README.md

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license: apache-2.0
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<img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;">

# HiRO-ACE

The HiRO-ACE framework enables efficient generation of 3 km precipitation fields over decades of simulated climate and arbitrary regions of the globe.
HiRO (High Resolution Output) is a diffusion model which generates downscaled fields at 3 km resolution from 100 km resolution inputs. The HiRO checkpoint included in this model generates 6-hourly averaged surface precipitation rates at 3 km resolution. The Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheric variability from the time scale of days to centuries. For usage with the HiRO downscaling model, we include a checkpoint for ACE2S. Compared to previous ACE models, ACE2S uses an updated training procedure and can generate stochastic predictions. For more details, please see the accompanying HiRO-ACE paper linked below.

### Quick links

- 📃 [Paper](https://arxiv.org/pdf/2512.18224)
- 💻 [Code](https://github.com/ai2cm/ace)
- 💬 [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/)
- 📂 [All ACE Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4)


### 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 ACE inference with `python -m fme.ace.inference inference_config.yaml`.

5. Update paths in the `downscaling_config.yaml`. Specifically, update `experiment_dir`, `model.checkpoint_path`, and `data.coarse`. `data.coarse` data path(s) should point to the saved ACE inference output from step 4. 


### Strengths and weaknesses

#### ACE2S
The strengths of ACE2S are:
- stochastic (generative) emulator of 100km coarsened X-SHiELD 
- improved small scale variability compared to deterministic ACE2 (especially surface precipitation)
- exact conservation of global dry air mass and moisture
- low time-mean biases for most variables 
- compatible with HiRO model with no observed degradation

Some known weaknesses of ACE2S are:
- trained only on 9 years of X-SHiELD (2014-2022)
- not expected to generalize outside of the limited forcing conditions used during training (e.g., SSTs and CO2)
- not suitable for weather forecasting (e.g., not trained on reanalysis data only climate model output)
- model was not tested as rigorously as ACE2-ERA5 (e.g., cannot comment on stratospheric variability, etc) 
- overestimation of tropical cyclone generation compared to X-SHiELD
- some aspects of the pre-training and fine-tuning methodologies did not have ablations
- exactly pre-training and fine-tuning methodologies are subject to potential significant changes for future versions 

## License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).