This paper demonstrates the feasibility of democratizing AI-driven global
weather forecasting models among university research groups by leveraging
Graphics Processing Units (GPUs) and freely available AI models, such as
NVIDIA’s FourCastNetv2. FourCastNetv2 is an NVIDIA’s advanced neural network
for weather prediction and is trained on a 73-channel subset of the European
Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset
at single levels and different pressure levels. Although the training
specifications for FourCastNetv2 are not released to the public, the training
documentation of the model’s first generation, FourCastNet, is available to all
Benutzer. The training had 64 A100 GPUs and took 16 hours to complete. Although
NVIDIA’s models offer significant reductions in both time and cost compared to
traditional Numerical Weather Prediction (NWP), reproducing published
forecasting results presents ongoing challenges for resource-constrained
university research groups with limited GPU availability. We demonstrate both
(ich) leveraging FourCastNetv2 to create predictions through the designated
application programming interface (API) Und (ii) utilizing NVIDIA hardware to
train the original FourCastNet model. Further, this paper demonstrates the
capabilities and limitations of NVIDIA A100’s for resource-limited research
groups in universities. We also explore data management, training efficiency,
and model validation, highlighting the advantages and challenges of using
limited high-performance computing resources. Consequently, this paper and its
corresponding GitHub materials may serve as an initial guide for other
university research groups and courses related to machine learning, climate
science, and data science to develop research and education programs on AI
weather forecasting, and hence help democratize the AI NWP in the digital
economy.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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