---
license: cc-by-sa-4.0
metrics:
- mse
pipeline_tag: graph-ml
---

# AIFS Single - v0.2.1

<!-- Provide a quick summary of what the model is/does. -->

Here, we introduce the **Artificial Intelligence Forecasting System (AIFS)**, a data driven forecast
model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). 

We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and
tropical cyclone tracks. AIFS is run four times daily alongside ECMWF’s physics-based NWP model and forecasts
are available to the public under ECMWF’s open data policy.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, 
and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses. 
It has a flexible and modular design and supports several levels of parallelism to enable training on
high resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses
and direct observational data. 

- **Developed by:** ECMWF
- **Model type:** Encoder-processor-decoder model
- **License:** CC BY-SA 4.0


### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [Anemoi](https://anemoi-docs.readthedocs.io/en/latest/index.html)
- **Paper:** https://arxiv.org/pdf/2406.01465

## Uses

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->


### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}

## How to Get Started with the Model

Use the code below to get started with the model.

**TODO: Fill

## Training Details

### Training Data

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AIFS is trained to produce 6-hour forecasts. It receives as input a representation of the atmospheric states
at \\(t_{−6h}\\), \\(t_{0}\\), and then forecasts the state at time \\(t_{+6h}\\). 

The full list of input and output fields is shown below:

| Field                                                                                                                                                       | Level type                                                                   | Input/Output |
|-------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------|
| 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         |
| 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         |
| Total precipitation, convective precipitation                                                                                                               | Surface                                                                      | Output       |
| 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        |

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

- Pre-training was performed on ERA5 for the years 1979 to 2020 with a cosine learning rate (LR) schedule and a total
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
of \\(3 × 10^{-7}\\).
- The pre-training is then followed by rollout on ERA5 for the years 1979 to 2018, this time with a LR
of \\(6 × 10^{-7}\\). As in [Lam et al. [2023]](https://doi.org/10.48550/arXiv.2212.12794) we increase the
rollout every 1000 training steps up to a maximum of 72 h (12 auto-regressive steps).
- Finally, to further improve forecast performance, we fine-tune the model on operational real-time IFS NWP
analyses. This is done via another round of rollout training, this time using IFS operational analysis data
from 2019 and 2020


#### Training Hyperparameters

- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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{{ speeds_sizes_times | default("[More Information Needed]", true)}}

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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{{ testing_data | default("[More Information Needed]", true)}}

#### Factors

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#### Metrics

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### Results

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#### Summary

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## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

{{ model_examination | default("[More Information Needed]", true)}}

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** {{ hardware_type | default("[More Information Needed]", true)}}
- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}

## Technical Specifications [optional]

### Model Architecture and Objective

{{ model_specs | default("[More Information Needed]", true)}}

### Compute Infrastructure

{{ compute_infrastructure | default("[More Information Needed]", true)}}

#### Hardware

{{ hardware_requirements | default("[More Information Needed]", true)}}

#### Software

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## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

If you use this model in your work, please cite it as follows:

**BibTeX:**

```
@article{lang2024aifs,
  title={AIFS-ECMWF's data-driven forecasting system},
  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},
  journal={arXiv preprint arXiv:2406.01465},
  year={2024}
}
```

**APA:**

```
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.
```
 
## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

{{ glossary | default("[More Information Needed]", true)}}

## More Information

[More Information Needed](https://arxiv.org/pdf/2406.01465)