Bris Forecaster Pretrained

This repository contains the Boiling Blizzard pretrained-model configs and a single pinned uv environment for both anemoi-inference and anemoi-training.

It is organized in the same artifact-oriented style as ~/bris-forecaster: runnable configs are collected under a single top-level configs/ directory, and the repository root describes how to use them.

configs/config_anemoi_inference.yaml: anemoi-inference config for the pretrained checkpoint
configs/config_training_r1.yaml: stage 1 retraining config
configs/config_training_r2.yaml: stage 2 retraining config
configs/config_training_r3_6.yaml: stages 3-6 retraining config
configs/config_training_r6_ifs.yaml: IFS-based stage 6 retraining config
pyproject.toml: shared uv project metadata for inference and training
uv.lock: shared locked dependency set

Layout

The repository is split by concern rather than by tool:

configs/ contains the runnable YAML configurations.
the repository root contains the shared pyproject.toml and uv.lock.

Usage

Create the shared environment from the repository root:

uv sync --locked

Anemoi inference:

uv run --locked anemoi-inference run configs/config_anemoi_inference.yaml

Training with the default stage-1 config:

uv run --locked anemoi-training train --config-path=configs --config-name=config_training_r1.yaml

Training with a different stage:

uv run --locked anemoi-training train --config-path=configs --config-name=config_training_r2.yaml

Notes

Training configs live in configs/, so their local Hydra search path points one level up to the repository root.
The shared environment includes both anemoi-inference and anemoi-training.
The configs still reference the same checkpoint and dataset locations as before.
The environment currently depends on pytorch-wavelets from /leonardo_work/DestE_330_25/enordhag/repos/pytorch_wavelets, so uv sync --locked requires that path to exist.

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