A Four-Dimensional Variational Informed Generative Adversarial Network for Data Assimilation

Abstract

Data-driven weather prediction (DDWP) has made significant advancements in recent years. However, weather prediction using DDWPs still requires an accurate initial field as the input. To fulfill this requirement, the four-dimensional variational (4DVar) approach can offer initial fields. Recent studies have demonstrated the potential of deep learning (DL)-based methods in accelerating 4DVar. In this study, we propose a novel model called the 4DVar-informed generative adversarial network (4DVarGAN), which combines prior knowledge from 4DVar with the conditional generative network (CGAN). We employ a CGAN to non-iteratively solve the 4DVar cost function and utilize a cycle-consistent adversarial learning framework for data augmentation. Additionally, we incorporate a 4DVar-based adaptive adjustment to the output of the proposed model's analysis increment-generating component, which promotes reasonable stabilization. Experimental results using 500 hPa geopotential fields from the WeatherBench data set demonstrate that our approach achieves a 73-fold acceleration compared to the 4DVar implemented by the DDWP model. Furthermore, our model exhibits the lowest initial and forecast errors, outperforming state-of-the-art DL-based data assimilation (DA) methods. Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results. These findings highlight the potential of CGANs in enhancing the reliability of data-driven DA by incorporating the prior knowledge of the 4DVar method.