Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review

Nowadays, meteorological data plays a crucial role in various fields such as remote sensing, weather forecasting, climate change, and agriculture. The regional and local studies call for high spatial resolution gridded meteorological data to identify refined details, which however is generally limited due to the models, platforms, sensors, etc. Downscaling has been a significant and practical way to improve spatial resolution. In recent years, with superior feature extraction and expression abilities, deep learning (DL) has outperformed traditional methods in various areas, and exhibits huge potential to establish a complicated mapping between large-scale and local-scale meteorological data. Therefore, this paper provides a systematic review of DL in statistical downscaling for deriving high spatial resolution gridded meteorological data. This review first presents the background, including a taxonomy of downscaling methods, the role of DL in statistical downscaling, and the analogy between downscaling and image super-resolution. It shows evidence of how downscaling can benefit from DL, particularly super-resolution networks. Subsequently, this review focuses on the recent development of the DL-based statistical downscaling of gridded meteorological data, especially the deep architectures, including convolutional neural networks to capture the spatial dependencies of meteorological variables, recurrent neural networks to reveal the temporal states from time series, and generative adversarial networks to facilitate the reconstruction of high-frequency details, as well as the major structure residual learning and attention mechanism. In addition, this review demonstrates the specific issues towards downscaling, including scaling factors, spatial–temporal and variable correlations, and paired datasets construction, and then gives a comprehensive summary of the status of datasets, toolsets and metrics. The future challenges from the perspective of unsupervised models, transformer architecture, data fusion, physical-informed learning, generalization capacity, and uncertainty quantification for downscaling are finally discussed.