Did you know? A forecast of tomorrow’s weather may begin with measurements taken thousands of kilometres away. Weather systems move, interact and change, so scientists combine observations with physical models—and increasingly with trained AI—to estimate what could happen next.
Quick answer: collect observations → estimate the current atmosphere → run a forecasting model → compare possible outcomes → check the forecast against real observations. Machine learning can help model how weather evolves, but it does not make storms or rainfall perfectly predictable.
Imagine watching dark clouds approach a playground. You could guess that it may rain soon. A professional forecasting system must also consider temperature, pressure, humidity, winds and the conditions over a large surrounding region.
Weather forecasting in 30 seconds
- Observation: a measurement from weather stations, satellites, balloons, aircraft or other instruments.
- Initial conditions: the best available estimate of the atmosphere at the beginning of a forecast.
- Numerical weather prediction: forecasting through mathematical representations of physical processes.
- Machine-learning weather model: a model trained to forecast how atmospheric conditions change.
- Deterministic forecast: one predicted evolution from a defined setup.
- Ensemble: a collection of forecasts used to investigate uncertainty and alternative outcomes.
An everyday weather app may display a simple icon, but creating the underlying forecasts can involve specialised scientific centres, large datasets and substantial checking.
Step 1: Measure the world before predicting it
A useful forecast starts from suitable evidence. Ground stations can measure temperature, pressure and other variables. Satellites observe broad regions, while aircraft and weather balloons provide information about conditions above the ground.
No instrument covers every point in the atmosphere. Measurements have different accuracy, locations and times. A forecasting system must combine them into a consistent estimate rather than pretend it has perfectly observed every cloud.
The European Centre for Medium-Range Weather Forecasts (ECMWF) explains that its operational AI forecasting system starts with initial conditions drawn from a physics-based data-assimilation process using many types of observations. The AI model does not simply look at a single photograph of the sky.
Step 2: Create a starting picture of the atmosphere
Scientists refer to data assimilation when combining observational information with previous model estimates to produce a coherent initial state. That state can contain wind, temperature, pressure, moisture and many other variables across a geographic grid.
Imagine trying to describe a classroom using reports from five people who looked in from different doors. One saw the windows, another saw the whiteboard and a third heard the air conditioner. Combining partial views is better than treating one description as complete.
Atmospheric analysis is far more mathematical, but the principle is similar: the starting state matters because later predictions depend on it.
Step 3: What traditional weather models do
Conventional numerical weather prediction uses mathematical representations of atmospheric physics to estimate how the state changes over time. Computers calculate how relevant physical variables interact on a chosen grid or other representation.
Some processes occur on scales smaller than the model resolves. These effects require appropriate approximations, and their accuracy is an important part of scientific model development.
Such models have decades of scientific development behind them. AI-based forecasts are valuable additions, not evidence that conservation laws, observations or atmospheric expertise have become unnecessary.
Step 4: What changes when AI predicts the next state
A machine-learning forecast model can learn relationships from carefully prepared historical weather analyses and other suitable datasets. Given the current atmospheric state, it predicts later states using its learned parameters.
Instead of numerically solving every component of a conventional physics system during inference, some AI models estimate the atmospheric evolution directly from patterns learned in training. That can make the forecast calculation significantly faster for supported tasks.
But a trained model can still make physical or statistical errors. Developers check important constraints, test performance against observational records and compare results with established forecasting systems.
The important distinction: Faster forecast computation is not the same as a guarantee that every local shower or storm will be predicted correctly.
A real example: ECMWF’s AIFS
ECMWF put its Artificial Intelligence Forecasting System (AIFS) Single into operational use on 25 February 2025, alongside its physics-based Integrated Forecasting System. The ensemble version, AIFS ENS, began operational service on 1 July 2025.
According to ECMWF’s current model information, both systems were upgraded to version 2 in May 2026. The deterministic model produces a single forecast, while the ensemble approach produces multiple possible evolutions that can help forecasters assess uncertainty.
ECMWF describes the AI and physics-based systems as complementary. Forecast skill depends on the weather variable, location, lead time and verification method; it would be inaccurate to say AI always beats every conventional method.
Step 5: Why run more than one forecast?
The weather system is sensitive to initial conditions and model assumptions. Two plausible starting estimates can develop differently. An ensemble explores a range of possible outcomes instead of presenting only one future.
For example, suppose 30 of 50 fictional ensemble members predict rain under a carefully defined event and period. The fraction is 30 ÷ 50 = 60%. That is a teaching calculation, not automatically a calibrated 60% rain probability.
Actual probabilistic forecasts depend on ensemble design, verification, calibration and a precise definition of the event. The important lesson is that several possible outcomes help communicate uncertainty that one deterministic track may hide.
Why local rain can be hard to predict
A region-wide model may accurately capture a broad weather pattern while missing a short-lived shower over one street. Small storm cells, local wind patterns and the limited resolution of data make precise timing and placement difficult.
Imagine two neighbouring parks. One receives a brief downpour while the other remains mostly dry. A forecast valid over a broad grid cell may not be a guarantee for either exact location.
For lightning, floods or severe weather, readers should use relevant official alerts and timely observations rather than relying on an illustrative AI response or one outdated forecast screenshot.
What does a fair comparison of forecast models look like?
A good evaluation checks the same weather variable, geographic region, forecast horizon and period against reliable reference observations. Different scoring methods reveal different types of error.
For temperature, one option is mean absolute error. Imagine three made-up predictions of 28°C, 30°C and 32°C, with actual observations of 29°C, 27°C and 34°C. The absolute errors are 1, 3 and 2 degrees. Their mean is (1 + 3 + 2) ÷ 3 = 2°C.
That value describes only our fictional three-case sample. Real forecast centres assess many dates, atmospheric levels and variables; some scores evaluate entire probabilistic distributions.
Weather forecasting is not the same as climate projection
Weather forecasting concerns the evolution of conditions over coming hours and days, and sometimes further. Climate analysis and projection study longer-term statistical patterns and changes under stated conditions and scenarios.
The methods can share data and mathematical tools, but one accurate short-range forecast does not settle every long-term climate question. Likewise, a long-term climate trend is not a precise forecast of rain at 3 pm on a particular street.
Try it: make and check a tiny forecast
On paper, record three successive daily temperature observations from a fictional town: 27°C, 28°C and 29°C. Predict tomorrow using a simple rule, such as assuming the last observed temperature continues. Then imagine tomorrow is actually 31°C.
Your one-day forecast error is two degrees. Now discuss what information you omitted: wind, cloud cover, season, humidity and surrounding regional conditions.
This is not a real weather-forecasting model, but it demonstrates why a naive time-series rule can be useful as a baseline and why additional evidence matters.
Five misconceptions worth avoiding
- “AI forecasts run without weather observations.” Operational systems still depend on well-prepared atmospheric initial conditions.
- “One predicted map tells the exact future.” Uncertainty and alternative outcomes matter.
- “Faster computation proves universal superiority.” Skill differs across variables and conditions.
- “A global weather grid predicts every street’s rainfall precisely.” Local resolution and phenomena impose limits.
- “A weather app icon is the entire forecast.” Professional products contain richer probabilistic and scientific information.
Frequently asked questions
How does AI predict the weather?
A trained model uses suitably prepared atmospheric information to estimate future conditions based on patterns learned from past data.
Does AI replace physics-based weather models?
Not generally. Centres such as ECMWF operate AI-based and physics-based systems together, with complementary strengths and ongoing verification.
What is an ensemble weather forecast?
It is a set of forecasts designed to explore alternative outcomes and help characterise forecast uncertainty.
Why can a weather forecast change between mornings?
New observations and updated initial conditions can alter the predicted evolution of a complex atmospheric system.
Can AI guarantee whether my street will flood?
No. Flood risk needs appropriate local data, hydrology and official warnings; an AI-generated forecast should not be treated as a guarantee.
Your one-minute takeaway
- Weather prediction starts with quality-controlled observations and an estimate of the current atmosphere.
- AI can forecast atmospheric changes quickly and complement physics-based models.
- Local uncertainty, verification and official alerts remain essential.
Authoritative reading and related AI guides
- ECMWF: AIFS Machine-Learning Forecast Data — current AIFS Single and ensemble versions.
- ECMWF: AI Forecasts Become Operational — how the first system works.
- ECMWF: Ensemble AI Forecasts — why multiple scenarios matter.
- Geoscientific Model Development: AIFS Single Technical Description — constraints, training and scientific details.
- How AI Forecasts Sales, Traffic and Demand — compare weather with ordinary time-series forecasting.
- How AI Measures Confidence and Uncertainty — interpreting forecast probabilities.
- How AI Works: Complete Overview — the full series.