A ResNet-Based Super-Resolution Approach for Constructing a High-Resolution Temperature Dataset from ERA5 Reanalysis

Temperature data, as a key meteorological parameter, holds an indispensable position in meteorological research and social management. High-resolution data can significantly enhance these tasks, whether it is accurate climate prediction or the prevention of meteorological disasters. Unfortunately, due to economic or geographical factors, among others, some regions are unable to obtain detailed temperature data, which is a concern for researchers. This study proposes a ResNet-based model aimed at high-resolution reconstruction of 2 m temperature data. In this study, we utilized the ERA5 dataset and applied the method to the South China region (SC). The paper constructs a neural network architecture that integrates a sub-pixel convolution module with a residual structure, which can effectively capture regional temperature characteristics and achieve high-precision data reconstruction. Compared with traditional interpolation methods, this method is more accurate, reduces the initial parameter settings, and lowers the risk of excessive human intervention. Moreover, it is not restricted by the super-resolution ratio. In this paper, experiments with 2× and 4× super-resolution were conducted, respectively. These outcomes indicate that the neural network model presented in this article is a promising approach for generating high-resolution climate data, which holds significant importance for climate research and related applications.