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--- |
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license: mit |
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base_model: |
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- microsoft/aurora |
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tags: |
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- atmospheric-dynamics |
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- aurora-model |
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--- |
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# Cerrora: A High-Resolution Regional Weather Model for Europe |
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This repository contains the trained checkpoints for Cerrora, an AI-based regional weather model for Europe |
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based on [Microsoft's Aurora](https://huggingface.co/microsoft/aurora). |
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The model is trained on the [Copernicus European Regional Reanalysis (CERRA)](https://doi.org/10.1002/qj.4764) dataset provided by ECMWF. |
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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). |
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For more detailed information about the model, the training procedure, and the evaluation results, read our technical report (TODO: MISSING LINK). |
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## Model Architecture |
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Aurora is a 1.3B parameter model consisting of an encoder that projects the input into a fixed-size latent, |
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a backbone to process the latent, and a decoder to recreate the original data shape. |
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The encoder and decoder use a Perceiver architecture, while the backbone is a Swin Transformer. |
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It is pretrained on a variety of weather and climate datasets, with the goal of providing a foundation model |
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that can be finetuned for diverse downstream tasks. |
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For more information, read the [Aurora paper](https://www.nature.com/articles/s41586-025-09005-y). |
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Cerrora is based on the 0.25° pretrained model with 6h lead time. |
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We mostly leave the model architecture unchanged, with the exception of the patch size, which we increase from 4 to 8. |
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## Training Procedure |
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We adopt a two-stage training procedure consisting of a 6h pretraining stage, and a rollout finetuning stage. |
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In the first stage, the model is trained on CERRA data to forecast the weather state in 6 hours. |
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This done to adapt the model to the change in data domain and input resolution. |
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The 6h model is published as `cerrora-base.ckpt`. |
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In the second stage, we finetune the model to autoregressively roll out predictions for lead times up to 30 hours. |
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As we train a regional model, we face the issue that the CERRA input data lacks the global context necessary for performing longer forecasts. |
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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) |
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as lateral boundary conditions. |
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The rollout trained model is published as `cerrora-rollout.ckpt`. |
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