Update README.md

README.md

@@ -12,11 +12,23 @@ Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheri
 
 ACE2-ERA5 is trained on the [ERA5 dataset](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) and is described in [ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses](https://arxiv.org/abs/2411.11268).
 
-Quick links:
+### Quick links
 - 📃 [Paper](https://arxiv.org/abs/2411.11268)
 - 💻 [Code](https://github.com/ai2cm/ace)
 - 💬 [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/)
 - 📂 [All Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4)
+- 💾 [Training Data](https://console.cloud.google.com/storage/browser/ai2cm-public-requester-pays/2024-11-13-ai2-climate-emulator-v2-amip/data)
+
+### Inference quickstart
+1. Download this repository. Optionally, you can just download a subset of the `forcing_data` and `initial_conditions` for the period you are interested in.
+
+2. Update paths in the `inference_config.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`.
+
+3. Install code dependencies with `pip install fme`.
+
+4. Run inference with `python -m fme.ace.inference inference_config.yaml`.
+
+### Strengths and weaknesses
 
 Briefly, the strengths of ACE2-ERA5 are:
 - accurate atmospheric warming response to combined increase of sea surface temperature and CO2 over last 80 years