A Review of Super Resolution Technology for Extended-Range Precipitation Forecasting

Jiahan Chen, Chengsheng Yuan

Abstract: In the field of meteorology, precipitation holds significant importance for human activities and environment. Extended-range precipitation forecasting, covering a timeframe of weeks to months, is crucial across various sectors, including water resource management, agriculture, and disaster prevention. However, the conventional meteorological forecasting models often fail to satisfy the precise demand for accurate and detailed precipitation predictions due to the inherent limitations in resolution and data collection frequency associated with these methods. Recently, super resolution technology has emerged as a promising solution with the potential to transform the resolution of meteorological data. By leveraging advanced algorithms and computational prowess, this technology can generate high-resolution images from low-resolution inputs. In this comprehensive review, we meticulously dissect the state-of-the-art super resolution techniques utilized in extended-range precipitation forecasting tasks. By classifying and analyzing the literature based on various methodologies, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, we uncover their performance characteristics, strengths, and limitations. Our in-depth exploration reveals that while these methods have shown promise, they also face significant challenges. For example, CNNs struggle with capturing ultrafine details, GANs exhibit training instability, and diffusion models require further optimizations in terms of their computational efficiency. Future research efforts should focus on enhancing model architectures, seamlessly incorporating physical constraints to ensure the physical consistency of the generated data, and optimizing the current data utilization strategies. Furthermore, it is crucial to develop comprehensive evaluation metrics, conduct extensive long-term and regional validation studies, and establish effective uncertainty quantification methods. Additionally, the seamless integration of super resolution forecasts into practical applications such as decision support systems for flood prevention and water resource management, as well as their applications in climate change studies, will be pivotal for unlocking the full potential of this technology and addressing the longstanding challenges faces in precipitation forecasting scenarios.