A Closed-loop Circular Regression Network For 2m Air Temperature Downscaling Over Southwestern China

IEEE Transactions on Geoscience and Remote Sensing(2024)

引用 0|浏览0
High-quality meteorological grid data are essential for meteorological research and applications, especially in regional scales. Statistical downscaling (SD) is an efficient method to provide more detailed information at spatial scale, and has already been implemented in many regions. In recent years, deep convolutional neural networks have exhibited promising performance in SD, effectively learning non-linear mappings from the low-resolution (LR) meteorological data to its corresponding high-resolution (HR) one. Nevertheless, existing deep-learning-based SD approaches may encounter two potential limitations. First, most of the previous deep-learning-based downscaling algorithms utilize a supervised learning framework, which necessitates the formation of data pairs consisting of HR labels and LR data for model training. However, the acquisition of meteorological data at regional scale is more challenging, making it difficult to meet the training requirements of traditional supervised-learning-based downscaling models in some cases. Second, learning the non-linear mapping between LR and HR meteorology data is typically an ill-posed issue, which means that there are infinite HR solutions for the same LR sample, making it harder to find the optimal solution within the large solution space, especially in the case of insufficient HR training labels. In this study, we propose a closed-loop circular regression network for simultaneous restoration of medium-resolution (MR) and HR 2m air temperature over Sichuan and surrounding areas, China. The model leverages the circular structure consistency to train both the downscaling and upscaling networks simultaneously. Specifically, in terms of insufficient HR labels, we introduce an additional constraint of MR supervision information to reduce the space of possible functions, forming a gradual downscaling process from LR to MR to HR data. Besides, we also establish an extra upscaling mapping from HR to MR to LR, which forms a circular consistency constraint on LR and MR data to provide additional supervision. Extensive experiments demonstrate that the proposed algorithm attains a Root Mean Square Error (RMSE) of 0.84 when utilizing 50% of the training data and 0.77 when using 75%. This performance surpasses that of many classic supervised-learning-based SD methods, even the complete supervised information is not utilized.
2m air temperature,Statistical downscaling,Insufficient HR labels,Closed-loop circular regression network
AI 理解论文
Chat Paper