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README.md

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-NASA and IBM have teamed up to create an AI Foundation Model for Weather and Climate, using [MERRA 2](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/) data. 
-By embracing the principles of open AI and 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. More details on Prithvi can be 
-found in the joint IBM NASA technical paper.
 
-prithvi.wxc.2300m.v1 is the first Weather and Climate Foundation Model. A list of successful downstream applications will be provided soon. Please feel free to use this model
-and submit a PR to link to your downstream application.
+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
+future. The model takes data from two timestamps as input and generates a single, possibly future, timestamp as output. Currently Prithvi WxC comes in two flavors:
+- (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.
 
 ![gravity wave](https://huggingface.co/Prithvi-WxC/Gravity_wave_Parameterization/resolve/a29b9eb40fe3940a0e617a2cea7f9248b6cebc3e/flux_prediction_prithvi_finetuning.gif)
 ![Hurricane Ida](https://huggingface.co/datasets/Prithvi-WxC/Hurricane/resolve/f0adb1f759ba3b416cde1bfb39589e5178048749/2021C4Ida_2021082700.gif)