## 5 Conclusions  
We presented a model that combines sub-pixel convolution with ResNet architecture to perform super-resolution reconstruction on temperature data in the SC region. By inputting training data obtained from downsampling real 2 m temperature data into the model, the model learns to extract temperature characteristics within the corresponding area. Diverging from traditional upsampling approaches in conventional CNNs, the model employs a special periodic shuffling mechanism to learn the numerical positions and weights within the padded feature maps. This process effectively utilizes the feature information learned from the deep structure of the model and reduces the impact of researchers' subjective influences due to excessive hyperparameter settings, thereby enhancing the model's performance in SR tasks. Figure 17: SR results for bilinear interpolation between single and average moments: (**a**) SR data for a single moment; (**b**) SR data for the average moment; (**c**) comparison between true data and SR data for a single moment; (**d**) comparison between true data and SR data for the average moment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(RMSE\) & \(PSNR\) & \(SSIM\) \\ \hline \hline Bilinear interpolation & 0.5644 & 52.7789 & 0.9415 \\ LASSO & 0.5426 & 53.1317 & 0.9465 \\ SVM & 0.5835 & 52.5193 & 0.9379 \\ CNN & 0.4075 & 53.0385 & 0.9584 \\ ResNet-Subpixel & 0.2897 & 58.8929 & 0.9721 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The \(RMSE\), \(PSNR\), and \(SSIM\) of different SR methods. After conducting multiple rounds of super-resolution experiments with varying dimensions, the results demonstrate that the model has successfully extracted temperature-related feature information from the SC region, thereby achieving excellent performance. Both the statistical outcomes derived from the evaluation coefficients and the intuitive analysis of the comparison charts post-super-resolution underscore the model's superior capabilities. It is noteworthy that as the time steps for super-resolution reconstruction data increase, the impact of minor errors also gradually diminishes. This is specifically manifested as small deviations that may exist for a particular observation point at a single moment, but on average, these minor deviations have little effect on the results, and the model performance improves. Moreover, compared to the over-smoothing issues encountered in traditional interpolation methods, this model is significantly capable of preserving more detailed information in temperature data. This study has once again demonstrated that the use of CNN for the super-resolution reconstruction of temperature data is feasible and advantageous. In fact, we have identified numerous issues and aspects that require further research and development. Building upon the findings of this study, our future research directions are divided into two main areas:  
1. Further enhancing the model's precision in super-resolution tasks. It is widely acknowledged that meteorological data within the same region are correlated [54; 55; 56; 57], such as temperatures at different altitudes, wind fields, and atmospheric humidity. By incorporating multiple feature parameters to describe a more detailed temperature field, the model can extract and learn details that are difficult to discern from single temperature data, thereby yielding more refined SR results. 2. In the field of research concerning the super-resolution reconstruction of meteorological data, the reliability of the model's generalizability has always been a matter of concern [58; 59; 60; 22]. In our future work, we will further discuss the generalizability of the model, with the verification process divided into two parts. Firstly, we will conduct super-resolution experiments using the model on temperature data from different regions, which is also the issue of greatest concern to most researchers.