KARINA: An Efficient Deep Learning Model for Global Weather Forecast

arXiv:2403.10555v1 Announce Type: new
Abstract: Deep learning-based, data-driven models are gaining prevalence in climate research, particularly for global weather prediction. However, training the global weather data at high resolution requires massive computational resources. Therefore, we present a new model named KARINA to overcome the substantial computational demands typical of this field. This model achieves forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only 4 NVIDIA A100 GPUs and less than 12 hours of training. KARINA combines …

abstract arxiv climate computational cs.ai cs.cv cs.lg data data-driven deep learning forecast global however massive physics.ao-ph prediction research resources training type weather weather data weather prediction