Quality Control Method and Treatment for Urumqi Meteorology Tower Gradient Observation Data
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.