GAN-based forecasting model via self-adaptive clustering approach

Deep learning has been rapidly adopted in short-term precipitation prediction, such as simulating precipitation movement and predicting extreme weather events. Recently, generative adversarial neural networks (GANs) have been shown to be effective at dealing with field smoothing with increasing lead time. Several studies (Jing et al., 2019; Ravuri et al., 2021) demonstrated the potential of GAN by solving spatial smoothing problems and demonstrating reliable predictive performance. However, despite promising results from GANs, unbalanced datasets and human annotations can limit the predictive ability of deep learning and induce biased results. In addition, precipitation is a complex process that depends on various factors. Thus, approximating the model into a single latent space is a challenge, and furthermore, there is a risk of mode collapse. This study introduces an algorithm for predicting precipitation by clustering precipitation types using self-supervised learning (SSL) and estimating rainfall distribution according to precipitation types. First, we derive precipitation-type labels by self-clustering a generator that is a multi-layer ConvGRU. And then, we predict six-hour precipitation based on the gaussian distribution of each type. SSL improves the performance of precipitation forecasting based on type-specific representation learning through adaptive sampling in latent space. The proposed methodology was verified using hybrid surface rainfall (HSR) dataset at a spatial resolution of 500m with a resolution of 2,305 (longitude) × 2,881 (latitude) and a temporal resolution of 5 min. The images consist of 256×256 pixels from scaling down to a resolution of 4 km and are extracted at 30-minute intervals. Experimental results show that our method outperforms a state-of-the-art method on a six-hour prediction basis with a mean squared error and critical success index on unseen datasets. Also, the proposed algorithm can predict various precipitation types without spatial smoothing.