---
license: mit
base_model:
- microsoft/aurora
tags:
- atmospheric-dynamics
- aurora-model
---

# Cerrora: A High-Resolution Regional Weather Model for Europe

This repository contains the trained checkpoints for Cerrora, an AI-based regional weather model for Europe 
based on [Microsoft's Aurora](https://huggingface.co/microsoft/aurora).
The model is trained on the [Copernicus European Regional Reanalysis (CERRA)](https://doi.org/10.1002/qj.4764) dataset provided by ECMWF.
You can find the training and inference code, as well as information about how to use the checkpoints in our [GitHub repository](https://github.com/HPI-DeepLearning/Cerrora).
For more detailed information about the model, the training procedure, and the evaluation results, read our technical report (TODO: MISSING LINK).

## Model Architecture

Aurora is a 1.3B parameter model consisting of an encoder that projects the input into a fixed-size latent, 
a backbone to process the latent, and a decoder to recreate the original data shape.
The encoder and decoder use a Perceiver architecture, while the backbone is a Swin Transformer.
It is pretrained on a variety of weather and climate datasets, with the goal of providing a foundation model
that can be finetuned for diverse downstream tasks.
For more information, read the [Aurora paper](https://www.nature.com/articles/s41586-025-09005-y).
Cerrora is based on the 0.25° pretrained model with 6h lead time.
We mostly leave the model architecture unchanged, with the exception of the patch size, which we increase from 4 to 8.

## Training Procedure

We adopt a two-stage training procedure consisting of a 6h pretraining stage, and a rollout finetuning stage.
In the first stage, the model is trained on CERRA data to forecast the weather state in 6 hours.
This done to adapt the model to the change in data domain and input resolution.
The 6h model is published as `cerrora-base.ckpt`.

In the second stage, we finetune the model to autoregressively roll out predictions for lead times up to 30 hours.
As we train a regional model, we face the issue that the CERRA input data lacks the global context necessary for performing longer forecasts.
We address this by using the IFS-HRES forecasts provided in the [WeatherBench2 GCP bucket](https://weatherbench2.readthedocs.io/en/latest/data-guide.html#ifs-hres)
as lateral boundary conditions.
The rollout trained model is published as `cerrora-rollout.ckpt`.