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README.md
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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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- 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
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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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| 66 |
- Convert geopotential height into greopotential
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| 67 |
- Create the initial state
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| 68 |
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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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