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从再分析数据和机器学习方法来绘制中国各地每天10公里的扩散太阳辐射

Qinghai Qi¹, Jinyang Wu¹, Christran A Gueymard²

¹Hubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.

Scientific data

|July 11, 2024

概括

为中国创建了一个新的41年每日扩散太阳辐射 (DSR) 数据集. 这种高分辨率数据集解决了DSR数据的稀缺性,有利于可再生能源和农业应用.

科学领域:

地球和环境科学地球和环境科学
可再生能源的研究研究.
农业科学农业科学

背景情况:

分散太阳辐射 (DSR) 对可再生能源和农业至关重要.
现有的DSR数据集缺乏高精度,长期连续性和空间覆盖,特别是在中国.

研究的目的:

为中国构建一个高精度,长期和空间连续的每日DSR数据集.
解决1982年至2022年中国的DSR数据缺口.

主要方法:

开发了一种结合REST2模型用于清空辐射和机器学习与重新分析衍生的云数据的新型组合模型.
创建了一个每日扩散太阳辐射 (CHDSR) 数据集,空间分辨率为10公里.
通过地面测量验证数据集.

主要成果:

该CHDSR数据集跨越41年 (1982-2022年),具有很高的空间和时间连续性.
混合型号表现出强的性能,其中R=0.94,RMSE=13.9 W m−2,MBE=-0.49 W m−2.
多年平均的DSR是74.51W m-2.2.

结论:

新建的CHDSR数据集有效地填补了中国DSR的关键数据缺口.
数据集的高质量和可用性支持太阳能,农业和气候变化方面的研究.
为了更广泛的科学用途,CHDSR数据集可以自由访问.

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