How Weather Forecasts Work

How a Forecast Is Made

Weather forecasting starts with observations: hundreds of millions of them, every few hours, from weather stations, satellites, ocean buoys, weather balloons, aircraft, and radar. These measurements are fed into computer models that simulate the atmosphere using the laws of physics.

The models divide the atmosphere into a three-dimensional grid and calculate how temperature, pressure, humidity, and wind will evolve at each point over time. This process is called numerical weather prediction (NWP) and it runs on some of the most powerful supercomputers in the world.

Because no set of observations is perfect, forecasters also run ensemble models: many slightly different simulations with varied starting conditions. When the simulations agree, confidence is high. When they diverge, the forecast is less certain.

How Far Ahead Can You Trust a Forecast?

Forecast accuracy drops with time. Here’s a rough guide to what you can expect:

Lead Time	Reliability	What It’s Good For
Today	Very high	Hourly planning, commute, outdoor events
1–3 days	High	Weekend plans, travel prep, event scheduling
4–5 days	Good	General planning; expect some details to shift
6–7 days	Moderate	Broad trends only: warm vs. cold, dry vs. wet
8–10 days	Low	Rough tendencies; specifics are unreliable
Beyond 10 days	Very low	Only large-scale patterns, not day-to-day detail

A useful rule of thumb: forecast skill has improved by roughly one extra day of accuracy per decade over the past 40 years. Today’s 5-day forecast is significantly more accurate than it was just a few decades ago.

The atmosphere has a hard predictability limit of roughly two weeks, established by researchers in the 1960s. Beyond that, the atmosphere is too chaotic for any model, no matter how powerful, to produce a meaningful day-by-day forecast.

Why Forecasts Change

The atmosphere is a chaotic system. Tiny differences in starting conditions, a fraction of a degree here, a small shift in humidity there, can compound into large differences in the outcome days later. This sensitivity to initial conditions is sometimes called the butterfly effect: the idea that a small change in one place can cascade into a vastly different outcome elsewhere. The real-world implication is that observation networks can never measure the entire atmosphere with perfect precision, so some uncertainty is always baked in from the start.

Forecasts change for three main reasons:

• New data arrives. Every few hours, fresh observations update the picture of the current atmosphere, which shifts the starting point for the next model run.
• Small errors grow. Even tiny inaccuracies in the initial state amplify over time, which is why longer-range forecasts are less reliable.
• Models have limits. Global weather models divide the atmosphere into a grid, roughly 10 to 25 km across depending on the model and configuration. Weather that happens at scales smaller than that has to be approximated, which introduces uncertainty. Regional and local models use finer grids and can resolve smaller-scale weather like thunderstorms more accurately, but they cover a limited area and still depend on global models for their boundary conditions.

A forecast changing isn’t a sign that it was wrong; it’s a sign that the system is working as intended, incorporating better information as it becomes available.

What’s Harder to Forecast?

Not all weather is equally predictable. As a general rule, larger and slower-moving systems are easier to forecast than smaller, faster ones.

Easier to Forecast	Harder to Forecast
Large pressure systems and fronts	Thunderstorms and convective storms
General temperature trends	Precipitation type (rain vs. snow vs. ice)
Hurricane tracks	Hurricane intensity
Broad wind patterns	Fog and local effects

Thunderstorms are especially difficult: they form at scales below what models can resolve and can develop in under an hour. Winter precipitation type is tricky because the difference between rain, sleet, and snow often comes down to just a degree or two in a narrow layer of the atmosphere. Complex terrain (mountains, coastlines, valleys) creates local effects that global models struggle to capture.

Short-Range vs. Long-Range Forecasts

Not all forecasts are trying to do the same thing. In the first few days, models produce detailed, location-specific predictions: temperature by the hour, rain starting at 2 pm, wind picking up in the evening. This is where forecasts are at their best.

Further out, the approach shifts. Rather than telling you exactly what will happen on a specific day, longer-range forecasts deal in probabilities and broad trends: whether the coming week will be warmer or cooler than average, wetter or drier. The further out you go, the less a forecast can say about any single day, and the more it becomes a guide to the general pattern.

Seasonal outlooks stretch months ahead, but these aren’t really weather forecasts at all. They’re driven by slow-moving climate signals like El Niño, La Niña, and other large-scale ocean and atmospheric patterns, and they describe tendencies rather than specific conditions.

Tips for Reading Forecasts

• Check forecasts closer to the date. A 7-day forecast gives you a direction; a 1–2 day forecast gives you the detail.
• Rain percentage ≠ duration. A 60% chance of rain doesn’t mean it will rain for 60% of the day. It means there’s a 60% probability that rain will fall at any given point in the forecast area during that period.
• Forecasts are for areas, not pinpoints. Your backyard conditions may differ from the nearest forecast point, especially in hilly or coastal areas.

How Airpult Shows Forecasts

On Airpult, forecast data is refreshed regularly as new model runs become available. Use the explore page to search for any location and see its full forecast.