Full Model Emulation
File size: 2,225 Bytes
f760fd3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
---
license: apache-2.0
---

<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

<!-- will leave to Andre and Troy to decide what to list here -->

## 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).