README.md

---
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). 

<div style="display: flex; justify-content: center;">
  <img src="aifs_10days.gif" alt="AIFS 10 days Forecast" style="width: 50%;"/>
</div>


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. (https://www.ecmwf.int/en/forecasts/datasets/open-data)

## 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. 

<div style="display: flex; justify-content: center;">
  <img src="encoder_graph.jpeg" alt="Encoder graph" style="width: 50%;"/>
  <img src="decoder_graph.jpeg" alt="Decoder graph" style="width: 50%;"/>
</div>

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

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


- **Repository:** [Anemoi](https://anemoi-docs.readthedocs.io/en/latest/index.html)
  Anemoi is an open-source framework for creating machine learning (ML) weather forecasting systems, which ECMWF and a range of national meteorological services across Europe have co-developed.
- **Paper:** https://arxiv.org/pdf/2406.01465

## How to Get Started with the Model

To be able to run AIFS to generate a new forecast, you can use ai-models https://github.com/ecmwf-lab/ai-models.
`ai-models` command can be used to run different models, since in this case we are looking at using AIFS we need to speficy
`anemoi` as `model-name` and then pass the path to the checkpoint (`aifs_single_v0.2.1.ckpt`) and the initial conditions. 
You can find an example of a set of initial conditions in the GRIB file `example_20241107_12_n320.grib`.

Use the code below to get started with the model. 


```
# 1st create the conda environment
export CONDA_ENV=aifs-env
conda create -n ${CONDA_ENV} python=3.10
conda activate ${CONDA_ENV}

pip install torch=2.4
pip install anemoi-inference[plugin] anemoi-models==0.2
pip install ninja
pip install flash-attn --no-build-isolation

# 2nd Run ai-models to generate weather forecast

ai-models anemoi --checkpoint aifs_single_v0.2.1.ckpt --file example_20241107_12_n320.grib
```

**Note** we train AIFS using `flash_attention` (https://github.com/Dao-AILab/flash-attention)
There are currently some issues when trying to install flash attention with the latest PyTorch version 2.5 and CUDA 12.4 (https://github.com/Dao-AILab/flash-attention/issues/1330)
For that reason we recommen you install PyTorch 2.4

After running the `ai-models` command the output of the forecast should be written into `anemoi.grib` 
Below you can find an example to read that file and load it as numpy array or xarray. 

```

import earthkit.data as ekd

source_filename='anemoi.grib'
aifs_forecast = ekd.from_source('file',source_filename)

# to load it as a numpy array
aifs_forecast.to_numpy()

# to load it as a xarray array
aifs_forecast.to_xarray()

```

## Training Details

### Training Data

<!-- 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. -->

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}\\). 

<div style="display: flex; justify-content: center;">
  <img src="aifs_diagram.png" alt="High-level AIFS diagram" style="width: 80%;"/>
</div>

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        |

Input and output states are normalised to unit variance and zero mean for each level. Some of
the forcing variables, like orography, are min-max normalised.

### 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**: It 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}\\).
- **Fine-tuning I**: 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]](doi: 10.21957/slk503fs2i) we increase the
rollout every 1000 training steps up to a maximum of 72 h (12 auto-regressive steps).
- **Fine-tuning II**: 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

- **Optimizer:** We use *AdamW* (Loshchilov and Hutter [2019]) with the \\(β\\)-coefficients set to 0.9 and 0.95.

- **Loss function:** The loss function is an area-weighted mean squared error (MSE) between the target atmospheric state
and prediction.

- **Loss scaling:** A loss scaling is applied for each output variable. The scaling was chosen empirically such that
all prognostic variables have roughly equal contributions to the loss, with the exception of the vertical velocities,
for which the weight was reduced. The loss weights also decrease linearly with height, which means that levels in 
the upper atmosphere (e.g., 50 hPa) contribute relatively little to the total loss value.

#### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

Data parallelism is used for training, with a batch size of 16. One model instance is split across four 40GB A100
GPUs within one node. Training is done using mixed precision (Micikevicius et al. [2018]), and the entire process
takes about one week, with 64 GPUs in total. The checkpoint size is 1.19 GB and it does not include the optimizer 
state.

## Evaluation

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

AIFS is evaluated against ECMWF IFS (Integrated Forecast System) for 2022. The results of such evaluation are summarized in 
the scorecard below that compares different forecast skill measures across a range of
variables. For verification, each system is compared against the operational ECMWF analysis from which the forecasts
are initialised. In addition, the forecasts are compared against radiosonde observations of geopotential, temperature
and windspeed, and SYNOP observations of 2 m temperature, 10 m wind and 24 h total precipitation. The definition
of the metrics, such as ACC (ccaf), RMSE (rmsef) and forecast activity (standard deviation of forecast anomaly,
sdaf) can be found in e.g Ben Bouallegue et al. ` [2024].

<div style="display: flex; justify-content: center;">
  <img src="aifs_v021_scorecard.png" alt="Scorecard comparing forecast scores of AIFS versus IFS (2022)" style="width: 80%;"/>
</div>


. Forecasts are initialised on 00 and 12
UTC. Shown are relative score changes as function of lead time (day 1 to 10) for northern extra-tropics (n.hem),
southern extra-tropics (s.hem), tropics and Europe. Blue colours mark score improvements and red colours score
degradations. Purple colours indicate an increased in standard deviation of forecast anomaly, while green colours
indicate a reduction. Framed rectangles indicate 95% significance level. Variables are geopotential (z), temperature
(t), wind speed (ff), mean sea level pressure (msl), 2 m temperature (2t), 10 m wind speed (10ff) and 24 hr total
precipitation (tp). Numbers behind variable abbreviations indicate variables on pressure levels (e.g., 500 hPa), and
suffix indicates verification against IFS NWP analyses (an) or radiosonde and SYNOP observations (ob). Scores
shown are anomaly correlation (ccaf), SEEPS (seeps, for precipitation), RMSE (rmsef) and standard deviation of
forecast anomaly (sdaf, see text for more explanation).

## Technical Specifications

### Hardware

<!--  {{ hardware_requirements | default("[More Information Needed]", true)}} -->

We acknowledge PRACE for awarding us access to Leonardo, CINECA, Italy. In particular, this version of the AIFS has been trained 
on 64 A100 GPUs (40GB).

### Software

The model was developed and trained using the [AnemoI framework](https://anemoi-docs.readthedocs.io/en/latest/index.html).
AnemoI is a framework for developing machine learning weather forecasting models. It comprises of components or packages
for preparing training datasets, conducting ML model training and a registry for datasets and trained models. AnemoI
provides tools for operational inference, including interfacing to verification software. As a framework it seeks to
handle many of the complexities that meteorological organisations will share, allowing them to easily train models from
existing recipes but with their own data.

## 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.
```


## More Information

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