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| title: README | |
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| ## Welcome to Destination Earth on Hugging Face! | |
| [Destination Earth (DestinE)](https://destine.ecmwf.int) is a flagship initiative of the European Commission to develop a highly accurate digital model of the Earth. It is implemented by [ECMWF](https://www.ecmwf.int), [ESA](https://www.esa.int) and [EUMETSAT](https://www.eumetsat.int). | |
| This organisation hosts the artificial intelligence (AI) and machine learning (ML) work led by ECMWF and its partners within DestinE. AI and ML play an increasingly central role in DestinE: ML-driven models and AI-based software boost the ability to quantify uncertainty in digital twin simulations and make the DestinE system more interactive, using the supercomputing infrastructure of the [EuroHPC Joint Undertaking](https://eurohpc-ju.europa.eu). | |
| This work builds on ECMWF's own ML efforts with its Member States, in particular: | |
| - [Anemoi](https://anemoi.readthedocs.io), the open-source ML framework now powering several state-of-the-art data-driven forecasting models in Europe. | |
| - The [Artificial Intelligence Forecasting System (AIFS)](https://huggingface.co/ecmwf), ECMWF's operational AI-based weather forecasting system. | |
| - [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. | |
| ## Objectives | |
| In Phase II of DestinE, AI activities focus on: | |
| - Developing ML-based Earth system model components, working towards a European ML-based Earth system model. | |
| - Enhancing the [Digital Twin Engine](https://destine.ecmwf.int/digital-twin-engine/) to support training and running ML models on EuroHPC systems. | |
| - Applying AI to support uncertainty quantification of digital twin simulations. | |
| - Using AI to make the system more interactive and digital twin data easier to use. | |
| ## ML Earth system components | |
| ECMWF is developing [ML-based Earth system components](https://destine.ecmwf.int/ml-earth-system-components/) that will be coupled with the AIFS to form a fully AI-driven Earth system model: | |
| | Component | Description | | |
| | --- | --- | | |
| | [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. | | |
| | [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. | | |
| | [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. | | |
| | [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. | | |
| | [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. | | |
| ## Learn more | |
| - [AI in DestinE](https://destine.ecmwf.int/ai-in-destination-earth/): news, videos and podcasts on AI and ML in DestinE. | |
| - [AI in DestinE: the explainer](https://destine.ecmwf.int/artificial-intelligence-in-destine-the-explainer/) | |
| - [Forecast-in-a-Box](https://destine.ecmwf.int/news/forecast-in-a-box-portable-ai-forecasting-workflows-within-the-destine-digital-twin-engine/): portable AI forecasting workflows within the Digital Twin Engine. | |
| - [AIFS blog series](https://www.ecmwf.int/en/about/media-centre/aifs-blog) | |
| ## Get involved | |
| - **Training**: ECMWF offers advanced ML and AI training, including the Machine Learning for Earth System Modelling course series. See the [ML training page](https://destine.ecmwf.int/ml-training/). | |
| - **AI Weather Quest**: an international competition to advance weather forecasting with AI and ML. See the [AI Weather Quest](https://destine.ecmwf.int/ai-weather-quest/). | |
| - **Tenders**: ECMWF regularly opens invitations to tender for AI and ML development in DestinE. See [DestinE procurement](https://www.ecmwf.int/en/about/suppliers/destine-procurement/update-destine-itts) and the [opportunities to be involved](https://destine.ecmwf.int/opportunities-to-be-involved/). | |
| ## Support | |
| For questions about DestinE, please use the [DestinE contact page](https://destine.ecmwf.int/contact/). For the ECMWF tools behind this work: | |
| - To follow announcements and discuss with other users and ECMWF staff, visit the [ECMWF User Forum](https://forum.ecmwf.int/). | |
| - To report a bug or contribute to Anemoi, open a GitHub issue in line with the [contribution guidelines](https://anemoi.readthedocs.io/en/latest/contributing/contributing.html). | |
| If you spot an issue specific to our entries on Hugging Face, you may raise it through Hugging Face discussions. Please note that we do not guarantee support through Hugging Face. | |
| --- | |
| Destination Earth is funded by the European Union and implemented by ECMWF, ESA and EUMETSAT. | |