The latest GRAF® weather model improvements
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Key takeaways
- Accurate weather forecasting combines real-time data, advanced models, and expert interpretation.
- High-resolution models, such as GRAF®, multi-model ensembles like WxMix, and new AI methodologies are transforming forecast precision on a global scale.
- Forecast accuracy directly impacts decision-making across various industries, including aviation, media, advertising, utilities, government sectors, and the daily lives of people everywhere.
What is the origin of weather forecasting?
Weather forecasts have been around since the beginning of civilization, when humans used recurring meteorological and astronomical events to better monitor weather patterns and plan for seasonal changes. Initially based on simple observations of the sky, wind, and temperature, these forecasts have thankfully evolved into more advanced and reliable ones.
Incorporating technology into weather forecasting began in the 1700s with the development of the barometer and thermometer. These basic yet advanced tools not only paved the way for more accurate weather predictions but also inspired generations of weather enthusiasts interested in advancing the science.
Today, individuals and businesses alike rely on accurate weather forecasting to anticipate severe weather and drive daily decision-making. But how truly reliable are weather forecasts?
To understand reliability, you must define what an accurate weather forecast is
An accurate weather forecast is a measure of how closely the forecast matches reality. To produce an accurate forecast, scientists combine complex data analysis, modeling, and human expertise. Forecasts can range from short-term to long-range predictions, each with varying degrees of accuracy.
Short-range weather forecasts (1–14 days) are typically generated by physics-driven models that ingest global weather data and simulate outcomes using advanced techniques, with AI increasingly contributing to the precision of these forecasts within this timeframe. These are considered more reliable due to their frequent model updates and high-resolution input data.
Long-range weather forecasts (15+ days) are largely based on historical data and pattern recognition to predict what’s ahead. Forecasts beyond 15 days are inherently less precise because of how rapidly the atmosphere can change. This means accuracy is likely to decrease the further out the forecast goes.
What type of weather forecast is the most reliable?
Forecast accuracy largely depends on the forecast range. Short-term forecasts typically demonstrate higher accuracy than longer-term. For example:
- A 7-day forecast can accurately predict the weather about 80% of the time.
- A 5-day forecast can accurately predict the weather approximately 90% of the time.
Accuracy drops as the forecast range increases, but advanced models and AI are helping to improve even long-range predictions.
Why is weather forecast accuracy important?
Weather significantly impacts people’s daily lives, and accurate weather forecasting enables communities and businesses to better prepare for the effects of changing weather conditions. By leveraging this information, utilities can strengthen grids, schools can decide whether to open, airlines can assess routes and safety parameters, and first responders and insurance companies can prepare for potential emergency situations.
As extreme weather events become more common, people and businesses rely on weather forecast accuracy more than ever to try to mitigate losses, influence safety measures, increase productivity, and improve business-related decisions.
According to research from the National Centers for Environmental Information (NCEI), between 2021 and 2024, the United States saw a total of 93 individual $1B+ weather and climate disasters (20 in 2021, 18 in 2022, 28 in 2023, and 27 in 2024). Prior to 2020, the highest number for a single year was 16. In 2024 alone, there were more than 150 unprecedented climate disasters globally and $182.7 billion in U.S. weather-related damages, making it the fourth-highest year on record.
This critical need for reliability and understanding underscores why accurate weather forecasting helps instill confidence, drive informed decisions, and protect people and communities. That’s why we’re committed to continuous innovation of current and future solutions. From rerouting flights to adjusting supply chains, better forecasts reduce risk and support operational confidence.
How do meteorologists predict the weather?
Weather forecasting is the process of combining scientific insights, data, and technology to assess future atmospheric conditions. Meteorologists observe, study, and predict changes in precipitation, temperature, wind, and more.
In today’s data-rich environment, meteorologists combine real-time observations, advanced modeling techniques, and expert interpretation to produce accurate forecasts. But how do weather forecasters predict the weather, and how are weather predictions made?
How a weather forecast is made
The process of creating a weather forecast begins with gathering data and using that information to feed forecasting models. These simulations help anticipate everything from temperature changes to the path of major storms, including tornadoes, dangerous winds, and severe precipitation.
At The Weather Company, our Human-Over-The-Loop (HOTL) model combines human meteorological expertise with advanced AI to create forecasting capabilities that neither could achieve on its own. Our team of over 100 expert meteorologists works in real-time with our AI systems, providing critical oversight and adding invaluable human intelligence to the process without slowing things down.
Weather forecasting is a four-pronged approach:
1. Observe: What the weather is like now
We ingest data from a collection of instruments to observe conditions on the surface and in the upper atmosphere, including:
- Weather radar: Detects precipitation and storm intensity.
- Weather balloons: Measure upper-atmosphere conditions.
- Barometers and thermometers: Monitor pressure and temperature.
- Satellites: Observe cloud cover and storm systems globally.
- Weather stations: Collect ground-level conditions.
- IoT sensors: Deliver hyperlocal temperature, humidity, and pressure data.
2. Model: How the weather evolves
Numeric Weather Prediction (NWP) models take current atmospheric conditions as a starting point to project a forecast. There are many well-known models, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), the Global Forecast System (GFS), and The Weather Company’s proprietary, hyperlocal Global High-Resolution Atmospheric Forecasting System (GRAF®).
Instead of relying on a single model, our AI-driven, multi-model ensemble, WxMix, synthesizes and optimizes over 100 models, ensuring that we always leverage the best available science.
3. Produce: Combining models with meteorologist expertise
We combine advanced modeling with human meteorological expertise to translate model outputs into actionable insights, such as daily highs/lows, severe weather alerts, and turbulence maps.
4. Deliver: Share forecasts across channels
Forecasts are delivered instantly across apps and websites, such as The Weather Channel, Weather Underground, and Storm Radar, which feature APIs that provide real-time and historical data, aviation dashboards, broadcast media systems, and displays, as well as mission planning tools.
The role of AI in accurate weather forecasting
AI is rapidly transforming weather forecasting, significantly enhancing accuracy and speed. This rapid processing enables more frequent forecast updates, which is crucial for quickly evolving weather events, such as severe thunderstorms. In particular, new deep learning-based AI models (DL-NWP) are showing promise in improving the accuracy, granularity, and cost-effectiveness of traditional models while also demonstrating an enhanced ability to depict the range and likelihood of potential weather outcomes.
Long before “AI” became a buzzword, The Weather Company was harnessing the power of sophisticated algorithms, statistical models, and data-driven computational methods to improve weather forecasting and deliver actionable insights to consumers and businesses globally.
Today, we’re working with partners like NVIDIA to actively develop new deep learning approaches and incorporate the latest AI models into our forecasting processes to continuously improve forecast precision.
Benefits of accurate weather forecasting
Weather affects nearly every sector, from supply chains and staffing to safety and customer engagement. NCEI research shows it impacts an estimated $3 trillion of the U.S. economy annually and influences 30% of global GDP. The National Retail Federation cites weather directly impacts an average of 3.4% of retail sales, influencing about $1T per year globally.
Simply, better accuracy means better decisions. Accurate weather forecasting can deliver measurable value across industries:
Aviation: For airlines, accurate forecasts are crucial for planning routes, minimizing delays, and enhancing safety. According to the Federal Aviation Association (FAA), weather is responsible for nearly 75% of flight delays, highlighting the importance of accurate, proactive forecasting for keeping flights on schedule and passengers safe. Turbulence prediction, wind shear detection, and runway condition forecasts enable flight crews to make informed decisions that protect passengers and optimize fuel consumption.
Advertising: Weather impacts consumer behavior, and accurate forecasts enable brands to align their messaging with what people are experiencing in the moment. The Weather Company’s advertising solutions use real-time weather and location insights to power smarter campaign delivery, reaching consumers when and where it matters most.
Media: Reliable forecasts built into broadcast media solutions keep viewers informed and engaged. Localized, timely forecasts build trust, improve viewer retention, and support higher ad revenue. Broadcasters can promote their accuracy, backed by The Weather Company, as a differentiator in competitive media markets.
Government & Defense: From storm response to mission planning, government and defense agencies rely on accurate forecasts for operational readiness. Whether preparing for hurricanes or managing logistics during winter storms, accurate data helps leaders act decisively and allocate resources efficiently.
Global industries: Businesses from retail and CPG to utilities and insurance can use our Weather Data APIs to turn climate uncertainty into operational control. Access diverse weather analytics and intelligence, from basic conditions and almanac data to high-resolution radar imagery and personal weather station feeds, all customizable to any industry’s specific needs.
What is the future of forecasting?
Forecasting is evolving to become faster, more personalized, and more precise. Key innovations include:
- Probabilistic forecasting: Instead of offering a single deterministic outcome, probabilistic forecasts show a range of possible scenarios and the likelihood of each one. This helps decision-makers understand risk and uncertainty more clearly. For example, while a deterministic forecast might indicate an expected snowfall amount of 5 inches in the next 24 hours, a probabilistic forecast reveals there’s also a chance of as little as 1 inch or as many as 10 inches of snowfall in that time, which might prompt a user to change plans or prepare alternatives accordingly.
- AI-powered modeling: Artificial intelligence and machine learning are increasingly playing a role in enhancing forecast accuracy. These systems can rapidly process massive volumes of historical and real-time data, identify subtle patterns, and provide a range of outcomes known as ensemble modeling, which supports probabilistic forecasting and gives a more complete picture of the weather’s nuances and variability. AI is particularly useful for refining forecasts in dynamic or hard-to-model environments.
- Street-level resolution: Forecasts are becoming hyper-local, not just citywide but down to neighborhoods and even individual streets. This level of detail supports everything from route planning in logistics to micro-targeted alerts for consumers.
Seeing the forecast others miss. The Weather Company’s AI-powered super-resolution technology transforms standard weather model data into hyper-local wind forecasts 10x more detailed.
Precision, preparedness, and progress
Ultimately, accurate weather forecasting isn’t just about knowing if it will rain tomorrow; it’s about making smarter, more informed decisions that protect lives, livelihoods, and economies. The synergy of human meteorological insight and advanced AI is pushing the boundaries of what’s possible. Moving forward, our unwavering commitment to precision will continue to empower individuals and businesses to thrive in an ever-changing world.
Frequently Asked Questions (FAQ)
Short-range weather forecasts (1–14 days) offer the highest reliability. A 5-day forecast accurately predicts the weather about 90% of the time, while a 7-day forecast is accurate approximately 80% of the time. Forecasts beyond 15 days rely on historical data and pattern recognition, making them inherently less precise as atmospheric conditions change rapidly.
AI enhances weather forecasting by rapidly processing vast amounts of data to provide more frequent forecast updates, which is essential for tracking fast-moving events like severe thunderstorms. Deep learning-based models (DL-NWP) improve forecast precision, granularity, and cost-effectiveness while effectively predicting the range and likelihood of potential weather outcomes.
The Human-Over-The-Loop (HOTL) model combines artificial intelligence with human meteorological expertise. In this system, more than 100 expert meteorologists work in real time with AI systems to provide oversight, ensuring that automated model outputs are accurately translated into actionable insights.
Weather directly impacts an estimated $3 trillion of the U.S. economy annually and influences 30% of global GDP. Precise forecasts help airlines minimize delays and avoid turbulence, enable retailers to optimize supply chains and ad targeting, and allow governments and utilities to prepare for severe weather events and allocate emergency resources.
A deterministic forecast provides a single expected outcome (e.g., predicting exactly 5 inches of snow). A probabilistic forecast presents a range of possible scenarios along with the likelihood of each occurring (e.g., a chance of receiving between 1 and 10 inches of snow), helping decision-makers better evaluate risk and uncertainty.
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