--- license: apache-2.0 library_name: fme --- Logo for the ACE Project # HiRO-ACEv2 The HiRO-ACEv2 framework enables efficient generation of 3 km precipitation, 10 m eastward and northward winds, and sea level pressure 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 HiROv2 checkpoint included in this model generates 6-hourly averaged surface precipitation rates at 3 km resolution, and instantaneous 10 m eastward and northward winds and sea level pressure at 6-hour frequency. 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 HiROv2 downscaling model, we include a checkpoint for ACE2S-X-SHiELD. For more details, please see the accompanying HiRO-ACEv2 paper linked below. ### Quick links - 📃 [Paper](http://arxiv.org/abs/2610.17693) - 💻 [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. Install code dependencies with `pip install fme`. 3. Update paths in the ACE inference config file `ace2s_inference_config_global.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`. 4. Update paths in the HiROv2 downscaling inference config file `hiro_downscaling_ace2s_pnw_output.yaml`. Specifically, update `experiment_dir`, `model.checkpoint_path`, and `data.coarse`. The directory path in `data.coarse` should point to the same ACE inference `experiment_dir` from step 3. Optionally, if you wish to change the region and/or time selection of the area(s) to downscale you may edit those in the downscaling config (see [downscaling inference docs](https://ai2-climate-emulator.readthedocs.io/en/latest/downscaling_inference.html) for more details). An example of global downscaling is also provided in `hiro_downscaling_ace2s_global_output.yaml`. 4. Run the script `run-hiro-ace.sh`. ### Strengths and weaknesses #### ACE2S-X-SHiELD The strengths of ACE2S-X-SHiELD 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 relative to the 10-year training data available - compatible with HiRO model with no observed degradation - similar tropical cyclone generation to X-SHiELD Some known weaknesses of ACE2S-X-SHiELD 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., past/future SSTs and CO2) - may not produce statistically independent and representative samples of the base X-SHiELD model when run for significantly longer than 9 years - 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) - 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 #### HiROv2 The strengths of HiROv2 are: - Quickly generates surface precipitation rate, 10 m eastward and northward winds, and sea level almost anywhere on the globe analogous to X-SHiELD 3 km output - Reproduces surface precipitation rate, 10 m winds, and sea level pressure distribution of X-SHiELD out to the 99.9999th percentile and generates realistic fields for snapshots of events with high regional impacts (ex. tropical cyclones, downscaled winds, atmospheric rivers) - Low time-mean biases against X-SHiELD - Recovers time-mean characteristics of precipitation related to topography - Stochastic for easy ensemble generation of small-scale variability Some known weaknesses of HiROv2 are: - Not expected to generalize to climates outside of the 10-year training dataset - Only produces precipitation, wind, and pressure features representative of X-SHiELD data, not observations - Not trained or tested outside of 66S -- 70N, and cannot run outside of 88S or 88N - Downscaling regions larger than the 16x16 1-degree patch size used for training will have discontinuities for patch overlaps of 0 or some blending artifacting from averaging in the overlap region for overlaps > 0 ## 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). ## Attribution This model is trained using data processed from X-SHIELD (eXperimental System for High-resolution prediction on Earth-to-Local Domains) provided by the Geophysical Fluid Dynamics Laboratory (GFDL).