Quality Assessment of the Observational Data of Automatic Precipitation Stations in China

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.