Modernizing CNN-based Weather Forecast Model towards Higher Computational Efficiency

• URL: http://arxiv.org/abs/2507.10893v1
• Date: Tue, 15 Jul 2025 01:16:32 GMT
• Title: Modernizing CNN-based Weather Forecast Model towards Higher Computational Efficiency
• Authors: Minjong Cheon, Eunhan Goo, Su-Hyeon Shin, Muhammad Ahmed, Hyungjun Kim,
• Abstract summary: We introduce a modernized CNN-based model for global weather forecasting.<n> KAI-a incorporates a scale-invariant architecture and InceptionNeXt-based blocks within a geophysically-aware design.<n>It is trained on the ERA5 daily dataset with 67 atmospheric variables and completes training in just 12 hours on a single NVIDIA L40s GPU.
• Score: 5.781137818421603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, AI-based weather forecast models have achieved impressive advances. These models have reached accuracy levels comparable to traditional NWP systems, marking a significant milestone in data-driven weather prediction. However, they mostly leverage Transformer-based architectures, which often leads to high training complexity and resource demands due to the massive parameter sizes. In this study, we introduce a modernized CNN-based model for global weather forecasting that delivers competitive accuracy while significantly reducing computational requirements. To present a systematic modernization roadmap, we highlight key architectural enhancements across multiple design scales from an earlier CNN-based approach. KAI-a incorporates a scale-invariant architecture and InceptionNeXt-based blocks within a geophysically-aware design, tailored to the structure of Earth system data. Trained on the ERA5 daily dataset with 67 atmospheric variables, the model contains about 7 million parameters and completes training in just 12 hours on a single NVIDIA L40s GPU. Our evaluation shows that KAI-a matches the performance of state-of-the-art models in medium-range weather forecasting, while offering a significantly lightweight design. Furthermore, case studies on the 2018 European heatwave and the East Asian summer monsoon demonstrate KAI-a's robust skill in capturing extreme events, reinforcing its practical utility.

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