Graph Machine Learning
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  ---
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- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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- # Doc / guide: https://huggingface.co/docs/hub/model-cards
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  license: cc-by-sa-4.0
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-
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-
 
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  ---
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- # Model Card for {{ model_id | default("Model ID", true) }}
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  <!-- Provide a quick summary of what the model is/does. -->
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- {{ model_summary | default("", true) }}
 
 
 
 
 
 
 
 
 
 
 
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  ## Model Details
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  ## Model Card Contact
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- {{ model_card_contact | default("[More Information Needed]", true)}}
 
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  ---
 
 
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  license: cc-by-sa-4.0
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+ metrics:
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+ - mse
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+ pipeline_tag: graph-ml
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  ---
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+ # AIFS Single - v0.2.1
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  <!-- Provide a quick summary of what the model is/does. -->
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+ Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast
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+ model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).
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+
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+ AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor,
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+ and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses.
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+ It has a flexible and modular design and supports several levels of parallelism to enable training on
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+ high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses
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+ and direct observational data.
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+
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+ We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and
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+ tropical cyclone tracks. AIFS is run four times daily alongside ECMWF’s physics-based NWP model and forecasts
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+ are available to the public under ECMWF’s open data policy.
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  ## Model Details
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  ## Model Card Contact
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+ {{ model_card_contact | default("[More Information Needed]", true)}}