Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908927704170496 |
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| author | Lieberman, Kelsey Slivinski, Laura Bender, Matt Miller, Chris DaRosa, Josh Krall, Nick Alam, Mohammad Ridhwaan Silverman, Nick Frolov, Sergey |
| author_facet | Lieberman, Kelsey Slivinski, Laura Bender, Matt Miller, Chris DaRosa, Josh Krall, Nick Alam, Mohammad Ridhwaan Silverman, Nick Frolov, Sergey |
| contents | Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This study expands previous work to show the potential for integrating observation operators into artificial intelligence (AI) weather forecasting models. Specifically, this study shows that (1) deep-learned models can effectively predict the innovations (or differences between the simulated and observed radiances) used by data assimilation models and (2) deep-learned observation models suffer only minor degradations in performance when the model state is represented with fewer vertical levels, as is commonly used by AI forecasting models. Experiments were performed using the Unified Forecast System (UFS) replay dataset, including Gridpoint Statistical Interpolation (GSI) observational data for the Advanced Technology Microwave Sounder (ATMS) sensor from 2022 and 2023. Code is available at https://github.com/mitre/deep-obs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00082 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models Lieberman, Kelsey Slivinski, Laura Bender, Matt Miller, Chris DaRosa, Josh Krall, Nick Alam, Mohammad Ridhwaan Silverman, Nick Frolov, Sergey Atmospheric and Oceanic Physics Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This study expands previous work to show the potential for integrating observation operators into artificial intelligence (AI) weather forecasting models. Specifically, this study shows that (1) deep-learned models can effectively predict the innovations (or differences between the simulated and observed radiances) used by data assimilation models and (2) deep-learned observation models suffer only minor degradations in performance when the model state is represented with fewer vertical levels, as is commonly used by AI forecasting models. Experiments were performed using the Unified Forecast System (UFS) replay dataset, including Gridpoint Statistical Interpolation (GSI) observational data for the Advanced Technology Microwave Sounder (ATMS) sensor from 2022 and 2023. Code is available at https://github.com/mitre/deep-obs. |
| title | Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2604.00082 |