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--- |
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license: cc-by-sa-4.0 |
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metrics: |
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- mse |
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pipeline_tag: graph-ml |
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--- |
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# AIFS Single - v0.2.1 |
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<!-- Provide a quick summary of what the model is/does. --> |
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Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast |
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model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). |
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We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and |
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tropical cyclone tracks. AIFS is run four times daily alongside ECMWF’s physics-based NWP model and forecasts |
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are available to the public under ECMWF’s open data policy. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, |
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and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses. |
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<div style="display: flex;"> |
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<img src="encoder_graph.jpeg" alt="Encoder graph" style="width: 50%;"/> |
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<img src="decoder_graph.jpeg" alt="Decoder graph" style="width: 50%;"/> |
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</div> |
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It has a flexible and modular design and supports several levels of parallelism to enable training on |
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high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses |
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and direct observational data. |
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- **Developed by:** ECMWF |
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- **Model type:** Encoder-processor-decoder model |
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- **License:** CC BY-SA 4.0 |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Anemoi](https://anemoi-docs.readthedocs.io/en/latest/index.html) |
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- **Paper:** https://arxiv.org/pdf/2406.01465 |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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export CONDA_ENV=aifs-env |
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conda create -n ${CONDA_ENV} python=3.10 |
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conda activate ${CONDA_ENV} |
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pip install flash-attn |
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pip install anemoi-inference[plugin] anemoi-models==0.2 |
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ai-models anemoi --checkpoint aifs_single_v0.2.1.ckpt --file example_20241107_12_n320.grib |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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AIFS is trained to produce 6-hour forecasts. It receives as input a representation of the atmospheric states |
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at \\(t_{−6h}\\), \\(t_{0}\\), and then forecasts the state at time \\(t_{+6h}\\). |
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The full list of input and output fields is shown below: |
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| Field | Level type | Input/Output | |
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|-------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------| |
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| Geopotential, horizontal and vertical wind components, specific humidity, temperature | Pressure level: 50,100, 150, 200, 250,300, 400, 500, 600,700, 850, 925, 1000 | Both | |
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| Surface pressure, mean sea-level pressure, skin temperature, 2 m temperature, 2 m dewpoint temperature, 10 m horizontal wind components, total column water | Surface | Both | |
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| Total precipitation, convective precipitation | Surface | Output | |
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| Land-sea mask, orography, standard deviation of sub-grid orography, slope of sub-scale orography, insolation, latitude/longitude, time of day/day of year | Surface | Input | |
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Input and output states are normalised to unit variance and zero mean for each level. Some of |
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the forcing variables, like orography, are min-max normalised. |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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- **Pre-training**: It was performed on ERA5 for the years 1979 to 2020 with a cosine learning rate (LR) schedule and a total |
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of 260,000 steps. The LR is increased from 0 to \\(10^{-4}\\) during the first 1000 steps, then it is annealed to a minimum |
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of \\(3 × 10^{-7}\\). |
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- **Fine-tuning I**: The pre-training is then followed by rollout on ERA5 for the years 1979 to 2018, this time with a LR |
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of \\(6 × 10^{-7}\\). As in [Lam et al. [2023]](https://doi.org/10.48550/arXiv.2212.12794) we increase the |
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rollout every 1000 training steps up to a maximum of 72 h (12 auto-regressive steps). |
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- **Fine-tuning II**: Finally, to further improve forecast performance, we fine-tune the model on operational real-time IFS NWP |
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analyses. This is done via another round of rollout training, this time using IFS operational analysis data |
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from 2019 and 2020 |
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#### Training Hyperparameters |
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- **Optimizer:** We use *AdamW* (Loshchilov and Hutter [2019]) with the \\(β\\)-coefficients set to 0.9 and 0.95. |
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- **Loss function:** The loss function is an area-weighted mean squared error (MSE) between the target atmospheric state |
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and prediction. |
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- **Loss scaling:** A loss scaling is applied for each output variable. The scaling was chosen empirically such that |
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all prognostic variables have roughly equal contributions to the loss, with the exception of the vertical velocities, |
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for which the weight was reduced. The loss weights also decrease linearly with height, which means that levels in |
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the upper atmosphere (e.g., 50 hPa) contribute relatively little to the total loss value. |
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#### Speeds, Sizes, Times |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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Data parallelism is used for training, with a batch size of 16. One model instance is split across four 40GB A100 |
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GPUs within one node. Training is done using mixed precision (Micikevicius et al. [2018]), and the entire process |
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takes about one week, with 64 GPUs in total. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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{{ testing_data | default("[More Information Needed]", true)}} |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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{{ testing_factors | default("[More Information Needed]", true)}} |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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{{ testing_metrics | default("[More Information Needed]", true)}} |
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### Results |
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{{ results | default("[More Information Needed]", true)}} |
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#### Summary |
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{{ results_summary | default("", true) }} |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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{{ model_examination | default("[More Information Needed]", true)}} |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** {{ hardware_type | default("[More Information Needed]", true)}} |
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- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}} |
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- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}} |
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- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}} |
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- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}} |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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{{ model_specs | default("[More Information Needed]", true)}} |
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### Compute Infrastructure |
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{{ compute_infrastructure | default("[More Information Needed]", true)}} |
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#### Hardware |
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{{ hardware_requirements | default("[More Information Needed]", true)}} |
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We acknowledge PRACE for awarding us access to Leonardo, CINECA, Italy |
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#### Software |
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{{ software | default("[More Information Needed]", true)}} |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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If you use this model in your work, please cite it as follows: |
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**BibTeX:** |
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``` |
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@article{lang2024aifs, |
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title={AIFS-ECMWF's data-driven forecasting system}, |
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author={Lang, Simon and Alexe, Mihai and Chantry, Matthew and Dramsch, Jesper and Pinault, Florian and Raoult, Baudouin and Clare, Mariana CA and Lessig, Christian and Maier-Gerber, Michael and Magnusson, Linus and others}, |
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journal={arXiv preprint arXiv:2406.01465}, |
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year={2024} |
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} |
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``` |
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**APA:** |
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``` |
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Lang, S., Alexe, M., Chantry, M., Dramsch, J., Pinault, F., Raoult, B., ... & Rabier, F. (2024). AIFS-ECMWF's data-driven forecasting system. arXiv preprint arXiv:2406.01465. |
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``` |
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## More Information |
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[More Information Needed](https://arxiv.org/pdf/2406.01465) |
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