AIFS Single v2

The AIFS is ECMWF's Artificial Intelligence Forecasting System. It consists of two data-driven medium-range forecast models:

AIFS Single v2 (described here)
AIFS ENS v2 (described on the following page)

AIFS Single v2 was implemented on 12 May 2026 and supersedes version 1.1. It is run operationally by ECMWF, generating a single 15-day (6-hourly) global forecast four times per day.

What's new in v2
Quickstart
Model overview
Data details
Training details
Evaluation
Known limitations
Technical specifications
Citation

What's new in v2

The release of AIFS v2 introduces:

A new wave component, including 11 wave variables, marking ECMWF's first operational data-driven wave forecasts.
The addition of a new snow variable to the existing land component.
Improved vertical velocities, by changing parameter W from a prognostic to a diagnostic field.
An improved representation of the stratosphere, with the addition of pressure level fields at 10hPa.
An improved training regime, using 2 years of additional training data compared to AIFS Single v1.1.

For full details of the changes introduced with this upgrade, see the implementation page.

Quickstart

Access AIFS Single v2 model data

ECMWF generates operational forecasts from AIFS Single v2 four times per day (at 00, 06, 12 & 18 UTC). Users can access the forecast data free-of-charge through various open data platforms.

Generate a forecast with AIFS Single v2

To generate a forecast using the AIFS Single v2 model, follow the notebook example.

The notebook demonstrates:

installing packages and imports
retrieving initial conditions from ECMWF Open Data
loading the pretrained checkpoint
running inference with anemoi-inference
visualising the forecast

anemoi-inference also provides a command line interface:

anemoi-inference run inference.yaml

or, if using uv from this repository:

uv run --extra inference anemoi-inference run inference.yaml

Model overview

Model description

AIFS Single v2 is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor.

The model 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 numerical weather prediciton (NWP) analyses and direct observational data.

Developed by: ECMWF
Model type: Encoder-processor-decoder model
License: Inference 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/. The demonstration notebook and other script files are published under an Apache 2.0 licence. To view a copy of this license, visit https://www.apache.org/licenses/LICENSE-2.0.txt.

Model resolution

AIFS Single v2 introduces a new pressure level to the stratospheric component.

Model	Vertical resolution [pressure levels] (hPa)
AIFS-single v2.0	10 (new), 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000

There are no changes in horiztonal resolution compared to previous version AIFS Single v1.1.

Component	Horizontal Resolution [kms]	Vertical Resolution [levels]
Atmosphere	~ 31	14

Model Sources

Repository: Anemoi is an open-source framework for creating data-driven weather forecasting systems. Anemoi is co-developed by ECMWF and national meteorological services across Europe.
Papers:

Data details

Training data

The data used for pre-training AIFS Single v2 remains the same as for the previous model version (v1.1):

Pre-training was performed on ERA5 data covering the years 1979–2022 over 260,000 training steps.

The data used for fine-tuning AIFS Single v2 has been updated:

Fine-tuning was performed on 2 years of additional data compared to AIFS Single v1.1.
In particular, fine-tuning was performed on ECMWF operational analysis data and IFS 50r1 esuite analysis data, covering the years 2018-2024 over 7,900 training steps.

Note: The IFS 50r1 esuite analysis data used for fine-tuning is not available to users. It consists of prototype data from early versions of IFS Cycle 50r1.

As in previous versions of AIFS Single, IFS fields are interpolated from their native O1280 resolution (approximately 0.1°) using MARS default interpolation tools down to N320 (approximately 0.25°) for fine-tuning and initialisation of the model during inference.

Data parameters

New parameters

AIFS Single v2 introduces 12 new parameters, which were used for model training and are output by the model during a forecast.

Short Name	Name	Units
h1012	Significant wave height of all waves with periods within the inclusive range from 10 to 12 seconds	(m)
h1214	Significant wave height of all waves with periods within the inclusive range from 12 to 14 seconds	(m)
h1417	Significant wave height of all waves with periods within the inclusive range from 14 to 17 seconds	(m)
h1721	Significant wave height of all waves with periods within the inclusive range from 17 to 21 seconds	(m)
h2125	Significant wave height of all waves with periods within the inclusive range from 21 to 25 seconds	(m)
h2530	Significant wave height of all waves with periods within the inclusive range from 25 to 30 seconds	(m)
wmb	Model bathymetry	(m)
swh	Significant wave height	(m)
mwd	Mean wave direction	(Degree true)
mwp	Mean wave period	(s)
cdww	Coefficient of drag with waves	(dimensionless)
fscov	Fraction of snow cover	(Proportion)

Training Details

Training Data

AIFS Single v2 is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses as described in the training data section.

AIFS Single v2 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 table below shows all parameters used and output by AIFS Single v2. New parameters and levels are marked bold.

Field	Level type	Input/Output
Geopotential (Z), horizontal and vertical wind components (U, V), temperature (T)	Pressure levels: 10, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000	Both ("Prognostic")
Specific humidity (Q)	Pressure levels: 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000	Both ("Prognostic")
Vertical velocity (W)	Pressure levels: 10, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000	Output ("Diagnostic")
Specific humidity (Q)	Pressure level: 50	Output ("Diagnostic")
Surface pressure (SP), mean sea-level pressure (MSL), sea-surface temperature (SST), skin temperature (SKT), 2m temperature (2T), 2m dewpoint temperature (2D), 10m horizontal wind components (10U, 10V), total column water (TCW), mean wave period (MWP), mean wave direction (MWD), coefficient of drag with waves (CDWW), significant wave height (SWH), significant wave height of all waves with periods within the inclusive range from: - 10 to 12 seconds (H1012) - 12 to 14 seconds (H1214) - 14 to 17 seconds (H1417) - 17 to 21 seconds (H1721) - 21 to 25 seconds (H2125) - 25 to 30 seconds (H2530)	Surface	Both ("Prognostic")
Volumetric soil moisture (VSW) and soil temperature (SOT), both at soil depth 1 and 2	Soil layer	Both ("Prognostic")
100m horizontal wind components (100U, 100V), surface short-wave (solar) radiation downwards (SSRD), surface long-wave (thermal) radiation downwards (STRD), cloud variables (TCC, HCC, MCC, LCC), runoff water equivalent (ROWE) and snow fall (SF), total precipitation (TP), convective precipitation (CP), fraction of snow cover (FSCOV)	Surface	Output ("Diagnostic")
Standard deviation of sub-gridscale orography (SDOR), slope of sub-gridscale orography (SLOR), land-sea mask (LSM), Geopotential (Z), insolation, latitude/longitude, time of day/day of year	Surface	Input ("Forcings")

Training Procedure

This upgrade does not introduce any changes to the model architecture; the model remains the same as the existing operational model v1.1. Technical details about the model architecture are detailed in the arXiv preprints here and here.

Speeds, Sizes, Times

Data parallelism is used for training, with a batch size of 16. One model instance is split across one 120GB GH200 GPUs. Training is done using mixed precision (Micikevicius et al. [2018]), and the entire process takes about 4 days, with 16 GPUs in total. The checkpoint size is 994MB and as mentioned above, it does not include the optimizer state.

Evaluation

Interactive scorecards presenting the performance of AIFS Single v2 between January-March 2026 are now available. The scorecards compare performance when initialised from 49r1 and 50r1 IFS initial conditions:

[AIFS Single v2 (initialised from 50r1) compared with AIFS Single v1.1](initialised from 49r1)(https://sites.ecmwf.int/aifs/scorecards/AIFS-Single-v2%20vs%20AIFS-Single-v1.1.html)
AIFS Single v2 compared with AIFS Single v1.1 (both initialised from 50r1)

Known limitations

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

Technical specifications

Hardware

AIFS Single v2 was trained on 16 GH200 GPUs (120GB).

Software

The model was developed and trained using the Anemoi framework. The Anemoi framework provides a complete toolkit to develop data-driven weather models – from data preparation through to inference. The development is primarily driven by a number of European Meterological Organisations but open to contributions from any organisation or any individual. The framework is composed of several packages which target the different components necessary to construct data-driven weather models. To aid development and deployment, each package collects metadata that can be used by the subsequent packages. The framework builds upon on established Python tools including PyTorch, Lighting, Hydra, Zarr, Xarray and earthkit.

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

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