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 # 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).
-You can find the training code and more information about how to use the checkpoint in our [GitHub repository](https://github.com/HPI-DeepLearning/Cerrora)
+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`.