The real-time data processing and lightning extraction technology of lightning imager on FY-4 meteorological satellite

Zhang, Shoucai; Bao, Shulong; Liang, Hua; Wang, Kun; Qin, Zidong

doi:10.1117/12.2505573

Cited by 1 publication

(2 citation statements)

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“…On the other hand, infrared channels offer observations in all weather conditions (G. Q. Wang, 2004). 9-11 channel of FY4A provides comprehensive data on atmospheric water vapor across different vertical layers.…”

Section: Fy4a Satellite Datamentioning

confidence: 99%

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AttUnet_R_SFT: A Novel Network to Explore the Application of Complex Terrain Information in Satellite Precipitation Estimating

Zhang,

Zhou,

Guan

et al. 2024

Earth and Space Science

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Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research.

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Section: Fy4a Satellite Datamentioning

confidence: 99%

“…FY4 has a higher spatial resolution for visible light channels, but they are only accessible during daylight hours. On the other hand, infrared channels offer observations in all weather conditions (G. Q. Wang, 2004). 9–11 channel of FY4A provides comprehensive data on atmospheric water vapor across different vertical layers.…”

Section: Datamentioning

confidence: 99%