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自动雨量站降雨资料质量评估方法研究
Quality Assessment of the Observational Data of Automatic Precipitation Stations in China
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摘要: 利用19种不同型号的自动雨量站2005年6—9月在北京 (54511)、宜昌 (57461) 和南京 (58238) 3站的实测对比观测资料, 进行降雨资料质量评估, 不同的评估方法将会导致评估结果的差异。结果表明:采用自动气象站降雨资料和雨量筒人工定时观测记录作为降雨量参考标准对自动雨量站进行评估时, 由于降雨量参考标准本身存在的不确定性, 或多或少影响了评估结果的准确性。在广泛试验的基础上, 提出了采用基于各自动雨量站观测结果的拟合降雨量作为降雨量参考标准的新思路, 并对拟合降雨量的可行性、适用性以及计算方法进行了详细论述。Abstract: Using the observational data of 19 different types of automatic precipitation stations located in Beijing, Yichang and Nanjing from June to September in 2005, the method of quality assessment of precipitation data is studied. Different methods can lead to different assessment results. It is mainly discussed by the present study how to choose standard precipitation in the assessment. The results show that it may affect the veracity of the assessment more or less due to the uncertainty of standard precipitation when exploiting the Automatic Weather Stations (AWS) data and timed manned observation record as standard precipitation to examine the automatic precipitation stations. On the basis of extensive experiments, a new method to define standard precipitation as fitting precipitation derived from the automatic precipitation stations data is put forward. Moreover, the feasibility, applicability and computation method of fitting precipitation are also addressed in the study.Comparison results show that when employing the observed precipitation obtained from AWS as a standard to assess the automatic precipitation stations, the errors of monthly precipitation in the respective automatic precipitation stations are generally higher (above 8%) and the differences of the assessment results are larger because of the uncertainty of the observed precipitation in AWS. In comparison to AWS, the uncertainty in the timed manned observations is low and the veracity of data is high. It is therefore more reliable for the estimate results to use the timed manned observation as standard precipitation for comparison assessment. However, low temporal precision of the timed manned observational data restricts its role in the assessment. The observed rainfall can be estimated by fitting precipitation relative accurately under the conditions of more samples. Time series of precipitation based on the fitting precipitation have high credibility as well as temporal precision for comparison. The assessment results using the fitting precipitation as standard precipitation to evaluate automatic precipitation stations are basically consistent with those utilizing timed manned observations as a standard. The consistency between them demonstrates their respective credibility.The fitting precipitation is based on the observational results of different automatic precipitation stations under the same conditions, therefore, it is much more reliable. The fitting method is considered to be used in the setting of standard series under the conditions of more samples. The present study shows a new idea of quality assessment of automatic meteorological instruments data.
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图 1 2005年6—9月19种自动雨量站降雨量分别与自动气象站和人工雨量筒的百分误差对比
Fig. 1 Comparison of monthly precipitation errors among 19 automatic precipitation stations, AWS data and timed man-made observations
图 2 2005年19种自动雨量站与自动气象站实测降雨量对比
Fig. 2 Comparison of 19 automatic precipitation stations data and AWS observations in 2005
图 5 南京站2005年6—9月不同雨强典型降水比较
Fig. 5 The precipitation under the conditions of different rainfall intensity from June to September in 2005 of Nanjing
图 6 2005年6—9月北京、宜昌、南京自动雨量站与自动站、雨量筒和拟合降雨量对比误差分布图
Fig. 6 Comparison of precipitation errors in 19 automatic precipitation stations, AWS data and timed man-made observations among Beijing, Yichang and Nanjing from June to September in 2005
表 1 拟合降雨量与业务自动站实测降雨量误差对比分析
Table 1 Comparison of fitting precipitation and AWS data errors
表 2 拟合降雨量与雨量筒人工观测降雨量误差对比分析
Table 2 Comparison of fitting precipitation and timed man-made observation errors
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