RN-STLSTM-GAN: Spatiotemporal-Guided Generative Adversarial Network for Time-Evolving Precipitation Downscaling

Generative adversarial networks (GANs) have been widely applied in the field of meteorological research, particularly in the downscaling of images due to their ability to generate super-resolution images. In recent years, numerous researchers have combined GANs with recurrent neural networks (RNNs) to address the issue of meteorological super-resolution. However, these models do not take into account the spatial variations of meteorological sequences. In this paper, we propose a super-resolution method named RN-STLSTM-GAN, which combines GANs with RN-STLSTM and ESA networks to learn the spatiotemporal features of meteorological sequences. Specifically, we first apply the RN-STLSTM at the initialization of the generator and discriminator to learn the spatiotemporal relationships between sequential images. Second, an ESA network is combined with the RN-STLSTM structure to enhance the learning of spatial features. Thirdly, LeakyReLU is used as the activation function for both the generator and discriminator to minimize the loss of image data during model training. Experiments conducted on the NJU-CPOL datasets demonstrate that our proposed method outperforms other existing methods and can generate realistic and temporally consistent super-resolution sequences for datasets at different heights.