AIFS Single - v1.0

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).

The release of AIFS Single v1.0 marks the first operationally supported AIFS model. Version 1 supersedes the existing experimental version, 0.2.1 AIFS-single. The new version, 1.0, brings changes to the AIFS Single model, including among many others:

Improved performance for upper-level atmospheric variables (AIFS Single still uses 13 pressure-levels, so this improvement mainly refers to 50 and 100 hPa)
Improved skill for total precipitation.
Additional output variables, including 100 meter winds, snow-fall, surface solar-radiation and land variables such as soil-moisture and soil-temperature.

AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and tropical cyclone tracks. AIFS Single 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). Note that due to the non-determinism of GPUs, users will be unable to exactly reproduce an official AIFS forecast when running AIFS Single themselves.

For more details please refer to https://confluence.ecmwf.int/display/FCST/Implementation+of+AIFS+Single+v1

Data Details

Data parameters

New parameters

More detailed information about the new parameters introduced with AIFS Single v1.0 is provided in the table below.

Short Name	Name	Units	Component Type	Lev.Type
ssrd	Surface short-wave (solar) radiation downwards	$J m^{-2}$	AIFS	sfc
strd	Surface long-wave (thermal) radiation downwards	$J m^{-2}$	AIFS	sfc
lcc	Low cloud cover	$(0 - 1)$	AIFS	sfc
mcc	Medium cloud cover	$(0 - 1)$	AIFS	sfc
hcc	High cloud cover	$(0 - 1)$	AIFS	sfc
sf	Snowfall water equivalent	$kg m^{-2}$	AIFS	sfc
tcc	Total cloud cover	$(0 - 1)$	AIFS	sfc
100u	100 metre U wind component	$m s^{-1}$	AIFS	sfc
100v	100 metre V wind component	$m s^{-1}$	AIFS	sfc
rowe	Runoff water equivalent (surface plus subsurface)	$kg m^{-2}$	AIFS	sfc
vsw	Volumetric soil moisture	$m^3 m^{-3}$	AIFS	sol
sot	Soil temperature	$K$	AIFS	sol

Changes to existing parameters

There are no changes to existing parameters already introduced with AIFS Single v0.2.1.

Note Regarding precipitation units, it's worth noting that AIFS model was trained on $m^{3}/m^{2}$ and will therefore produce precip in that units. If one wants to retrieve precipitation from Open data, the units will be $m m$ .

Discontinued parameters

No parameters have been discontinued with regards to the previous version of AIFS Single v0.2.1.

Model Details

Model Description

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: These model weights are published under a Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

Model resolution

There are no changes in resolution compared to previous version AIFS Single v0.2.1.

	Component	Horizontal Resolution [kms]	Vertical Resolution [levels]
Atmosphere	AIFS-single v1.0	~ 36	13

Model Sources

Repository: 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 generate a new forecast using AIFS, you can use anemoi-inference. In the following notebook, a step-by-step workflow is specified to run the AIFS using the HuggingFace model:

Install Required Packages and Imports
Retrieve Initial Conditions from ECMWF Open Data

Select a date
Get the data from the ECMWF Open Data API
Get input fields
Add the single levels fields and pressure levels fields
Convert geopotential height into geopotential
Create the initial state

Load the Model and Run the Forecast

Download the Model's Checkpoint from Hugging Face
Create a runner
Run the forecast using anemoi-inference

Inspect the generated forecast

Plot a field

🚨 Note we train AIFS using flash_attention (https://github.com/Dao-AILab/flash-attention). The use of 'Flash Attention' package also imposes certain requirements in terms of software and hardware. Those can be found under #Installation and Features in https://github.com/Dao-AILab/flash-attention

🚨 Note the aifs_single_v1.0.ckpt checkpoint just contains the model’s weights. That file does not contain any information about the optimizer states, lr-scheduler states, etc.

How to train AIFS Single v1.0

To train this model you can use the configuration files included in this repository and the following Anemoi packages:

anemoi-training==0.3.1
anemoi-models==0.4.0
anemoi-graphs==0.4.4

and run the pretraining stage as follows,

export DATASETS_PATH=???????
export OUTPUT_PATH=???????
anemoi-training train --config-name=config_pretraining.yaml

Now, you can fine-tune your model for rollout using the run_id of your previous run, Note - this run_id refers to the run_id and you can find it looking at the checkpoint folder path. For more details, please refer to https://anemoi.readthedocs.io/projects/training/en/latest/user-guide/training.html#restarting-a-training-run

export PRETRAINING_RUN_ID=???????
anemoi-training train --config-name=config_finetuning.yaml

Training Details

Training Data

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
Soil moisture and soil temperature (layers 1 & 2)	Surface	Both
100m horizontal wind components, solar radiation (Surface short-wave (solar) radiation downwards and Surface long-wave (thermal) radiation downwards), cloud variables (tcc, hcc, mcc, lcc), runoff and snow fall	Surface	Output
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

Based on the different experiments we have made - the final training recipe for AIFS Single v1.0 has deviated slightly from the one used for AIFS Single v0.2.1 since we found that we could get a well trained model by skipping the ERA5 rollout and directly doing the rollout on the operational-analysis (extended) dataset. When we say 'extended' we refer to the fact that for AIFS Single v0.2.1 we used just operational-analysis data from 2019 to 2021, while in this new release we have done the fine-tunning from 2016 to 2022.

The other important change in the fine-tuning stage is that for AIFS Single v0.2.1 after the 6hr model training the optimiser was not restarted (ie. rollout was done with the minimal lr of $3 × 10^{-7}$ ). For this release we have seen that restarting the optimiser for the rollout improves the model's performance. For the operational-fine tuning rollout stage, the learning rate cycle is restarted, gradually decreasing to the minimum value at the end of rollout.

Pre-training: It was performed on ERA5 for the years 1979 to 2022 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 local learning rate used for this stage is $3.125 × 10^{-5}$ .
Fine-tuning: The pre-training is then followed by rollout on operational real-time IFS NWP analyses for the years 2016 to 2022, this time with a local learning rate of $8 × 10^{−7}$ , which is decreased to $3 × 10^{−7}$ . Rollout steps increase per epoch. In this second stage the warm up period of the optimiser is 100 steps to account for shorter length of this stage. Optimizer step are equal to 7900 ( 12 epoch with ~630 steps per epoch).

As in the previous version of aifs-single for fine-tuning and initialisation of the model during inference, IFS fields are interpolated from their native O1280 resolution (approximately $0.1 °$ ) down to N320 (approximately $0.25 °$ ).

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

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 as mentioned above, it does not include the optimizer state.

Evaluation

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].

AIFS Single v1.0 vs AIFS Single v0.2.1 (2023)

Scorecard comparing forecast scores of AIFS versus IFS (2022)

AIFS Single v1.0 vs IFS (2024)

Forecasts are initialised on 00 and 12 UTC. The scorecard show 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).

Known limitations

This version of AIFS shares certain limitations with some of the other data-driven weather forecast models that are trained with a weighted MSE loss, such as blurring of the forecast fields at longer lead times.
AIFS exhibits reduced forecast skill in the stratosphere forecast owing to the linear loss scaling with height
AIFS currently provides reduced intensity of some high-impact systems such as tropical cyclones.

Please refer to https://confluence.ecmwf.int/display/FCST/Known+AIFS+Forecasting+Issues for further details

Technical Specifications

Hardware

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. 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 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

Find the paper here

ecmwf
/

aifs-single-1.0