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There is no useful single score for deciding whether AI weather forecasts are better than traditional numerical weather prediction (NWP). A credible evaluation matches forecast cases, initial conditions, lead times, variables, regions and verifying data, then uses several deterministic, probabilistic and spatial metrics. Results must be broken down by lead time, geography, season and event type, with uncertainty and model versions disclosed.
ECMWF’s published assessment of its Artificial Intelligence Forecasting System (AIFS) versus the physics-based Integrated Forecasting System (IFS) found lower errors for most assessed medium-range variables, while also finding a weakness in tropical-cyclone intensity. That is evidence about those systems, versions and tests—not a universal verdict on AI or NWP.
What a fair AI-versus-NWP comparison must specify
Start by defining the forecast task before downloading scores. “AI forecast” and “traditional model” cover many different systems, and a medium-range deterministic forecast is answering a different question from an ensemble probability or a sub-seasonal outlook.
Forecast initialization and valid time
Record every initialization date and the valid time being scored. A 24-hour forecast initialized at 00 UTC cannot be compared fairly with a 48-hour forecast initialized at 12 UTC, even if both verify at the same clock time. State the lead-time range explicitly—for example, 1–15 days from each model run—and use the same valid times for all systems.
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Variables, grids and domain
Name the variables and units: 2-metre temperature, 10-metre wind, mean-sea-level pressure, geopotential height, precipitation accumulation or another quantity. Define the grid or station set, horizontal resolution, vertical level and geographic region. A global mean can hide a model’s poor performance over mountains, coasts, the tropics or a particular country.
Forecast type and configuration
Separate deterministic output from an ensemble. A single forecast gives one value; an ensemble gives a predictive distribution from which probabilities, spread and quantiles can be evaluated. Record model version, ensemble size, initial-condition source, resolution, data-assimilation configuration and any post-processing. Missing forecasts and failed runs must be listed rather than silently removed.
Build a matched experiment step by step
- Choose the question. Decide whether you are measuring medium-range point accuracy, the probability of an event, precipitation placement, tropical-cyclone track, or another user-relevant outcome. Do not combine these into one undifferentiated ranking.
- Set the case list. Use identical initialization dates, valid times and lead-time bins for every system. A hindcast period, seasonal mix or start-date distribution that differs between models can change the apparent winner.
- Freeze versions. Write down the exact model and release, including any operational upgrade during the sample. If a system changed during the period, analyze the segments separately or explain the break.
- Align resolution and post-processing. Regrid forecasts with a documented method, and state whether bias correction, downscaling or calibration was applied. A post-processed AI output should not be compared with an untouched NWP output without identifying that difference.
- Define the verification domain. Specify land and ocean treatment, station or grid cells, missing-data rules and regional masks. Keep the same mask for each model.
- Choose the verifying reference. Identify whether each score uses observations, an analysis or a reanalysis, and use the same reference for all systems unless the experiment is explicitly testing reference sensitivity.
- Pre-register metrics and slices. Decide in advance which scores, lead bins, regions, seasons and event definitions will be reported. This limits selective reporting after seeing the results.
- Retain the raw distributions. Save case-level errors and probabilities, not only a final average. They are needed for confidence intervals, paired significance tests and event analysis.
Choose the verifying reference and baseline carefully
The verifying dataset is part of the experiment, not a neutral detail. ECMWF’s AI Weather Quest uses near-real-time ERA5T and warns that a reference can favor systems with similar model dynamics, parameterizations or data-assimilation characteristics. ERA5T is useful for consistent large-scale comparison, but it is not independent ground truth. Where practical, repeat key results against independent observations or an alternate analysis and show whether the ranking changes.
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Every skill score also needs a named baseline. Common choices are climatology, persistence, a previous operational forecast or a specific NWP version. “Skill score” without its reference forecast is uninterpretable: a model can improve substantially over climatology while still losing to the latest NWP system.
Use a metric set, not a single number
Each metric answers a different question. Report units, aggregation, sample, baseline and uncertainty next to every value.
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| Metric or diagnostic | What it measures | Important qualification |
|---|---|---|
| RMSE | Typical error magnitude, with large errors weighted more heavily | State variable, units, lead-time aggregation and reference |
| MAE | Average absolute error | Less sensitive to extremes than RMSE; does not show directional bias |
| Bias | Systematic tendency to be too high or too low | A near-zero mean bias can conceal poor timing, extremes or spread |
| Skill score | Improvement relative to a named baseline | The baseline, sign convention and uncertainty must be shown |
| CRPS | Accuracy of a full predictive distribution | For finite ensembles, use a fair correction when comparing different ensemble sizes |
| Fair CRP skill score | Distributional skill relative to climatology with finite-ensemble treatment | State ensemble size and climatological reference |
| Brier score and fair Brier score | Accuracy of probabilities for binary or categorical events | Fair forms account for finite ensemble size; report the event threshold |
| ROC AUC | Discrimination between event and non-event cases | It does not establish probability calibration |
| Reliability diagram | Calibration: observed frequency for each forecast probability | Small samples or ensembles can make the curve unstable |
| SEEPS | 24-hour precipitation category skill | Uses local climatology-based dry, light and heavy categories, allowing station climate differences |
| Neighborhood precipitation scores | Whether precipitation occurs in a nearby area rather than at one exact grid point | Report neighborhood radius and threshold |
| Object-based precipitation scores | Placement, size and intensity of coherent precipitation features | State object-identification rules and matching tolerances |
Deterministic continuous forecasts
Use RMSE or MAE for ordinary error magnitude and bias for systematic offset. Pair those with a skill score against an explicit baseline. A model with lower RMSE may still have a damaging wet or dry bias in a particular region, so do not treat one average as a complete quality assessment.
Ensemble and probability forecasts
Evaluate the whole predictive distribution with CRPS or a fair CRP skill score, then test event probabilities with Brier-based scores. ROC AUC tells you whether high-probability cases tend to be events, but not whether a stated 70% probability occurs about 70% of the time. Reliability diagrams provide that calibration check. Keep finite-ensemble corrections consistent; an uncorrected small ensemble can look worse or better simply because of sampling effects.
Evaluate precipitation as a spatial problem
Point-by-point precipitation scores penalize a forecast when a rain band is displaced by one grid cell, even if the forecast captures its shape and intensity. Include neighborhood measures that give credit for nearby precipitation and object-based measures that compare coherent structures. A 2025 United States comparison of GraphCast and NWP precipitation used grid-based, neighborhood and object-oriented metrics against ERA5 and Stage IV analyses, illustrating why a single point score is inadequate for this variable. State the neighborhood radius, accumulation period, thresholds, object-matching method and verification references so another analyst can reproduce the result.
Stratify results instead of hiding weaknesses in an average
Publish a matrix of results by lead time, variable, region and season. Add event-specific slices for hazards that matter to users.
- Lead time: Show early, medium and late ranges separately; a model can lead at days 1–5 and trail at days 10–15.
- Variable: Temperature, pressure, wind, precipitation and upper-air fields have different error behavior.
- Region: Separate tropics, mid-latitudes, polar areas, land, ocean, mountains and coastal zones when those distinctions affect use.
- Season: Seasonal circulation, convection and observing-network coverage can change rankings.
- Event type: Test heat, heavy rain, high winds, severe storms and tropical cyclones with definitions relevant to the warning decision.
- Impact threshold: Where possible, evaluate whether the forecast supports an action—such as exceeding a rainfall threshold—rather than relying only on average field error.
Rare events require enough cases or an uncertainty statement. A few spectacular successes cannot establish reliability, and a global average can conceal a dangerous regional failure.
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Quantify uncertainty and statistical significance
A reported difference is an estimate from a finite sample, not a fact about all future weather. Use paired case-level comparisons because both models verify against the same events. Provide confidence intervals or another uncertainty estimate, explain the resampling or statistical test, and account for serial dependence when forecasts from nearby dates are not independent.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteEnsemble size, hindcast length and start-date design influence uncertainty. A large ensemble can estimate probabilities more smoothly than a small one, while a long hindcast can make a tiny difference statistically significant without making it operationally important. Report both practical effect size and uncertainty about the ranking. If multiple regions, variables and lead times are tested, identify whether significance was adjusted for multiple comparisons or treat individual discoveries as exploratory.
What current ECMWF evidence actually shows
ECMWF’s 8 May 2026 account of its 2025 evaluation says AIFS had errors 5–15% lower than IFS for most assessed surface and upper-air variables in the medium range, out to 15 days. The same account says AIFS improved tropical-cyclone location and track prediction but tended to under-predict cyclone intensity. Those statements are attributed to ECMWF’s operational assessment and apply to the tested versions, variables, period and verification design.
ECMWF’s Forecast performance 2025 report states that AIFS Single v1 entered operation on 25 February 2025 and AIFS-ENS launched on 1 July 2025. In its machine-learning-versus-physics-based comparison, it reports about a 10% medium-range precipitation improvement. That figure belongs to the report’s stated comparison context and should not be merged with the separate May 2026 summary.
The May 2026 summary also describes a 0.5–1% improvement in precipitation forecast skill for IFS. This is an IFS improvement statement, not an AIFS-versus-IFS result. Keeping these figures separate is essential: they use different comparisons and should never be presented as one estimate of AI’s advantage.
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ECMWF’s evaluation lead Matthieu Chevallier described user feedback as vital to forecast assessment: “We’re a user-driven organisation, and feedback and assessment from our Member and Co-operating States on ECMWF forecast performance are extremely valuable. Forecast evaluation is hugely enriched by user feedback and is an absolutely vital aspect of what we do and a key driver of our work: delivering high-quality forecast products that meet users’ needs.”
Use a comparison table that exposes scope
A publication-ready comparison should make the experimental boundaries visible rather than placing two headline scores side by side.
| Field | What to report |
|---|---|
| Initialization and lead | Dates, run times, valid times and lead-time bins |
| Model and version | Model name, release, deterministic or ensemble configuration and resolution |
| Variable and region | Variable, level, units, grid or stations and geographic mask |
| Reference and baseline | Observation, analysis or reanalysis; named baseline forecast |
| Metric and ensemble treatment | Metric definition, thresholds, neighborhood or object settings and finite-ensemble correction |
| Score and uncertainty | Point estimate, confidence interval or significance result, sample size and practical effect |
| Event behavior | High-impact strengths, failures, event definition and number of cases |
| Post-processing | Calibration, bias correction, downscaling or regridding applied to each system |
Common shortcuts that produce misleading rankings
Comparing different cases
Using one model’s available runs and another model’s different dates confounds weather difficulty with model quality. Restrict both to identical cases and list missing forecasts.
Reporting a score without its baseline
A percentage improvement has no meaning until the reader knows the reference forecast, sample, variable, lead and sign convention.
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A high ROC AUC can coexist with overconfident probabilities. Pair discrimination with reliability diagrams and Brier-based scores.
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Using only point precipitation error
Displaced but realistic rain systems can be penalized severely. Add neighborhood and object-based diagnostics.
Pooling all regions and leads
Global averages can erase a tropical-cyclone intensity weakness or a regional gain. Publish slices before any aggregate headline.
Ignoring version changes
An operational upgrade can create an apparent performance jump unrelated to the AI-versus-NWP question. Identify the change date and evaluate homogeneous periods.
Treating a reanalysis as unquestionable truth
Reanalysis has its own model and assimilation characteristics. Test sensitivity to independent observations or another reference when the conclusion matters.
Test robustness, transfer and physical consistency
The World Meteorological Organization identifies additional evaluation questions for training-data-based systems: robustness, transfer to other regions, fairness and consistency with physical principles. These are dimensions to test, not proof that every AI model fails them.
- Robustness: Check performance during unusual circulation, observing outages, sensor changes and extreme events.
- Geographic transfer: Train and evaluate with clearly separated regions or periods when assessing whether skill generalizes beyond familiar conditions.
- Fairness: Compare errors and warning quality across regions and communities that have different observing coverage and hazard exposure.
- Physical consistency: Check conservation-related behavior, plausible spatial and temporal evolution, and consistency among linked variables.
- Operational stability: Track latency, failed runs, missing fields and behavior after model or data-pipeline changes.
These tests complement, rather than replace, standard forecast verification. A lower average error is not enough if the system is unreliable in an underrepresented region or produces physically implausible combinations during extremes.
A practical reporting checklist
- State the exact question, forecast type, initialization period and maximum lead.
- Name every model version, resolution, ensemble size and initial-condition source.
- Use matched valid times, variables, regions and missing-data rules.
- Identify observations, analysis or reanalysis used for verification and explain its limitations.
- Name the baseline for every skill score.
- Report RMSE or MAE, bias and a baseline-relative score for continuous variables.
- Report CRPS or fair CRP skill, Brier-based scores, ROC AUC and reliability for ensembles.
- Add neighborhood or object-based metrics for precipitation.
- Break down results by lead, variable, region, season and event type.
- Show sample size, confidence intervals and significance treatment.
- Record post-processing and finite-ensemble corrections.
- Discuss robustness, transfer, fairness and physical consistency where relevant.
How to state the conclusion responsibly
Write the verdict at the same scope as the experiment. A defensible sentence names the systems, versions, period, geography, variables, lead time, reference and metrics, then states where one led and where it did not. For example, ECMWF’s 2025 assessment supports saying that AIFS outperformed IFS on most assessed medium-range variables in that evaluation while showing a tropical-cyclone intensity weakness. It does not support saying that AI forecasts are universally better than physics-based NWP.
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