For decades, weather forecasting has often focused on finding the single most likely outcome. Will the storm hit here or there? Will it snow or rain? Will severe weather develop or not? But a recent forecasting experiment suggests the future of weather prediction may be less about choosing one answer and more about explaining a range of possibilities. New tools help meteorologists explain not just what might happen, but why — and when they'll know more.

A new testbed experiment explored new ways to use large collections of weather forecast models — known as ensembles — to understand uncertainty and communicate risk. The first Ensemble Clustering and Sensitivity Analysis Testbed was a collaboration between National Weather Service forecasters and researchers at CIRES and partner institutions. The experiment was held virtually from March 30 to April 3, with the final report released at the beginning of June.

While researchers expected participants to find value in advanced forecasting tools, they were surprised by how quickly forecasters embraced one tool in particular: Ensemble sensitivity analysis. 

“I did not expect them to gravitate to the ensemble sensitivity analysis, because of its learning curve,” said Austin Coleman, a researcher at CIRES and NOAA Weather Prediction Center who led the testbed. “Once they realized how to use it, we witnessed a series of lightbulb moments from the forecasters.”

While initially viewed as a research tool, the ensemble sensitivity analysis became a communication and impact-based decision-support tool by the end of the week. "Not only do I have a better understanding of why there are differences in output from the ensemble, but now I can get an idea of when I may have better clarity," one participant noted. "This is forecast gold to the partners." 

Weather forecasts are often uncertain because the atmosphere is chaotic. Tiny differences in current conditions can grow into dramatically different outcomes days later. Ensemble sensitivity analysis helps forecasters to do meteorological detective work to identify which atmospheric features are responsible for that uncertainty and when they’ll have greater confidence in their forecasts. 

During the testbed, participants used ensemble sensitivity analysis to analyze a late-February Nor'easter scenario. Six days before the storm, forecast confidence was low. "Of course this winter storm is uncertain," one forecaster explained. "The trough responsible for it is still south of Alaska." They learned from the ensemble sensitivity analysis to expect greater clarity in the forecast once the parent system approached the West Coast. 

More importantly, ensemble sensitivity analysis showed when confidence would improve. Once that parent weather system approached the West Coast around three days before the storm, forecasters expected a significant increase in predictability. For decision-makers, that information can be just as valuable as the forecast itself.

Researchers say uncertainty can actually provide a more realistic picture of risk. Rather than presenting a single outcome that may change from day to day, forecasters can explain the range of possible scenarios and the factors they are monitoring. That shift aligns with growing evidence from social science research showing that people often make better decisions when they understand probabilities and risks rather than acting on a single prediction.

Participants found this approach can also build trust. Simply saying a forecast is uncertain can leave users frustrated. But explaining why it is uncertain — and when confidence is expected to increase — provides transparency and context. Forecasters described this as a major advantage when communicating with emergency managers, transportation officials, utility operators, and other partners who rely on weather information to make decisions.

Forecasters also emphasized the need for simple graphics and messaging tools to translate complex ensemble information into actionable guidance for the public and core partners. Researchers are now working on training, new applications, and additional use cases to help bring these concepts into everyday forecasting operations.

Tools like ensemble clustering and sensitivity analysis may help bridge the "last mile" between complex forecast science and practical decision-making. They could help people understand what meteorologists are watching, why uncertainty exists, and when greater confidence is likely to emerge — giving communities the information they need to make better decisions before high-impact weather strikes.