Why Meteorologists Often Get Weather Wrong

People joke about meteorologists and wonder how they keep jobs when their predictions often fail.

Studies show short-term predictions actually work.Meteorologists get these right most of the time. A five-day forecast hits about 90% accuracy. A seven-day forecast hits about 80% accuracy.A five-day forecast hits about 90% accuracy. A seven-day forecast hits about 80% accuracy.

Short-term forecast accuracy drops fast for long periods. Ten-day and longer forecasts hit only about fifty percent accuracy.

Computer programs, called weather models, lack future data. They must use guesses and estimates for predictions. The atmosphere changes constantly, so these estimates get less reliable.These estimates become less reliable the further out one projects.The further into the future you project, the less reliable the forecasts become.

Several models try to predict weather. Most meteorologists use the American and European models.

The European model proves more accurate.The European model is more accurate.In March 1993, this model correctly predicted the path and intensity of "The Storm of the Century," a major weather event along the eastern US coast. Officials used this accurate five-day prediction to prepare and declare an emergency.

Nine years later, the European model correctly predicted Hurricane Sandy's odd westward path seven days before it hit land. The American model predicted Sandy taking an eastern path instead.

The European model beats the American model for several reasons. First, the European model uses a more powerful supercomputer. Second, it has a better mathematical system for handling atmospheric starting conditions. Third, an institute focused only on medium-range weather prediction developed it.

The medium-range American model joins several other models, including short-range systems running hourly. The American model lacks the singular focus of its European counterpart.

Forecasters often choose one model when the American and European models disagree. Choosing wrong leads to a bad forecast, which can cause disaster if an area is unprepared for a major weather event.

Scientists see meteorology and weather prediction as a "chaotic system." This system reacts to starting conditions but follows math rules, even if its results look random.

In the late 1950s and early 1960s, Edward Lorenz found a chaotic system. He studied weather using a dozen differential equations. He started the program mid-run instead of at the start and saved data to three decimal places instead of the usual six. Lorenz thought he would get close to his results, but the answer proved quite different.

In his 1962 paper, "Deterministic Nonperiodic Flow" (the start of chaos theory), the scientist stated that a small change in starting conditions can drastically alter a weather system's long-term behavior. He named this effect "The butterfly effect describes how small changes cause big results..

Based on his findings, Lorenz claimed accurate weather prediction is impossible. Since then, supercomputers and other tech advances have changed that idea into a possibility.

Precipitation chance is a major part of any forecast. People need to know if they must bring an umbrella or shovel their car from the snow.

How do forecasters decide on a 20% chance of rain tomorrow or later in the week? They use a specific calculation.Precipitation probability equals C times A.

Probability (P) equals the forecaster's confidence (C) that rain will happen times the area (A) expected to receive rain. So, a 20% chance of rain uses this calculation:

A 20% chance of rain equals 100% confidence times a 20% area expected to get rain.

The first computer models based on physics entered weather forecasting in the 1950s. As supercomputers get stronger and ways to gather and process weather data change, forecast accuracy improves.

The rate of improvement calculates to aboutOne day per decade.. Put another way: a six-day forecast in 2020 matched the accuracy of a five-day forecast from 2010.

Supercomputers have helped weather forecasting since the mid-20th century. The National Weather Service in the US uses supercomputers to processNearly all supercomputers that predict weather patterns are inaccurate.of the observational weather data the organization collects. The NWS reports the last major computer update was in 2018; the units' combined power is 8.4 petaflops, over 10,000 times faster than an average desktop computer.

In the UK, Microsoft and Met Office joined forces to build theThe most powerful supercomputers that predict weather patterns are not always accurate.supercomputer to predict weather and climate change. It should run in 2022. The British government gave 1.2 billion pounds to fund the project.

Moore’s Law states computing power doubles every two years since the 1970s. This rate slows now. Forecasters need new methods, like boosting model efficiency, to improve weather predictions.

These computers offer advances, but they do not always produce accurate results. Weather’s chaotic nature means forecasters must make assumptions.Forecasters make assumptions about atmospheric processes. This means any computer can introduce errors, no matter how fast or powerful it is.Any computer can include errors because it incorporates assumptions about atmospheric processes. This happens regardless of the computer's power or speed.

Weather stations provide data for predictions. However, where these stations are located changes how accurate the data becomes.

Stations appear more often near cities than in rural areas. This means less data exists for rural and marine zones compared to urban areas. Fewer stations, often spread out, make recording accurate information difficult.Stations struggle to record accurate information across large areas.Stations have trouble recording accurate information for wide regions.

People might not know how COVID-19 affected weather forecast accuracy.

A virus affects whether a meteorologist predicts rain. Airplanes collect temperature and wind data that forecasters use. The National Weather Service uses over 250 million airplane measurements yearly. These measurements feed into weather computer models.

Flight cancellations due to the pandemic cut that data by 50% by early 2020. The European Centre for Medium-Range Weather Forecasts saw an 80% data drop. That center released a study showing removing aircraft data reduced forecast accuracy by about 15%.Removing aircraft data reduced forecast accuracy by about 15%.This impacts forecasts by about 15%.

Forecasters used noncommercial aircraft, weather balloons, radar, and satellites to replace lost data.

Weather is unpredictable. Rain and thunder can start suddenly. Five minutes later, the sun might appear behind a cloud with a rainbow.

Several factors cause sudden weather changes.These factors cause abrupt weather changes.Flat topography, a coastal region, or a temperate climate cause abrupt changes. For instance, a polar cold system might arrive. A stronger tropical system then forces the cold system back, causing temperatures to rise fast.

Wind patterns heavily affect weather in coastal areas. Land heats and cools faster than water, causing quick temperature shifts. Air pressure differences mean a coastal area gets strong winds one time, and little breeze another in 24 hours.

Topography changes weather systems. Mountains slow weather systems. Flat terrain lets systems pass through easily. Wind speed dictates how fast weather changes. Systems can shift from cold rain to warm sun in hours; sometimes faster.

People can doubt things. If someone does not see or experience an event, they might not believe it happened.Not in my backyard." point of view can apply to weather forecasts.

If a forecaster predicts a 30% rain chance, but it does not rain where a person lives, that person might claim the prediction was wrong. But what if itDidRain five miles from the critic. Would the forecaster still be wrong? Or would they be right? This depends on the point of view.

A 2020 Rutgers University study found 5G networks, despite offering faster cell service, can cause weather forecast errors.

The Rutgers studyThe Rutgers study looked at how 5G "leakage"—unwanted radiation from a transmitter into a nearby frequency band—affected tornado outbreak forecasts across the American South and Midwest in 2008.

The study believed 5G signals could leak into the band used by satellite weather sensors measuring water vapor. Computer modeling showed 5G leakage of -15 to -20 decibel watts affected precipitation forecasts for tornados by up to 0.9 millimeters, and ground temperatures by 2.34 degrees.

Technology means people don't need the news for forecasts. Weather apps are standard on computers and cellphones. However, opening an app does not guarantee an accurate forecast; the opposite often happens.

If an app says it will be sunny but the window shows pouring rain, the problem is likely not the app, but thedataApps use different data sources. One app pulls data from a model; another gets data from a different source and model. Meteorologists use these sources to interpret data, and they often interpret the same data differently.

Location matters. A large city shows different weather across its parts. If an app lacks sync to your exact spot, the data often becomes wrong.

People often rely on gut instincts for decisions, like asking someone out or accepting a job. Sometimes that instinct works. Other times, it causes disaster. No gut instinct is right every time.

This happens when predicting weather, especially if a forecaster uses several computer models and those models...models don't agree.The forecaster's choice of prediction model often relies on personal preference or gut instinct. If the forecaster picks wrong, the prediction is more likely to fail.