2022
DOI: 10.3390/s23010081
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Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning

Abstract: Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s n… Show more

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Cited by 11 publications
(10 citation statements)
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“…To evaluate the performance of the model in areas without radar observations, GPM precipitation data is utilized in the experiment to assess the model's reconstruction of CREF. This was primarily because the precipitation is moderately correlated with radar reflectivity; although GPM cannot fully quantify the effectiveness of CREF reconstruction, it can serve as supplementary information to indicate the distribution of radar reflectivity in the absence of radar coverage [34,43]. Additionally, the precision of GPM precipitation data is relatively high compared to other satellite precipitation products due to the integration of multiple satellites and rain gauge data [44,45].…”
Section: Gpm Datamentioning
confidence: 99%
“…To evaluate the performance of the model in areas without radar observations, GPM precipitation data is utilized in the experiment to assess the model's reconstruction of CREF. This was primarily because the precipitation is moderately correlated with radar reflectivity; although GPM cannot fully quantify the effectiveness of CREF reconstruction, it can serve as supplementary information to indicate the distribution of radar reflectivity in the absence of radar coverage [34,43]. Additionally, the precision of GPM precipitation data is relatively high compared to other satellite precipitation products due to the integration of multiple satellites and rain gauge data [44,45].…”
Section: Gpm Datamentioning
confidence: 99%
“…Environmental science examples: Attention U-nets have been used to estimate radar reflectivity (Yang et al, 2023), precipitation (Trebing et al, 2021; Gao et al, 2022a), and cloud detection (Guo et al, 2020) from satellite imagery. U-Nets with bidirectional LSTM and attention mechanism (Garnot and Landrieu, 2021; Ghosh et al, 2021) leverage features from time-series satellite data to identify temporal patterns of each land cover class and automate land cover classification.…”
Section: A New Generation Of Neural Networkmentioning
confidence: 99%
“…Efforts have been made to merge observations from radars and satellites for the monitoring of convective storms (Mishra, 2018;Kumar et al, 2020). Recent studies have focused on the reconstruction of radar reflectivity for the identification of weather events by merging radar observations with satellite signatures for weather events (Duan et al, 2021;Yang et al, 2023). The present study focuses on the development of reconstructed radar reflectivity by integrating observations from DPR with a recently developed storm index (Mishra et al, 2022) from the Indian geostationary satellite mission INSAT-3D.…”
Section: Introductionmentioning
confidence: 99%