卷积长短期记忆网络用于每天生成100米近地表空气温度
Mengqi Sun1,2, Qingyan Meng3,4, Linlin Zhang1,5
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Scientific data
|May 14, 2025
概括
这项研究为城市气候研究创建了一个高分辨率的温度数据集. 这些数据有助于为发达城市制定热浪减缓策略.
科学领域:
- 城市气候学 城市气候学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 全球变暖和城市化对城市气候产生重大影响.
- 精细的气候研究对于了解发达城市的温度变化至关重要.
研究的目的:
- 为城市地区生成近地表空气温度的高时空分辨率数据集.
- 支持城市气候研究,并为热浪减缓政策提供信息.
主要方法:
- 使用了卷积式长期短期记忆 (ConvLSTM) 深度学习模型.
- 综合多源数据:ERA5温度,地形,地表和植被部分.
- 为每日温度数据 (最大,最小,平均) 获得100米的空间分辨率.
主要成果:
- 开发的数据集涵盖了2019-2023年的夏季月份 (六月至八月).
- 在MAE (0.564-0.784°C),RMSE (0.733-1.027°C) 和R2 (0.892-0.943) 证实了高精度.
- 该数据集提供了最新的每日温度数据,分辨率为100米.
结论:
- 高分辨率数据集对城市和城市内气候研究有价值.
- 这个资源可以为城市的热浪缓解和适应策略提供信息.
- 该研究强调了深度学习对于生成详细的气候数据的有用性.
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