Journal Article

CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting

Gong, Bing, Xiefei Zhi, Langguth, Michael, Mozaffari, Amirpasha, Ji, Yan

2023

Overview

The prediction of precipitation patterns up to 2 h ahead, also known as precipitation nowcasting, at high spatiotemporal resolutions is of great relevance in weather-dependent decision-making and early warning systems. In this study, we are aiming to provide an efficient and easy-to-understand deep neural network – CLGAN (convolutional long short-term memory generative adversarial network) – to improve the nowcasting skills of heavy precipitation events. The model constitutes a generative adversarial network (GAN) architecture, whose generator is built upon a u-shaped encoder–decoder network (U-Net) and is equipped with recurrent long short-term memory (LSTM) cells to capture spatiotemporal features. The optical flow model DenseRotation and the competitive video prediction models ConvLSTM (convolutional LSTM) and PredRNN-v2 (predictive recurrent neural network version 2) are used as the competitors. A series of evaluation metrics, including the root mean square error, the critical success index, the fractions skill score, and object-based diagnostic evaluation, are utilized for a comprehensive comparison against competing baseline models. We show that CLGAN outperforms the competitors in terms of scores for dichotomous events and object-based diagnostics. A sensitivity analysis on the weight of the GAN component indicates that the GAN-based architecture helps to capture heavy precipitation events. The results encourage future work based on the proposed CLGAN architecture to improve the precipitation nowcasting and early warning systems.

• Publisher: Copernicus GmbH
• Subject: Algorithms / Architecture / Artificial neural networks / Coders / Competitors / Datasets / Decision making / Deep learning / Early warning systems / Generative adversarial networks / Heavy precipitation / Long short-term memory / Modelling / Neural networks / Nowcasting / Optical flow (image analysis) / Precipitation / Precipitation patterns / Prediction models / Predictions / Recurrent neural networks / Sensitivity analysis / Warning systems