Weather forecasting improves with AI, but we still need humans

Weather forecasts are notoriously unreliable. Most people can relate to booking a trip or making plans expecting a sunny day, only to have it disappointingly rained out. While seven-day weather forecasts are accurate about 80 percent of the time, that figure drops to around 50 percent when extended to 10 days or more. Recent staffing cuts at the National Weather Service have already led to reduced weather balloon data collection, which experts warn could further degrade forecast accuracy. That’s a minor inconvenience when it ruins a picnic, but it can be life-threatening if a forecast fails to predict a tornado or hurricane.

But a series of recent advances in artificial intelligence–based forecasting models from big tech firms like Microsoft and Google might offer a silver lining. This new generation of AI systems, some of which are already being used in parts of Europe’s weather centers, are faster to build and easier to update than traditional models, which can take years to develop. Early testing shows several of these systems are also more accurate than conventional models at predicting weather up to 15 days in advance. Researchers have previously noted that extending reliable forecasts from 10 to 15 days could yield “enormous socioeconomic benefits” by helping people better prepare for the impacts of extreme weather.

Aurora, a new Microsoft model detailed this week in the journal Nature, outperformed the traditional models used by the European Centre for Medium-Range Weather Forecasts in more than 90 percent of the forecasts tested. It also proved more effective at predicting several extreme weather events, including typhoons and sandstorms. Though AI systems still rely on the foundational equations used in traditional models, these recent advances point to a future where researchers can respond more quickly to evolving weather patterns and deliver more accurate forecasts. All of that work, however, still depends on the continued rapid collection of accurate real-world weather data.

AI weather models are faster to build and cheaper to run 

Traditional weather forecasting is an expensive and time-consuming enterprise. The current process, in place for roughly 70 years, relies on supercomputers to solve complex mathematical equations that factor in variables like ocean currents and solar heating. Real-world weather data is then input into these equations. Eventually, the models produce several predictive outputs, which are reviewed by a human meteorologist who uses their expertise to finalize the forecast. These models can take years to build and are not easily updated.

AI-based weather systems, by contrast, are smaller and more easily “fine-tuned” with newer environmental data. These models are broadly trained on large datasets of weather and climate information to recognize patterns. That pattern recognition allows them to make predictions about what could come next.

In the case of Microsoft’s Aurora, the “foundation model” was trained on over one million hours of data collected from satellite radar, weather stations, simulations, and other forecasts. Researchers involved in the study believe this represents the largest collection of atmospheric data ever assembled to train a forecasting model. That base model can then be tailored to predict specific types of weather events by adding additional data related to the event in question.

When it came to hurricanes, Aurora was able to generate forecasts for hypothetical storms five days in advance with 15–20 percent greater accuracy than the top traditional model. It went on to outperform seven major forecasting models on all cyclone track predictions globally during the 2022–2023 season. The reported improvements weren’t limited to hurricanes. Microsoft says its model accurately predicted the date and location of the devastating Typhoon Doksuri’s landfall in the Philippines in July 2023—days before it occurred. At the time, official forecasts using traditional models had incorrectly identified the storm’s landfall location. Aurora also successfully predicted the onset of a major sandstorm in Iraq one day before it happened. Researchers say the model made that prediction much faster and at a significantly lower cost than traditional forecasting methods.

“Earth’s climate is perhaps the most complex system we study—with interactions spanning from quantum scales to planetary dynamics,” University of Pennsylvania associate professor and paper co author Paris Perdikaris said in a statement. “With Aurora, we addressed a fundamental challenge in Earth system prediction: how to create forecasting tools that are both more accurate and dramatically more computationally efficient.”

The findings come just months after similarly impressive results were documented for GenCast, an AI weather forecasting model developed by Google DeepMind. In a separate Nature paper, researchers reported that GenCast significantly outperformed traditional models in medium-term forecasting accuracy. Specifically, it surpassed the Global Ensemble Prediction System, a model used by 35 countries, 97.2 percent of the time in 15-day forecasts. Like Aurora, GenCast was trained on a vast dataset of weather events, in this case spanning 40 years (from 1978 to 2018), and uses that information to predict future conditions. The model presents its forecasts probabilistically, drawing from a set of 50 or more predictions to generate its results. All of this occurs far more quickly than with traditional forecasting methods.

Federal funding cuts will make predicting weather patterns harder, even with AI’s help 

The rapidly evolving field of AI weather forecasting is unfolding against the backdrop of job cuts and reduced capacity at the U.S. National Weather Service. These cuts—spearheaded by Elon Musk’s cost-cutting organization DOGE—have reportedly led to the elimination of several critical weather balloon launches during tornado and hurricane season. These balloon launches, which typically occur twice daily at over 100 locations, provide meteorologists with real-time atmospheric data used to predict where severe weather events may strike. 

AI weather systems may be able to draw from massive training datasets to outperform traditional models in many cases, but they still rely on timely, location-specific data—like that collected by weather balloons—to make accurate forecasts about rapidly developing storms such as tornadoes.