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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchResearchers make weather predictions more accurate by improving the observations that describe current conditions, combining those observations with previous forecasts to estimate the atmosphere’s present state, refining forecast models, and using ensembles to account for uncertainty. Artificial-intelligence systems are now joining physics-based models, but no method eliminates uncertainty or is best in every situation.
Why better forecasts start with better observations
A forecast depends on how well a system describes the atmosphere before projecting how it will change. Observations from satellites and ground-based instruments provide that starting evidence. NOAA says satellite data and imagery feed its weather prediction models (NOAA: Improving weather forecasts).
The type and coverage of observations matter as much as their quantity. ECMWF reports that microwave radiances and radio occultation observations are especially important for defining large-scale temperature and humidity conditions, while additional wind information is still needed (ECMWF: From space to forecast). Gaps in observations mean the atmosphere cannot be measured everywhere at once.
How data assimilation creates a starting forecast
Data assimilation combines available measurements with information from a previous short-term forecast to estimate the atmosphere’s current state. A new forecast begins from that estimate. Because small errors in the starting conditions can grow over time, more accurate and timely observations can help—but only if the data are sufficiently reliable and cover the conditions being forecast.
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Update frequency alone does not guarantee improvement. NOAA experiments found that hourly updates improved skill for some variables and regions when dense, accurate observations were available. When aircraft observations were withheld, hourly cycling degraded the fit (NOAA Physical Sciences Laboratory, October–December 2025 publication report). The useful update schedule depends on the quality and availability of observations.
How models and computing turn conditions into predictions
Numerical weather prediction models use mathematical representations of atmospheric motion and physical processes, including cloud formation, alongside relevant land and marine processes. They calculate how conditions may evolve from the estimated starting state. Since some weather processes occur at scales too small for a model to resolve directly, they must be approximated; computing capacity also limits the detail and number of forecasts that can be produced.
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Model resolution, frequent observations, and ensembles all contribute to forecasting work, but a model’s output is guidance rather than certainty. Forecasters assess multiple models and account for their different strengths and weaknesses. NOAA describes these elements in its overview of how forecasts are improved; ECMWF explains the modelling challenges in Modelling and Prediction.
How ensembles show forecast uncertainty
A single forecast can make one possible outcome look more certain than it is. An ensemble runs forecasts from slightly different initial conditions to show a range of plausible outcomes. Differences between those runs reveal how sensitive a prediction is to small changes in the starting state.
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ECMWF describes its medium-range ensemble, or ENS, as 51 forecast realisations: one control forecast and 50 perturbed members. That count describes the system, not its accuracy. Ensemble information can help people make decisions by showing the likelihood and spread of possible outcomes; it does not promise that any one member will occur (ECMWF: Quantifying forecast uncertainty).
What AI adds—and what it has not settled
AI forecasting systems are now operating alongside established physics-based systems. ECMWF says it launched the first operational version of its Artificial Intelligence Forecasting System (AIFS) in February 2025, followed by an ensemble version in July 2025 (ECMWF: Modelling and Prediction).
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NOAA reports that its AI-based AIGEFS has 31 members, uses 9% of the computing resources of operational GEFS, and has comparable forecast skill. NOAA also reports that its hybrid HGEFS outperforms both GEFS and AIGEFS across most major verification metrics. Those findings apply to the systems and measures NOAA reports; they do not establish universal superiority for AI or hybrid forecasting (NOAA deployment announcement).
AI can also support very short-range nowcasting. The World Meteorological Organization describes deep-learning approaches that combine radar, satellite observations, and local meteorological and hydrological records to improve predictions of precipitation and hazards such as hail, gusts, and lightning. It also highlights questions about robustness, performance across regions, fairness, and physical consistency. Faster output alone does not prove a forecast is more accurate (WMO: Forecasting the Future).
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How researchers can tell whether an improvement is real
Forecast comparisons are meaningful only when systems are judged on comparable tasks. Researchers need to account for the forecast variable, lead time, geography, and verification period, and distinguish the accuracy of a single forecast from the skill of probability forecasts. Resource use, performance on rare or high-impact events, and whether a method transfers to different regions and observation conditions also matter.
The WMO describes work toward fair evaluation of AI, physics-based, and hybrid forecasts. This is essential because a result for one region, time horizon, or metric cannot establish that a system is better everywhere. No single historical accuracy figure captures progress without specifying what was predicted, where, how far ahead, and against which baseline.
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