A spatial context-aware model for climate model data fusion

doi:10.1080/17538947.2025.2509099

2025-05-25

Jinyu Meng

Zengchuan Dong *

Yongze Song *

DOI：10.1080/17538947.2025.2509099

原文链接

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摘要

En 中文

获取更准确的气候模型数据对于开展精确的区域气候研究至关重要。大多数研究使用线性加权方法或单个机器学习模型融合多个气候模型数据集以降低不确定性。然而，这些方法在全球范围内使用相同的模型，并忽略了各种气候模型的局部特性。本研究开发了一种空间上下文感知融合（SCAF）模型，通过在不同空间位置构建不同的模型来融合多源气候数据，捕捉局部气候特征并增强气候数据的区域适用性。开发的SCAF模型在融合黄河上游22个CMIP6气候模型的辐射数据时得到实施。结果表明，SCAF能够有效融合区域辐射数据，且融合数据与观测数据之间的相关系数在任何空间位置均高于0.95。因此，SCAF可以通过空间局部建模有效捕捉区域气候特征。此外，未来辐射趋势分析显示，辐射下降速率随着情景模型的增强而加速，下降幅度在每十年0.123至0.7771 W/m²之间。该模型展现出显著优势，并具有广泛潜力以有效融合区域气候模型。

Keyword:

Spatial context-aware model

climate change

climate model data fusion

radiation

期刊

International Journal of Digital Earth

IF:

4.9

论文数:

2.0K

被引数:

4.7K

机构

Hohai University

学者数:

2.3W

论文数: 1.8W

被引数: 2.1W

Curtin University

学者数:

1.5W

论文数: 1.8W

被引数: 2.8W

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