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A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN)
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DOI：10.1007/s00382-023-06971-9.png)
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High-resolution weather data is crucial for assessing future climate change impacts on local environments, yet downscaling low-resolution Global Climate Models (GCMs) outputs and addressing associated uncertainty remain significant challenges. In this study, we propose a novel spatial weather generator using generative networks, specifically a numerical conditional deep convolutional generative adversarial network (cDCGAN), as a promising solution. The cDCGAN generates high-resolution weather data from low-resolution GCM outputs and was applied to four case areas in China under four Shared Socio-economic Pathway (SSP) scenarios. The results demonstrate the cDCGAN's accuracy, consistency, and stability, with low uncertainties. The model performs optimally in low-elevation plains and tropical regions. The cDCGAN offers advantages in uncertainty analysis over traditional downscaling methods, serving as a valuable tool for climate change analysis, response estimation, and environmental management decision-making within the spatial statistics domain.
Keyword:
Spatial weather generator
Adversarial network
Convolution neural network
GCM downscaling
Uncertainty analysis
期刊
IF:
3.7
论文数:
8.9K
被引数:
2.9W
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