Prithvi WxC: Foundation Model for Weather and Climate
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| Format: | Preprint |
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2024
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| _version_ | 1866917780453851136 |
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| author | Schmude, Johannes Roy, Sujit Trojak, Will Jakubik, Johannes Civitarese, Daniel Salles Singh, Shraddha Kuehnert, Julian Ankur, Kumar Gupta, Aman Phillips, Christopher E Kienzler, Romeo Szwarcman, Daniela Gaur, Vishal Shinde, Rajat Lal, Rohit Da Silva, Arlindo Diaz, Jorge Luis Guevara Jones, Anne Pfreundschuh, Simon Lin, Amy Sheshadri, Aditi Nair, Udaysankar Anantharaj, Valentine Hamann, Hendrik Watson, Campbell Maskey, Manil Lee, Tsengdar J Moreno, Juan Bernabe Ramachandran, Rahul |
| author_facet | Schmude, Johannes Roy, Sujit Trojak, Will Jakubik, Johannes Civitarese, Daniel Salles Singh, Shraddha Kuehnert, Julian Ankur, Kumar Gupta, Aman Phillips, Christopher E Kienzler, Romeo Szwarcman, Daniela Gaur, Vishal Shinde, Rajat Lal, Rohit Da Silva, Arlindo Diaz, Jorge Luis Guevara Jones, Anne Pfreundschuh, Simon Lin, Amy Sheshadri, Aditi Nair, Udaysankar Anantharaj, Valentine Hamann, Hendrik Watson, Campbell Maskey, Manil Lee, Tsengdar J Moreno, Juan Bernabe Ramachandran, Rahul |
| contents | Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prithvi WxC: Foundation Model for Weather and Climate Schmude, Johannes Roy, Sujit Trojak, Will Jakubik, Johannes Civitarese, Daniel Salles Singh, Shraddha Kuehnert, Julian Ankur, Kumar Gupta, Aman Phillips, Christopher E Kienzler, Romeo Szwarcman, Daniela Gaur, Vishal Shinde, Rajat Lal, Rohit Da Silva, Arlindo Diaz, Jorge Luis Guevara Jones, Anne Pfreundschuh, Simon Lin, Amy Sheshadri, Aditi Nair, Udaysankar Anantharaj, Valentine Hamann, Hendrik Watson, Campbell Maskey, Manil Lee, Tsengdar J Moreno, Juan Bernabe Ramachandran, Rahul Machine Learning Atmospheric and Oceanic Physics Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face. |
| title | Prithvi WxC: Foundation Model for Weather and Climate |
| topic | Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2409.13598 |