在 GraphCast 上对中国大陆的降雨预测基础的评估
Zihuang Yan1, Xianghui Lu2, Lifeng Wu1,3
1School of Soil and Water Conservation, Nanchang Institute of Technology, Nanchang, 330099, China.
Scientific reports
|April 28, 2025
概括
深度学习模型GraphCast在中国的1-3天累积降水预测中显示出高准确度,表现优于ECMWF. 它的预测能力随着预测时间的延长而下降.
科学领域:
- 气象学和气候科学 气象学和气候科学
- 环境科学中的人工智能
背景情况:
- 准确的累积降水预测对于水资源管理和减轻自然灾害至关重要.
- 深度学习和大数据集成是降雨预测的有希望的进步.
- 现有的大型模型需要用现场数据进行验证.
研究的目的:
- 为了评估GraphCast模型的降雨预测性能.
- 通过中国大陆的现场数据来评估GraphCast的准确性.
- 将GraphCast的性能与欧洲中期天气预报中心 (ECMWF) 的模型进行比较.
主要方法:
- 利用来自2393个观测站 (2020-2021) 的累积降水数据.
- 使用高分辨率的GraphCast模型 (0.25° × 0.25°网格,13层).
- 评估了1-3天累积降水预报,并与ECMWF进行了比较.
主要成果:
- 对于1-3天的预测,GraphCast的根平均平方误差 (RMSE) 从0.44到9.38毫米/日不等.
- 随着预测期的延长,GraphCast的预测准确性下降.
- 在中国大陆的各个电台中,GraphCast的预测准确性高于ECMWF.
- 在中国东北部温带潮湿/半潮湿地区,GraphCast表现优越.
结论:
- 在降雨预测方面,GraphCast具有显著的潜力和高准确性.
- 模型的性能是通过现场数据验证的,解决了当前大型模型中的差距.
- GraphCast为研究区域的短期降水预测提供了一个比ECMWF更准确的替代方案.
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