A Physics-Informed Machine Learning Approach utilizing Multiband Satellite Data for Solar Irradiance Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929410431516672 |
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| author | Sasaki, Jun Okada, Maki Utsunomiya, Kenji Yamaguchi, Koji |
| author_facet | Sasaki, Jun Okada, Maki Utsunomiya, Kenji Yamaguchi, Koji |
| contents | Solar irradiance is fundamental data crucial for analyses related to weather and climate. High-precision estimation models are necessary to create areal data for solar irradiance. In this study, we developed a novel estimation model by utilizing machine learning and multiband data from meteorological satellite observations. Particularly under clear-sky and thin clouds, satellite observations can be influenced by surface reflections, which may lead to overfitting to ground observations. To make the model applicable at any location, we constructed the model incorporating prior information such as radiative transfer models and clear-sky probability, based on physical and meteorological knowledge. As a result, the estimation accuracy significantly improved at validation sites. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Physics-Informed Machine Learning Approach utilizing Multiband Satellite Data for Solar Irradiance Estimation Sasaki, Jun Okada, Maki Utsunomiya, Kenji Yamaguchi, Koji Atmospheric and Oceanic Physics Solar irradiance is fundamental data crucial for analyses related to weather and climate. High-precision estimation models are necessary to create areal data for solar irradiance. In this study, we developed a novel estimation model by utilizing machine learning and multiband data from meteorological satellite observations. Particularly under clear-sky and thin clouds, satellite observations can be influenced by surface reflections, which may lead to overfitting to ground observations. To make the model applicable at any location, we constructed the model incorporating prior information such as radiative transfer models and clear-sky probability, based on physical and meteorological knowledge. As a result, the estimation accuracy significantly improved at validation sites. |
| title | A Physics-Informed Machine Learning Approach utilizing Multiband Satellite Data for Solar Irradiance Estimation |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2407.04283 |