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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.15706 |
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| _version_ | 1866914047102812160 |
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| author | Yang, Chi Wang, Fu Yang, Xiaofei Huang, Hao Cao, Weijia Chu, Xiaowen |
| author_facet | Yang, Chi Wang, Fu Yang, Xiaofei Huang, Hao Cao, Weijia Chu, Xiaowen |
| contents | Cloud phase profiles are critical for numerical weather prediction (NWP), as they directly affect radiative transfer and precipitation processes. In this study, we present a benchmark dataset and a baseline framework for transforming multimodal satellite observations into detailed 3D cloud phase structures, aiming toward operational cloud phase profile retrieval and future integration with NWP systems to improve cloud microphysics parameterization. The multimodal observations consist of (1) high--spatiotemporal--resolution, multi-band visible (VIS) and thermal infrared (TIR) imagery from geostationary satellites, and (2) accurate vertical cloud phase profiles from spaceborne lidar (CALIOP\slash CALIPSO) and radar (CPR\slash CloudSat). The dataset consists of synchronized image--profile pairs across diverse cloud regimes, defining a supervised learning task: given VIS/TIR patches, predict the corresponding 3D cloud phase structure. We adopt SGMAGNet as the main model and compare it with several baseline architectures, including UNet variants and SegNet, all designed to capture multi-scale spatial patterns. Model performance is evaluated using standard classification metrics, including Precision, Recall, F1-score, and IoU. The results demonstrate that SGMAGNet achieves superior performance in cloud phase reconstruction, particularly in complex multi-layer and boundary transition regions. Quantitatively, SGMAGNet attains a Precision of 0.922, Recall of 0.858, F1-score of 0.763, and an IoU of 0.617, significantly outperforming all baselines across these key metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SGMAGNet: A Baseline Model for 3D Cloud Phase Structure Reconstruction on a New Passive Active Satellite Benchmark Yang, Chi Wang, Fu Yang, Xiaofei Huang, Hao Cao, Weijia Chu, Xiaowen Computer Vision and Pattern Recognition Artificial Intelligence Atmospheric and Oceanic Physics Cloud phase profiles are critical for numerical weather prediction (NWP), as they directly affect radiative transfer and precipitation processes. In this study, we present a benchmark dataset and a baseline framework for transforming multimodal satellite observations into detailed 3D cloud phase structures, aiming toward operational cloud phase profile retrieval and future integration with NWP systems to improve cloud microphysics parameterization. The multimodal observations consist of (1) high--spatiotemporal--resolution, multi-band visible (VIS) and thermal infrared (TIR) imagery from geostationary satellites, and (2) accurate vertical cloud phase profiles from spaceborne lidar (CALIOP\slash CALIPSO) and radar (CPR\slash CloudSat). The dataset consists of synchronized image--profile pairs across diverse cloud regimes, defining a supervised learning task: given VIS/TIR patches, predict the corresponding 3D cloud phase structure. We adopt SGMAGNet as the main model and compare it with several baseline architectures, including UNet variants and SegNet, all designed to capture multi-scale spatial patterns. Model performance is evaluated using standard classification metrics, including Precision, Recall, F1-score, and IoU. The results demonstrate that SGMAGNet achieves superior performance in cloud phase reconstruction, particularly in complex multi-layer and boundary transition regions. Quantitatively, SGMAGNet attains a Precision of 0.922, Recall of 0.858, F1-score of 0.763, and an IoU of 0.617, significantly outperforming all baselines across these key metrics. |
| title | SGMAGNet: A Baseline Model for 3D Cloud Phase Structure Reconstruction on a New Passive Active Satellite Benchmark |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2509.15706 |