EPT-2提升地球系统预测精度，尤其在风速与温度预报上超越主流模型。
EPT-2 Technical Report
- 基于变压器架构的EPT-2模型，统一建模多种气候变量
- 在0-240小时预报范围内，风速、气温等关键指标优于ECMWF和微软模型
- 配套的随机扰动集成模型EPT-2e以极低算力实现超前概率预报
我们提出EPT-2，是面向地球系统预报的地球物理变换器（EPT）系列的最新版本。EPT-2相比前代EPT-1.5有显著提升，在0-240小时预报周期内，对10米和100米风速、2米温度及地表太阳辐射等能源相关变量的预测性能达到新基准。其表现持续优于微软Aurora等领先人工智能气象模型，以及欧洲中期天气预报中心（ECMWF）的运行数值预报系统IFS HRES。同时，我们引入了基于扰动的EPT-2集成模型EPT-2e，用于概率预报。令人瞩目的是，EPT-2e在中长期预报中大幅超越被视为行业金标准的ECMWF ENS均值，且计算成本仅为后者的极小部分。EPT系列模型及第三方预报结果可通过app.jua.ai平台获取。
原文摘要 · Abstract (English)
We present EPT-2, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI models for Earth system forecasting. EPT-2 delivers substantial improvements over its predecessor, EPT-1.5, and sets a new state of the art in predicting energy-relevant variables-including 10m and 100m wind speed, 2m temperature, and surface solar radiation-across the full 0-240h forecast horizon. It consistently outperforms leading AI weather models such as Microsoft Aurora, as well as the operational numerical forecast system IFS HRES from the European Centre for Medium-Range Weather Forecasts (ECMWF). In parallel, we introduce a perturbation-based ensemble model of EPT-2 for probabilistic forecasting, called EPT-2e. Remarkably, EPT-2e significantly surpasses the ECMWF ENS mean-long considered the gold standard for medium- to longrange forecasting-while operating at a fraction of the computational cost. EPT models, as well as third-party forecasts, are accessible via the app.jua.ai platform.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。