## 4 Results
### Overall Validation Results  
To clearly understand the advantages and disadvantages of PSAC CWV data, we not only used AERONET data to assess the quality of PSAC CWV data but compared its accuracy with that of widely used and fully validated MODIS CWV data derived from MODIS onboard Terra (MOD05) [11; 37; 38]. Existing validation results show that MOD05 has a high correlation with ground-based CWV data, but MOD05 has a significant overestimation [39; 40]. Since the accuracy of MOD05 derived from Terra/MODIS and MYD05 derived from MODIS/Aqua is close [40], MOYD05 is not used together with MOD05. The above MODIS CWV data can be downloaded free of charge from the Internet ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), last accessed on 26 January 2022). A total of five parameters were used for quality assessment of remote sensing CWV data, including root mean square error (RMSE), mean absolute error (MAE), mean bias (MB), relative error (RE), and percentage of CWV data with error within \(\pm(0.05+0.1*CWVAERONET)\) (PER10). The calculation equations of RMSE, MAE, MB, and RE are as follows:  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{N}\left(V_{cuv}^{i}-V_{cuv}^{i^{\prime}}\right)^{ 2}/N}, \tag{7}\]  
\[\text{MAE}=\sum_{i=1}^{N}\left|V_{cuv}^{i}-V_{cuv}^{i^{\prime}}\right|/N, \tag{8}\]  
\[\text{MB}=\sum_{i=1}^{N}\left(V_{cuv}^{i}-V_{cuv}^{i^{\prime}}\right)/N, \tag{9}\]  
\[\text{RE}=\sum_{i=1}^{N}\left|V_{cuv}^{i}-V_{cuv}^{i^{\prime}}\right|/\sum_{i= 1}^{N}V_{cuv}^{i^{\prime}}, \tag{10}\]  
where \(N\) is the number of effective matching results between AERONET CWV data and remote sensing CWV data, \(V_{cuv}^{i}\) is the value of remote sensing CWV data of the ith matching result, and \(V_{cuv}^{i^{\prime}}\) is the value of ground-based CWV data corresponding to \(V_{cuv}^{i}\).  
In this work, we used the temporal mean of ground-based observations within half an hour of the satellite observation time to match the spatial average of remote sensing CWV data within the range of 9 \(\times\) 9 km centered on the ground-based site [27; 39]. The scatter plots of the matching results between PSAC CWV data, MODIS CWV data, and AERONET data are shown in Figure 5. From Figure 5, it can be found that there is no obvious overestimation or underestimation in PSAC CWV data (MB = \(-0.03\) cm). However, there is a large overestimation in the MODIS CWV dataset (MB = 0.47 cm), which is consistent with the existing quality assessment results of MODIS CWV data [39; 40]. The RMSE, MAE, RE, and PER10 of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE. By analyzing the values of above statistical parameters, it can be found that, whether the MAE and RMSE used to characterize absolute error or the RE used to characterize relative error is selected for quality assessment of remote sensing CWV data, the accuracy of PSAC CWV data is significantly better than that of MODIS CWV data.  
The obvious overestimation of MODIS CWV data is the main reason why the absolute error and relative error of MODIS CWV data are obviously worse than those of PSAC CWV data. In addition, due to the big systematic error of MODIS CWV data, the PER10 of PSAC CWV data (78.19%) is significantly larger than that of MODIS CWV data (16.55%). Since the PER10 of PSAC CWV data is greater than 68.27%, it can be considered that the expected error of PSAC CWV data is less than \(\pm(0.05+0.1*CWVAERONET)\). In summary, it can be concluded that PSAC CWV data has no large systematic deviation and has a high accuracy, which is higher than that of MODIS CWV data.