标题： 面向更高计算效率的基于CNN的天气预报模型现代化 显示英文标题

标题： Modernizing CNN-based Weather Forecast Model towards Higher Computational Efficiency

摘要： 最近，基于人工智能的天气预报模型取得了令人印象深刻的进展。 这些模型已经达到了与传统数值天气预报（NWP）系统相当的准确性，标志着数据驱动天气预测的一个重要里程碑。 然而，它们大多采用基于Transformer的架构，这通常会导致由于参数规模庞大而产生的高训练复杂性和资源需求。 在本研究中，我们引入了一个现代化的基于卷积神经网络（CNN）的全球天气预报模型，在显著降低计算需求的同时实现了具有竞争力的准确性。 为了提出一个系统化的现代化路线图，我们从早期的基于CNN的方法出发，强调了多个设计尺度上的关键架构改进。 KAI-a结合了尺度不变的架构和基于InceptionNeXt的块，并在地球系统数据结构的背景下进行了地理物理感知的设计。 该模型在包含67个大气变量的ERA5每日数据集上进行训练，包含约700万个参数，并且仅需在一块NVIDIA L40s GPU上训练12小时。 我们的评估显示，KAI-a在中程天气预报中的表现与最先进的模型相当，同时提供了一个显著轻量级的设计。 此外，对2018年欧洲热浪和东亚夏季季风的案例研究表明，KAI-a在捕捉极端事件方面表现出强大的能力，加强了其实际应用价值。 显示英文摘要

摘要： 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.