DeepDT: Generative Adversarial Network for High-Resolution Climate Prediction
Jianxin Cheng, Jin Liu, Qiuming Kuang, Zhou Xu, Chenkai Shen, Wang Liu, Kang Zhou
Abstract
Climate prediction is susceptible to a variety of meteorological factors, and downscaling technology is used for high-resolution climate prediction. This technology can generate small-scale regional climate prediction from large-scale climate output information. Inspired by the concept of image super resolution, we propose to apply the convolutional neural network (CNN) to downscaling technology. However, some unpleasant artifacts always appear in the final climate images generated by existing CNN-based models. To further eliminate these unpleasant artifacts, we present a new training strategy for the generative adversarial network, termed DeepDT. The key idea of our DeepDT is to train a generator and a discriminator separately. More specifically, we apply the residual-in-residual dense block as the basic frame structure to fully extract the features of the input. Additionally, we innovatively use a CNN model to fuse multiple climate elements to generate trainable climate images, and build a high-quality climate data set. Finally, we evaluate the DeepDT using the proposed climate data sets, and the experiments indicate that DeepDT performs best compared to most CNN-based models in climate prediction.