一个用于机器学习模型的数据集,用于对流动启动检测和中国东南部的现在广播
Yujia Liu1, Anyuan Xiong2, Na Liu1
1National Meteorological Information Center, CMA, Beijing, China.
Scientific data
|March 1, 2026
概括
本研究介绍了用于人工智能 (AI) 模型的Convective Initiation Dataset (CIDS),以改善恶劣天气预报. CIDS有助于高精度地识别和预测对流动启动 (CI) 事件.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 人工智能用于天气预报
- 对于恶劣天气的远程传感
背景情况:
- 对于严重的对流天气预警系统来说,对流动启动 (CI) 的有效识别和预测至关重要.
- 人工智能 (AI) 为增强CI预测和预警能力提供了一个有希望的途径.
研究的目的:
- 引入Convective启动数据集 (CIDS),这是一个专门为人工智能模型设计的新型数据集,用于识别和预测CI.
- 为中国东南部的强烈对流天气事件提供全面的特征数据和标签.
主要方法:
- 该CIDS数据集是使用雷达马赛克产品和FY-4A卫星辐射数据从2018年到2023年编制的.
- 利用雷达复合反射因子开发了一种算法,以识别初始风暴细胞,并根据30分钟进化分配CI类别标签.
- 数据包括10个雷达马赛克产品和10分钟间隔的9个光谱波段的卫星辐射.
主要成果:
- 该CIDS数据集包含136,728个样本,识别了超过410万个CI,其中近180万被归类为发展中CI.
- 该数据集捕捉了短时间的暴雨,雷暴风和冰事件,提供了详细的空间和时间信息.
结论:
- CIDS数据集为训练人工智能模型提供了宝贵的资源,以提高对流动启动预测的准确性和及时性.
- 这一数据集促进了对严重的对流天气的预警系统的进步,利用人工智能和多源气象数据.
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