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docs: clarify AIFL is part of ECMWF's wider ML activities
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README.md
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@@ -19,6 +19,7 @@ This work builds on ECMWF's own ML efforts with its Member States, in particular
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- [Anemoi](https://anemoi.readthedocs.io), the open-source ML framework now powering several state-of-the-art data-driven forecasting models in Europe.
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- The [Artificial Intelligence Forecasting System (AIFS)](https://huggingface.co/ecmwf), ECMWF's operational AI-based weather forecasting system.
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## Objectives
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| [Land](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-land/) | **aiLand**, a global deep learning emulator of land surface processes, trained on ECMWF's physical land model. |
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| [Waves](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-waves/) | An AI wave model trained on over 40 years of ECMWF physical wave model output, driven by the AIFS. |
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| [Hydrology](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-hydrology/) |
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| [Sea ice](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-sea-ice/) | Learns sea ice and surface ocean evolution from decades of reanalysis data, capturing seasonal cycles and ice-edge behaviour. |
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| [Ocean](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-oceans/) | Models the full-depth ocean: temperature, salinity, currents and sea surface height anomaly. |
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- [Anemoi](https://anemoi.readthedocs.io), the open-source ML framework now powering several state-of-the-art data-driven forecasting models in Europe.
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- The [Artificial Intelligence Forecasting System (AIFS)](https://huggingface.co/ecmwf), ECMWF's operational AI-based weather forecasting system.
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- [AIFL (AI for Flood Forecasting)](https://huggingface.co/ecmwf), ECMWF's global deep learning model for daily streamflow at catchment scale, including in ungauged basins.
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## Objectives
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| --- | --- |
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| [Land](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-land/) | **aiLand**, a global deep learning emulator of land surface processes, trained on ECMWF's physical land model. |
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| [Waves](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-waves/) | An AI wave model trained on over 40 years of ECMWF physical wave model output, driven by the AIFS. |
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| [Hydrology](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-hydrology/) | Builds on ECMWF's [AIFL](https://huggingface.co/ecmwf) model, developed as part of ECMWF's wider ML activities. |
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| [Sea ice](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-sea-ice/) | Learns sea ice and surface ocean evolution from decades of reanalysis data, capturing seasonal cycles and ice-edge behaviour. |
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| [Ocean](https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-oceans/) | Models the full-depth ocean: temperature, salinity, currents and sea surface height anomaly. |
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