| --- |
| model_name: calm-coriolis |
|
|
| base_model: met-no/bris-forecaster-pretrained |
| base_model_relation: finetune |
|
|
| license: apache-2.0 |
| language: |
| - en |
|
|
| tags: |
| - weather-forecasting |
| - weather |
| - meteorology |
| - ensemble-forecasting |
| - probabilistic-forecasting |
| --- |
| |
| # Bris Forecaster |
|
|
| This repository contains the Bris forecaster checkpoints and matching training and inference configurations. |
|
|
| The intended use is training with Anemoi and forecast inference from the published model artifacts. |
|
|
| ## Contents |
|
|
| - `bris-crpsfft_inference.ckpt`: inference checkpoint |
| - `bris-crpsfft_training.ckpt`: training checkpoint artifact |
| - `configs/config_inference.yaml`: inference configuration |
| - `configs/config_training.yaml`: training configuration |
| - `pyproject.toml`: pinned Python project metadata for inference |
| - `uv.lock`: locked dependency set for the inference environment |
|
|
| ## Scope |
|
|
| This is an artifact repository. It provides model weights and configs, but not input datasets. |
|
|
| The source code used for training is open and available through Anemoi Core: https://github.com/ecmwf/anemoi-core |
|
|
| The configurations in [configs/config_inference.yaml](configs/config_inference.yaml) and [configs/config_training.yaml](configs/config_training.yaml) are the references for concrete settings. |
|
|
| ## Usage |
|
|
| Training is performed with the Anemoi codebase. |
|
|
| Training and fine-tuning of this model require the forked Anemoi Core branch `evenmn/anemoi-core:feat/crps-fft-loss` rather than current upstream Anemoi Core: |
|
|
| https://github.com/evenmn/anemoi-core/tree/feat/crps-fft-loss |
|
|
| That branch carries the custom spectral CRPS loss used by the training config (`anemoi.training.losses.CRPSFFTLoss`; see `training/src/anemoi/training/losses/afcrps_fft.py` in the fork). It is based on an older Anemoi version, so training or fine-tuning against newer upstream Anemoi releases is not expected to work without porting the loss implementation and any related training code. |
|
|
| ## Environment |
|
|
| This repository includes a `pyproject.toml` and `uv.lock` for the pinned environment. |
|
|
| Setup with `uv`: |
|
|
| 1. Install `uv`. |
| 2. Ensure you have SSH access to `github.com/metno/bris-inference`, since the `bris` dependency is fetched from Git. |
| 3. Create the environment with `uv sync`. |
|
|
| You can then run commands inside the environment with `uv run`. |
|
|
| This pinned environment is primarily for inference and artifact inspection. If you want to train or fine-tune, set up Anemoi from the fork/branch above and run training from that checkout while using this repository's `configs/config_training.yaml` as the reference configuration. |
|
|
| From the forked Anemoi checkout, run training with this repository's config on the Hydra search path, for example: |
|
|
| `uv run anemoi-training train --config-path=/path/to/bris-forecaster/configs --config-name=config_training.yaml` |
|
|
| Inference is intended to be performed with Anemoi Inference. |
|
|
| The inference checkpoint is included here, and the Anemoi Inference config will be added once that interface is finalized. |
|
|
| In practice, this repo is meant to be used as: |
|
|
| - the published checkpoint artifact for inference |
| - companion configs showing how the model was trained and run |
|
|
| If you need operational details, use the config directly rather than this README. |
|
|
| The environment is pinned in this repository through `pyproject.toml` and `uv.lock`. |
|
|
| ## Notes |
|
|
| - `bris-crpsfft_inference.ckpt` is the checkpoint intended for inference. |
| - `bris-crpsfft_training.ckpt` is kept as a training artifact. |
| - The configs are included to make the artifacts easier to interpret and reuse. |
|
|
| ## Citation |
|
|
| If you use these artifacts, cite: |
|
|
| Even Marius Nordhagen, Håvard Homleid Haugen, Aram Farhad Shafiq Salihi, Magnus Sikora Ingstad, Thomas Nils Nipen, Ivar Ambjørn Seierstad, Inger-Lise Frogner, "High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid," arXiv:2511.23043, 2025. |
|
|
| Reference: https://arxiv.org/abs/2511.23043 |
|
|
