Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915039569510400 |
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| author | Sunil, Akshay Deepthi, B Ganjir, Gaurav Rashid, Muhammed Sreedhar, Rahul S, Adarsh |
| author_facet | Sunil, Akshay Deepthi, B Ganjir, Gaurav Rashid, Muhammed Sreedhar, Rahul S, Adarsh |
| contents | The growing adoption of machine learning (ML) in modelling atmospheric and oceanic processes offers a promising alternative to traditional numerical methods. It is essential to benchmark the performance of both ML and physics-informed ML (PINN) models to evaluate their predictive skill, particularly for short- to medium-term forecasting. In this study, we utilize gridded sea surface temperature (SST) data and six atmospheric predictors (cloud cover, relative humidity, solar radiation, surface pressure, u-component of velocity, and v-component of velocity) to capture both spatial and temporal patterns in SST predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19031 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Sunil, Akshay Deepthi, B Ganjir, Gaurav Rashid, Muhammed Sreedhar, Rahul S, Adarsh Atmospheric and Oceanic Physics The growing adoption of machine learning (ML) in modelling atmospheric and oceanic processes offers a promising alternative to traditional numerical methods. It is essential to benchmark the performance of both ML and physics-informed ML (PINN) models to evaluate their predictive skill, particularly for short- to medium-term forecasting. In this study, we utilize gridded sea surface temperature (SST) data and six atmospheric predictors (cloud cover, relative humidity, solar radiation, surface pressure, u-component of velocity, and v-component of velocity) to capture both spatial and temporal patterns in SST predictions. |
| title | Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2411.19031 |