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Global Navigation Satellite System (GNSS) technology provides an effective means to invert the Zenith tropospheric delay (ZTD), which can be obtained with high accuracy and high temporal resolution independent of weather variability. As a key parameter in climate change studies, ZTD is widely used in El Niño-Southern Oscillation (ENSO), air quality, and drought and flood monitoring studies. However, due to the sensor or observation equipment, some ZTD data are wrong or missing, which leads to biased analysis results of related studies. Therefore, in this paper, the daily-scale ZTD of 40 GNSS stations in the Chinese region from 2011 to 2020 is used as experimental data, and the K-Nearest Neighbor (KNN) and Periodic Model (PM) methods are used to complete the data and evaluate the accuracy considering the missing rate of 10%, 20% and 30%, where The KNN method takes the number of neighboring values K from 1 to 10, and the PM method includes the annual and semi-annual Periodic information of ZTD. The results show that for K=1, the missing rate is 10%, 20%, and 30%, and the mean RMS values of KNN/PM-ZTD and GNSS ZTD are 9.9/7.2, 14.3/10.3, 16.7/12.1mm, respectively, and the RMS difference between KNN/PM-ZTD and GNSS ZTD increases with the value of K from 2.7 to 0.5/4 to 0.7/ 4.6 to 0.7 mm. Therefore, it is verified that the PM method is superior to KNN, and the present method is important for the study of the complementary long-time series data.
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