Evaluating the Quality of Temperature Measured at Automatic Weather Stations in Beijing

Yang Ping; Liu Weidong; Zhong Jiqin; Yang Jie

Vol.22, NO.6, 2011

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2011 > 22(6): 706-715

Yang Ping, Liu Weidong, Zhong Jiqin, et al. Evaluating the quality of temperature measured at automatic weather stations in Beijing. J Appl Meteor Sci, 2011, 22(6): 706-715.

Citation:

Yang Ping, Liu Weidong, Zhong Jiqin, et al. Evaluating the quality of temperature measured at automatic weather stations in Beijing. J Appl Meteor Sci, 2011, 22(6): 706-715.

Yang Ping, Liu Weidong, Zhong Jiqin, et al. Evaluating the quality of temperature measured at automatic weather stations in Beijing. J Appl Meteor Sci, 2011, 22(6): 706-715.

Citation:

Yang Ping, Liu Weidong, Zhong Jiqin, et al. Evaluating the quality of temperature measured at automatic weather stations in Beijing. J Appl Meteor Sci, 2011, 22(6): 706-715.

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Evaluating the Quality of Temperature Measured at Automatic Weather Stations in Beijing

1.
China Meteorological Administration Training Center, Beijing 100081
2.
Institute of Urban Meteorology of Beijing, China Meteorological Administration, Beijing 100089
3.
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000

Received Date: 2011-04-19
Rev Recd Date: 2011-10-14
Publish Date: 2011-12-31

Abstract

Abstract

Data quality is a basic assurance for meteorological researches and data applications. Considering data integrality, veracity and confidence, the standard for AWS (automatic weather station) data quality assessment is defined band a set of index is established for AWS data quality assessment and a feasible observation data quality control flow is designed. Following the definition and principia, AWS data can be categorized as correct, mistake and dubious data. The spatial and temporal consistent detections are employed in the data quality control flow. Based on the quality control flow and assessment index, hourly data measured by 187 AWS in Beijing from 1998 to 2009 is evaluated. The results show that the AWS net of Beijing is set up following a fine layout. Although the distribution of AWS is uneven, most AWS is located in urban area such as Haidian and Chaoyang districts, especially in early time of the AWS construction process, some AWS are set up in mountainous area according to the need of different region representative. It is beneficial for urban and rural climate comparing, data sequence reconstruction and regional climate research. The severe missing data is rare, and discrete and slight continuous missing data is just found in concentrated regions and with regional consistent characteristic. The highest error rate of AWS temperature data is 3.8% and it is below 1% in most years. The amount of dubious data is much more than the mistaking data. But more than half of dubious data can be got back after space consistency check. It means that the AWS data in Beijing is reliable. The rates of mistaken data are above 20% after 2004. It shows that the amount of dubious data is not related with the rate of mistake data and on the other hand the uncertainty of AWS data set is reduced after 2004. In conclusion, the assessment result reveals that the AWS data in Beijing are accurate, reliable and show great potential in the future application.
- Beijing;
- automatic weather stations;
- temperature;
- data-lacking;
- quality evaluation

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References(16)

Figures and Tables

Fig. 1 Flow chart of quality evaluation about temperature

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图 2 北京地区自动站运行情况 (a) 自动站逐年运行站点个数, (b) 自动站运行时长的空间分布

(数字代表该站点的运行时长，单位：a；+：运行时长为10 a；E：运行时长为11 a；T：运行时长为12 a)

Fig. 2 Results of Beijing AWS's working length (a) the number of sites with first working year, (b) the distribution of AWS's working length

(figures stand for the working length, unit:a; +:10 a, E:11 a, T:12 a)

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图 3 不同类型气温缺测年平均缺测过程的空间分布

(a) 离散型, (b) 轻度连续型, (c) 中度连续型, (d) 重度连续型

Fig. 3 Spatial distribution of frequency of temperature data-lacking per year on different type

(a) discrete, (b) mild continuous, (c) moderate continuous, (d) serious continuous

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Fig. 4 Interannual variation of error data (a) extreme value error, (b) unchangeable error, (c) jumping error, (d) entire error

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图 5 可疑数据的年际变化

(a) 全部站点平均可疑数据个数，(b) 全部站点判定为错误数据的平均个数，(c) 判定为错误数据的比例

Fig. 5 Interannual variation of suspicious data

(a) average suspicious numbers per station, (b) average determiated error numbers per station, (c) the ratio of determinated error data

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图 6 年平均错误数据个数的空间分布

(a) 错误检查，(b) 可疑检查，(c) 检查为错误的数据

Fig. 6 The distribution of annual average error numbers

(a) error check, (b) suspect check, (3) determinated error data

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Fig. 7 Annual ratio of data-lacking before and after the assessment of quality

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Table 1 Classification and definication of data-lacking

缺测类型	定义	缺测值个数范围
离散型	缺测量的邻近时刻均为非缺测量
轻度连续型	连续缺测值个数不高于1 d的总时次	[2,24]
中度连续型	连续缺测值个数介于1 d和1个月总时次之间	(24，720]
重度连续型	连续缺测值个数超过1个月的总时次	(720，+∞)

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