High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909864240873472 |
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| author | Xiao, Haixia Zhang, Feng Wang, Lingxiao Pan, Baoxiang Zhu, Yannian Wang, Minghuai Li, Wenwen Guo, Bin Li, Jun |
| author_facet | Xiao, Haixia Zhang, Feng Wang, Lingxiao Pan, Baoxiang Zhu, Yannian Wang, Minghuai Li, Wenwen Guo, Bin Li, Jun |
| contents | Clouds play a critical role in Earth's hydrological and energy cycles, and accurately representing their properties is essential for effective numerical modeling and weather forecasting. Machine learning methods have been widely used for cloud property retrieval; however, most existing techniques are deterministic and do not incorporate uncertainty quantification. Generative machine learning has made significant advances in various domains, including natural language processing, image generation, and notably weather forecasting, where it has enabled ensemble predictions and the quantification of forecast uncertainty. This ability to quantify uncertainty offers valuable opportunities for cloud remote sensing. In this study, we propose a novel cloud property retrieval method, CloudDiff, based on a generative diffusion model. By leveraging thermal infrared observations from the Himawari-8 Advanced Himawari Imager (AHI), CloudDiff generates high spatiotemporal resolution cloud properties for both daytime and nighttime conditions, increasing the resolution of Himawari-8/AHI cloud retrievals from 2 km to 1 km. Unlike deterministic retrieval methods, CloudDiff generates multiple samples from the underlying probability distribution, allowing for a diverse range of plausible retrievals and taking steps towards providing uncertainty assessment. Additionally, CloudDiff produces sharper samples and better captures fine local features, enhancing the precision of cloud property retrieval. By averaging over the ensemble of generated samples, we demonstrate that both the accuracy and reliability of the retrievals are significantly improved. These high-resolution cloud properties have been successfully applied to analyze extreme weather events, such as typhoons, providing potentially valuable insights into atmospheric processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite Xiao, Haixia Zhang, Feng Wang, Lingxiao Pan, Baoxiang Zhu, Yannian Wang, Minghuai Li, Wenwen Guo, Bin Li, Jun Atmospheric and Oceanic Physics Clouds play a critical role in Earth's hydrological and energy cycles, and accurately representing their properties is essential for effective numerical modeling and weather forecasting. Machine learning methods have been widely used for cloud property retrieval; however, most existing techniques are deterministic and do not incorporate uncertainty quantification. Generative machine learning has made significant advances in various domains, including natural language processing, image generation, and notably weather forecasting, where it has enabled ensemble predictions and the quantification of forecast uncertainty. This ability to quantify uncertainty offers valuable opportunities for cloud remote sensing. In this study, we propose a novel cloud property retrieval method, CloudDiff, based on a generative diffusion model. By leveraging thermal infrared observations from the Himawari-8 Advanced Himawari Imager (AHI), CloudDiff generates high spatiotemporal resolution cloud properties for both daytime and nighttime conditions, increasing the resolution of Himawari-8/AHI cloud retrievals from 2 km to 1 km. Unlike deterministic retrieval methods, CloudDiff generates multiple samples from the underlying probability distribution, allowing for a diverse range of plausible retrievals and taking steps towards providing uncertainty assessment. Additionally, CloudDiff produces sharper samples and better captures fine local features, enhancing the precision of cloud property retrieval. By averaging over the ensemble of generated samples, we demonstrate that both the accuracy and reliability of the retrievals are significantly improved. These high-resolution cloud properties have been successfully applied to analyze extreme weather events, such as typhoons, providing potentially valuable insights into atmospheric processes. |
| title | High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2405.04483 |