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投稿时间：2015-08-17 修订日期：2016-02-21
投稿时间：2015-08-17 修订日期：2016-02-21
中文摘要: 本文介绍了乌鲁木齐市建成区内5座100 m气象塔地理位置，根据逻辑极值检查、僵值检查、时间一致性检查、空间一致性检查和人工干预检查等组成的一套针对气象塔观测资料的质量控制方法和四点滑动平均插值、要素垂直分布拟合和线性回归方法组成的数据插值方法，对乌鲁木齐市从南至北5座100 m气象塔2012年4月1日至2014年4月30日的资料进行质量控制和数据插值。结果表明：该套方案能很好地找出缺测、错误和可疑的数据，结合人工干预，使得检验结果更为可靠。5座气象塔资料的质量是比较好的，正常数据占总数据的97.17%，其中红光山数据质量最好（正常数据占总数据的99.01%）。非正常数据只是少数情况（占总数据的2.83%），包括缺测和错误数据，其中缺测数据占非正常数据的6.23%（出现在米东和燕南立交），错误数据占非正常数据的93.77%。虚假数据占错误数据的89.99%（大部分为风速、风向），僵值数据占错误数据的5.47%（大部分为气温和湿度），超出逻辑极值数据占错误数据的0.41%（只出现在水塔山气温），不符合一致性数据占错误数据的4.13%（主要为湿度，主要在燕南立交）。利用四点中央插值法、每座气象塔要素垂直分布拟合和不同气象塔之间线性回归方法插值气温和相对湿度的效果较好。
中文关键词: 乌鲁木齐市， 气象塔， 质量控制， 插值， 观测资料
Abstract:This paper introduced geographical position of 5 100 m high meteorological towers in Urumqi. A series of quality control methods were advanced to deal with observation data, such as logical extreme check, static check, time consistency check and similarity check. Besides, a series of data interpolation methods were advanced to interpolation controlled data, such as four point moving average interpolation, elements of vertical distribution fitting and linear regression. Especially in the treatment process, using manual intervention can make the test results more reliable. The quality control method was applied to data from the 5100 m high meteorology towers from 1 April 2012 to 30 April 2014 in Urumqi. The results showed that the method can differentiate the missing, error and dubious. The data of 5 towers are reliable with normal data accounting for 97.17% of the total data, which is the best quality of Hongguangshan tower (normal data accounting for 99.01% of the total data). Abnormal data only account for a small proportion (accounting for 2.83% of the total data), including missing data and error data. Missing data accounts for 6.23% of the abnormal data, which mainly occurred in Midong Tower and Yannanlijiao; error data accounts for 93.77% of the abnormal data. Among error data, false data accounts for 89.99% of the error data (mainly wind speed and wind direction); static data accounts for 5.47% of the error data (mainly air temperature and relative humidity); extreme error data accounts for 0.41% of the error data (mainly air temperature in Shuitashan Tower); consistency error data accounts for 4.13% of error data (mainly relative humidity in Yannanlijiao Tower). The effect of temperature and relative humidity interpolation are better by using the four points moving average interpolation method, elements of vertical distribution fitting method and linear regression method between different meteorological towers.
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基金项目:中国沙漠气象科学研究基金(Sqj2015009)、中央级公益性科研院所基本科研业务费专项资金(IDM201505)、公益性行业(气象)科研专项(GYHY(QX)201506001 14)共同资助
引用文本：
金莉莉,何清,李振杰,缪启龙,买买提艾力·买买提依明,赵逸舟,2016.乌鲁木齐市气象塔梯度观测资料质量控制与处理[J].气象,42(6):732-742.
JIN Lili,HE Qing,LI Zhenjie,MIAO Qilong,ALI Mamtimin,ZHAO Yizhou,2016.Quality Control Method and Treatment for Urumqi Meteorology Tower Gradient Observation Data[J].Meteor Mon,42(6):732-742.
金莉莉,何清,李振杰,缪启龙,买买提艾力·买买提依明,赵逸舟,2016.乌鲁木齐市气象塔梯度观测资料质量控制与处理[J].气象,42(6):732-742.
JIN Lili,HE Qing,LI Zhenjie,MIAO Qilong,ALI Mamtimin,ZHAO Yizhou,2016.Quality Control Method and Treatment for Urumqi Meteorology Tower Gradient Observation Data[J].Meteor Mon,42(6):732-742.