Modernizing CNN-based Weather Forecast Model towards Higher Computational Efficiency

TL;DR

This paper presents a modernized CNN-based weather forecast model that achieves comparable accuracy to Transformer-based models but with significantly reduced computational complexity, enabling efficient global weather prediction.

Contribution

The paper introduces a systematic modernization of CNN architectures for weather forecasting, incorporating scale-invariant and geophysically-aware design enhancements for efficiency and accuracy.

Findings

Matches state-of-the-art accuracy in medium-range forecasting

Contains about 7 million parameters, trained in 12 hours on a single GPU

Effectively captures extreme weather events like heatwaves and monsoons

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.