The Statistic of Automatic Weather Station's Efficiency

Li Yan; Liang Haihe; Meng Zhaolin; Pei Chong; Shi Cheng

Vol.20, NO.4, 2009

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2009 > 20(4): 504-509

Li Yan, Liang Haihe, Meng Zhaolin, et al. The statistic of automatic weather station's efficiency. J Appl Meteor Sci, 2009, 20(4): 504-509.

Citation:

Li Yan, Liang Haihe, Meng Zhaolin, et al. The statistic of automatic weather station's efficiency. J Appl Meteor Sci, 2009, 20(4): 504-509.

Li Yan, Liang Haihe, Meng Zhaolin, et al. The statistic of automatic weather station's efficiency. J Appl Meteor Sci, 2009, 20(4): 504-509.

Citation:

Li Yan, Liang Haihe, Meng Zhaolin, et al. The statistic of automatic weather station's efficiency. J Appl Meteor Sci, 2009, 20(4): 504-509.

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The Statistic of Automatic Weather Station's Efficiency

CMA Meteorological Observation Center, Beijing 100081

Received Date: 2008-04-22
Rev Recd Date: 2009-03-03
Publish Date: 2009-08-31

Abstract

Abstract

Over 2100 state automatic weather stations (AWS) can be monitored by the Atmospheric Observing System Operations and Monitoring (ASOM) platform of CMA Meteorological Observation Center at present. They are divided into six groups by areas, which are north, northeast, east, south central, southwest, northwest China. The efficiencies of the AWS are statically analyzed in the respect of data arrival rate, equipment availability and reliability. Influencing factors on the data format errors of datagram and the qualities of data element errors are also analyzed. The results indicate that the data arrival rate, equipment availability and the reliability of all the 2100 state AWS maintain over 80%, with a generally consistent trend. The AWS operation efficiencies of the northeast region are the highest and those of the southwest are the lowest. The data arrival rate is the precondition of the equipment availability and reliability. With the data arrival rate fixed, the format error of datagram has more influence on the equipment availability and reliability comparing to the data elements errors. The 1st line data format errors have more influence on AWS operation efficiencies than the 2nd line data format errors. The ground temperature is the main elements affecting AWS operation efficiencies. Among the ground temperature factors, the different levels of ground temperature have different impacts on AWS efficiencies; 320 cm ground temperature has the most significant influence, while the 5 cm ground temperature's influence is relatively lower. It shows a trend that deeper layers of the geothermal elements have greater influences on AWS efficiencies.
- ASOM;
- AWS;
- operation efficiency

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References(23)

Figures and Tables

图 1 2007年9-10月自动站运行效能统计

Fig. 1 The statistic of the AWS's efficiency in September and October of 2007

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图 2 2007年9-10月数据报文格式错误、数据质量错误和异常情况

Fig. 2 The datagram format error, data quality error and data abnormal in September and October of 2007

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图 3 2007年9-10月数据报文格式错误中各类格式错误出错量对比图

Fig. 3 The percentage graph of each datagram format errors in September and October of 2007

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图 4 2007年9-10月数据质量错误中各类要素出错量对比图

Fig. 4 The percentage graph of each data quality errors in September and October of 2007

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图 5 2007年9-10月数据质量错误中地温各要素之间出错量对比图

Fig. 5 The percentage graph of each ground temperature errors in September and October of 2007

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表 1 2007年9-10月数据可用性和可靠性与数据质量情况和报文格式错误之间偏相关分析

Table 1 The partial correlations analysis between AWS's availability, reliability and data quality abnormal and datagram format error in September and October of 2007

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