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一个基于站点的0.1度每日网格集成降水数据集,用于印度
Anagha Peringiyil1, Manabendra Saharia2, Sreejith O P3
1Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Scientific data
|February 25, 2025
概括
这项研究介绍了印度降雨集团数据集 (IPED),这是改善印度水文建模的新工具. 整体产品为关键降水数据提供了更高的可靠性,特别是在季风极端期间.
科学领域:
- 水文学的水文学
- 气象学 天气学
- 地理空间数据科学数据科学
背景情况:
- 确定性网格式降水数据存在重大不确定性,限制了它们在水文建模和数据同化中的使用.
- 像印度这样的发展中国家面临着稀疏的观测网络,复杂的地形和频繁的极端天气事件的挑战,加剧了数据限制.
- 印度气象局目前的降水数据集是决定性的,使用基本的插值技术.
研究的目的:
- 为印度开发基于观测的集成降水数据集,以解决现有决定性产品的局限性.
- 为印度的水文气象应用提供更可靠,更准确的降水数据集.
- 利用一个全面的降水计网络和先进的空间回归技术.
主要方法:
- 开发了印度降水组合数据集 (IPED),使用印度各地最大的降水测量站网络.
- 采用局部加权空间回归方法,结合地形变化 (高度,斜率,方面).
- 在0.1°和0.25°分辨率下创建了每日30个组合降水产品,涵盖1991年至2020年.
主要成果:
- 在所有降水门上,IPED表现出卓越的区分和可靠性,包括诸如季风期间的99百分位等极端事件.
- IPED的整体性质为高级建模提供了关键的不确定性.
- 该数据集可以解释印度复杂的地形,增强其适用性.
结论:
- IPED是印度第一个基于观测的集成降水产品,在现有的决定性数据集上提供了显著的改进.
- 这一数据集预计将提高印度水文建模和数据同化系统的准确性和可靠性.
- 在数据稀缺的地区,IPED为应对与水气极端相关的挑战提供了宝贵的资源.
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