fix the READMEs

Author: brianhenn

PACKAGE_README.md

@@ -1,11 +1,8 @@
 [![Docs](https://readthedocs.org/projects/ai2-climate-emulator/badge/?version=latest)](https://ai2-climate-emulator.readthedocs.io/en/latest/)
+[![PyPI](https://img.shields.io/pypi/v/fme.svg)](https://pypi.org/project/fme/)
 
 # FME: Weather/Climate Model Emulation
-This package contains code to train and evaluate weather/climate model emulators as seen in
-"ACE: A fast, skillful learned global atmospheric model for climate prediction" ([arxiv:2310.02074](https://arxiv.org/abs/2310.02074))
-and "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity"
-([JGR-ML](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JH000136)).
-
+This package contains code to train and evaluate weather/climate model emulators.
 
 ## Installation
 

README.md

@@ -1,72 +1,29 @@
-# ACE: AI2 Climate Emulator
-This repo contains the inference code accompanying "ACE: A fast, skillful learned global atmospheric model for climate prediction" ([arxiv:2310.02074](https://arxiv.org/abs/2310.02074)).
+[![Docs](https://readthedocs.org/projects/ai2-climate-emulator/badge/?version=latest)](https://ai2-climate-emulator.readthedocs.io/en/latest/)
+[![PyPI](https://img.shields.io/pypi/v/fme.svg)](https://pypi.org/project/fme/)
 
-## DISCLAIMER
-This is rapidly changing research software. We make no guarantees of maintaining backwards compatibility.
+<img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;">
 
 # Ai2 Climate Emulator
 
 Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries.
 
-This repo contains code accompanying four papers describing ACE models:
+This repo contains code accompanying five papers describing ACE models:
 - "ACE: A fast, skillful learned global atmospheric model for climate prediction" ([link](https://arxiv.org/abs/2310.02074))
 - "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" ([link](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JH000136))
-- "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" ([link](https://arxiv.org/abs/2411.11268))
+- "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" ([link](https://www.nature.com/articles/s41612-025-01090-0))
 - "ACE2-SOM: Coupling to a slab ocean and learning the sensitivity of climate to changes in CO2" ([link](https://arxiv.org/abs/2412.04418))
+- "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" ([link](https://arxiv.org/abs/2505.08742))
 
-### 1. Install
+## Installation
 
-Clone this repository. Then assuming [conda](https://docs.conda.io/en/latest/)
-is available, run
 ```
-make create_environment
+pip install fme
 ```
-to create a conda environment called `fme` with dependencies and source
-code installed. Alternatively, a Docker image can be built with `make build_docker_image`.
-You may verify installation by running `pytest fme/`.
 
-### 2. Download data and checkpoint
+## Documentation
 
-The checkpoint and a 1-year subsample of the validation data are available at
-[this Zenodo repository](https://zenodo.org/doi/10.5281/zenodo.10791086).
-Download these to your local filesystem.
-
-Alternatively, if interested in the complete dataset, this is available via a public
-[requester pays](https://cloud.google.com/storage/docs/requester-pays)
-Google Cloud Storage bucket. For example, the 10-year validation data (approx. 190GB)
-can be downloaded with:
-```
-gsutil -m -u YOUR_GCP_PROJECT cp -r gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-netCDFs/validation .
-```
-It is possible to download a portion of the dataset only, but it is necessary to have
-enough data to span the desired prediction period. The checkpoint is also available on GCS at
-`gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/checkpoints/ace_ckpt.tar`.
-
-### 3. Update configuration and run
-Update the paths in the [example config](examples/config-inference.yaml). Then in the
-`fme` conda environment, run inference with:
-```
-python -m fme.fcn_training.inference.inference examples/config-inference.yaml
-```
-
-## Configuration options
-See the `InferenceConfig` class in [this file](fme/fme/fcn_training/inference/inference.py) for
-description of configuration options. The [example config](examples/config-inference.yaml)
-shows some useful defaults for performing a 400-step simulation (100 days, with the 6-hour time step).
+See complete documentation [here](https://ai2-climate-emulator.readthedocs.io/en/latest/) and a quickstart guide [here](https://ai2-climate-emulator.readthedocs.io/en/latest/quickstart.html).
 
 ## Model checkpoints
 
-## Analyzing output
-Various climate performance metrics are computed online by the inference code. These can be viewed via
-[wandb](https://wandb.ai) by setting `logging.log_to_wandb` to true and updating `logging.entity`
-to your wandb entity. Additionally, raw output data is saved to netCDF by the inference code.
-
-## Available datasets
-Two versions of the complete dataset described in [arxiv:2310.02074](https://arxiv.org/abs/2310.02074)
-are available on a [requester pays](https://cloud.google.com/storage/docs/requester-pays) Google Cloud Storage bucket:
-```
-gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-zarrs
-gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-netCDFs
-```
-The `zarr` format is convenient for ad-hoc analysis. The netCDF version contains our
-train/validation split which was used for training and inference.
+Pretrained model checkpoints, and datasets to run them, are available in the [ACE Hugging Face](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4) collection.