Machine-learned weather forecast models have started to rival or outperform physics-based numerical weather prediction (NWP) models in recent years . For both training and forecasting, these machine-learned forecast models mostly depend on the Copernicus ERA5 reanalysis dataset produced by ECMWF and operational analysis by ECMWF's physics-based integrated forecasting system (IFS).

ECMWF has developed the artificial intelligence forecasting system (AIFS) , its own machine-learned forecast model. After a successful pre-operational test phase running four times daily since October 2023, with forecasts publicly available under ECMWF's open data policy, AIFS has now transitioned to operational status. The first operational version, AIFS 1.0.0 replacing AIFS 0.2.1, was implemented on 25 February 2025. The current operational version, AIFS 1.1.0 described here, was released on 27 August 2025 to correct a precipitation forecast issue in the initial version. The model is trained with a mean-squared error (MSE) loss function and is referred to as AIFS Single, to distinguish it from the probabilistically trained version, the AIFS ENS .

Although such MSE-trained forecast models have been shown to smooth forecast fields at longer lead times to avoid the double-penalty of incorrectly positioned weather phenomena , they still display physically robust characteristics and are able to make useful predictions of extreme events . The cheaper training costs associated with MSE-trained models (compared to probabilistically trained models) make them attractive for prototyping new features and model components.

To date, most machine-learned weather forecast models only include a limited subset of forecast variables available from current NWP systems. Here, we include for the first time in the AIFS soil moisture, soil temperature and runoff together with energy sector variables such as cloud cover, 100 metre winds and solar radiation. The choice of additional variables has been guided by utility to users and with considerations of future applications of the model, alongside pragmatic considerations on data availability and readiness. Surface solar radiation and 100-metre wind speeds have been included, important for renewable energy sectors. We added an initial characterization of the land surface with prognostic soil moisture and soil temperature, important for drought forecasting. We also include snowfall, improving the representation of distinct precipitation types in the model. Finally, we have added run-off as a diagnostic model output, pushing towards a hydrological component for the AIFS.

Despite their ability to produce skilful forecasts, machine-learned forecast models are prone to producing outputs that violate known physical relationships and limits (e.g., negative precipitation or mass imbalances). In current applications, including the pre-operational version of AIFS, post-processing of forecasts is commonly applied to remove such physical inconsistencies. Instead, we propose an additional final layer of activation functions that bound certain variables within physically meaningful limits and enforce physical constraints between related quantities. This simplifies the learning task by constraining the model output space to physically plausible regimes. This bounding strategy also proves particularly beneficial for variables with non-Gaussian distributions, such as precipitation, where the model must effectively distinguish between rain and no-rain states. Enforcing precipitation non-negativity resolves a gradient ambiguity at the zero-precipitation boundary under MSE training, greatly reducing drizzle bias and improving forecast skill in the light-precipitation regime.

In this paper we begin by outlining the training setup of the model and how this differs from the previous AIFS version. Then we motivate and describe the new bounding strategy to make the model forecast more physically consistent. We demonstrate the improved performance of the revised AIFS version via evaluation results and selected case studies. We conclude by summarizing main results and future work in the discussion and conclusions.

The architecture of AIFS follows an encoder-processor-decoder design. Here, encoder and decoder are attention-based graph neural networks, and the processor is a transformer with a sliding window attention (see for details).

The model operates on a reduced Gaussian grid, (N320, approximately 0.25° resolution). The processor (or hidden) grid is an O96 octahedral reduced Gaussian grid with 40 320 grid points, approximately 1° resolution, and consists of 16 processor layers.

AIFS is trained to produce 6 h forecasts t+6h using past and present atmospheric states at t-6,h and t0 (from ERA5 or ECMWF's operational analyses at initialization, or from the model forecast itself). Longer lead times are produced auto-regressively by feeding the model's predictions back as inputs, a process commonly referred to as rollout.

Machine-learned forecast models for numerical weather prediction show very good forecast skill, yet they are prone to producing outputs that violate known physical laws or expected statistical consistency. Unlike traditional numerical models, which are governed by equations ensuring mass conservation, positivity, or energy bounds, machine-learned forecast models lack such guarantees by default. As a result, physically implausible outputs, such as negative precipitation, can emerge. We show that incorporating constraints into the model design to enforce physical realism improves forecast skill. In this section, we first identify specific issues in the output of the previous AIFS version related to total precipitation, and then introduce a simple yet effective method to bound the model outputs using activation functions. The proposed method is not restricted to total precipitation but can be equally applied to other variables.

Unless otherwise stated, all verification results presented in this section are based on twice-daily forecasts initialised at 00:00 and 12:00 UTC for every day of 2023, verified against operational IFS analyses.

The revised AIFS version delivers highly skilled forecasts, as shown by anomaly correlation scores for 2023 in the Northern Hemisphere (Fig. ). In the medium range (3–10 d), AIFS outperforms the IFS by 12 to 24 h in skill. Forecast skill is also clearly improved compared to the previous AIFS version. This performance gain can be attributed to the combined effect of increased training data, improvements in rollout fine-tuning, the implementation of output bounding, and the inclusion of new prognostic variables. To quantify the specific contribution of expanded training data, we present a controlled comparison in Fig. . We verify against the operational IFS analysis, which is also used to initialise the forecasts.

As shown in Fig. , the expanded training dataset contributes to the most important portion of the overall performance gain. This indicates that data availability (ERA5 extended to 2022 and rollout fine-tuning expanded from 2019–2020 to 2016–2022) plays a major role. The remaining improvement stems from other system modifications, including rollout fine-tuning schedules, output bounding layers, and expanded prognostic variables. Due to the high computational cost, a detailed ablation study to isolate the impact of each individual modification beyond data expansion was not performed; thus, the observed improvements represent the cumulative result of these integrated system updates. It should be noted that the close agreement between AIFS revised (2020 data) and AIFS previous in ACC should be interpreted with caution, as these configurations differ in their training protocols: AIFS previous includes a rollout fine-tuning phase on ERA5 which AIFS revised (2020) does not, and uses only 2 years (2019–2020) of operational data for final rollout fine-tuning compared to 6 years (2016–2022) in the full revised version. Furthermore, similar ACC scores do not imply equivalent forecast quality. As shown in Fig. , AIFS revised (2020) exhibits less mesoscale smoothing than AIFS previous despite comparable ACC, indicating that the changes introduced in the revised system do contribute positively to forecast quality in ways not fully captured by ACC alone.

Additionally, imposing a minimum on the loss weights in the stratosphere leads to significant improvements in the data-driven forecasts at 100 and 50 hPa (Fig. ). For temperature at 100 hPa, the new version of the AIFS outperforms the IFS, while for 50 hPa wind speed, the gap in skill between the previous version of AIFS and the IFS in the stratosphere is significantly reduced.

Forecast skill for key surface variables, such as 2 m temperature and 10 m wind speed, verified against SYNOP observations, is similarly improved (Fig. ). Overall, the new AIFS version exhibits improvements of around 4 %–6 % across all variables, lead times, and pressure levels relative to the previous AIFS version, as shown in the scorecard presented in Fig. . The performance of the model for tropical cyclone prediction is similar to that of the previous version (see ), with some small improvements to track position. The training configuration, including a maximum rollout length of 12 (72 h), was retained from the previous AIFS version, as shown in Sect. . This parameter is known to influence spectral characteristics, with longer rollouts leading to enhanced damping. No explicit tuning was performed to target spectral behaviour.

The resulting Z500 power spectral density shown in Fig.  are very similar to those of the previous AIFS across scales, including the ∼500 km range (zonal wavenumbers 70–90), with slightly improved agreement with the IFS analysis at longer lead times. At the same time, the RMSE-based scorecard (Fig. ) shows overall improvements. Taken together, these results indicate that the skill gains are not achieved at the expense of degraded spatial variability.

Figure  presents verification metrics for several variables introduced in the new version. In line with those already present in earlier versions, AIFS shows a gain in forecast skill of around 1 d in the medium range for surface short-wave downwards radiation verified against geostationary satellite observation via CMSAF and 100 m wind speed verified against ECMWF operational analysis, relative to the IFS. The population distribution for total cloud cover verified against SYNOP observations, however, highlights the inherent limitations of MSE-trained AI models. While the observed distribution follows a U-shape, with high frequency at the tails of the distribution (clear skies and overcast conditions), AIFS produces a much flatter distribution, under-predicting these extremes and over-estimating intermediate values. This behaviour is closely linked to the smoothing effect introduced by the MSE loss function, which tends to penalize large deviations and thereby suppress extremes (see Sect. ).

The forecasting skill of the model with respect to 24 h accumulated total precipitation is significantly improved. The new AIFS version is compared against both the previous AIFS version and the operational IFS (cycles 47r3 and 48r1) in Fig. . The Stable Equitable Error in Probability Space (SEEPS) skill score is used as the primary verification metric, with 24 h accumulated precipitation SYNOP observations serving as the reference. Results show a consistent and statistically significant improvement across all lead times and in the Northern Hemisphere and the Southern Hemisphere. The revised AIFS demonstrates approximately a 1 d gain in forecast skill relative to both IFS and the previous AIFS version. The forecast fields also exhibit noticeable improvements, as illustrated in Fig. . The new version of the AIFS produces no negative values in the output and substantially reduces light precipitation, aligning more closely with the 24 h total precipitation accumulation fields derived from the IFS operational short-range forecasts.

Figure  reveals where the improvement originates. The Frequency Bias Index (FBI, ), defined as the ratio of predicted to observed event frequency at a given threshold (FBI=(H+FA)/(H+M), where H are hits, FA false alarms, and M misses), and the Peirce Skill Score (PSS, also known as the Hanssen–Kuipers discriminant; ), defined as the difference between the probability of detection and the probability of false detection (PSS=H/(H+M)-FA/(FA+CN), where CN are correct negatives), are shown for the Northern Hemisphere for different thresholds. The previous AIFS version exhibits a strong tendency to over-predict light precipitation events (<1 mm) across all lead times, as shown by the FBI. This bias is substantially corrected due to the bounding (see Sect. ) in the revised AIFS.

While the AI model still slightly over-predicts light precipitation compared to the IFS, it demonstrates competitive skill for light precipitation. The AIFS excels at medium-intensity events (1–10 mm), with PSS scores significantly higher than those of the IFS. At higher thresholds (>10 mm), corresponding to moderate to heavy precipitation, the AIFS diverges from the IFS, with a marked under-prediction (FBI < 1). This is likely caused by smoothing introduced by the loss function, in combination with the model’s coarser spatial resolution.

This under-prediction plays an important role in the metrics concerning more extreme events, since both the previous and the revised AIFS models underperform IFS for thresholds exceeding 10 mm in terms of PSS, but remains competitive. This suggests that although the AI models predict fewer high-intensity events, their predictions are more accurate when they do occur. Finally, the revised AIFS shows a marginal improvement in terms of PSS compared against the previous AIFS version, possibly due to improvements in the learning-rate scheduling used for fine-tuning and additional training data.

The revised AIFS version (1.1.0) presented here improves upon the pre-operational release through a revised training regime with more data, new forecast variables, improved stratospheric loss weights, and a bounding strategy that enforces physical constraints on the output variables. Overall, this leads to improvements of around 4 %–6 % across all variables, lead times, and pressure levels. The largest improvements, up to 12 % gains in normalized difference in the short range, are observed in total precipitation forecasting, which benefits from the newly introduced bounding. We showed that this has a significant impact on the prediction of no rain and light precipitation. The model displays good forecast performance for out-of-training-sample case studies, accurately capturing extreme precipitation and snowfall events.

Data plays a crucial role in the performance of AI models. Most of the improvements non-related to precipitation in the revised version of the AIFS stem from the expansion of the training dataset and the use of more recent operational ECMWF analyses for rollout fine-tuning, as demonstrated by the controlled comparison in Fig. . Since the AIFS relies on these analyses for real-time forecasting, it is important to fine-tune them regularly using up-to-date data. Regular fine-tuning with recent ECMWF analyses helps the models to adapt to shifts in the data due to new IFS model cycles.

Recent global AI forecasting systems, including GraphCast, Pangu-Weather, FuXi, and CREDIT, have reported persistent challenges in representing light precipitation. Positive frequency bias in the drizzle regime appears to be a recurring feature across models trained with symmetric regression losses on strictly non-negative, intermittent variables. Although these systems differ substantially in backbone architecture, from graph neural networks to transformer-based designs and modular physically constrained frameworks, the drizzle problem appears largely independent of architecture. Instead, it is closely tied to how precipitation is parameterized and constrained during training. Physical constraint methodologies offer multiple pathways for mitigating precipitation biases. Global conservation schemes may reduce drizzle indirectly by regulating total moisture budgets. The present analysis suggests that non-negativity enforcement addresses a more fundamental issue: the local gradient asymmetry at the zero boundary and the superposition of dry and wet states around zero. By introducing a hard geometric boundary at zero, the optimization landscape is reshaped such that dry and wet regimes become separable. This mechanism operates independently of large-scale conservation principles and may therefore represent a structural requirement for stable training of intermittent variables under MSE. Alternative activation functions such as LeakyReLU, which scale negative inputs by a small factor α (typically 0.01), would permit gradient flow in the negative space while heavily attenuating the loss contribution from dry predictions (by a factor of α2). We expect that similar regime separation would still emerge, since the cost of placing dry states deep in the negative space becomes negligible. The main practical difference is that LeakyReLU produces non-physical slightly negative output values at inference, requiring post-processing clipping. More broadly, alternative formulations that explicitly decouple the dry and wet regimes during training, such as asymmetric loss functions or dedicated classification heads for the no-rain state, represent promising directions for future work.

The bounding strategy presented here enforces physical realizability, non-negativity, boundedness, and inter-variable consistency, but does not impose global conservation of mass or energy. For the medium-range timescales considered in this work (up to 10 d), we expect conservation violations to remain small relative to forecast errors dominated by chaotic error growth, though a systematic quantification of mass and energy drift over extended AIFS integrations remains to be carried out.

Rollout fine-tuning emerges as an important factor shaping forecast behaviour, including the degree of spatial smoothing in the outputs. As the model is trained on extended lead times and optimised using a mean squared error objective, some degree of smoothing is expected. Training hyperparameters such as learning rate scheduling, number of optimisation steps, and rollout configuration can influence this behaviour and warrant further systematic investigation. In the present study, the training configuration, including a maximum rollout length of 12, was retained from the previous AIFS version to ensure consistency. The resulting Z500 power spectra (Fig. ) are broadly comparable to those of the previous model across scales, including the ∼500 km range, with slightly improved agreement with the analysis at longer lead times. Importantly, these comparable spectral characteristics are achieved alongside overall improvements in RMSE-based skill (Fig. ). This indicates that the skill gains are not obtained at the expense of degraded spatial variability. While more aggressive rollout strategies may further optimise headline verification scores, understanding their impact on spatial characteristics remains an important area for future work.

Alongside making updates to the training schedule, we have also added new variables to the AIFS while achieving improvements in forecast skill for headline atmospheric metrics. In particular, the inclusion of soil moisture and soil temperature as prognostic variables represents an initial step toward a more complete Earth system representation within AIFS. Targeted ablation studies are planned as the land-surface component is extended in future versions. However, it remains to be seen if adding more variables and earth-system components will eventually require an increase to the latent space of the model. The additional earth-system and energy-sector variables in AIFS establish a foundation for future extensions, including ocean and wave components, expanding the number of cryospheric processes with enhanced snow modelling, and increasing the hydrological capabilities of the model. These new variables are currently taken from a consistent data source with the rest of the model variables. In the future, there is the potential to look at datasets tailored to specific earth-system components, such as ERA5-Land and the ocean and sea-ice reanalysis system (ORAS6) .

AIFS currently operates at approximately 0.25° spatial resolution with a 6 h timestep, and future work will focus on increasing both spatial and temporal resolution.

The AIFS development has now transitioned to the new Anemoi framework . Anemoi provides tools for the whole data-driven modelling workflow, from the generation of training datasets, to scalable probabilistic training and running real-time inference with such models. Anemoi also allows for the cataloguing and archiving of model and data checkpoints to ensure reproducibility and traceability of training and inference runs and ensure that any models developed within this framework have a clear lineage. The Anemoi framework is now being used by an increasing number of Member States of ECMWF and collaborating organisations supported by ECMWF.

After a successful experimental phase, AIFS has transitioned to operational status at ECMWF on 25 February 2025. It is supported 24/7 alongside ECMWF's physics-based system, the IFS. The MSE trained model is labeled AIFS Single, and its forecasts are available earlier than the ones from the physics-based model chain, due to the fast runtime of AIFS.

Results presented in this paper show that AIFS forecasts are highly skilful and they outperform the IFS forecasts across the vast majority of lead times and variables. They highlight the relevance of AIFS for weather prediction. Future developments will focus on including more surface variables and exploring a wider range of applications such as climate reanalysis. The operational release of the AIFS demonstrates the commitment of ECMWF to pursue the best possible weather forecasts with both physics-based and machine learning methods.

Code and model availability.

AIFS version 1.1.0 was fully trained using the Anemoi framework https://github.com/ecmwf/anemoi (last access: 31 March 2026). The frozen versions of the Anemoi modules used for training, together with the configuration files and the trained model checkpoint, are available in the permanent archive under 10.5281/zenodo.17349820.