Aries：面向能源行业的专有中期天气预报模型
Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry
- InCommodities（InCommodities公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文介绍InCommodities开发的基于SwinTransformer的专有中期天气预报模型Aries，在ERA5数据上训练，在2025年ECMWF分析场上评估，风速预报优于ECMWF HRES和AIFS，证明工业界开发竞争性天气模型可行。
中文摘要 AI 辅助
中期天气预报支撑着能源行业的运营和规划决策。开发具有竞争力的天气模型曾经是国家气象中心的专属领域，但近期机器学习天气预报（MLWP）的进展已将该领域向工业界开放。我们介绍了Aries，一个由InCommodities开发的基于SwinTransformer的MLWP模型。Aries在0.25度分辨率的ERA5再分析数据上训练，预测74个预报变量和11个诊断大气变量。我们在2025年ECMWF分析初始场上评估该模型，确保所有比较模型都使用近期且严格样本外的测试时段。在10米风速方面，Aries在长达四天的预报时效内，其RMSE优于ECMWF HRES和AIFS；而在2米温度方面，其RMSE与AIFS业务运行版本相当。这些结果表明，专有开发具有竞争力的天气模型在技术上是可行的，为能源行业的运营和规划应用提供了更广泛的预报支持。
英文摘要
Medium-range weather forecasting underpins operational and planning decisions across the energy industry. Developing competitive weather models was once the domain of national meteorological centers, but recent advances in machine-learned weather prediction (MLWP) have opened the field to industry. We present Aries, a SwinTransformer-based MLWP model developed at InCommodities. Aries is trained on ERA5 reanalysis data at 0.25\textdegree{} resolution, predicting 74 prognostic and 11 diagnostic atmospheric variables. We evaluate the model on 2025 ECMWF Analysis initializations, ensuring a recent and strictly out-of-sample test period for all models compared. On 10-metre wind speed, Aries outperforms both ECMWF HRES and AIFS in terms of RMSE for lead times up to four days, while on 2-metre temperature it achieves RMSE on par with AIFS operational. These results demonstrate that proprietary development of competitive weather models is technically viable, supporting a broader set of forecasts available for operational and planning applications in the energy industry.
