Graph Machine Learning
AnemoI
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@@ -57,19 +57,19 @@ and direct observational data.
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  To generate a new forecast using AIFS, you can use [anemoi-inference](https://github.com/ecmwf/anemoi-inference). In the [following notebook](run_AIFS_v0_2_1.ipynb), a
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  step-by-step workflow is specified to run the AIFS using the HuggingFace model:
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- - 1. Install Required Packages and Imports
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- - 2. Retrieve Initial Conditions from ECMWF Open Data
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  - Select a date
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  - Get the data from the [ECMWF Open Data API](https://www.ecmwf.int/en/forecasts/datasets/open-data)
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  - Get input fields
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  - Add the single levels fields and pressure levels fields
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  - Convert geopotential height into greopotential
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  - Create the initial state
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- 3. Load the Model and Run the Forecast
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  - Download the Model's Checkpoint from Hugging Face
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  - Create a runner
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  - Run the forecast using anemoi-inference
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- 4. Inspect the generated forecast
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  - Plot a field
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  To generate a new forecast using AIFS, you can use [anemoi-inference](https://github.com/ecmwf/anemoi-inference). In the [following notebook](run_AIFS_v0_2_1.ipynb), a
58
  step-by-step workflow is specified to run the AIFS using the HuggingFace model:
59
 
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+ 1. **Install Required Packages and Imports**
61
+ 2. **Retrieve Initial Conditions from ECMWF Open Data**
62
  - Select a date
63
  - Get the data from the [ECMWF Open Data API](https://www.ecmwf.int/en/forecasts/datasets/open-data)
64
  - Get input fields
65
  - Add the single levels fields and pressure levels fields
66
  - Convert geopotential height into greopotential
67
  - Create the initial state
68
+ 3. **Load the Model and Run the Forecast**
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  - Download the Model's Checkpoint from Hugging Face
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  - Create a runner
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  - Run the forecast using anemoi-inference
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+ 4. **Inspect the generated forecast**
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  - Plot a field
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