## 4 Experiments and Results  
The aim of the study was to perform SR reconstruction on temperature data in the SC region with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). To validate the model's capability in this regard, we conducted multiple experiments for verification. To assess the model's performance in SR reconstruction of temperature data at different scales, we subjected the original data to downsampling operations by factors of 2 and 4, resulting in LR data with spatial resolutions of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) and 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), respectively, as shown in Figure 7. In terms of network performance evaluation, the study employed a variety of metrics to compare the differences between the SR results \(x\) and the original data \(y\). In the experiments, we conducted tests on data with resolutions of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) and 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) to evaluate the performance of two types of SR tasks: (1) the model performance for SR at a specific moment; (2) the model performance for SR over a period. Additionally, we compare the results of SR reconstruction using CNN with those obtained using traditional methods (such as bilinear interpolation). Initially, we utilized commonly employed evaluation metrics in the fields of machine learning and computer vision, namely Root Mean Square Error (\(RMSE\)), Mean Absolute Error (\(MAE\)), Mean Absolute Percentage Error (\(MAPE\)), correlation coefficient (\(Corr\)), Peak Signal-to-Noise Ratio (\(PSNR\)), and structural similarity (\(SSIM\)):  
\[RMSE(x,y)=\sqrt{\frac{\sum_{j=1}^{P}\sum_{i=1}^{N}\left(x_{i}^{j}-y_{i}^{j} \right)^{2}}{N\cdot P}} \tag{7}\]  
Figure 6: (a) ResNet-Subpixel (2\(\times\)) structure and experimental flow, containing some residual Bblocks. (b) Residual block containing 2 convolutional layers, 2 BatchNorm layers, and an activation function Relu. (c) Upscaling Block. Figure 7: Example temperature fields (shaded color) from the original dataset at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (a); low-resolution dataset at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) (b); and low-resolution dataset at 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) (c). \[MAE(x,y)=\frac{\sum_{j=1}^{p}\sum_{l=1}^{N}\left|x_{i}^{j}-y_{i}^{j} \right|}{N\cdot P} \tag{8}\]  
\[MAPE(x,y)=\frac{100}{N\cdot P}\sum_{j=1}^{P}\sum_{i=1}^{N}\left|\frac{x_{i}^{j}- y_{i}^{j}}{x_{i}^{j}}\right| \tag{9}\]  
\[Corr(x,y)=\frac{\sum_{j=1}^{P}\sum_{i=1}^{N}\left(x_{i}^{j}-\overline{x}^{j} \right)\left(y_{i}^{j}-\overline{y}^{j}\right)}{\sqrt{\sum_{j=1}^{p}\sum_{i=1} ^{N}\left(x_{i}^{j}-\overline{x}^{j}\right)^{2}}\sqrt{\sum_{j=1}^{p}\sum_{i=1} ^{N}\left(y_{i}^{j}-\overline{y}^{j}\right)^{2}}} \tag{10}\]  
\[PSNR(x,y)=10\cdot\log_{10}\left(\frac{MAX^{2}}{RMSE(x,y)^{2}}\right) \tag{11}\]  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+C_{2}\right)} \tag{12}\]  
where \(P\) is the total number of the samples (when a single moment case \(P=1\)); \(N\) is the total number of pixels in one sample; \(x_{i}^{j}\) and \(y_{i}^{j}\) denote the \(i\)-th pixel at the \(j\)-th moment in the original results and the SR data, respectively; \(\overline{x}^{j}\) and \(\overline{y}^{j}\) are the mean values of \(x^{j}\) and \(y^{j}\) at the \(j\)-th moment, respectively. \(RMSE\), \(MAE\), and \(MAPE\) are commonly used metrics for evaluating model performance [48; 49; 50]. They provide a direct representation of the model's performance, allowing us to clearly understand the deviation between the temperature data reconstructed by the model and the actual temperatures, including the magnitude of the deviation and whether the SR data are generally higher or lower than the actual values. However, relying on a single evaluation metric can lead to errors in assessment, as such metrics may be sensitive to outliers, amplify relative errors, or ignore the direction of errors.