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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAI weather models can generate forecast guidance far faster and with far less computing than traditional numerical weather prediction (NWP), but speed is not the same as accuracy. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Oceanic and Atmospheric Administration (NOAA) now run AI systems alongside established physics-based models. Their output can add useful guidance, especially at longer lead times, but performance still depends on the weather variable, location, forecast horizon, and event.
How does AI predict the weather?
Traditional NWP systems represent the atmosphere on a three-dimensional grid and repeatedly calculate how physical processes—such as winds, temperature, moisture, and pressure—change over time. The calculations are demanding because the model must advance the atmospheric state through many small time steps.
Learned weather models take a different route. They are trained on historical weather states and atmospheric analyses, learning patterns that connect one state of the atmosphere to a later one. Once trained, a model can infer a forecast without repeating the full set of physics-based calculations used by conventional systems. Training and development still require substantial data, computing, and meteorological expertise; the savings apply primarily to generating each forecast.
AI output is best understood as forecast guidance: information meteorologists and forecasting systems can assess alongside other evidence. It does not mean AI has replaced the established modeling and operational forecasting process.
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Why can AI models produce forecasts faster?
Inference with a trained model can take much less computation than running a full numerical simulation. Published examples illustrate the potential, but they describe different systems, hardware, and workflows—not a single controlled comparison:
- GraphCast: Google DeepMind reported in 2023 that the model generated a 10-day forecast of 35 GB in under 60 seconds on Cloud TPU hardware. In its evaluation, GraphCast outperformed ECMWF’s HRES on 89.3% of 2,760 evaluated variable-and-lead-time pairs. That figure is the share of comparisons won, not an accuracy percentage. Google DeepMind’s GraphCast announcement.
- ECMWF AIFS: ECMWF said in its February 2025 operational announcement that AIFS used approximately 1,000 times less energy to make a forecast than its traditional system. The claim concerns energy use per forecast, not a general measure of accuracy. ECMWF’s AIFS announcement.
- NOAA AIGFS: In December 2025, NOAA said a 16-day AIGFS forecast used 0.3% of the computing resources of operational GFS and completed in approximately 40 minutes. This is NOAA’s description of AIGFS v1.0, not a universal figure for AI weather models. NOAA’s operational AI-model announcement.
These comparisons should not be read as a direct ranking: the systems, forecast setups, hardware, and measures differ. The practical point is that AI inference can reduce the time and computing needed to produce some global forecast guidance.
Which AI weather models are in use?
AI systems are entering operational forecasting, but they are being used alongside traditional models rather than replacing them.
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ECMWF’s AIFS
ECMWF made AIFS Single operational on 25 February 2025, alongside its physics-based Integrated Forecasting System (IFS). It reported gains on selected verification measures. ECMWF’s ensemble version of AIFS became operational on 1 July 2025, also alongside IFS. Its 2026 performance report says AIFS skill is similar to several other machine-learning forecasts and notes a small decrease in skill over the preceding 12 months—one reason model rankings should be tied to a particular period and measure. ECMWF’s ensemble announcement and ECMWF’s 2025 forecast performance report.
NOAA’s AIGFS, AIGEFS, and HGEFS
NOAA announced its operational AI global-model suite on 17 December 2025. AIGFS is its AI-based global forecast system. AIGEFS is a 31-member AI ensemble, while HGEFS is a hybrid system combining AIGEFS with the conventional GEFS ensemble. NOAA reported improvements for selected large-scale features and longer-range tropical-cyclone tracks, but also said AIGFS v1.0 degraded tropical-cyclone intensity forecasts. Track and intensity are different forecast questions; progress in one does not establish progress in the other.
NOAA administrator Neil Jacobs described the deployment as “a significant leap forward in American weather model innovation.” That is NOAA’s characterization of the launch, not an independent performance assessment. Operational verification information is available from NOAA’s AIGEFS verification page; its current status and cycle details may change.
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GraphCast and WeatherNext
GraphCast is a prominent research model whose published results demonstrate what learned global forecasting can do under its evaluation setup. Those results do not establish that operational services use the same model weights, inputs, hardware, or verification method.
Google also documents WeatherNext model and data access through options including BigQuery, Earth Engine, Cloud Storage, and managed inference through Vertex AI Model Garden. Its documentation describes the forecasts as experimental and explains specific limitations. Availability and access terms can change. Google’s WeatherNext documentation.
Are AI weather forecasts more accurate?
There is no single answer for every forecast. A model can perform better on one variable or lead time and worse on another. For example, a model that improves large-scale patterns several days ahead may still struggle with localized precipitation or the intensity of a particular storm.
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Published results must stay attached to their evaluation context. GraphCast’s 89.3% result is the proportion of evaluated variable-and-lead-time pairs in which it outperformed ECMWF HRES—not an overall accuracy score. ECMWF reported gains of up to 20% on selected AIFS measures, while its 2026 report also noted a small decrease in AIFS skill over the previous 12 months. NOAA reported selected improvements for AIGFS and, at the same time, a weakness in tropical-cyclone intensity forecasts.
To compare forecasts for a real need, look for results that match the forecast question:
- Variable: temperature, wind, rainfall, storm track, and storm intensity are distinct measures.
- Place and scale: global or broad-area performance does not guarantee precision for a specific location or localized weather.
- Lead time: a model’s skill can change as the forecast extends further into the future.
- Verification method and period: deterministic scores and probabilistic ensemble measures answer different questions, and rankings can shift over time.
- Operational context: consider the model’s status, initialization and training data, forecast latency, computing requirements, and documented failure modes.
Why do ensembles matter?
A single forecast trajectory shows one possible evolution of the atmosphere; it cannot, on its own, communicate the full range of plausible outcomes. An ensemble runs multiple forecasts to show how outcomes vary, which helps represent uncertainty rather than implying that one exact path is certain.
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NOAA describes AIGEFS as a 31-member AI ensemble. Its HGEFS combines that AI ensemble with the physics-based GEFS ensemble, bringing both forms of guidance into a hybrid system. Combining forecasts can offer complementary information, but it does not remove uncertainty or guarantee a correct outcome.
What are the limits of AI weather models?
AI systems have documented weaknesses, and they vary by model. NOAA’s reported AIGFS v1.0 weakness in tropical-cyclone intensity is one operational example. Google’s WeatherNext documentation describes additional issues that users should understand when interpreting its output:
- Smoothing at longer lead times: deterministic machine-learning forecasts can become progressively smoother, losing fine-scale structure as predictions average plausible outcomes.
- Precipitation and data quality: rainfall guidance can be affected by the quality and biases of training data. Google says WeatherNext 3 combines multiple precipitation sources to address some of these issues.
- Local observations may not match training data: reanalysis products used to train models have limited resolution and biases, and may differ from ground measurements, particularly for localized variables. Bias correction may be needed.
- Artifacts: Google notes visible artifacts in some outputs, particularly certain station and precipitation products.
These caveats are model- and output-specific; they do not establish that every AI forecast has the same limitations. Google’s guidance for WeatherNext 3 is explicit: “For the protection of life and property, never rely on WeatherNext 3 as a sole source of information and always defer to official alerts and advisories from your national meteorological service and local emergency authorities.” For decisions affecting life or property, follow official alerts and advisories from the relevant weather service and emergency authorities rather than relying on an experimental AI forecast alone.
What AI forecasting means for weather services
AI models can make it cheaper and faster to generate some forecast guidance, and operational deployments from ECMWF and NOAA show how agencies can add that capability while retaining established physics-based systems. The useful question is not whether AI is categorically better, but where a particular model adds reliable information: for which variable, location, lead time, and type of event—and with what uncertainty.
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