Update README.md

README.md

@@ -9,6 +9,24 @@ tags:
 - IBM
 - MERRA2
 ---
+NASA and IBM have teamed up to create an AI Foundation Model for Weather and Climate - [Prithvi WxC](https://huggingface.co/Prithvi-WxC/prithvi.wxc.2300m.v1), using [MERRA-2](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/) data.
+By embracing the principles of open science, both organizations are actively contributing to the global mission of promoting knowledge sharing and accelerating 
+innovations in addressing critical environmental challenges. With Hugging Face’s platform, they simplify model training and deployment, making it accessible for 
+open science users, startups, and enterprises on multi-cloud AI platforms like [watsonx](https://www.ibm.com/watsonx). 
+Additionally, Hugging Face enables easy sharing of the pipelines of the model family, which our team calls Prithvi WxC, within the community, fostering global 
+collaboration and engagement.
+Prithvi WxC has been fine-tuned for several tasks including climate downscaling (released as part of the enterprise-grade [IBM granite](https://huggingface.co/ibm-granite) family of models) and 
+gravity wave parameterization. More details on Prithvi WxC can be found in the joint [IBM NASA technical paper](https://arxiv.org/abs/2409.13598).
+
+Current downstreams tasks of PrithviWxC are (please feel free to submit a PR if you want to add yours):  
+[Downscaling](https://huggingface.co/ibm-granite/granite-geospatial-wxc-downscaling) (IBM Granite Model) <b><i>>>>[Try it on Colab](https://colab.research.google.com/github/IBM/granite-wxc/blob/4d14cd19ec86ae1a5d32c311e6362fb1d42ff693/examples/granitewxc_downscaling/notebooks/granitewxc_downscaling_inference.ipynb)<<<</i></b> (Please select the T4 GPU runtime)  
+[Gravity Wave](https://github.com/NASA-IMPACT/gravity-wave-finetuning)  
+
+
+More information:[Paper](https://arxiv.org/abs/2409.13598), [Code](https://github.com/NASA-IMPACT/Prithvi-WxC), [Model V1](https://huggingface.co/Prithvi-WxC/prithvi.wxc.2300m.v1)
+PRs: [prithvi-weather-climate-foundation-model (NASA)](https://www.earthdata.nasa.gov/learn/blog/prithvi-weather-climate-foundation-model-background-benefits), [weather-climate-foundation-model (IBM)](https://research.ibm.com/blog/weather-climate-foundation-model)
+[nasa-ibm-weather-climate-foundation-model (NASA)](https://www.earthdata.nasa.gov/learn/blog/nasa-ibm-weather-climate-foundation-model)
+
 
 Prithvi WxC is a 2.3 billion parameter model trained on 160 different variables from MERRA-2 data. It has been pretrained on both forecasting and masked
 reconstruction objectives. I.e.~the model is capable of reconstructing atmospheric state from partial information as well as propagating state into the
@@ -16,16 +34,24 @@ future. The model takes data from two timestamps as input and generates a single
 - (This model) `prithvi.wxc.2300m.v1` has been pretrained with a 50% masking ratio. The time delta between input timestamps is variable as is the forecast lead time.
   During pretraining, the input delta was chosen from [-3, -6, -9, -12] hours while the forecast lead time was chosen from [0, 6, 12, 24] hours. We recommend using
   `prithvi.wxc.2300m.v1` for generic use cases that do not focus on forecasting.
+- `prithvi.wxc.rollout.2300m.v1` has been through further training cycles to be optimzed for autoregressive rollout. Here, we restricted the input delta
+  as well as the lead time to 6 hours. We recommend using `prithvi.wxc.rollout.2300m.v1` for forecasting applications.
 
- <b> Zero-shot reconstruction </b>
 <div style="display: flex; justify-content: center;">
+<b> Zero-shot reconstruction </b>
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/6488f1d3e22a0081a561ec8f/ftaxww2youmdS8XER31RC.png" alt="Reconstruction" width="1024"/>
 </div>
 
-Current downstream tasks of PrithviWxC are (please feel free to submit a PR if you want to add yours):  
-[Downscaling](https://huggingface.co/ibm-granite/granite-geospatial-wxc-downscaling) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [<b><i>>>Try it on Colab<<</i></b> (Please select the T4 GPU runtime)](https://colab.research.google.com/github/IBM/granite-wxc/blob/4d14cd19ec86ae1a5d32c311e6362fb1d42ff693/examples/granitewxc_downscaling/notebooks/granitewxc_downscaling_inference.ipynb)  
-[Gravity Wave](https://github.com/NASA-IMPACT/gravity-wave-finetuning)  
+<div style="display: flex; justify-content: center;">
+
+<b>Gravity Wave</b>
+<img src="https://huggingface.co/Prithvi-WxC/Gravity_wave_Parameterization/resolve/20f8a120752b4e48364a2a606d6d2db26b2aa8b9/prithvi_downstream_gwflux_animation.gif" alt="Gravity Wave" width="512"/>
+
+<b>Hurricane Ida - Zero-Shot Rollout</b>
+<img src="https://huggingface.co/datasets/Prithvi-WxC/Hurricane/resolve/6edc7c6838d59c1694755508917dce7a203fb9e8/2021C4Ida_2021082700_ground_truth_prediction.gif" alt="Hurricane Ida" width="475"/>
+
+</div>
 
 ## Citation
 If you use this work, consider citing our paper