How artificial intelligence is transforming weather forecasting for the future

Huang, Jianping; Chen, Bin

doi:10.1360/tb-2024-0100

Chin. Sci. Bull.

2024

DOI: 10.1360/tb-2024-0100

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How artificial intelligence is transforming weather forecasting for the future

Jianping Huang,

Bin Chen

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2024

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Cited by 2 publications

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Large AI models: Pioneering innovations in accurate weather forecasting

Zhang,

Huang,

et al. 2024

Chin. Sci. Bull.

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Large AI models: Pioneering innovations in accurate weather forecasting

Zhang,

Huang,

et al. 2024

Chin. Sci. Bull.

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Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network

Zheng,

Wu,

et al. 2024

Sensors

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To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network’s perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson’s correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.

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How artificial intelligence is transforming weather forecasting for the future

Cited by 2 publications

References 30 publications

Large AI models: Pioneering innovations in accurate weather forecasting

Large AI models: Pioneering innovations in accurate weather forecasting

Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network

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