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Co-authored-by: Johannes Schmude <johannesschmude@users.noreply.huggingface.co>

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- # Model card for granite-geospatial-downscaling
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- `granite-geospatial-downscaling` is a fine-tuned foundation model for the downscaling of weather and climate data. It is based on the [Prithvi WxC foundation model](https://huggingface.co/Prithvi-WxC). `granite-geospatial-downscaling` has been used to downscale both MERRA-2 data as well as EURO-CORDEX climate simulations. The weights for the former are included here.
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  <b>6x downscaling of MERRA-2 2m temperature</b>
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  <center><img src="downscaling_T2M_coolwarm_animated.gif" alt="Downscaling of MERRA-2 T2M" width=462></center>
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  ## Architecture
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  From an architecture point of view, we embed Prithvi WxC's transformer layers into a series of convolutional layers. That is, we typically increase resolution before and after the pre-trained transformer stages.
 
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+ # Model card for granite-geospatial-wxc-downscaling
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+ `granite-geospatial-wxc-downscaling` is a fine-tuned foundation model for the downscaling of weather and climate data. It is based on the [Prithvi WxC foundation model](https://huggingface.co/Prithvi-WxC). `granite-geospatial-downscaling` has been used to downscale both MERRA-2 data as well as EURO-CORDEX climate simulations. The weights for the former are included here.
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  <b>6x downscaling of MERRA-2 2m temperature</b>
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  <center><img src="downscaling_T2M_coolwarm_animated.gif" alt="Downscaling of MERRA-2 T2M" width=462></center>
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+ More information: [Code](https://github.com/IBM/granite-wxc), [base model](https://huggingface.co/Prithvi-WxC), paper (to appear).
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  ## Architecture
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  From an architecture point of view, we embed Prithvi WxC's transformer layers into a series of convolutional layers. That is, we typically increase resolution before and after the pre-trained transformer stages.