Deep Learning Reconstruction Method of Meteorological Radar Echo Data based on Satellite Data

Su, Jiang; Huang, Yunxin; Zhang, Fuhan

doi:10.1145/3444370.3444550

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies 2020

DOI: 10.1145/3444370.3444550

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Deep Learning Reconstruction Method of Meteorological Radar Echo Data based on Satellite Data

Jiang Su

Yunxin Huang

Fuhan Zhang

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2024

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Cited by 2 publications

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“…Hu et al [6] present a deep learning architecture consisting of four U-Net sub-networks for precipitation bias correction. Jiang et al [7] propose a deep learning-based method to supplement the missing radar echo data by reconstruction from satellite data. By combining model-data-knowledge-driven and deep learning techniques, Wang et al [8] suggest a method to overcome the illconditioned problem of land surface temperature retrieval by combining model-data-knowledge-driven and deep learning.…”

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Deep Learning for Near-Surface Air Temperature Estimation From FengYun 4A Satellite Data

Yang,

Ren,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Near-surface air temperature is a crucial weather parameter that significantly impacts human health and is widely utilized in numerical weather forecasting and climate prediction studies. However, the most common ground-based meteorological station observation and radar observation are often limited by geographic and natural constraints. With the advantages of global coverage and high spatiotemporal resolution, satellite remote sensing has become a valuable support in overcoming data scarcity issues related to ground-based station and radar observations in complex geographic and natural conditions. Although remote sensing indirectly reflects atmosphere variables (e.g., near-surface air temperature), accurately estimating the atmosphere variables through satellite remote sensing remains a significant challenge. This paper introduces a deep learning Transformer-based neural network (TaNet) for near-surface air temperature estimation. TaNet automatically extracts information from imageries captured by China's new-generation geostationary meteorological satellite FengYun-4A and generates grid near-surface air temperature data in near real-time. Extensive experiments conducted using the state-of-the-art operational reanalysis product ERA5 and meteorological station observations as benchmark standards demonstrate the effectiveness and superiority of TaNet. It achieves an impressive Pearson's correlation coefficient (CC) of 0.990 with ERA5 and 0.959 with station observations, outperforming the other products, such as CFSv2, CRA, and U-Net, on root mean square error (RMSE) and CC metrics. TaNet reduces the RMSE of CFSv2, CRA, and U-Net by a margin of 10.

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Deep Learning for Near-Surface Air Temperature Estimation From FengYun 4A Satellite Data

Yang,

Ren,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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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Deep Learning Reconstruction Method of Meteorological Radar Echo Data based on Satellite Data

Cited by 2 publications

References 6 publications

Deep Learning for Near-Surface Air Temperature Estimation From FengYun 4A Satellite Data

Deep Learning for Near-Surface Air Temperature Estimation From FengYun 4A Satellite Data

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

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