bias Adjustment and Analysis of Chinese Daily Historical Radiosonde Temperature Data

作     者：Zhe CHEN Zijiang ZHOU Zhiquan LIU Qinglei LI Xiaoling ZHANG Zhe CHEN;Zijiang ZHOU;Zhiquan LIU;Qinglei LI;Xiaoling ZHANG

作者机构：National Meteorological Information CenterChina Meteorological AdministrationBeijing 100081China National Center for Atmospheric ResearchBoulderColorado 80301USA Beijing Meteorological Information CenterBeijing Meteorological ServiceBeijing 100089China 

出 版 物：《Journal of Meteorological Research》 (气象学报（英文版）)

年 卷 期：2021年第35卷第1期

页      面：17-31页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：Supported by the National Innovation Project for Meteorological Science and Technology (CMAGGTD003-5) China Meteorological Administration Special Public Welfare Research Fund (GYHY201506002) National Key Research and Development Program of China (2017YFC1501801) 

主　　题：daily radiosonde temperature data mandatory levels significant levels bias adjustment 

摘      要：The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while d