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The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also poses significant challenges, particularly regarding the substantial costs associated with processing extensive data and the limitations of computational resources. Inspired by the Neural Image Compression (NIC) task in computer vision, this study seeks to compress weather data to address these challenges and enhance the efficiency of downstream applications. Specifically, we propose a variational autoencoder (VAE) framework tailored for compressing high-resolution datasets, specifically the High Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) with a spatial resolution of 1 km. Our framework successfully reduced the storage size of 3 years of HRCLDAS data from 8.61 TB to just 204 GB, while preserving essential information. In addition, we demonstrated the utility of the compressed data through a downscaling task, where the model trained on the compressed dataset achieved accuracy comparable to that of the model trained on the original data. These results highlight the effectiveness and potential of the compressed data for future weather research., Comment: 19 pages

The atmosphere affects humans in a multitude of ways, from loss of life due to adverse weather effects to long-term social and economic impacts on societies. Computer simulations of atmospheric dynamics are, therefore, of great importance for the well-being of our and future generations. Here, we propose AtmoRep, a novel, task-independent stochastic computer model of atmospheric dynamics that can provide skillful results for a wide range of applications. AtmoRep uses large-scale representation learning from artificial intelligence to determine a general description of the highly complex, stochastic dynamics of the atmosphere from the best available estimate of the system's historical trajectory as constrained by observations. This is enabled by a novel self-supervised learning objective and a unique ensemble that samples from the stochastic model with a variability informed by the one in the historical record. The task-independent nature of AtmoRep enables skillful results for a diverse set of applications without specifically training for them and we demonstrate this for nowcasting, temporal interpolation, model correction, and counterfactuals. We also show that AtmoRep can be improved with additional data, for example radar observations, and that it can be extended to tasks such as downscaling. Our work establishes that large-scale neural networks can provide skillful, task-independent models of atmospheric dynamics. With this, they provide a novel means to make the large record of atmospheric observations accessible for applications and for scientific inquiry, complementing existing simulations based on first principles.

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligence (AI) research and applications. We detail its system architecture, parallel, distributed model training, and benchmarks indicating its outstanding performance. We exemplify its potential for research application by presenting large-scale AI research highlights from various scientific fields that require such a facility., Comment: 12 pages, 5 figures. Accepted at ISC 2021, Workshop Deep Learning on Supercomputers. This is a duplicate submission as my previous submission is on hold for several weeks now and my attempts to contact the moderators failed