Paper

Weather fronts play an important role in atmospheric science. Their correlation to severe natural hazards such as extreme precipitation, cyclones or thunderstorms makes localization and understanding of frontal systems an important factor in weather forecasting. Despitetheir importance weather fronts are mostly studied on horizontal slices, ignoring their three-dimensional characteristics. In this paper we present an efficient GPU-based parallelization for the detection of three-dimensional weather fronts. We achieve comparable skill to our previous CPU-based method, on which we based our algorithm, while being more than two orders-of-magnitude faster. Furthermore, we extend our previous method by providing additional information for warm, cold, occluded, and stationary fronts. Thus, our approach drastically increases the ability to provide statistical evaluations of three-dimensional fronts for different setups. Even faster runtimes can be achieved by using multiple GPUs with linear scaling

The Cloud Model version 1 is an atmospheric model that allows for idealized studies of atmospheric phenomena. A new Lagrangian microphysics capability has been added, enabling a significantly more accurate representation than the traditional bulk or multi-moment approaches frequently found in mesoscale atmospheric models. We have utilized a directive-based approach to enable a single source code to efficiently support execution on both CPU and GPU-based computing platforms. In addition to the use of accelerator directives, changes to the data structures and the message-passing approach used by the Lagrangian particle-based microphysics module were necessary to enable efficient execution for a large number of particles. We focus on a configuration that will be used to investigate the impact of oceanic sea-spray on the atmospheric boundary layer within a hurricane. We observe a factor of $5.1 \times$ reduction in time to the solution when comparing the execution time for 256 NVIDIA A100 GPUs versus 256 AMD Epyc\textsuperscript{TM} Milan-based compute nodes using 1 billion particles.

The inclusion of atmospheric chemistry in global climate projections is currently limited by the high computational expense of modelling the many reactions of chemical species. Recent rapid advancements in artificial intelligence (AI) provide us with new tools for reducing the cost of numerical simulations. The application of these tools to atmospheric chemistry is still somewhat nascent and multiple challenges remain due to the reaction complexities and the high number of chemical species. In this work, we present GAIA-Chem, a global AI-accelerated atmospheric chemistry framework for large-scale, multi-fidelity, data-driven chemical simulations; GAIA-Chem provides an environment for testing different approaches to data-driven species simulation. GAIA-Chem includes curated training and validation datasets, support for offline and online training schemes, and comprehensive metrics for model intercomparison. We use GAIA-Chem to evaluate two DNN models; a standard autoencoder scheme based on convolutional LSTM nodes, and a transformer-based model. We show computational speedups of up to 1,280 times over numerical methods for the chemical solver and a 2.8 times reduction in RMSE when compared to previous works.