What if weather forecasts were always right?

A forecast is a guess about tomorrow's weather, made by scientists.

Most of the time it is right. What if it were right every single time?

Every day, forecasters tell us whether to expect sunshine, rain or snow. Their forecasts are often right, especially for the next few days, but further ahead they get less reliable.

What if forecasts were always right, even weeks ahead? In this thought experiment, you can follow three paths: why tiny changes in the air make perfect forecasts so hard, how giant computers build forecasts, and how satellites and weather balloons watch the atmosphere.

Modern forecasts come from computer models that start from a snapshot of the atmosphere and calculate how it will change. NOAA's NESDIS notes that models must use estimates and assumptions, because we cannot collect data from the future, so their reliability falls with lead time.

This thought experiment imagines a forecast that is always right, at any range. Edward Lorenz's discovery of chaos shows why that is out of reach: in a chaotic system, immeasurably small differences in the starting state grow until the forecast and reality part ways. The benefits of perfect forecasts are ideas; the limits are well-established science.

Forecasts for the next few days are usually right.

Forecasts for many days ahead are right only some of the time. The further ahead, the harder it gets.

A 2025 NOAA guide says a five-day forecast is right about 90 percent of the time, and a seven-day forecast about 80 percent of the time. But a forecast for ten days or more is right only about half the time.

Forecasts also use chances. When a forecast says '40 percent chance of rain', the National Weather Service explains that there is a 40 percent chance that your spot will get at least a hundredth of an inch of rain. It does not mean it will rain for 40 percent of the day.

A 2025 explainer from NOAA's NESDIS gives rough accuracy figures: about 90 percent for five-day forecasts, about 80 percent for seven-day forecasts and about half for ten days or more. The decline reflects how errors in the starting estimate grow as the model runs forward.

Because certainty is impossible, forecasts state probabilities. The NWS defines the probability of precipitation (PoP) as the chance that a given forecast point will receive at least 0.01 inch of rain. A 40 percent PoP is therefore a statement about likelihood at a place, not about how much of the day or the area will be wet. Probabilities let people weigh risks rather than trust a single guess.

Let's pretend forecasts were always right.

You would always know when to bring an umbrella. Farmers would know just when to plant. This is only pretend.

Now imagine forecasts that were never wrong, even a month ahead. You could plan a birthday picnic weeks early and know it would be sunny. Farmers could plant and harvest at exactly the right time. Airports and ships could plan around every storm long before it formed.

But there might be surprises too. If everyone knew rain was coming on a certain day, events might crowd onto the sunny days. These are ideas, not things that can happen: scientists think perfect long-range forecasts are impossible.

A perfect forecast at any range would turn weather from a risk to be managed into a schedule. Agriculture, energy supply, transport and emergency planning might all be organized weeks or months ahead with certainty. Probabilistic language, such as PoP, might become unnecessary.

These are reasoned ideas, and they rest on an impossibility. Perfect forecasts would require a perfect description of the atmosphere's present state, down to every gust and eddy, plus a perfect model of how it evolves. MIT notes that chaos led meteorologists to conclude it may be fundamentally impossible to predict weather beyond two or three weeks with reasonable accuracy.

The air is always moving and changing.

A tiny change today can grow into a big change later. That is why forecasts far ahead are hard.

In 1961, a scientist named Edward Lorenz at MIT ran a weather simulation on a computer. When he ran part of it again, he rounded one number from .506127 to .506, a tiny change. To his surprise, the simulated weather turned out completely different over two months.

Lorenz realized that in weather, small changes can have large consequences. In 1972 he gave a talk asking whether the flap of a butterfly's wings in Brazil could set off a tornado in Texas. The idea became known as the butterfly effect.

Lorenz's model had 12 variables, representing quantities such as temperature and wind speed. Restarting a run with one value rounded from .506127 to .506, a change in the fourth decimal place, he found the new run diverged until the whole pattern changed over two months of simulated weather. He published the result in 1963 as 'Deterministic Nonperiodic Flow'.

This is sensitive dependence on initial conditions: a deterministic system can still be unpredictable in the long run. His 1972 talk, 'Predictability: Does the Flap of a Butterfly's Wings in Brazil Set Off a Tornado in Texas?', gave the idea its name. Because observations can never capture the atmosphere exactly, chaos sets a limit on forecasting.

Weather balloons float up high and measure the air.

Satellites watch the clouds from space. Big computers use all of it to make the forecast.

Forecasts start with measurements. Every day, at the same times around the world, weather balloons rise from more than 800 stations, carrying instruments that measure temperature, humidity and pressure as they climb more than 35 kilometers high.

Satellites add a view from space: NOAA's polar-orbiting satellites circle Earth 14 times a day and see every part of the planet twice daily. The National Weather Service feeds all this data, plus radar and buoys, into supercomputers that run around the clock.

The NWS has launched radiosondes since the late 1930s. Each 60-to-80-gram package rises about 300 meters a minute, reporting pressure, temperature, humidity and GPS position every second, and can climb beyond 35 km. More than 800 stations worldwide launch at the same times each day and share data internationally; the NWS fact sheet lists 92 NWS stations.

NOAA's GOES satellites, about 22,000 miles up, watch weather continuously, while JPSS satellites about 500 miles up orbit pole to pole 14 times a day. NWS supercomputers, in Reston, Virginia, and Orlando, Florida, combine these observations with radar and buoy data, and share their output worldwide. Better data and models keep improving forecasts, but they cannot remove chaos.