## 4 Experimentation and Results
### Comparison of Compression Effects under Different Metrics  
To explore the rationale behind multi-channel compression, we conducted compression tests using both single-channel and multi-channel approaches and compared their mean squared error (MSE) values. As shown in Figure 6, for different data variables, the MSE after multi-channel compression was lower than that of single-channel compression. The performance of compression varied for different datasets due to different data distributions. However, overall, multi-channel compression outperformed single-channel compression in terms of performance. The compression ratio of JPEG is controlled by adjusting its compression quality. Table 2 presents a comprehensive performance comparison at various quality levels, encompassing compression ratio (CR), mean absolute error (MAE), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR). The results in Table 1 reveal a discernible pattern: with increasing compression quality, the model's compression ratio gradually increases, accompanied by a significant reduction in both MAE and RMSE, while correspondingly, there is an observable upward trend in PSNR. This phenomenon arises from the fact that, at higher quality settings, JPEG's quantization table values are smaller, giving the compressed data higher precision. Conversely, lower quality settings result in larger quantization table values, leading to the discarding of more information. It is noteworthy that across different compression ratios, our model consistently exhibits lower MAE and RMSE compared to JPEG, coupled with a corresponding increase in PSNR. This indicates the superior reconstruction performance of our method at relatively lower quality levels, demonstrating its efficacy in preserving image information and achieving lower error levels. \begin{table} \begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Quality**} & \multirow{2}{*}{**CR**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**PSNR**} \\ \cline{3-8}  & & **JPEG** & **OURS** & **JPEG** & **OURS** & **JPEG** & **OURS** \\ \hline
50 & 27.3 & 0.0169 & 0.0075 & 0.0156 & 0.0104 & 47.94 & 50.73 \\
70 & 23.7 & 0.0132 & 0.0069 & 0.0123 & 0.0090 & 50.07 & 51.12 \\
90 & 17.0 & 0.0080 & 0.0053 & 0.0073 & 0.0069 & 53.27 & 54.47 \\ \hline \hline \end{tabular}  \endThis study proposes an error-controlled lossy compression scheme for spatiotemporal data, aiming to achieve a large compression ratio while minimizing reconstruction error. Therefore, we further conducted comparative experiments on the compression performance and reconstruction performance of this scheme on spatiotemporal data. Currently, the most common lossless compression formats include ZIP, RAR, and 7 Z. We compared the compression performance of the hybrid compression scheme on meteorological and oceanographic data with these traditional lossless compression schemes. As shown in Figure 7, our scheme outperforms traditional lossless compression schemes. It can compress 1.5 GB of data to 64.7 MB, achieving a compression ratio of nearly 24 times, while the compression ratio achieved by these traditional lossless compression schemes is only below 5 times. To compare the reconstruction performance of our proposed compression method, we conducted a comparison experiment with other lossy compression schemes, namely JPEG, SZ, and autoencoders (AEs), at the same compression ratio in terms of PSNR. We used JPEG as the baseline. As shown in Figure 8, at any compression ratio, our compression scheme significantly outperforms the lossy compression scheme based on autoencoders. Furthermore, compared to the baseline model, our proposed compression scheme outperforms JPEG compression overall, with performance improvement becoming more significant as the compression ratio increases. For the most competitive error-bounded lossy compression scheme (SZ), we adjusted the error of SZ to achieve the same compression performance as our scheme based on the achievable compression ratio.