--- license: apache-2.0 --- Logo for the ACE Project # 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).