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SHEN Wangbin, LI Xin, QIN Zhengkun, et al. 2022. Restoration Method for Automatic Station Temperature Observation Data Based on EOF Iteration [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(2): 406−418. doi: 10.3878/j.issn.1006-9895.2103.21021
Citation: SHEN Wangbin, LI Xin, QIN Zhengkun, et al. 2022. Restoration Method for Automatic Station Temperature Observation Data Based on EOF Iteration [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(2): 406−418. doi: 10.3878/j.issn.1006-9895.2103.21021

Restoration Method for Automatic Station Temperature Observation Data Based on EOF Iteration

  • With the construction of about 70,000 automatic weather stations across China, a comprehensively automatic meteorological observation has been realized. However, the real application of this kind of observation always suffers from their low quality. A large number of error data seriously affects the practical application of observation. Therefore, it is a particularly important task to repair these abnormal observations. Using a total of 168 times of hourly surface temperature observations of automatic weather station during December 1–7, 2019, which is provided by the Jiangsu meteorological bureau, a restoration method based on the empirical orthogonal function method is proposed. The accuracy analysis of ideal restoration experiments shows that the new restoration method can well repair wrong observations with an error of about 0.48 degrees centigrade. The methods based on the Cressman interpolation, which rely on a single point observation information, are more vulnerable to small-scale signal interferences and introduce unnatural observation information, with the surface temperature repair error reaching up to 1.55 degrees centigrade. The analysis of the actual repair results also proves that the new repair method makes full use of the time-space separation and modal orthogonality of the EOF analysis method and gradually eliminates the influence of wrong data through an iterative method to obtain better space-time continuity repair results with the surrounding observation data.
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